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faizankshaikh/Project
trials/trial5-Copy2.ipynb
1
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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: GeForce GT 740M (CNMeM is disabled)\n" ] } ], "source": [ "# import modules\n", "%matplotlib inline\n", "\n", "import os\n", "import pylab\n", "import random\n", "import pandas as pd\n", "import numpy as np\n", "import cPickle as pkl\n", "from skimage.util import crop\n", "from skimage import transform\n", "from lasagne import layers, updates\n", "from scipy.misc import imread, imresize\n", "from theano.tensor.nnet import softmax\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.cross_validation import train_test_split\n", "from nolearn.lasagne import NeuralNet, BatchIterator\n", "\n", "project_root = 'workspace/.project/project'\n", "script_root = os.path.join(os.path.expanduser('~'), project_root, 'scripts')\n", "model_root = os.path.join(os.path.expanduser('~'), project_root, 'models')\n", "data_root = os.path.join(os.path.expanduser('~'), project_root, 'datasets')\n", "chars74k_root = os.path.join(data_root, 'English')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load train_test set\n", "# chars74k\n", "data = pd.read_csv(os.path.join(script_root, 'LISTFILE.txt'), sep = ' ', header = None)\n", "data_x = np.zeros((data.shape[0], 1, 32, 32))\n", "data_y = np.ones((data.shape[0], ), dtype = int)\n", "\n", "mms = MinMaxScaler()\n", "\n", "for idx, path in enumerate(data[0]):\n", " img = imread(os.path.join(chars74k_root, path))\n", " img = imresize(img, (32, 32))\n", " if len(img.shape) == 3:\n", " data_x[idx, ...] = mms.fit_transform(img.dot([0.299, 0.587, 0.144]))\n", " else:\n", " data_x[idx, ...] = mms.fit_transform(img.astype(float))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# preprocess\n", "# chars74k\n", "data_x /= data_x.std(axis = None)\n", "data_x -= data_x.mean()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# double data and concat\n", "data1_x = data_x\n", "data2_x = data_x\n", "\n", "data1_y = data_y\n", "data2_y = np.zeros((data_y.shape[0], ), dtype = 'int32')\n", "\n", "final_data_x = np.concatenate([data1_x, data2_x], axis = 0)\n", "final_data_y = np.concatenate([data1_y, data2_y], axis = 0)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15410, 1, 32, 32) (15410,)\n" ] } ], "source": [ "# check again\n", "final_data_x = final_data_x.astype('float32')\n", "final_data_y = final_data_y.astype('int32')\n", "\n", "print final_data_x.shape, final_data_y.shape" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class TransIterator(BatchIterator):\n", " def fast_warp(self, img, tf, output_shape, mode = 'nearest'):\n", " return transform._warps_cy._warp_fast(img, tf.params, output_shape = output_shape, mode = mode)\n", " \n", " def transform(self, Xb, yb):\n", " Xb, yb = super(TransIterator, self).transform(Xb, yb)\n", " \n", " if yb is None:\n", " return Xb, yb\n", " else:\n", " Xb_pos = Xb[yb > 0]\n", " yb_pos = yb[yb > 0]\n", "\n", " Xb_neg = Xb[yb == 0]\n", " yb_neg = yb[yb == 0]\n", "\n", " Xb_aug_pos = np.empty(shape = (Xb_pos.shape[0], 1, 32, 32), dtype = 'float32')\n", " Xb_aug_neg = np.empty(shape = (Xb_neg.shape[0], 1, 32, 32), dtype = 'float32')\n", "\n", " dorotate = random.randint(-5,5)\n", "\n", " trans_1_pos = random.randint(-4,4)\n", " trans_2_pos = random.randint(-4,4)\n", "\n", " trans_1_neg = random.choice([random.randint(-31,-10), random.randint(10, 31)])\n", " trans_2_neg = random.choice([random.randint(-31,-10), random.randint(10, 31)])\n", "\n", " center_shift = np.array((32, 32)) / 2. - 0.5\n", " tform_center = transform.SimilarityTransform(translation=-center_shift)\n", " tform_uncenter = transform.SimilarityTransform(translation=center_shift)\n", "\n", " tform_aug_pos = transform.AffineTransform(\n", " rotation = np.deg2rad(dorotate),\n", " translation = (trans_1_pos, trans_2_pos))\n", "\n", " tform_aug_neg = transform.AffineTransform(\n", " rotation = np.deg2rad(dorotate),\n", " translation = (trans_1_neg, trans_2_neg))\n", "\n", " tform_pos = tform_center + tform_aug_pos + tform_uncenter\n", " tform_neg = tform_center + tform_aug_neg + tform_uncenter\n", "\n", " for j in range(Xb_pos.shape[0]):\n", " Xb_aug_pos[j][0] = self.fast_warp(Xb_pos[j][0], tform_pos,\n", " output_shape = (32, 32))\n", "\n", " for j in range(Xb_neg.shape[0]):\n", " Xb_aug_neg[j][0] = self.fast_warp(Xb_neg[j][0], tform_neg,\n", " output_shape = (32, 32))\n", "\n", " Xb_aug = np.concatenate([Xb_aug_pos, Xb_aug_neg], axis = 0)\n", " yb_aug = np.concatenate([yb_pos, yb_neg], axis = 0)\n", "\n", " return Xb_aug, yb_aug" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# setting nn \n", "net = NeuralNet(\n", " layers = [\n", " ('input', layers.InputLayer),\n", " ('conv1', layers.Conv2DLayer),\n", " ('pool1', layers.MaxPool2DLayer),\n", " ('dropout1', layers.DropoutLayer),\n", " ('conv2', layers.Conv2DLayer),\n", " ('pool2', layers.MaxPool2DLayer),\n", " ('dropout2', layers.DropoutLayer),\n", " ('conv3', layers.Conv2DLayer),\n", " ('dropout3', layers.DropoutLayer),\n", " ('hidden4', layers.DenseLayer),\n", " ('output', layers.DenseLayer),\n", " ],\n", "\n", " input_shape = (None, 1, 32, 32),\n", " conv1_num_filters = 32, conv1_filter_size = (5, 5),\n", " pool1_pool_size = (2, 2),\n", " dropout1_p = 0.2,\n", " conv2_num_filters = 64, conv2_filter_size = (5, 5),\n", " pool2_pool_size = (2, 2),\n", " dropout2_p = 0.3,\n", " conv3_num_filters = 128, conv3_filter_size = (5, 5),\n", " dropout3_p = 0.5,\n", " hidden4_num_units = 128,\n", " output_num_units = 2, output_nonlinearity = softmax,\n", "\n", " batch_iterator_train = TransIterator(batch_size = 256),\n", " batch_iterator_test = TransIterator(batch_size = 256),\n", "\n", " update=updates.adam,\n", "\n", " regression = False,\n", " max_epochs = 200,\n", " verbose = 1,\n", ")" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 20 \u001b[36m0.36742\u001b[0m 0.92503 0.39720 0.66376 13.19s\n", " 21 0.64655 \u001b[32m0.40303\u001b[0m 1.60425 0.88431 12.85s\n", " 22 0.37878 0.47318 0.80050 0.85757 12.85s\n", " 23 \u001b[36m0.36143\u001b[0m 0.51342 0.70396 0.83624 12.84s\n", " 24 0.42710 0.41248 1.03544 0.86809 12.84s\n", " 25 0.38977 0.49440 0.78838 0.81280 12.91s\n", " 26 0.99950 3.78441 0.26411 0.53696 12.85s\n", " 27 0.88138 0.51375 1.71557 0.80288 12.84s\n", " 28 0.47281 \u001b[32m0.39685\u001b[0m 1.19142 0.87428 12.85s\n", " 29 \u001b[36m0.33457\u001b[0m 0.40743 0.82118 0.87061 12.85s\n", " 30 0.36014 0.46808 0.76939 0.85457 12.86s\n", " 31 0.36482 \u001b[32m0.34075\u001b[0m 1.07064 0.88221 12.85s\n", " 32 \u001b[36m0.27936\u001b[0m 0.35392 0.78935 0.87590 12.84s\n", " 33 \u001b[36m0.25393\u001b[0m \u001b[32m0.27427\u001b[0m 0.92582 0.90325 12.86s\n", " 34 \u001b[36m0.24657\u001b[0m 0.38679 0.63748 0.85457 12.86s\n", " 35 0.27386 \u001b[32m0.20800\u001b[0m 1.31662 0.93179 12.85s\n", " 36 \u001b[36m0.18779\u001b[0m 0.25970 0.72313 0.89483 12.84s\n", " 37 0.19578 \u001b[32m0.19423\u001b[0m 1.00797 0.93780 12.84s\n", " 38 \u001b[36m0.16007\u001b[0m 0.20686 0.77382 0.92939 12.83s\n", " 39 \u001b[36m0.12585\u001b[0m \u001b[32m0.11915\u001b[0m 1.05625 0.95493 12.85s\n", " 40 \u001b[36m0.11385\u001b[0m 0.22039 0.51659 0.92067 12.86s\n", " 41 0.14130 0.17138 0.82450 0.93780 12.96s\n", " 42 \u001b[36m0.10335\u001b[0m 0.33411 0.30933 0.87169 12.85s\n", " 43 0.17599 0.78945 0.22292 0.73317 12.85s\n", " 44 0.44183 0.35432 1.24698 0.87109 12.84s\n", " 45 0.27015 0.22315 1.21061 0.91406 12.84s\n", " 46 0.17012 0.22891 0.74315 0.90565 12.84s\n", " 47 0.21042 0.18359 1.14619 0.93239 12.85s\n", " 48 0.17883 0.15645 1.14308 0.93960 12.98s\n", " 49 0.11833 0.12718 0.93040 0.95012 12.85s\n", " 50 0.10604 0.12355 0.85828 0.94832 12.84s\n" ] } ], "source": [ "# train and test nn\n", "net.fit(final_data_x, final_data_y);" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net.save_params_to(model_root + '/detector_4.pkl')" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(768, 1024, 3)\n" ] }, { "data": { "image/png": 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RoilIGj+w6LmfokY9DmJPa0t9JmlCYIX6LeOtpECug9I1ddpLbivVHfMAisaO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R8j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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f96493c9850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# test image\n", "from scipy.misc import imread as ims\n", "img = ims(data_root + '/svt/svt1/img/00_13.jpg')#[292:450, 176:850, :]#img -> 00_12\n", "print img.shape\n", "# image[y:y + image_height, x:x + image_width, :]\n", "pylab.imshow(img)\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": 253, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(138, 189, 1, 80, 80, 3)\n" ] } ], "source": [ "from sklearn.feature_extraction import image\n", "patches = image.extract_patches(img, (80, 80, 3), extraction_step = 5)\n", "print patches.shape" ] }, { "cell_type": "code", "execution_count": 254, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26082\n" ] } ], "source": [ "from scipy.misc import imresize\n", "new_lst = []\n", "for i in range(patches.shape[0]):\n", " for j in range(patches.shape[1]):\n", " new_lst.append(imresize(patches[i, j, 0, :, :, :], (32, 32)))\n", " \n", "print len(new_lst)" ] }, { "cell_type": "code", "execution_count": 255, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_list = np.stack(new_lst)\n", "new_list = new_list.dot([0.299, 0.587, 0.144])\n", "tester = new_list.reshape(patches.shape[0]*patches.shape[1], 1, 32, 32)" ] }, { "cell_type": "code", "execution_count": 256, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tester /= tester.std(axis = None)\n", "tester -= tester.mean()\n", "tester = tester.astype('float32')" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(26082, 1, 32, 32)\n" ] } ], "source": [ "print tester.shape" ] }, { "cell_type": "code", "execution_count": 258, "metadata": { "collapsed": false }, "outputs": [], "source": [ "preder = net.predict_proba(tester)" ] }, { "cell_type": "code", "execution_count": 259, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(138, 189)\n" ] } ], "source": [ "heatmap = preder[:, 1].reshape((patches.shape[0], patches.shape[1]))\n", "print heatmap.shape" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dRliYtSZHkZA/oJ/1Qb+8Vk/J7bHctSqWDFMpRsCzWygny6QfzTsRERERUwhR0t9jqHS0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f96551748d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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45zGbO4CbgYLH6om2txzsZyW/Sj3oEKksstgCzISg60H/NcoFiKtTO2pflIBA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\nn0HwTKcT7n10imkiL17f5+q+5ez+faZHt9D6yxSDEbNZhY0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8o5IJ9axkfjTjbHZEFgdk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\nt8OgvNB5ntlSJOakJiyl4JeeTGXc77TBl0KOG1ApVdVWKTeTIpv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f96483a05d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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VNM9wThro+qAIEFKQceMGVyit08iXWU7ZxCSsIvHmJW8641ZOJfFQBByl6ozb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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9649773110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.pcolor(heatmap[::-1])\n", "pylab.axis('off')\n", "pylab.show()\n", "pylab.imshow(img)\n", "pylab.axis('off')\n", "pylab.show()\n", "pylab.plot(heatmap[::-1].ravel())\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(26082, 2)" ] }, "execution_count": 261, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preder.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jegibbs/phys202-2015-work
assignments/assignment04/TheoryAndPracticeEx02.ipynb
1
22936
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Theory and Practice of Visualization Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Violations of graphical excellence and integrity" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Find a data-focused visualization on one of the following websites that is a *negative* example of the principles that Tufte describes in *The Visual Display of Quantitative Information*.\n", "\n", "* [CNN](http://www.cnn.com/)\n", "* [Fox News](http://www.foxnews.com/)\n", "* [Time](http://time.com/)\n", "\n", "Upload the image for the visualization to this directory and display the image inline in this notebook." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "bd4340d93d2efdf5c3864b5caca1f6ba", "grade": true, "grade_id": "theorypracticeex02a", "points": 2 } }, "outputs": [ { "data": { "image/png": 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Hx8OHD09OTkbEX8xrZmYm/jc8EwAAKKgDADtpbW0tAvToyP+F4lFAu3v86fbt250fEXzw\nwQfxv7i7nx0xzcgcMn9YXFyM3CMyBPsCAJAAwIMToXy7DKFz/rBhPYEOZmZmYnS3/wGADhQBgp2U\nhXNWV1cPHz5c7n/z5s1igHbj5l38AwcOZC3eTBUGBgaGh4fzAUJ47rnnavWCQOU0I6YZcf/y8vLS\n0lLnIkYAAJ4AwA6LYD1C9mPHjhV9lutq9ZZ8OiQAGc3PzMzEwCMjI6OjozGpiPWnpqayzZ/MB6Jn\n5AbZZ3Z2Nv7HwNl9rM4uAAA68AQAdl2G6RH6j42NdRgsnwBEfJ8VfFM2DBpZQfTP0kHx7cx90Sc+\nxlfZ5GiH2sMAABIAeBAmJyezxE5E6p3L95+oa+6ZbwZYWFgYHR3NnqN15cHm5uZiyIGBgcgiIjHI\nKsj5LEJ7oACABAAeXPS/vLyc9+m3FogXYxW1CJrNz89H3D8yMhL/L168GH2yJkC+jmDDlw8AAD1F\nHQDYFWtra5cuXYr4e3BwMILyitF/hPJFBd9Chu/tmg/KN3+Njo7GYAsLC/HxxIkTWRkgOuJj9LQ7\nAICCJwCw81ZXVy9fvhz/s4B+xbGyuH9fX18kDOW6wlk3oN1d/Ij+a/ff/JVPCQ4cOJBfHTx4sHb/\n1QEAAMkTANhha2trEf3H/4mJiYbo/9KlS/mm3paypaC1uoaetVJZoIZMY35+3pu/AAAJADw0EeJH\nXD42NtbQImcW7yluz8fHM2fOREqQN/hr9UZCa/Wau+Wb/dmCULvXezW8+StfPlC8gyxLE+WrAwAA\nkiJAsMOyBZ6pqamW3xbBfRbdiRh9eXk5I/jR0dGbN2/G6BHWZ54Q3TFAvlys+R5/vvmr3Gbo0NDQ\nYl0W/skCRdHTTgEAJACwW9rV1m1IAEZGRiLQHxgYKB4URLB+/vz5+fn5hYWFfCwQfSIriPSgOfqP\nueSbv8qljGLiFy5cyFcE1O43A6oJIACg7JH19XVbATbl3r17t27d2o9L/vjjjz/zzDPdvXdu3Lix\nT5f8yJEjflxOArvt6Tp7EHqcOgAAACABAAAAJAAAAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAAAkA\nAACwBY/aBAB7009/+tOf/OQn+3HJf/EXf/HRR11fACQAAGzGJ5988vHHH+/HJX+6zh4E2JsUAQIA\nAAkAAAAgAQAAACQAAACABAAAAJAAAAAAD4tmQAH2n9XV1YWFhfn5+fw4MDBwoq7KuEtLS4uLi8vL\ny/lxeHh4aGiov7+/YbCZmZmcfnw1MjLSMPH4am5ubmJiImZtdwBIAADYRRHBT01NRVw+OjpaBOsr\nKyu3b98u+rQzPT0d0X9fX18OGaPM142Pjw8ODhaDxfRjLtEn4vvoiLHW1tYiVchvozui/8gKRP8A\nEgAAdlcE+hn9X7hwIeL47BkdEaNHHH+srt24MUBE/9ER4xa3/A8ePBj5Q0zz/PnzGdDHLCLoj2lG\nVhAfDx8+PDk5GRF/kQDE8PG/4gMHAPYadQAA9pMIzWv1IkBFGZ5Q3Lwv92wQoxSBe7nAT4T1+XF2\ndjb7fPDBB7V6saL8mB1ra2sxhZxOZBGjo6NF+gGABACAXZfheIMI0ztnDqH5EUHmD5E8tJxmg8gi\nImFw+x9g/1IECGA/GRkZOXDgQK1+577oubKykh0d7soXwzQnAAcPHiyShJjsc889Vx7+9u3bOeWI\n+yNJiGEmJibsCAAJAAAPQgTi5dA/Fc0BdagAkAF9X13DV0WJoLt379bqZX4GBwcj0I/JxryyaFAk\nHrV6MaHO1Qzg4bpz78f/77+s7sclf/KJx37tl/vtQSQAAGwsIvUs+h/BeofQPIv3dC64X5QgGh8f\nn7kv0oNsI2hxcTGyiPPnz9vm7FkR/f/Jldn9uOS/+Wu//M2vnbIHkQAAsIEI/aempmr1e/+d2wCN\n0L9DDYFmo3XlPnNzcydOnBgYGMj6xFmpIBKDSDy0BwogAQDgQUT/k5OTtfq9/w3fAJAJQOdqvkVl\ngGbz8/Mx7sjISPy/ePFi9MmaAJF+xGKU2xUFYI/TChDAvrS4uBjRf4T1EYhvGP3XSs16Nj8HKLKC\ndgWE8s1fMZeI8hcWFuLjiRMnsjJAdMTH6GmPAEgAANgt8/Pz09PTEX9fvHixYpXcIgFofldAtvNT\nK71PoEFE/7X7b/66efNm/M+WiGr3HxrkqwMAkAAAsCvR/8zMTATrExMT5Xv2S0tLZ86caVfQP8L3\nHLg5AcjS/JFItHwCsLq6GnP05i+ArqEOAMB+khVwBwYGxsfHm8P0ciufk5OTEetHnpBDRv+RkZEY\nd3FxMTqKIvtZuD++HRsbaznHhjd/HT58OCZbPDTI1kXz1QGwL3zjP/zB8We/+H/88f9VZeDTv3v8\nxd8bfOqJx6L7w3/+wfW/ev+9736/PMDJF37jla/8dg5w7X/87Vt//X7525hRzO7yX/zN29/5ni2P\nBACArci79RF2nzlzpvnbopxPDJB3+mP4iO8z3B8eHr57925E/BcvXsx2/SOOj3ygVm/3s2Ut3nzz\nVznZGBoaWqzLwj/REclD9LRr2Pue/ZUvvPh7xyMorzJwxPRv/sc/iFEi4v/on36Q0fxr/37k6F+/\nH4F+kR5E9P9ZYvAP3z/6xS9Ed/x/47/OlfOH9z/8J9E/EgAAdksRxEcmcOzYsXwCUI7sR0dH46uI\n6WdmZrJPZAURvreM/tfW1vLNX+W6ATHkhQsX8hUBtfvNgGoCiL3v3B/+zskXfqP68BHNR/Qf4XsR\n0L/11+9/+8LLEdMv/MP3I+iPPi/+3mc/jcn/9v/Ex0gYIkP40r/99eu/8oX8NmYXfb7+X/67jY8E\nAICtG66rMmQ209lssK7KFPr6+lq+9itfDWZfsL9c/ou/ib8s/1Nl+Bzsw3++Xe4Z+UCE9Ue/eDBC\n/EgPiqJB8f+Tez++9cN/fepXvpDf1j67/T/49ne+F6PY+Ow1KgEDAL2ioQT/hp79lYNbm9Hp3z1+\n6PP/5q2/XrLNkQAAAOwDb3/nH2v15wDn/vB3ip5Hv/hZPrDwD59lER/+8w8+uffjepLwhfz20Of/\nt/j/0T/dfuqJx178vcFr/+Nvb/3wf9qSSAAAAPZFAvC9oij/lT/+d5EJnP7d4xHrR1ifcX+4/lef\n3eB/5Ssv1Oq3/CPuf++7n1UPyLoB6v6yZ6kDAADQKKL8V//sv3/r/3w5wvqI+7/xH/6g1tTQZ3TH\nYK985bff/eZ/Kr499Pl/E8lAOU8ACQAAwF4XQf9r/37kztqPr//V0skXno+wvlZvGijj/mKwt7/z\nvYY7/ad/d/DWD/9nDhPDRzJQa/MOAXhYFAECAPg5Ee6/+R//IP5//b/83xHKv3TxW0Xb/0VM39Lx\nZ7948oXfyLq/V/743+WjgBg9PkY68aV/++u2LRIAAIA95+QLz2eB/qIWb6QBRYv+xbuBmxVv/opY\n/9n6CwFixJjI9b96vz7icdsWCQAAwJ6TBX4a2vCJyD4L9kT0nw3+NIig//izX8xhGqaQ9YmLVweA\nBAAAYA+5s/bjIoj/+f4/yo5bP/zX5rFe+cpve/MXEgAAgL3u+LNffPeb/2n24h8VLwnOID4+NuQA\nOUB829zCT8Obv/Lef/GKgPvvFf6BpoHYC7QCBAD0SqBfVMONeL1ozOfkC79RqxfsiW8z9H/vu98/\n/p3v1d8A8GI29p+jx19E9pf/4m8appxv/sri/tknpvDiP/8gEoBzf/g7t374rydfeL722XsD3rcX\nkAAAADwIEYhnoJ9e+cpvx99LF78VIXvW2f3k3o/LzXRGlB/JwND//uvZ9GetflO/4T0A5anV7r8X\nrHD2m/+t3Azotf/6t5oBZY94ZH193VaATbl3796tW7f245I//vjjzzzzTHfvnRs3buzTJT9y5EhD\nn4/r9uO6PF3nJGDXPHjf/f6//MmV2f245L/5a7/8za+dcoXlwVAHAAAAJAAAAIAEAAAAkAAAAAAS\nAAAAQAIAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAJAAAABAr3nUJoAe93HdflzyQ4cOPfHEE/bg\nfnHjxo19uuRHjhyx+4Bu4gkAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAJAAAAAAu857AICqJicn\nl5eXr169uuGQq6urCwsL8/Pz+fHYsWMnTpwYHBwsD7O4uDgzM7O2thbdo6Ojw8PD5W9jRjG7sbGx\nGNGWd4xt7RiLAywH6O/vHxkZaTiW4qu5ubmJiYmBgQFbHpAAAPyclZWViJYiMqsycMbuEVRFWB8f\n7969m+NG+BUBfRF7RXAWw0TEFhOP7vg/Pj5eDs4ypLPxHWNbO8ampqaWlpbiAIvBomN6ejqyzSLP\njO6I/iMrEP0DEgCARhE5LS4uVhw44qoIvKLj1KlTEcFnzwMHDkSIHxOJaCx7RuwV/7/61a9G+BWj\nROgWIVqEgBmNxZDRZ2JiwsZ3jG3tGItjKY6ovr6+zCoPHz4cCUMcdUUCEAPHfxkm0JvUAQA2MDY2\ndvXq1SLS6iwCryzVU47nioIZN2/eLA+T4X5EaQcPHiy+zfQgIrOKc8Qx1nyMffDBB8UBVnTEWKur\nq7V68aEYd3R0NI49Gx+QAAC01lC6up2I4TLYKpesuHv3bvUZzc/PR3w2MjJimzvGdukYm5mZ6e/v\nd/sf6FmKAAE77Pz58w19lpaWyhFexG19fX1ra2tFmZ/bt2/X6uU0smT26OhoxGe2JFs+xp577rla\n/VlB9swDLI66OK6yvJkCZkAv8wQA2EUR0E9PT2dLLGNjY0VYnzf4Z2dna/Vb/jFYVtbMugFuzbLN\nYyzrl8dX2T+PtOKoO1Zn0wE9yxMAYLdcuHAhi1xnZFYO64eHh/v6+mZmZs6cOVO73wxoDBzhmpLZ\n7MgxNj4+PnNfZAXxMVKCxcXFlZWV5gcIABIAgB1w8eLFbNsnwvqGRhhr9dv8DXf65+bmIlDLYYoW\n3AcGBqJPxdLhOMbKx9hoXcMxFkddHFSRNsQxlgWH4uiKsbQHCvQORYCAXRShVURg2RRjttLYbsjl\n5eX4NgtpXLp0KR8FRHhXu9+gu43JNo+xon55/I9DK5uaDflWgeJJAoAEAGAHQrSigf8OwVm++at4\nIcDw8HDxQKB44Sts7Rgr1y9fWFiIj9nUbB518TF62oxAj1AECNhJEb5PTU1FjHXhwoVyUf7szsZY\nWo5VvPkrb8QW1YUzqsum39UNYMvHWLl+eb4r4MCBA/lVvoYiXx0A0As8AQB2ODjLIL5ogTHlm5sy\n0mo2MzPjzV/s3jGmfjlAmScAwNZl4ekIqsbHxzN8HxgYiPisv7+/HM1n0yu1Nu17Nrz5K+/9F7Fd\nzKJ2/9UBNrhjbGvHWMObvw4fPhyTLZ4V5Ij56gAACQDA/wrCimq4Ea8XDa1keeu1tbX4NqOx+Gq5\nbnp6Ou/FRpiVg8VXzcFZlszO4v7ZJwtzR0yWU8iS2eWmXXCMbeoYyylnLeE0NDS0WJejR0ckGNHT\nXgAkAACfiTCrXLEyG1a/ePFi3lKN0CqCp3IznRMTEzF89C/GisFigJYlfGJStftvaCqcP3++3Azo\n6OioZkAdY1s7xiJzyDd/lUfP+gM5l9r9ZkC9fBroHY+sr6/bCrAp9+7du3Xr1n5c8scff/yZZ55p\n6Plx3X5cnUOHDj3xxBMNPW/cuLFPj6sjR450za55uq6Ld83+PQm03DXd5Lvf/5c/uTK7H5f8N3/t\nl7/5tVOusDwYKgEDAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAJAA\nAAAAEgAAAJAAAAAAEgAAAEACAAAASAAAAAAJAAAAIAEAAAAkAAAAgAQAAACQAAAAABIAAABAAgAA\nABIAAABAAgAAAEgAAAAACQAAACABAAAAJAAAAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAQAIAAABI\nAAAAAAkAAAAgAQAAAPa2R20C9qaPPvro7br8ODQ0dPLkyePHj1cZ9/r169euXXvzzTeL4d9///3o\nE9OM7pjOiy++eOjQoWL4O3fuvPTSS1/60pKeOJkAABedSURBVJcmJiZseaDXfPrppz/72c/245I/\n9thjn/ucW5kgAaArRNw/OTkZHRGpP/XUU9ER4fvCwkJ8fOWVVzqMGKH8W2+9FQlAQy7x6quvPvnk\nkzHuJ598Et9GPnDlypXoU8wu/neeMkC3Wl1d/dGPfrQfl/zQoUNPPPGEPQgSAPa9iM4z+p+YmDh5\n8mT2PH78+NmzZyN2j3wg0oDOaUODSAni/+nTp3PEyAdiFjFwfrx161ZkFxH9F/kAAEAX8+CMPSeD\n+EOHDhXRfzh69Gh+jGj+zp07LUeMAd55553mG/kR7ucUikll3J8fI6mIebVLKgAAJACwiz766KMM\nzYeGhhq+ygL9Ef2/9957HabQPGLn2RWPAgAAJADwoOXd+vDss882fFXcwi+GqSgzh6wBXLt/7z8r\nAV+7di2+LT9qAACQAMCDU5TMKbfS09CnGKai06dP1+o1BGLESB4WFhayfFF0xEe3/wGAnqISMHs0\nAehQJbddHYB2jh49+uabb167du3ll1+u1asKZJXf6DM0NFSxaVGAnrW2tra4uDgzM5MfBwYGTtRt\naiJLS0tTU1PRcfXq1aJnTjamH92jo6PDw8PlUZaXlycnJ8fGxjY7L0ACwH6yS03xRJR/5cqVcp98\nIBCJQXZHMhB5Rcy9aCwIgLCyshJReMTog4ODEfpHn4WFhenp6Qjox8fH+/r6qkxkdXU1RmnoOT8/\nH9F/TDOmHHOJ7vgf0ywPcOzYMdE/7DhFgNhbstX/WsdyPs2lgzYrYv2I+E+ePBmTio64tkWG8M47\n78T/a3V2BECtfu8/o/+IwiM0H667cOFCf3//8vJyc0zfztTUVN7mL5ubm4v/X/3qV2OaY2NjkUtE\nUhE5QH67uLgYs2h4JgBIAOhCRXDfXM6nQ/WAzSre/BVzybeGnTt3rvgffTZbygigKxXlc0ZGRoqe\nEannx4jXI0avMpEI6wcHB8s9o09OOZ8qxDQPHjwYHTdv3izSg8g6jh07Zi+ABIAuV5TI//DDDxu+\nKprx2Wap/Ugk3nrrrdOnTz/55JM5lyfrsiOzi802NATQlSLEzxi9v7+/3L+I5nOAzlOYn58fHh7e\nVCgfo6yurpazDkACQNc6evRoxvcLCwsNX2VQHjH6NhOAzq8TBiAVN+mfe+65hq/6+voyJeicAGTR\n/8gfRkdHG76Knll/oCjzc/v27fh/+PDhmOnc3FyM0pB1ABIAula+yvfWrVtZUCflG7tq9VI6RUXh\nCOW//OUvnz17tnrDoA1v/sq3Ddy5cyencKuutu2HDABdoCiNc+DAgeZvs8TOWl27KWSzP+V6vWV5\ng392drZWv+Vf1DPOugHq/sLu0QoQe87Ro0dfe+21N954Y3JyMsLxrBacFXMjNyi/6Pett97KmP79\n99/Pl3nF8Fmmv1Z/hnCorjzxmE5Mv3jzV+QSkQzEKFkPOB8yRJ9daowIYB8pIvvOTf3EYC0HKFr1\naXcjf3h4OEaMwc6cOVO73wzo6upqJAPRXbF9IUACQJeIKP/KlSvvvfdeEc1Hn4jaG27Mnz59OgP6\n7P/2229HHF98+3bdxMREEe7Hxwjxs+nPQiQV2RZQfBVxf3xUOghgm4qi/w11fxs0v09gbm4uEoZs\n/Cdyg5hIrV5eaMNJARIA9r2jdVkcqJ0X64qPJ+s6DN9ugA1HBOhBxT34DoV8QvMN/uXl5ampqZZF\n/2vtnxjkiIuLi2NjY9F96dKllZWVmELE/VN14+PjcgCQAAAAu+Xw4cPZcffu3eZvs85uy+I9WT0g\nwvcs29MgG1xu+X7f4s1f+UKAvPFfqxcWigQgvpUAgAQAANgt2VDP2traBx980PBV9FxdXa2V2gMt\ni6zg6tWrDT0XFxfzxWHnz5/Ptv8b5FsFJiYmavXmg8rZRQ6frRKpGwDbpxUgAKC1bKgnIu+MyMvB\nenZs4UVdLaP/Wr3Evzd/gQQAAHiYIiLP2/DZNGfKdvpr9ZI5Rbw+Pz9/5syZS5cuNaQKFTW8+Stn\nWrwiIN83XLw6ANgmRYB4QD799NOf/exn+3HJH3vssc99TqoM9KIIuMfHx6emphYXFyPuz5v3CwsL\nEaxH6F+u45spQYTsEay3bMI/g/iM9bNkf0NGET2LMj/5QoCY2vT09MGDB/PVkA1jARIA9rq4Wvzo\nRz/aj0t+6NChJ554wh4EelME4hcuXIgEIGL0LPkTfUZGRhqi/OgzMzMTXzWX4ZmcnCyi/1q9qE+4\nePFiEe7Hx9r94kaF8+fPl5sBzeaA7A6QAAAAu66vr2+4rsMwHQbIer0djNU19x+ts/1hxynYAAAA\nEgAAAEACAAAASAAAAAAJAAAAsMc8sr6+biuwS/7yL//SRgCALfj93/99G4FdohlQnLwAAHqIIkAA\nACABAAAAJAAAAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAHrNozYB\ne8Fbb711/fr1Tz75JLqfffbZk3X51a1bt954442hoaHTp083jxijXLt27e23347uQ4cOvfLKK1/6\n0pfyqw8//DC+ev/996P7+PHj586diwHyq/fee29ycvLNN9+MeW04i4rKS5JzjIXJ6Ye362KRojuW\n8LXXXms5kXaDxcRjgWOxc/u8+OKLxWo2L3xuzNnZ2e3skc6rE0sSs8jlbNi2e3N1NlyjQizA2bNn\n4/CLbzc8VmOYYjk7rFTD8Ra+/vWvP/XUU+0Og+3vnYbZlXX4XcQEY+1iC0R3bIH4qmGti71Q8Xje\n5m+/w6JWXKOYeHwVHbG1Y4+Uf+ANu6Ddbt2U8pLkNoxJxVyaZ9GweascYNVPaBW32zZXp/wzv3z5\ncmzJ4oDf/im64XjbkTUqH7TNx0Pn88z2T2XxMbZSrkLDkPBQ/MLrr79uK/BwxZU4TotxrozT+ssv\nvxx94kT5wx/+8IUXXoie2R0n/eeff7553Bjl85///J/92Z/FiCsrK3GR+K3f+q3oE2fb/Orb3/52\nfPXOO+9ER5xw47x/6tSp+Pjpp5/G9SwG2HAWVcTsJiYmouPKlSt/9Ed/FHP8x3/8x7gwxPTjXB9X\njliwb3zjG1/72te+/OUvR/dHH33UfPbvMFisS8wiViEmnpecnHLDwsf1Ka4rsTEfe+yx7SQznVcn\nFuDVV1+NrReLGhszrpqxPUdHR/fs6my4RuVgJS7tMXDMPZaheTqxeH//93+fB1WMG1P7pV/6pTxs\n2q1Uw/EWcUYsSQz5q7/6q1uLADqvS8PsmseN5Yyd9ad/+qcxYoQ7sZty32UYF9s5d2vsr3fffTcm\n0rwXKh7P2/ztd1jUimsUk4qDM5YzDtfYMhEf55Sbd0GH3VpdRIExozhs8nQU2zCW5M///M/ztJMx\neixkDBNTjlnkwlQ8wKqf0Cput22uTjlJiC0cc4/+sT23f4qO/g3H246s0bW6GD0nEuvV4Qjf8VNZ\nDBkbM3ZlrmzeQIlJNWdT8MAoAsRDlvd1yvcv44wcZ+E458ZZMqKxuAR2uEEVihtUGbHlx5hsnI6L\nm7gxwfgYF/7ojutH+eZu51lUFKf7O3fuxKSKE3pcGGKNcjFikfJ/3gMLxTKXtRssFjtWMzZLTjzW\nJTpyXRoWPuYYl7Tt31jqvDrxVfTJ/0/VxSUwb1juzdXZcI3Kg3W+s7iwsBALn/fIh4aG4n+EAp1X\nquF4i69ipbZz4e+8Lg2za47qYpi8kZk/mViX/BhLG+ueAVBMOQOj+Bk274WKx/M2f/sdFrXKGuXC\nx2LnaeF4XfSJb5t3QbvduqlnMhH8xYoU9/WLxwuxv4rfSx5sR48ebTeLdktS/YRWcbttc3WKweJj\ny1R5y6fo5uNt+2sUU4gpxyyKh0sxl5h1PljY8Dyz/VNZymMg554bRwCABIDeFafmOGk2nHzL14kO\n8iRe3LPJq2aei2Pc6CgCi5hgfNxamFLljmycyuOi3hDVxfU7LhtxRc9nzXk5z+GffPLJ5um0Gywj\ngPJVNuZ1q+6hrE5c/4qYI3Zf+RK4B1enyhrlx7hyxxU6b66380pdJgm5Ig94pSquSzsZYZfL2ORP\nJoPO8sOQDjFKxeN5m7/9dotacY3yx56h9oZnlXa7tbrY8rH9i21SBM2xALltYwmLKPP69eu55NWX\npPoJreJ22+bqFLloLEnDYDt+it7+GhVlh8o9iyczG46+I6eyK3U5esw0VlwRIB4udQB4yIqbImV5\neq0SPJVLYMdlNS6ccZ34pK5l8e7ov+NPXctXtZb30p6tK+56Rp+4orS8WLYcLKdfvjOd84oL4daK\n9m5zdYrL8NmzZ3OxW5Zm3iOrU3GN4pL89ttvf+Mb39gwgC66s3B59nlgK1Vx77QTIxb3XGOVIzCK\nKCSWMIOq8qKWQ5yKe3Znf/vtFrXiGuXEm4dveVZpt1ura7flc0/FTHNJijoJ5Yh2wyXZ1Amt4nbb\n/urk4sVvv3MMvf1T9PbXKHd6y6Su5UG4e6eyU6dO5Qpu/7EzbJMnAHSDLJUe59xyALdhMPTgvVW3\nYRXDloNVuVA9SPncPC5jcRX8+te/vq9XJ1ZhcnJyUzUL33jjjRjrtddeKwcxe20fdRAxSqzy8ePH\ny/V3N/uTqXg878aiVhxss3uk5W7dQVn6KLZYbrpNLclm907F7bY1RRHNB3mK3tU12vIBv6ljbLYu\ntsPZs2f34BWKnuIJAA/Zk08+mc9M8+QeH+PknvcdK0ZjGYDGZbK4KmTB9ObzckxwNypd5XIWBamv\nXbuWl6iGm7XRPy+ZnZ/8Ng+WN2iLm4jFhXOXYpQqq5MFamv3m7PIwtwtiwI/9NWpskZZxrecxuSV\nPi7VLQ+YHDKSn2IVHthKVTzYOos9EvFlOY7JyZbvjufPp1yEZsM9uxu//eZFrbhGebu3OcbqcFZp\n3q3VFbfGa/drNmc7P8Wjlfgqe2YaEN3Xr19vt1INS7KFE1rF7bbl1Ynpx7enTp0qZyyxRi1vbO/I\nKXo7a1QUqYqJx0RiLWJd4ocTh0f1fb3NU1n8WrOoZD7QiA0SSdSuZs4gAWBPi5NpBltxdo54K87O\nEQrkV1WewmejjXE+zYHj/Jtn1aGhoWy1Lc/FGeF1KKu6zZgs5pL1yWJ53n333ZdeeinWImaaz7tr\n9cY94qu4OublLZYtQqvm60TLweJClUXtiwg7tlXWRXsoq1MuxtB5UnthdSruoOJgi+MkwppYtXZV\naeN4i7UoSj3FNGPIB7ZSVdals2zrM3ZKUco/ppNlKsoJQIZ67RKYisfzNn/77Ra14hplay3FYC0L\ngnferdXXJc4tMcrCwkJskGyVNQvIFds2w+gqW6nlkmzqhFZxu21ndc7VlUPzdqngjpyit7lGWf87\nxFwmJibif2bOtcplvbZ5Kss5xrcP69kFNFMEiIcsg6c4n+a9k+jOhiNattHeLC48We8qW3nL5+YZ\nmxalVGv3WzjZ1BV9U+K0HtMvnunnhSH71+6XL4/VuX79+rX7mm90tRssLlHlTRRziY52jYg/gNXJ\nJS9eoZDL3BxX7Z3V2XCNmrV7Op8lgGMtcnUiLMjpPMiV2uy6NKxXNnsSsV2uQsTceSM2fh2xN3Oy\nGaXFlFuGRxWP523+9jssapU1yhaNssh4RmDZ4lDLlKzdbq0uxo3IsmiZJ5YnZpRbKauV573t/NXk\nUrUMdtstSfUTWsXtts3VadZuFts/RW9/jSJViNnFoZUbMz7GNKM7+284+vZPZbn3i+dpMWRz9Xd4\nwB5ZX1+3FXjoyu++qZVefxPn2SLQifNvw/PlvFnbHFU0v3il/O6Ys2fPFm2b5JAdZlFdw7uZavUn\n+Hm9L69FcX1tfuNM58FiXXLi5ZfINGyfN998s7xBtlM8o8Pq1H7+jVExi4gJmuO/PbU6G65RcaWP\n2CKPw5bvtyofPEX8XRRrbrlSDcdbBAHlhhTblTLa8ro0zK4hosqbtQ1Ti+XP/K38frfiTVUNv7Is\npVPleN7mb7/zolZco2Li5Rc/NYwSS/7qq6922K3VNbw5q2GzFI/Oau3fNdbhAKt4Qms4wNptt+2v\nTpWV2sIpuvl4q/1826NbXqPyy7zKP/DmOTacZ7Z/KoufZ8O7zCre4QIJAD3kpZdeykYSouPb3/72\nflyFbCEnH3PnG2T29R7pstXpsjXqpnXpgt9+Q2RcVM/Y76W9u2x1IqOIWPxb3/pWJPx5I9+Vl17j\nTcDsOc8///zf/d3f5UtJt/xq3ofr83XXr19/9913X3755ZYvyLQ61si6dN9vv7wun376aSQzd+7c\nif2y39/52mWrExF/8f7sfAU19BpPAAAAoIeoBAwAABIAAABAAgAAAEgAAAAACQAAACABAAAAJAAA\nAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAQAIAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAJAAwD4w\nNTXVrrv5Y9Gn4WO7KXcepcNEiv4dZtE8tc2uxYYL2fyxyqbb5hJ22B2b2v7Vd0GHUSouYYdhOq9U\nxT3b4TjZcNtW37kdNmn1Raq+xRp6bvbw6Lyvt/Yr3tpxVeVQ33DxtnBKaZ5RlTWq8qvcwq6v/uPa\nwrl3U+eQLRyNmxpsw3NjxaMItulRmwC2nwOMj4+37FN0xP+GwYru5tHb9WyYZss+m13mDuO2HKb6\nem24hMW4nbfAZpeww+5oXrBN7daGsZrnXuVIaB6syppW3GgV99rWjuot7+sqP4fN7tMqq7OpDdu8\n/Jv6FVc/8CoeJFtbvA03zoYzanfwbG2Tdjga2816wx9XxR9782DtcuAOa7SdmXb4zVY5p4EEAPa6\nDtFM50tjjtthxGKYzQ6w5cSg5fS3s0idh6+y6RrCgg2XsENGUX3Bium0XICGiXQepcpab+pu34Yb\nbUfuHVZZlyoru4VFqrhPqxw8W5jvpvZLeZTmLdYuBK++3ar/2JsXZqdOKZ2P+S2fVTb8hVZJ1Cue\ne8sheJXzzBaOxs6bpWGUDsfAlg8P2CxFgGDHcoDNjlKE6Zu9JjVPpOV1K5dqOxfs5ulvdpG2fxkr\nL3/L5elwEe2w5J1Tr/IwHRag3fJsuM07T3b7R+OOhMUbrkv1xd7UCm5qn24YeG1qvpsdveXxsGGp\nmIoHyWYj1PLCbHnWG26E7R+r7abQsv9mf0pbPpd23mIbznSzm6XDjtja4QGb5QkA7HwOsKm7p9Vv\nE+7gnaGWdysrTr/zXbfmiTQ8+664Ctu8E7bh7ij36fDEYFO7oHmUKrf6qmzGbe76Dfd1c5+Wq7+r\nC7m1NLu85FUWYPuPpNpNrWGLdSjjtOFB0m5dyovXbpdt6glAyxk1bIQqW6y2E487NnycUuWntNnz\nxpbPMxvG6B0m2+HHVeV3BzvikfX1dVsBepYip7YAAL1GESDoaWJfWwAACQAAACABAAAAJAAAAIAE\nAAAAkAAAAAASAAAAQAIAAABIAAAAAAkAAAAgAQAAACQAAAAgAQAAACQAAACABAAAAJAAAAAAEgAA\nAEACAAAASAAAAAAJAAAAIAEAAAAkAAAAgAQAAAAkAAAAgAQAAACQAAAAABIAAABAAgAAAEgAAAAA\nCQAAACABAAAAJAAAAIAEAAAAkAAAAIAEAAAAkAAAAAASAAAAQAIAAABIAAAAAAkAAAAgAQAAACQA\nAACABAAAAJAAAAAAEgAAAEACAAAAEgAAAEACAAAASAAAAAAJAAAAIAEAAAAkAAAAgAQAAACQAAAA\nABIAAABAAgAAAEgAAABAAgAAAEgAAAAACQAAACABAAAAJAAAAIAEAAAAkAAAAAASAAAAQAIAAABI\nAAAAAAkAAABIAAAAAAkAAAAgAQAAACQAAACABAAAAJAAAAAAEgAAAEACAAAASAAAAAAJAAAAIAEA\nAAAJAAAAIAEAAAAkAAAAgAQAAACQAAAAABIAAABAAgAAAEgAAAAACQAAACABAAAAJAAAACABsAkA\nAEACAAAASAAAAAAJAAAAIAEAAAAkAAAAgAQAAACQAAAAABIAAABAAgAAAEgAAAAACQAAAEgAAAAA\nCQAAACABAAAAJAAAAIAEAAAAkAAAAAASAAAAQAIAAABs0f8Hmgbky1O7zwgAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add your filename and uncomment the following line:\n", "Image(filename='TheoryAndPracticeEx02graph.png')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Describe in detail the ways in which the visualization violates graphical *integrity* and *excellence*:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "51d112a58baebcf9db9e75eb596a408c", "grade": true, "grade_id": "theorypracticeex02b", "points": 8, "solution": true } }, "source": [ "Looking at this graph, I have no idea what it is trying to say; only one axis is labeled. According to the very small print at the bottom, it is showing \"GDP % quarterly change.\"\n", "The title of the article is \"U.S. economy looks weaker, as GDP data is revised.\" I do not get that from this graph. In fact, it shows growth from last quarter. \n", "This graph has so few data points that it cannot possibly have graphical integrity.\n", "It clearly violates graphical excellence because it takes a lot to figure out what it is trying to say." ] } ], "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
masve/saav-deliveries
project/NYC notebook 2.ipynb
1
3487599
null
mit
boya-zhou/kaggle_bimbo_reformat
notebooks/6_stack_model.ipynb
1
17613
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import tensorflow as tf\n", "from sklearn.cross_validation import train_test_split\n", "import xgboost as xgb\n", "from scipy import sparse\n", "from sklearn.feature_extraction import FeatureHasher\n", "from scipy.sparse import coo_matrix,csr_matrix,csc_matrix, hstack\n", "from sklearn.preprocessing import normalize\n", "from sklearn.utils import shuffle\n", "from sklearn import linear_model\n", "import gc\n", "from sklearn import preprocessing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1_predata.ipynb preprocessed_products.csv\r\n", "3_xgb.ipynb ruta_for_cliente_producto.csv\r\n", "3_xgb_prediction.ipynb \u001b[0m\u001b[01;34mstack_sub\u001b[0m/\r\n", "44fea_bst.model submission_10_new.csv\r\n", "4_keras_nn.ipynb submission_11_new.csv\r\n", "5_random_forest.ipynb submission_44fea.csv\r\n", "6_stack_model.ipynb submission_nn.csv\r\n", "agencia_for_cliente_producto.csv submission_nn_xgb\r\n", "canal_for_cliente_producto.csv train_pivot_56789_to_10_44fea.pickle\r\n", "model_nn_10_after_l2reg.h5 train_pivot_56789_to_10_new.pickle\r\n", "model_nn_10.h5 train_pivot_6789_to_11_new.pickle\r\n", "model_nn_10_whole.h5 train_pivot_xgb_time1_44fea.csv\r\n", "\u001b[01;34mold_submission\u001b[0m/ train_pivot_xgb_time1.csv\r\n", "\u001b[01;34morigin\u001b[0m/ train_pivot_xgb_time2_38fea.csv\r\n", "pivot_test.pickle train_pivot_xgb_time2.csv\r\n", "pivot_train_with_nan.pickle\r\n" ] } ], "source": [ "%ls" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### read train data\n", "-----------------------\n", "- for xgb\n", "- for nn\n", "-----------------" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20768652, 1)\n", "(20768652, 2)\n" ] } ], "source": [ "stack_train_nn_10= pd.read_pickle('stack_sub/stack_train_nn_10.pickle')\n", "stack_train_xgb_10= pd.read_csv('stack_sub/stack_train_xgb_10.csv',index_col = False,header = None)\n", "train_label = pd.read_csv('train_pivot_xgb_time1.csv',usecols = ['target'])\n", "\n", "print stack_train_nn_10.shape\n", "print stack_train_xgb_10.shape" ] }, { "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>xgb</th>\n", " <th>nn</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.767780</td>\n", " <td>3.470119</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.835551</td>\n", " <td>2.667063</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.999626</td>\n", " <td>1.882208</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.505517</td>\n", " <td>3.318728</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.278482</td>\n", " <td>4.153247</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xgb nn\n", "0 2.767780 3.470119\n", "1 2.835551 2.667063\n", "2 1.999626 1.882208\n", "3 3.505517 3.318728\n", "4 4.278482 4.153247" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stack_train_xgb_10.rename(columns = {1:'xgb'},inplace = True)\n", "stack_train_nn_10.rename(columns = {'predict':'nn'},inplace = True)\n", "stack_train = pd.DataFrame()\n", "stack_train['xgb'] = stack_train_xgb_10['xgb']\n", "stack_train['nn'] = stack_train_nn_10['nn']\n", "stack_train['target'] = train_label['target']\n", "stack_train.head()" ] }, { "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>xgb</th>\n", " <th>nn</th>\n", " <th>target</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.767780</td>\n", " <td>3.470119</td>\n", " <td>4.574711</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.835551</td>\n", " <td>2.667063</td>\n", " <td>2.639057</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.999626</td>\n", " <td>1.882208</td>\n", " <td>2.397895</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.505517</td>\n", " <td>3.318728</td>\n", " <td>3.784190</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.278482</td>\n", " <td>4.153247</td>\n", " <td>4.682131</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xgb nn target\n", "0 2.767780 3.470119 4.574711\n", "1 2.835551 2.667063 2.639057\n", "2 1.999626 1.882208 2.397895\n", "3 3.505517 3.318728 3.784190\n", "4 4.278482 4.153247 4.682131" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stack_train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### begin xgboost\n", "-------------------" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "param_10 = {'booster':'gbtree',\n", " 'nthread': 7,\n", " 'max_depth':5, \n", " 'eta':0.4,\n", " 'silent':1,\n", " 'subsample':0.7, \n", " 'objective':'reg:linear',\n", " 'eval_metric':'rmse'}" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_label_10 = stack_train['target']\n", "train_feature_10 = stack_train.drop(['target'],axis = 1)\n", "\n", "dtrain_10 = xgb.DMatrix(train_feature_10,label = train_label_10,missing= np.nan)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\ttrain-rmse:0.89846\ttest-rmse:0.898465\n", "[1]\ttrain-rmse:0.647989\ttest-rmse:0.647995\n", "[2]\ttrain-rmse:0.529401\ttest-rmse:0.529408\n", "[3]\ttrain-rmse:0.479527\ttest-rmse:0.479536\n", "[4]\ttrain-rmse:0.460222\ttest-rmse:0.460231\n", "[5]\ttrain-rmse:0.45305\ttest-rmse:0.453061\n", "[6]\ttrain-rmse:0.450429\ttest-rmse:0.450443\n", "[7]\ttrain-rmse:0.449472\ttest-rmse:0.449488\n", "[8]\ttrain-rmse:0.449121\ttest-rmse:0.44914\n", "[9]\ttrain-rmse:0.448989\ttest-rmse:0.44901\n", "[10]\ttrain-rmse:0.448935\ttest-rmse:0.448957\n", "[11]\ttrain-rmse:0.44891\ttest-rmse:0.448935\n", "[12]\ttrain-rmse:0.448898\ttest-rmse:0.448925\n", "[13]\ttrain-rmse:0.44889\ttest-rmse:0.448919\n", "[14]\ttrain-rmse:0.448885\ttest-rmse:0.448916\n", "[15]\ttrain-rmse:0.448879\ttest-rmse:0.448911\n", "[16]\ttrain-rmse:0.448874\ttest-rmse:0.448908\n", "[17]\ttrain-rmse:0.44887\ttest-rmse:0.448904\n", "[18]\ttrain-rmse:0.448867\ttest-rmse:0.448902\n", "[19]\ttrain-rmse:0.448863\ttest-rmse:0.4489\n", "[20]\ttrain-rmse:0.448859\ttest-rmse:0.448896\n", "[21]\ttrain-rmse:0.448854\ttest-rmse:0.448893\n", "[22]\ttrain-rmse:0.448851\ttest-rmse:0.44889\n", "[23]\ttrain-rmse:0.448848\ttest-rmse:0.448889\n", "[24]\ttrain-rmse:0.448845\ttest-rmse:0.448887\n", "[25]\ttrain-rmse:0.448842\ttest-rmse:0.448885\n", "[26]\ttrain-rmse:0.44884\ttest-rmse:0.448885\n", "[27]\ttrain-rmse:0.448838\ttest-rmse:0.448883\n", "[28]\ttrain-rmse:0.448836\ttest-rmse:0.448881\n", "[29]\ttrain-rmse:0.448834\ttest-rmse:0.448881\n", "[30]\ttrain-rmse:0.448833\ttest-rmse:0.44888\n", "[31]\ttrain-rmse:0.448831\ttest-rmse:0.44888\n", "[32]\ttrain-rmse:0.448829\ttest-rmse:0.448879\n", "[33]\ttrain-rmse:0.448827\ttest-rmse:0.448878\n", "[34]\ttrain-rmse:0.448826\ttest-rmse:0.448878\n", "[35]\ttrain-rmse:0.448824\ttest-rmse:0.448877\n", "[36]\ttrain-rmse:0.448822\ttest-rmse:0.448877\n", "[37]\ttrain-rmse:0.448821\ttest-rmse:0.448877\n", "[38]\ttrain-rmse:0.448819\ttest-rmse:0.448876\n", "[39]\ttrain-rmse:0.448818\ttest-rmse:0.448874\n", "[40]\ttrain-rmse:0.448817\ttest-rmse:0.448874\n", "[41]\ttrain-rmse:0.448816\ttest-rmse:0.448874\n", "[42]\ttrain-rmse:0.448815\ttest-rmse:0.448874\n", "[43]\ttrain-rmse:0.448814\ttest-rmse:0.448873\n", "[44]\ttrain-rmse:0.448813\ttest-rmse:0.448874\n", "[45]\ttrain-rmse:0.448811\ttest-rmse:0.448873\n", "[46]\ttrain-rmse:0.44881\ttest-rmse:0.448873\n", "[47]\ttrain-rmse:0.448809\ttest-rmse:0.448873\n", "[48]\ttrain-rmse:0.448808\ttest-rmse:0.448874\n", "[49]\ttrain-rmse:0.448807\ttest-rmse:0.448873\n", "[50]\ttrain-rmse:0.448806\ttest-rmse:0.448873\n", "[51]\ttrain-rmse:0.448805\ttest-rmse:0.448873\n", "[52]\ttrain-rmse:0.448805\ttest-rmse:0.448873\n", "[53]\ttrain-rmse:0.448804\ttest-rmse:0.448873\n", " test-rmse-mean test-rmse-std train-rmse-mean train-rmse-std\n", "45 0.448873 0.000128 0.448811 0.000033\n", "46 0.448873 0.000128 0.448810 0.000033\n", "47 0.448873 0.000128 0.448809 0.000033\n", "48 0.448874 0.000128 0.448808 0.000033\n", "49 0.448873 0.000129 0.448807 0.000033\n" ] } ], "source": [ "num_round = 1500\n", "\n", "cvresult = xgb.cv(param_10, dtrain_10, num_round, nfold=5,show_stdv=False,\n", " seed = 42, early_stopping_rounds=5,verbose_eval = 1)\n", "print(cvresult.tail())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### read submission file\n", "-----------------" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6999251, 2)\n", "(6999251, 2)\n" ] } ], "source": [ "stack_train_nn_10= pd.read_csv('stack_sub/submission_nn_2.csv',index_col=0)\n", "stack_train_xgb_10= pd.read_csv('stack_sub/submission_xgb_2.csv',index_col=0)\n", "\n", "stack_train_xgb_10.reset_index(inplace = True)\n", "stack_train_nn_10.reset_index(inplace = True)\n", "\n", "stack_train_xgb_10.rename(columns = {'Demanda_uni_equil':'xgb'},inplace = True)\n", "stack_train_nn_10.rename(columns = {'Demanda_uni_equil':'nn'},inplace = True)\n", "\n", "print stack_train_nn_10.shape\n", "print stack_train_xgb_10.shape" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stack_train_xgb_10['nn'] = stack_train_nn_10['nn']\n", "stack_train_xgb_10['nn'] = stack_train_xgb_10['nn'].apply(np.log1p)\n", "stack_train_xgb_10['xgb'] = stack_train_xgb_10['xgb'].apply(np.log1p)" ] }, { "cell_type": "code", "execution_count": 33, "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>id</th>\n", " <th>xgb</th>\n", " <th>nn</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1569352</td>\n", " <td>2.128232</td>\n", " <td>1.740466</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6667200</td>\n", " <td>3.627004</td>\n", " <td>3.629660</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1592616</td>\n", " <td>2.990720</td>\n", " <td>3.000720</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3909690</td>\n", " <td>4.172848</td>\n", " <td>4.207673</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3659672</td>\n", " <td>3.634951</td>\n", " <td>3.577948</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id xgb nn\n", "0 1569352 2.128232 1.740466\n", "1 6667200 3.627004 3.629660\n", "2 1592616 2.990720 3.000720\n", "3 3909690 4.172848 4.207673\n", "4 3659672 3.634951 3.577948" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stack_train_xgb_10.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3538385, 3)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>xgb</th>\n", " <th>nn</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1569352</td>\n", " <td>2.128232</td>\n", " <td>1.740466</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6667200</td>\n", " <td>3.627004</td>\n", " <td>3.629660</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1592616</td>\n", " <td>2.990720</td>\n", " <td>3.000720</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3909690</td>\n", " <td>4.172848</td>\n", " <td>4.207673</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3659672</td>\n", " <td>3.634951</td>\n", " <td>3.577948</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id xgb nn\n", "0 1569352 2.128232 1.740466\n", "1 6667200 3.627004 3.629660\n", "2 1592616 2.990720 3.000720\n", "3 3909690 4.172848 4.207673\n", "4 3659672 3.634951 3.577948" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stack_train_xgb_10 = stack_train_xgb_10.iloc[:3538385]\n", "print stack_train_xgb_10.shape\n", "stack_train_xgb_10.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ClaudioVZ/Metodos_numericos_II
06_Elementos_finitos/025_esfuerzo_plano.ipynb
1
27419
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Esfuerzo plano\n", "\n", "Ecuaciones de equilibrio interno del esfuerzo plano en función de los desplazamientos\n", "\n", "\\begin{align*}\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\frac{\\partial^{2} u}{\\partial x^{2}}\n", " + \\nu \\frac{\\partial^{2} v}{\\partial x \\ \\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial y^{2}}\n", " + \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\bigg)\n", " \\bigg] + F_{x} &= 0 \\\\\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\nu \\frac{\\partial^{2} u}{\\partial y \\ \\partial x}\n", " + \\frac{\\partial^{2} v}{\\partial y^{2}}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial x \\ \\partial y}\n", " + \\frac{\\partial^{2} v}{\\partial x^{2}} \\bigg)\n", " \\bigg] + F_{y} &= 0\n", "\\end{align*}\n", "\n", "Usando el método de Galerkin\n", "\n", "\\begin{align*}\n", " \\int_{0}^{t} \\int_{0}^{h} \\int_{0}^{b} \\bigg\\{\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\frac{\\partial^{2} u}{\\partial x^{2}}\n", " + \\nu \\frac{\\partial^{2} v}{\\partial x \\ \\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial y^{2}}\n", " + \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\bigg)\n", " \\bigg] + F_{x} \\bigg\\} (N_{1} + N_{2} + N_{3} + N_{4}) \\ dx \\ dy \\ dz &= 0 \\\\\n", " \\int_{0}^{t} \\int_{0}^{h} \\int_{0}^{b} \\bigg\\{\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\nu \\frac{\\partial^{2} u}{\\partial y \\ \\partial x}\n", " + \\frac{\\partial^{2} v}{\\partial y^{2}}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial x \\ \\partial y}\n", " + \\frac{\\partial^{2} v}{\\partial x^{2}} \\bigg)\n", " \\bigg] + F_{y} \\bigg\\} (N_{1} + N_{2} + N_{3} + N_{4}) \\ dx \\ dy \\ dz &= 0\n", "\\end{align*}\n", "\n", "El espesor es constante\n", "\n", "\\begin{align*}\n", " t \\int_{0}^{h} \\int_{0}^{b} \\bigg\\{\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\frac{\\partial^{2} u}{\\partial x^{2}}\n", " + \\nu \\frac{\\partial^{2} v}{\\partial x \\ \\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial y^{2}}\n", " + \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\bigg)\n", " \\bigg] + F_{x} \\bigg\\} (N_{1} + N_{2} + N_{3} + N_{4}) \\ dx \\ dy &= 0 \\\\\n", " t \\int_{0}^{h} \\int_{0}^{b} \\bigg\\{\n", " \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\nu \\frac{\\partial^{2} u}{\\partial y \\ \\partial x}\n", " + \\frac{\\partial^{2} v}{\\partial y^{2}}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial x \\ \\partial y}\n", " + \\frac{\\partial^{2} v}{\\partial x^{2}} \\bigg)\n", " \\bigg] + F_{y} \\bigg\\} (N_{1} + N_{2} + N_{3} + N_{4}) \\ dx \\ dy &= 0\n", "\\end{align*}\n", "\n", "Para reducir el tamaño de la ecuación se usara $N_{i}$\n", "\n", "\\begin{align*}\n", " t \\int_{0}^{h} \\int_{0}^{b}\n", " \\bigg\\{ \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\frac{\\partial^{2} u}{\\partial x^{2}}\n", " + \\nu \\frac{\\partial^{2} v}{\\partial x \\ \\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial y^{2}}\n", " + \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\bigg)\n", " \\bigg] + F_{x} \\bigg\\} N_{i} \\ dx \\ dy &= 0 \\\\\n", " t \\int_{0}^{h} \\int_{0}^{b}\n", " \\bigg\\{ \\frac{E}{1 - \\nu^{2}} \\bigg[\n", " \\nu \\frac{\\partial^{2} u}{\\partial y \\ \\partial x}\n", " + \\frac{\\partial^{2} v}{\\partial y^{2}}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial^{2} u}{\\partial x \\ \\partial y}\n", " + \\frac{\\partial^{2} v}{\\partial x^{2}} \\bigg)\n", " \\bigg] + F_{y} \\bigg\\} N_{i} \\ dx \\ dy &= 0\n", "\\end{align*}\n", "\n", "Expandiendo términos\n", "\n", "\\begin{align*}\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " N_{i} \\frac{\\partial^{2} u}{\\partial x^{2}}\n", " + \\nu N_{i} \\frac{\\partial^{2} v}{\\partial x \\ \\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " N_{i} \\frac{\\partial^{2} u}{\\partial y^{2}}\n", " + N_{i} \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\bigg) \\ dx \\ dy\n", " + t \\int_{0}^{h} \\int_{0}^{b} N_{i} F_{x} \\ dx \\ dy &= 0 \\\\\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\nu N_{i} \\frac{\\partial^{2} u}{\\partial y \\ \\partial x}\n", " + N_{i} \\frac{\\partial^{2} v}{\\partial y^{2}}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " N_{i} \\frac{\\partial^{2} u}{\\partial x \\ \\partial y}\n", " + N_{i} \\frac{\\partial^{2} v}{\\partial x^{2}} \\bigg) \\ dx \\ dy\n", " + t \\int_{0}^{h} \\int_{0}^{b} N_{i} F_{y} \\ dx \\ dy &= 0\n", "\\end{align*}\n", "\n", "Se reducira el orden de las derivadas parciales mediante integración por partes\n", "\n", "\\begin{equation*}\n", " \\int \\alpha \\ d\\beta = \\alpha \\beta - \\int \\beta \\ d\\alpha\n", "\\end{equation*}\n", "\n", "Para $u_{xx}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial x^{2}} \\ dx \\ dy =\n", " \\int_{0}^{h} \\bigg( \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial x^{2}} \\ dx \\bigg) \\ dy\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} u}{\\partial x^{2}} \\ dx \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial x} \\ dx & \\beta = \\frac{\\partial u}{\\partial x}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dy =\n", " \\int_{0}^{h} \\bigg(\n", " N_{i} \\frac{\\partial u}{\\partial x}\n", " - \\int_{0}^{b} \\frac{\\partial u}{\\partial x} \\frac{\\partial N_{i}}{\\partial x} \\ dx \\bigg) \\ dy =\n", " \\int_{0}^{h} N_{i} \\frac{\\partial u}{\\partial x} \\ dy\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial x} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $v_{xy}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial x \\ \\partial y} \\ dx \\ dy =\n", " \\int_{0}^{h} \\bigg( \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial x \\ \\partial y} \\ dx \\bigg) \\ dy\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} v}{\\partial x \\ \\partial y} \\ dx \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial x} \\ dx & \\beta = \\frac{\\partial v}{\\partial y}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dy =\n", " \\int_{0}^{h} \\bigg(\n", " N_{i} \\frac{\\partial v}{\\partial y}\n", " - \\int_{0}^{b} \\frac{\\partial v}{\\partial y} \\frac{\\partial N_{i}}{\\partial x} \\ dx \\bigg) \\ dy =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial v}{\\partial y} \\ dy\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial y} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $u_{yy}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial y^{2}} \\ dx \\ dy =\n", " \\int_{0}^{b} \\bigg( \\int_{0}^{h} N_{i} \\frac{\\partial^{2} u}{\\partial y^{2}} \\ dy \\bigg) \\ dx\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} u}{\\partial y^{2}} \\ dy \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial y} \\ dy & \\beta = \\frac{\\partial u}{\\partial y}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dx =\n", " \\int_{0}^{b} \\bigg(\n", " N_{i} \\frac{\\partial u}{\\partial y}\n", " - \\int_{0}^{h} \\frac{\\partial u}{\\partial y} \\frac{\\partial N_{i}}{\\partial y} \\ dy \\bigg) \\ dx =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial u}{\\partial y} \\ dx\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial y} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $v_{yx}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\ dx \\ dy =\n", " \\int_{0}^{b} \\bigg( \\int_{0}^{h} N_{i} \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\ dy \\bigg) \\ dx\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} v}{\\partial y \\ \\partial x} \\ dy \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial y} \\ dy & \\beta = \\frac{\\partial v}{\\partial x}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dx =\n", " \\int_{0}^{b} \\bigg(\n", " N_{i} \\frac{\\partial v}{\\partial x}\n", " - \\int_{0}^{h} \\frac{\\partial v}{\\partial x} \\frac{\\partial N_{i}}{\\partial y} \\ dy \\bigg) \\ dx =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial v}{\\partial x} \\ dx\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial x} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $u_{yx}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial y \\ \\partial x} \\ dx \\ dy =\n", " \\int_{0}^{b} \\bigg( \\int_{0}^{h} N_{i} \\frac{\\partial^{2} u}{\\partial y \\ \\partial x} \\ dy \\bigg) \\ dx\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} u}{\\partial y \\ \\partial x} \\ dy \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial y} \\ dy & \\beta = \\frac{\\partial u}{\\partial x}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dx =\n", " \\int_{0}^{b} \\bigg(\n", " N_{i} \\frac{\\partial u}{\\partial x}\n", " - \\int_{0}^{h} \\frac{\\partial u}{\\partial x} \\frac{\\partial N_{i}}{\\partial y} \\ dy \\bigg) \\ dx =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial u}{\\partial x} \\ dx\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial x} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $v_{yy}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial y^{2}} \\ dx \\ dy =\n", " \\int_{0}^{b} \\bigg( \\int_{0}^{h} N_{i} \\frac{\\partial^{2} v}{\\partial y^{2}} \\ dy \\bigg) \\ dx\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} v}{\\partial y^{2}} \\ dy \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial y} \\ dy & \\beta = \\frac{\\partial v}{\\partial y}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dx =\n", " \\int_{0}^{b} \\bigg(\n", " N_{i} \\frac{\\partial v}{\\partial y}\n", " - \\int_{0}^{h} \\frac{\\partial v}{\\partial y} \\frac{\\partial N_{i}}{\\partial y} \\ dy \\bigg) \\ dx =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial v}{\\partial y} \\ dx\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial y} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $u_{xy}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial x \\ \\partial y} \\ dx \\ dy =\n", " \\int_{0}^{h} \\bigg( \\int_{0}^{b} N_{i} \\frac{\\partial^{2} u}{\\partial x \\ \\partial y} \\ dx \\bigg) \\ dy\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} u}{\\partial x \\ \\partial y} \\ dx \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial x} \\ dx & \\beta = \\frac{\\partial u}{\\partial y}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dy =\n", " \\int_{0}^{h} \\bigg(\n", " N_{i} \\frac{\\partial u}{\\partial y}\n", " - \\int_{0}^{b} \\frac{\\partial u}{\\partial y} \\frac{\\partial N_{i}}{\\partial x} \\ dx \\bigg) \\ dy =\n", " \\int_{0}^{b} N_{i} \\frac{\\partial u}{\\partial y} \\ dy\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial y} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Para $v_{xx}$\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial x^{2}} \\ dx \\ dy =\n", " \\int_{0}^{h} \\bigg( \\int_{0}^{b} N_{i} \\frac{\\partial^{2} v}{\\partial x^{2}} \\ dx \\bigg) \\ dy\n", "\\end{equation*}\n", "\n", "identificando variables\n", "\n", "\\begin{matrix}\n", " \\alpha = N_{i} & d\\beta = \\frac{\\partial^{2} v}{\\partial x^{2}} \\ dx \\\\\n", " d\\alpha = \\frac{\\partial N_{i}}{\\partial x} \\ dx & \\beta = \\frac{\\partial v}{\\partial x}\n", "\\end{matrix}\n", "\n", "reemplazando\n", "\n", "\\begin{equation*}\n", " \\int ( \\alpha \\beta - \\int \\beta \\ d\\alpha ) \\ dy =\n", " \\int_{0}^{h} \\bigg(\n", " N_{i} \\frac{\\partial v}{\\partial x}\n", " - \\int_{0}^{b} \\frac{\\partial v}{\\partial x} \\frac{\\partial N_{i}}{\\partial x} \\ dx \\bigg) \\ dy =\n", " \\int_{0}^{h} N_{i} \\frac{\\partial v}{\\partial x} \\ dy\n", " - \\int_{0}^{h} \\int_{0}^{b} \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial x} \\ dx \\ dy\n", "\\end{equation*}\n", "\n", "Reemplazando y reordenando\n", "\n", "\\begin{align*}\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial x}\n", " + \\nu \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial y}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial x} \\bigg) \\ dx \\ dy &=\n", " t \\frac{E}{1 - \\nu^{2}} \\bigg[ \\int_{0}^{h}\n", " N_{i} \\frac{\\partial u}{\\partial x}\n", " + \\nu N_{i} \\frac{\\partial v}{\\partial y} \\ dy\n", " + \\frac{1 - \\nu}{2} \\int_{0}^{b}\n", " N_{i} \\frac{\\partial u}{\\partial y}\n", " + N_{i} \\frac{\\partial v}{\\partial x} \\ dx \\bigg]\n", " + t \\int_{0}^{h} \\int_{0}^{b} N_{i} F_{x} \\ dx \\ dy \\\\\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\nu \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial x}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial y}\n", " + \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial x} \\bigg) \\ dx \\ dy &=\n", " t \\frac{E}{1 - \\nu^{2}} \\bigg[ \\int_{0}^{b}\n", " \\nu N_{i} \\frac{\\partial u}{\\partial x}\n", " + N_{i} \\frac{\\partial v}{\\partial y} \\ dx\n", " + \\frac{1 - \\nu}{2} \\int_{0}^{h}\n", " N_{i} \\frac{\\partial u}{\\partial y}\n", " + N_{i} \\frac{\\partial v}{\\partial x} \\ dy \\bigg]\n", " + t \\int_{0}^{h} \\int_{0}^{b} N_{i} F_{y} \\ dx \\ dy\n", "\\end{align*}\n", "\n", "El lado derecho es reemplazado por su equivalente nodal $F_{j}$\n", "\n", "\\begin{align*}\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial x}\n", " + \\nu \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial y}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial x} \\bigg) \\ dx \\ dy &=\n", " F_{jx} \\\\\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\nu \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial u}{\\partial x}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial v}{\\partial y}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial u}{\\partial y}\n", " + \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial v}{\\partial x} \\bigg) \\ dx \\ dy &=\n", " F_{jy}\n", "\\end{align*}\n", "\n", "El desplazamiento horizontal es\n", "\n", "\\begin{equation*}\n", " u =\n", " \\begin{bmatrix}\n", " N_{1} & N_{2} & N_{3} & N_{4}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3} \\\\\n", " u_{4}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "El desplazamiento vertical es\n", "\n", "\\begin{equation*}\n", " v =\n", " \\begin{bmatrix}\n", " N_{1} & N_{2} & N_{3} & N_{4}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " v_{1} \\\\\n", " v_{2} \\\\\n", " v_{3} \\\\\n", " v_{4}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "Para reducir el tamaño de la ecuación $u = N_{j} u_{j}$ y $v = N_{j} v_{j}$; sus derivadas serán\n", "\n", "\\begin{equation*}\n", " \\begin{matrix}\n", " \\frac{\\partial u}{\\partial x} = \\frac{\\partial N_{j}}{\\partial x} u_{j} &\n", " \\frac{\\partial u}{\\partial y} = \\frac{\\partial N_{j}}{\\partial y} u_{j} \\\\\n", " \\frac{\\partial v}{\\partial x} = \\frac{\\partial N_{j}}{\\partial x} v_{j} &\n", " \\frac{\\partial v}{\\partial y} = \\frac{\\partial N_{j}}{\\partial y} v_{j}\n", " \\end{matrix}\n", "\\end{equation*}\n", "\n", "Reemplazando\n", "\n", "\\begin{align*}\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial x} u_{j}\n", " + \\nu \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial y} v_{j}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial y} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial x} v_{j} \\bigg) \\ dx \\ dy &=\n", " F_{jx} \\\\\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\nu \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial x} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial y} v_{j}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial y} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial x} v_{j} \\bigg) \\ dx \\ dy &=\n", " F_{jy}\n", "\\end{align*}\n", "\n", "En forma matricial\n", "\n", "\\begin{equation*}\n", " t \\frac{E}{1 - \\nu^{2}} \\int_{0}^{h} \\int_{0}^{b}\n", " \\begin{bmatrix}\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial x} u_{j}\n", " + \\nu \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial y} v_{j}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial y} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial x} v_{j} \\bigg)\\\\\n", " \\nu \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial x} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial y} \\frac{\\partial N_{j}}{\\partial y} v_{j}\n", " + \\frac{1 - \\nu}{2} \\bigg(\n", " \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial y} u_{j}\n", " + \\frac{\\partial N_{i}}{\\partial x} \\frac{\\partial N_{j}}{\\partial x} v_{j} \\bigg)\n", " \\end{bmatrix} \\ dx \\ dy =\n", " \\begin{bmatrix}\n", " F_{jx} \\\\\n", " F_{jy}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "Factorizando y reordenando\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b}\n", " \\begin{bmatrix}\n", " \\frac{\\partial N_{i}}{\\partial x} & 0 & \\frac{\\partial N_{i}}{\\partial y} \\\\\n", " 0 & \\frac{\\partial N_{i}}{\\partial y} & \\frac{\\partial N_{i}}{\\partial x} \\\\\n", " \\end{bmatrix} \\frac{E}{1 - \\nu^{2}}\n", " \\begin{bmatrix}\n", " 1 & \\nu & 0 \\\\\n", " \\nu & 1 & 0 \\\\\n", " 0 & 0 & \\frac{1 - \\nu}{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\frac{\\partial N_{j}}{\\partial x} & 0 \\\\\n", " 0 & \\frac{\\partial N_{j}}{\\partial y} \\\\\n", " \\frac{\\partial N_{j}}{\\partial y} & \\frac{\\partial N_{j}}{\\partial x}\n", " \\end{bmatrix} \\ t \\ dx \\ dy\n", " \\begin{bmatrix}\n", " u_{j} \\\\\n", " v_{j}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " F_{jx} \\\\\n", " F_{jy}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "Reemplazando las cuatro funciones de forma\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b}\n", " \\begin{bmatrix}\n", " \\frac{\\partial N_{1}}{\\partial x} & 0 & \\frac{\\partial N_{1}}{\\partial y} \\\\\n", " 0 & \\frac{\\partial N_{1}}{\\partial y} & \\frac{\\partial N_{1}}{\\partial x} \\\\\n", " \\frac{\\partial N_{2}}{\\partial x} & 0 & \\frac{\\partial N_{2}}{\\partial y} \\\\\n", " 0 & \\frac{\\partial N_{2}}{\\partial y} & \\frac{\\partial N_{2}}{\\partial x} \\\\\n", " \\frac{\\partial N_{3}}{\\partial x} & 0 & \\frac{\\partial N_{3}}{\\partial y} \\\\\n", " 0 & \\frac{\\partial N_{3}}{\\partial y} & \\frac{\\partial N_{3}}{\\partial x} \\\\\n", " \\frac{\\partial N_{4}}{\\partial x} & 0 & \\frac{\\partial N_{4}}{\\partial y} \\\\\n", " 0 & \\frac{\\partial N_{4}}{\\partial y} & \\frac{\\partial N_{4}}{\\partial x}\n", " \\end{bmatrix} \\frac{E}{1 - \\nu^{2}}\n", " \\begin{bmatrix}\n", " 1 & \\nu & 0 \\\\\n", " \\nu & 1 & 0 \\\\\n", " 0 & 0 & \\frac{1 - \\nu}{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\frac{\\partial N_{1}}{\\partial x} & 0 &\n", " \\frac{\\partial N_{2}}{\\partial x} & 0 &\n", " \\frac{\\partial N_{3}}{\\partial x} & 0 &\n", " \\frac{\\partial N_{4}}{\\partial x} & 0 \\\\\n", " 0 & \\frac{\\partial N_{1}}{\\partial y} &\n", " 0 & \\frac{\\partial N_{2}}{\\partial y} &\n", " 0 & \\frac{\\partial N_{3}}{\\partial y} &\n", " 0 & \\frac{\\partial N_{4}}{\\partial y} \\\\\n", " \\frac{\\partial N_{1}}{\\partial y} & \\frac{\\partial N_{1}}{\\partial x} &\n", " \\frac{\\partial N_{2}}{\\partial y} & \\frac{\\partial N_{2}}{\\partial x} &\n", " \\frac{\\partial N_{3}}{\\partial y} & \\frac{\\partial N_{3}}{\\partial x} &\n", " \\frac{\\partial N_{4}}{\\partial y} & \\frac{\\partial N_{4}}{\\partial x}\n", " \\end{bmatrix} \\ t \\ dx \\ dy\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " v_{1} \\\\\n", " u_{2} \\\\\n", " v_{2} \\\\\n", " u_{3} \\\\\n", " v_{3} \\\\\n", " u_{4} \\\\\n", " v_{4}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " F_{1x} \\\\\n", " F_{1y} \\\\\n", " F_{2x} \\\\\n", " F_{2y} \\\\\n", " F_{3x} \\\\\n", " F_{3y} \\\\\n", " F_{4x} \\\\\n", " F_{4y}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "Puede reescribirse como\n", "\n", "\\begin{equation*}\n", " \\int_{0}^{h} \\int_{0}^{b}\n", " \\begin{bmatrix}\n", " B_{1} \\\\\n", " B_{2} \\\\\n", " B_{3} \\\\\n", " B_{4}\n", " \\end{bmatrix} \\frac{E}{1 - \\nu^{2}}\n", " \\begin{bmatrix}\n", " 1 & \\nu & 0 \\\\\n", " \\nu & 1 & 0 \\\\\n", " 0 & 0 & \\frac{1 - \\nu}{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " B_{1} & B_{2} & B_{3} & B_{4}\n", " \\end{bmatrix} \\ t \\ dx \\ dy\n", " \\begin{bmatrix}\n", " d_{1} \\\\\n", " d_{2} \\\\\n", " d_{3} \\\\\n", " d_{4}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " F_{1} \\\\\n", " F_{2} \\\\\n", " F_{3} \\\\\n", " F_{4}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "y en forma compacta\n", "\n", "\\begin{equation*}\n", " \\iint \\limits_{A} \\mathbf{B}^{\\mathrm{T}} \\ \\mathbf{D} \\ \\mathbf{B} \\ t \\ dA \\ \\mathbf{d} = \\mathbf{F}\n", "\\end{equation*}\n", "\n", "o en forma general\n", "\n", "\\begin{equation*}\n", " \\mathbf{K} \\ \\mathbf{d} = \\mathbf{F}\n", "\\end{equation*}" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.6", "language": "julia", "name": "julia 0.3" }, "language_info": { "name": "julia", "version": "0.3.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
pycircle/presentations
Wprowadzenie_4.v3.ipynb
1
12987
{ "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" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Programowanie obiektowe" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Programowanie obiektowe:\n", "\n", "obiekt a klasa\n", "\n", "klasa: cechy, metody\n", "obiekt: warto\u015bci cech\n", "\n", "\n", "dziedziczenie\n", "\n", "zwierz\u0119\n", "pies kot mysz\n", "duza_rasa mala_rasa itp itd..." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Pierwsza klasa" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def __init__(self, name):\n", " self.name = name\n", " def print_name(self):\n", " print self.name\n", "gra1 = Game('nazwa1')\n", "gra1.print_name()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "nazwa1\n" ] } ], "prompt_number": 9 }, { "cell_type": "raw", "metadata": {}, "source": [ "Dziedziczenie:\n", "\n", "kolejno\u015b\u0107 szukania atrybut\u00f3w:\n", "szuka w obiekcie, \n", "nast\u0119pnie we wszystkich klasach powy\u017cej niego, \n", "od lewej do prawej w przypadku dziedziczenia wilokrotnego.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def __init__(self, name):\n", " self.name = name\n", " def print_name(self):\n", " print self.name\n", "\n", "class Planszowki(Game):\n", " def __init__(self, name, liczba_graczy):\n", " self.name = name\n", " self.liczba_graczy = liczba_graczy\n", " def print_name(self):\n", " print self.name + ' dla ' + str(self.liczba_graczy)\n", "\n", "gra2 = Game('nazwa2')\n", "gra3 = Planszowki('nazwa3', 2)\n", "gra2.print_name()\n", "gra3.print_name()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "nazwa2\n", "nazwa3 dla 2\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Wielodziedziczenie - kolejno\u015b\u0107" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def __init__(self, name):\n", " self.name = name\n", " def print_name(self):\n", " print self.name\n", "\n", "class Talia(object):\n", " def potasuj(self):\n", " print 'potasowane'\n", " def print_name(self):\n", " print 'karcianka: '+ self.name\n", "\n", "class Karcianki(Game, Talia):\n", " def __init__(self, name, liczba_talii):\n", " self.name = name\n", " self.liczba_talii = liczba_talii\n", "\n", "class Karcianki2(Talia, Game):\n", " def __init__(self, name, liczba_talii):\n", " self.name = name\n", " self.liczba_talii = liczba_talii\n", " \n", "gra1 = Karcianki('karcianka', 2)\n", "gra2 = Karcianki2('karcianka', 2)\n", "gra1.print_name()\n", "gra1.potasuj()\n", "gra2.print_name()\n", "gra2.potasuj()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "karcianka\n", "potasowane\n", "karcianka: karcianka\n", "potasowane\n" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "super a wywo\u0142anie metody konkretnej klasy" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "super" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def zagraj(self):\n", " print 'pograne'\n", "\n", "class Talia(Game):\n", " def zagraj(self):\n", " print 'potasowane'\n", " super(Talia, self).zagraj()\n", "\n", "class Karcianka(Game):\n", " def zagraj(self):\n", " print 'rozdane'\n", " super(Karcianka, self).zagraj()\n", "\n", "class Munchkin(Talia, Karcianka):\n", " def zagraj(self):\n", " print 'Munchkin'\n", " super(Munchkin, self).zagraj()\n", "gra1 = Munchkin()\n", "gra1.zagraj()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Munchkin\n", "potasowane\n", "rozdane\n", "pograne\n" ] } ], "prompt_number": 6 }, { "cell_type": "raw", "metadata": {}, "source": [ "metoda klasy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def zagraj(self):\n", " print 'pograne'\n", "\n", "class Talia(Game):\n", " def zagraj(self):\n", " print 'potasowane'\n", " Game.zagraj(self)\n", "\n", "class Karcianka(Game):\n", " def zagraj(self):\n", " print 'rozdane'\n", " Game.zagraj(self)\n", "\n", "class Munchkin(Talia, Karcianka):\n", " def zagraj(self):\n", " print 'Munchkin'\n", " Talia.zagraj(self)\n", " Karcianka.zagraj(self)\n", "gra1 = Munchkin()\n", "gra1.zagraj()" ], "language": "python", "metadata": { "scrolled": true }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Munchkin\n", "potasowane\n", "pograne\n", "rozdane\n", "pograne\n" ] } ], "prompt_number": 5 }, { "cell_type": "raw", "metadata": {}, "source": [ "Dziedziczenie z object\n", "class Game(object): #new style\n", "\tpass\n", "class Game:\t\t\t#old style\n", "\tpass\n", "\n", "Python 3 only has new-style classes. No matter if you subclass from object or not, classes are new-style in Python 3. It is however recommended that you still subclass from object.\n", "\n", "Important behavior changes between old and new style classes: \n", "super added \n", "MRO changed \n", "descriptors added \n", "new style class objects cannot be raised unless derived from Exception\n", "__slots__ added\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "@staticmethod and @classmethod" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Date(object):\n", "\n", " def __init__(self, day=0, month=0, year=0):\n", " self.day = day\n", " self.month = month\n", " self.year = year\n", " print 'success: ', year, month, day\n", "\n", " @classmethod\n", " def from_string(cls, date_as_string):\n", " day, month, year = map(int, date_as_string.split('-'))\n", " date1 = cls(day, month, year)\n", " return date1\n", "\n", " @staticmethod\n", " def is_date_valid(date_as_string):\n", " day, month, year = map(int, date_as_string.split('-'))\n", " return day <= 31 and month <= 12 and year <= 3999\n", "\n", "\n", "is_date = Date.is_date_valid('11-09-2012')\n", "\n", "date2 = Date.from_string('11-09-2012')\n", "\n", "print is_date\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "success: 2012 9 11\n", "True\n" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "ochrona przed zmianami" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Game(object):\n", " def __init__(self, name):\n", " self.name = name\n", " def print_name(self):\n", " return self.name\n", " def zagraj(self):\n", " print 'pograne w ' + self.__print_name()\n", " def zagraj2(self):\n", " print 'pograne w ' + self.print_name()\n", " __print_name = print_name\n", "\n", "class Planszowki(Game):\n", " def __init__(self, name, liczba_graczy):\n", " self.name = name\n", " self.liczba_graczy = liczba_graczy\n", " def print_name(self):\n", " return self.name + ' dla ' + str(self.liczba_graczy)\n", "\n", "gra1 = Planszowki('gra1', 2)\n", "gra1.zagraj()\n", "gra1.zagraj2()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "pograne w gra1\n", "pograne w gra1 dla 2\n" ] } ], "prompt_number": 3 }, { "cell_type": "raw", "metadata": {}, "source": [ "Napisz klas\u0119 Cz\u0142owiek. Ka\u017cdy cz\u0142owiek ma nadawane imi\u0119 i rok urodzenia. Ka\u017cdy cz\u0142owiek umie si\u0119 przedstawi\u0107, tzn. Wy\u015bwietli\u0107 swoje imi\u0119 oraz poda\u0107 sw\u00f3j wiek (wyliczony na podstawie roku urodzenia i aktualnej daty)\n", "Ludzi mo\u017cemy podzieli\u0107 na kobiety i m\u0119\u017cczyzn. Kobiety podczas podawania swojego wieku odejmuj\u0105 sobie 2 lata. M\u0119\u017cczy\u017ani natomiast maj\u0105 problem z liczeniem i za ka\u017cdym razem myl\u0105 si\u0119 o +/- 3 lata (losowo).\n", "Napisz klas\u0119 rodzic. Ka\u017cdy rodzic ma nadawany stopie\u0144 odpowiedzialno\u015bci od 0-5. Gdy podczas tworzenia obiektu rodzic nie podamy odpowiedzialno\u015bci nadawana jest warto\u015b\u0107 2. Rodzic podany o odpowiedzialno\u015b\u0107 odpowiada zgodnie z prawd\u0105.\n", "Ka\u017cda matka jest zar\u00f3wno kobiet\u0105 jak i rodzicem. Przedstawia si\u0119 m\u00f3wi\u0105c \u201eJestem /imi\u0119/ i jestem mam\u0105\u201d. Pytana o wiek odpowiada ile lat ma jej dziecko, kt\u00f3re urodzi\u0142a w wieku 20 lat (wykorzystaj funkcj\u0119 z klasy cz\u0142owiek do obliczania wieku, a nie z klasy kobieta!), je\u017celi nie ma 20 lat odpowiada jak kobieta.\n", "Ka\u017cdy ojciec jest zar\u00f3wno m\u0119\u017cczyzn\u0105 jak i rodzicem. Pytany o odpowiedzialno\u015b\u0107 zawy\u017ca j\u0105 o 1 (nawet je\u017celi wychodzi poza skal\u0119!).\n" ] }, { "cell_type": "code", "collapsed": true, "input": [ "matka1 = Matka('Anna', 1989, 2)\n", "matka2 = Matka('Julia', 2000, 1)\n", "ojciec1 = Ojciec('Radek', 1989)\n", "ojciec2 = Ojciec('Mateusz', 1989, 5)\n", "kobieta1 = Kobieta('Anita', 1989)\n", "czlowiek1 = Czlowiek('Gender', 1989)\n", "matka1.podaj_wiek()\n", "matka2.podaj_wiek()\n", "ojciec1.podaj_wiek()\n", "ojciec2.podaj_wiek()\n", "kobieta1.podaj_wiek()\n", "czlowiek1.podaj_wiek()\n", "ojciec1.podaj_odpowiedzialnosc()\n", "ojciec2.podaj_odpowiedzialnosc()\n", "matka1.podaj_odpowiedzialnosc()\n", "matka1.podaj_imie()\n", "kobieta1.podaj_imie()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": null } ], "metadata": {} } ] }
apache-2.0
ruohoruotsi/Wavelet-Tree-Synth
FourierTalkOSCON-master/09_AudioFiltering.ipynb
1
1131252
null
gpl-2.0
nikbearbrown/Deep_Learning
NEU/Sai_Raghuram_Kothapalli_DL/Creditcard Fraud detection using Autoencoders.ipynb
1
291717
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Credit Card fraud detection using Autoencoders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's Sunday morning, it's quiet and you wake up with a big smile on your face. Today is going to be a great day! Except, your phone rings, rather \"internationally\". You pick it up slowly and hear something really bizarre - \"Bonjour, je suis Michele. Oops, sorry. I am Michele, your personal bank agent.\". What could possibly be so urgent for someone from Switzerland to call you at this hour? \"Did you authorize a transaction for $3,358.65 for 100 copies of Diablo 3?\" Immediately, you start thinking of ways to explain why you did that to your loved one. \"No, I didn't !?\". Michele's answer is quick and to the point - \"Thank you, we're on it\". Whew, that was close! But how did Michele knew that this transaction was suspicious? After all, you did order 10 new smartphones from that same bank account, last week - Michele didn't call then.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Annual global fraud losses reached 21.8 billion dollars in 2015, according to Nilson Report.\n", "Probably you feel very lucky if you are a fraud. About every 12 cents per $100 were stolen in the US during the same year. Our friend Michele might have a serious problem to solve here.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we will train an Autoencoder Neural Network (implemented in Keras) in unsupervised (or semi-supervised) fashion for Anomaly Detection in credit card transaction data. The trained model will be evaluated on pre-labeled and anonymized dataset.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "import tensorflow as tf\n", "import seaborn as sns\n", "from pylab import rcParams\n", "from sklearn.model_selection import train_test_split\n", "from keras.models import Model, load_model\n", "from keras.layers import Input, Dense\n", "from keras.callbacks import ModelCheckpoint, TensorBoard\n", "from keras import regularizers\n", "\n", "%matplotlib inline\n", "\n", "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n", "\n", "rcParams['figure.figsize'] = 14, 8\n", "\n", "RANDOM_SEED = 42\n", "LABELS = [\"Normal\", \"Fraud\"]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading the data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset we're going to use can be downloaded from Kaggle. It contains data about credit card transactions that occurred during a period of two days, with 492 frauds out of 284,807 transactions.\n", "\n", "All variables in the dataset are numerical. The data has been transformed using PCA transformation(s) due to privacy reasons. The two features that haven't been changed are Time and Amount. Time contains the seconds elapsed between each transaction and the first transaction in the dataset.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"creditcard.csv\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exploration" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(284807, 31)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "31 columns, 2 of which are Time and Amount. The rest are output from the PCA transformation. Let's check for missing values:\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().values.any()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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SJRo2bJhKliyZ76EjOTk5MgxDrq6uKlmypCTlG5Odna1SpUpJUoFz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\nAwAAAAATIJwBAAAAgAkQzgAAAADABAh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z+vjjj1VVVaXc3Fy9/vrrkiSHw9FhxwEAdK6A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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f46392e9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "count_classes = pd.value_counts(df['Class'], sort = True)\n", "count_classes.plot(kind = 'bar', rot=0)\n", "plt.title(\"Transaction class distribution\")\n", "plt.xticks(range(2), LABELS)\n", "plt.xlabel(\"Class\")\n", "plt.ylabel(\"Frequency\");\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a highly imbalanced dataset on our hands. Normal transactions overwhelm the fraudulent ones by a large margin. Let's look at the two types of transactions:\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "frauds = df[df.Class == 1]\n", "normal = df[df.Class == 0]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(492, 31)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frauds.shape " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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FxDUAAAAUEtcAAABQSFwDAABAoTaJ682bN+e6667LmDFjMmLEiFx00UV57rnn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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f463851240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(normal.Amount, bins = 100)\n", "plt.xlim([0,20000])\n", "plt.ylim([0,10000])\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's have a more graphical representation:\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Frauds')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1f400457f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(nrows = 2, ncols = 1, sharex = True)\n", "\n", "axes[0].hist(normal.Amount, bins = 100)\n", "axes[0].set_xlim([0,20000])\n", "axes[0].set_ylim([0,10000])\n", "axes[0].set_title('Normal')\n", "\n", "\n", "axes[1].hist(frauds.Amount, bins = 50)\n", "axes[1].set_xlim([0,10000])\n", "axes[1].set_ylim([0,200])\n", "axes[1].set_title('Frauds')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autoencoders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets get started with autoencoders and we optimize the parameters of our Autoencoder model in such way that a special kind of error -Rreconstruction Error is minimized. In practice, the traditional squared error is often used:\n", "$$\\textstyle L(x,x') = ||\\, x - x'||^2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preparing the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's drop the Time column (not going to use it) and use the scikit's StandardScaler on the Amount. The scaler removes the mean and scales the values to unit variance:\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "data = df.drop(['Time'], axis=1)\n", "\n", "data['Amount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training our Autoencoder is gonna be a bit different from what we are used to. Let's say you have a dataset containing a lot of non fraudulent transactions at hand. You want to detect any anomaly on new transactions. We will create this situation by training our model on the normal transactions, only. Reserving the correct class on the test set will give us a way to evaluate the performance of our model. We will reserve 20% of our data for testing:\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "X_train, X_test = train_test_split(data, test_size=0.2, random_state=RANDOM_SEED)\n", "X_train = X_train[X_train.Class == 0]\n", "X_train = X_train.drop(['Class'], axis=1)\n", "\n", "y_test = X_test['Class']\n", "X_test = X_test.drop(['Class'], axis=1)\n", "\n", "X_train = X_train.values\n", "X_test = X_test.values\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(227451, 29)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building the model\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our Autoencoder uses 4 fully connected layers with 14, 7, 7 and 29 neurons respectively. The first two layers are used for our encoder, the last two go for the decoder. Additionally, L1 regularization will be used during training:\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "input_dim = X_train.shape[1]\n", "encoding_dim = 32\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "input_layer = Input(shape=(input_dim, ))\n", "\n", "encoder = Dense(encoding_dim, activation=\"relu\", \n", " activity_regularizer=regularizers.l1(10e-5))(input_layer)\n", "encoder = Dense(int(encoding_dim / 2), activation=\"sigmoid\")(encoder)\n", "\n", "decoder = Dense(int(encoding_dim / 2), activation='sigmoid')(encoder)\n", "decoder = Dense(input_dim, activation='relu')(decoder)\n", "\n", "autoencoder = Model(inputs=input_layer, outputs=decoder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's train our model for 200 epochs with a batch size of 32 samples and save the best performing model to a file. The ModelCheckpoint provided by Keras is really handy for such tasks. Additionally, the training progress will be exported in a format that TensorBoard understands.\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import h5py as h5py\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 227451 samples, validate on 56962 samples\n", "Epoch 1/100\n", "227451/227451 [==============================] - 17s 75us/step - loss: 0.9167 - acc: 0.4643 - val_loss: 0.9067 - val_acc: 0.5274\n", "Epoch 2/100\n", "227451/227451 [==============================] - 17s 73us/step - loss: 0.8659 - acc: 0.5431 - val_loss: 0.8769 - val_acc: 0.5646\n", "Epoch 3/100\n", "227451/227451 [==============================] - 17s 73us/step - loss: 0.8390 - acc: 0.5754 - val_loss: 0.8621 - val_acc: 0.5740\n", "Epoch 4/100\n", "227451/227451 [==============================] - 17s 74us/step - loss: 0.8262 - acc: 0.5891 - val_loss: 0.8503 - val_acc: 0.5928\n", "Epoch 5/100\n", "227451/227451 [==============================] - 17s 73us/step - loss: 0.8130 - acc: 0.6100 - val_loss: 0.8346 - val_acc: 0.6231\n", "Epoch 6/100\n", "227451/227451 [==============================] - 16s 72us/step - loss: 0.8007 - acc: 0.6277 - val_loss: 0.8352 - val_acc: 0.6189\n", "Epoch 7/100\n", "227451/227451 [==============================] - 17s 74us/step - loss: 0.7937 - acc: 0.6347 - val_loss: 0.8176 - val_acc: 0.6445\n", "Epoch 8/100\n", "227451/227451 [==============================] - 22s 97us/step - loss: 0.7847 - acc: 0.6491 - val_loss: 0.8126 - val_acc: 0.6504\n", "Epoch 9/100\n", "227451/227451 [==============================] - 19s 85us/step - loss: 0.7805 - acc: 0.6507 - val_loss: 0.8102 - val_acc: 0.6436\n", "Epoch 10/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7789 - acc: 0.6521 - val_loss: 0.8075 - val_acc: 0.6522\n", "Epoch 11/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7783 - acc: 0.6521 - val_loss: 0.8079 - val_acc: 0.6447\n", "Epoch 12/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7769 - acc: 0.6533 - val_loss: 0.8054 - val_acc: 0.6574\n", "Epoch 13/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7754 - acc: 0.6553 - val_loss: 0.8054 - val_acc: 0.6522\n", "Epoch 14/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7751 - acc: 0.6559 - val_loss: 0.8055 - val_acc: 0.6543\n", "Epoch 15/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7742 - acc: 0.6572 - val_loss: 0.8088 - val_acc: 0.6493\n", "Epoch 16/100\n", "227451/227451 [==============================] - 16s 72us/step - loss: 0.7741 - acc: 0.6567 - val_loss: 0.8037 - val_acc: 0.6584\n", "Epoch 17/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7735 - acc: 0.6571 - val_loss: 0.8030 - val_acc: 0.6552\n", "Epoch 18/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7732 - acc: 0.6575 - val_loss: 0.8058 - val_acc: 0.6518\n", "Epoch 19/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7730 - acc: 0.6575 - val_loss: 0.8043 - val_acc: 0.6564\n", "Epoch 20/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7728 - acc: 0.6580 - val_loss: 0.8035 - val_acc: 0.6576\n", "Epoch 21/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7725 - acc: 0.6580 - val_loss: 0.8081 - val_acc: 0.6478\n", "Epoch 22/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7726 - acc: 0.6588 - val_loss: 0.8032 - val_acc: 0.6557\n", "Epoch 23/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7714 - acc: 0.6608 - val_loss: 0.8002 - val_acc: 0.6619\n", "Epoch 24/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7704 - acc: 0.6628 - val_loss: 0.8002 - val_acc: 0.6618\n", "Epoch 25/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7692 - acc: 0.6661 - val_loss: 0.7991 - val_acc: 0.6609\n", "Epoch 26/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7691 - acc: 0.6661 - val_loss: 0.8004 - val_acc: 0.6621\n", "Epoch 27/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7688 - acc: 0.6672 - val_loss: 0.7981 - val_acc: 0.6707\n", "Epoch 28/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7684 - acc: 0.6677 - val_loss: 0.7984 - val_acc: 0.6667\n", "Epoch 29/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7690 - acc: 0.6660 - val_loss: 0.8006 - val_acc: 0.6684\n", "Epoch 30/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7687 - acc: 0.6656 - val_loss: 0.7987 - val_acc: 0.6620\n", "Epoch 31/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7669 - acc: 0.6690 - val_loss: 0.7972 - val_acc: 0.6666\n", "Epoch 32/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7670 - acc: 0.6685 - val_loss: 0.7965 - val_acc: 0.6718\n", "Epoch 33/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7662 - acc: 0.6694 - val_loss: 0.7960 - val_acc: 0.6694\n", "Epoch 34/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7659 - acc: 0.6688 - val_loss: 0.7961 - val_acc: 0.6655\n", "Epoch 35/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7654 - acc: 0.6701 - val_loss: 0.8080 - val_acc: 0.6347\n", "Epoch 36/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7656 - acc: 0.6697 - val_loss: 0.7952 - val_acc: 0.6701\n", "Epoch 37/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7656 - acc: 0.6697 - val_loss: 0.7948 - val_acc: 0.6721\n", "Epoch 38/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7655 - acc: 0.6698 - val_loss: 0.7949 - val_acc: 0.6725\n", "Epoch 39/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7657 - acc: 0.6696 - val_loss: 0.7953 - val_acc: 0.6681\n", "Epoch 40/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7655 - acc: 0.6699 - val_loss: 0.7961 - val_acc: 0.6643\n", "Epoch 41/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7649 - acc: 0.6707 - val_loss: 0.7969 - val_acc: 0.6648\n", "Epoch 42/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7653 - acc: 0.6691 - val_loss: 0.7977 - val_acc: 0.6655\n", "Epoch 43/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7652 - acc: 0.6705 - val_loss: 0.7958 - val_acc: 0.6709\n", "Epoch 44/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7649 - acc: 0.6710 - val_loss: 0.7948 - val_acc: 0.6726\n", "Epoch 45/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7654 - acc: 0.6702 - val_loss: 0.7951 - val_acc: 0.6703\n", "Epoch 46/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7649 - acc: 0.6705 - val_loss: 0.7949 - val_acc: 0.6707\n", "Epoch 47/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7649 - acc: 0.6705 - val_loss: 0.7944 - val_acc: 0.6750\n", "Epoch 48/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7648 - acc: 0.6704 - val_loss: 0.7949 - val_acc: 0.6718\n", "Epoch 49/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7648 - acc: 0.6709 - val_loss: 0.7965 - val_acc: 0.6674\n", "Epoch 50/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7650 - acc: 0.6705 - val_loss: 0.7951 - val_acc: 0.6732\n", "Epoch 51/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7648 - acc: 0.6707 - val_loss: 0.7957 - val_acc: 0.6662\n", "Epoch 52/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7646 - acc: 0.6707 - val_loss: 0.7966 - val_acc: 0.6693\n", "Epoch 53/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7647 - acc: 0.6703 - val_loss: 0.7954 - val_acc: 0.6720\n", "Epoch 54/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7648 - acc: 0.6706 - val_loss: 0.7958 - val_acc: 0.6697\n", "Epoch 55/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7648 - acc: 0.6705 - val_loss: 0.7948 - val_acc: 0.6730\n", "Epoch 56/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7645 - acc: 0.6714 - val_loss: 0.8004 - val_acc: 0.6493\n", "Epoch 57/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7650 - acc: 0.6702 - val_loss: 0.7953 - val_acc: 0.6709\n", "Epoch 58/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "227451/227451 [==============================] - 16s 70us/step - loss: 0.7647 - acc: 0.6712 - val_loss: 0.7976 - val_acc: 0.6633\n", "Epoch 59/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7643 - acc: 0.6717 - val_loss: 0.7952 - val_acc: 0.6685\n", "Epoch 60/100\n", "227451/227451 [==============================] - 17s 77us/step - loss: 0.7650 - acc: 0.6701 - val_loss: 0.7952 - val_acc: 0.6728\n", "Epoch 61/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7648 - acc: 0.6704 - val_loss: 0.7982 - val_acc: 0.6553\n", "Epoch 62/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7645 - acc: 0.6709 - val_loss: 0.8000 - val_acc: 0.6522\n", "Epoch 63/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7652 - acc: 0.6701 - val_loss: 0.7953 - val_acc: 0.6696\n", "Epoch 64/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7644 - acc: 0.6718 - val_loss: 0.7969 - val_acc: 0.6663\n", "Epoch 65/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7645 - acc: 0.6708 - val_loss: 0.7960 - val_acc: 0.6683\n", "Epoch 66/100\n", "227451/227451 [==============================] - 16s 71us/step - loss: 0.7644 - acc: 0.6719 - val_loss: 0.7988 - val_acc: 0.6603\n", "Epoch 67/100\n", "227451/227451 [==============================] - 17s 74us/step - loss: 0.7647 - acc: 0.6704 - val_loss: 0.7947 - val_acc: 0.6705\n", "Epoch 68/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7651 - acc: 0.6706 - val_loss: 0.7966 - val_acc: 0.6683\n", "Epoch 69/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7643 - acc: 0.6714 - val_loss: 0.7962 - val_acc: 0.6646\n", "Epoch 70/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7646 - acc: 0.6713 - val_loss: 0.7974 - val_acc: 0.6649\n", "Epoch 71/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7640 - acc: 0.6717 - val_loss: 0.7962 - val_acc: 0.6687\n", "Epoch 72/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7644 - acc: 0.6704 - val_loss: 0.7966 - val_acc: 0.6640\n", "Epoch 73/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7645 - acc: 0.6710 - val_loss: 0.7949 - val_acc: 0.6743\n", "Epoch 74/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7641 - acc: 0.6715 - val_loss: 0.7943 - val_acc: 0.6705\n", "Epoch 75/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7643 - acc: 0.6716 - val_loss: 0.7938 - val_acc: 0.6762\n", "Epoch 76/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7641 - acc: 0.6717 - val_loss: 0.7946 - val_acc: 0.6712\n", "Epoch 77/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7643 - acc: 0.6704 - val_loss: 0.7955 - val_acc: 0.6723\n", "Epoch 78/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7643 - acc: 0.6714 - val_loss: 0.7969 - val_acc: 0.6688\n", "Epoch 79/100\n", "227451/227451 [==============================] - 16s 70us/step - loss: 0.7637 - acc: 0.6718 - val_loss: 0.7957 - val_acc: 0.6696\n", "Epoch 80/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7644 - acc: 0.6715 - val_loss: 0.7961 - val_acc: 0.6681\n", "Epoch 81/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7637 - acc: 0.6722 - val_loss: 0.7953 - val_acc: 0.6655\n", "Epoch 82/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7638 - acc: 0.6715 - val_loss: 0.7945 - val_acc: 0.6740\n", "Epoch 83/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7643 - acc: 0.6715 - val_loss: 0.7941 - val_acc: 0.6721\n", "Epoch 84/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7634 - acc: 0.6725 - val_loss: 0.7962 - val_acc: 0.6653\n", "Epoch 85/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7639 - acc: 0.6721 - val_loss: 0.7968 - val_acc: 0.6731\n", "Epoch 86/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7639 - acc: 0.6723 - val_loss: 0.7941 - val_acc: 0.6694\n", "Epoch 87/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7643 - acc: 0.6714 - val_loss: 0.7945 - val_acc: 0.6758\n", "Epoch 88/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7637 - acc: 0.6720 - val_loss: 0.7953 - val_acc: 0.6735\n", "Epoch 89/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7634 - acc: 0.6721 - val_loss: 0.7975 - val_acc: 0.6667\n", "Epoch 90/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7634 - acc: 0.6727 - val_loss: 0.7937 - val_acc: 0.6719\n", "Epoch 91/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7660 - acc: 0.6696 - val_loss: 0.7968 - val_acc: 0.6578\n", "Epoch 92/100\n", "227451/227451 [==============================] - 16s 68us/step - loss: 0.7641 - acc: 0.6712 - val_loss: 0.7947 - val_acc: 0.6735\n", "Epoch 93/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7631 - acc: 0.6732 - val_loss: 0.7935 - val_acc: 0.6762\n", "Epoch 94/100\n", "227451/227451 [==============================] - 15s 67us/step - loss: 0.7640 - acc: 0.6719 - val_loss: 0.7946 - val_acc: 0.6687\n", "Epoch 95/100\n", "227451/227451 [==============================] - 15s 66us/step - loss: 0.7635 - acc: 0.6719 - val_loss: 0.7958 - val_acc: 0.6691\n", "Epoch 96/100\n", "227451/227451 [==============================] - 17s 75us/step - loss: 0.7648 - acc: 0.6708 - val_loss: 0.7966 - val_acc: 0.6602\n", "Epoch 97/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7656 - acc: 0.6694 - val_loss: 0.7999 - val_acc: 0.6550\n", "Epoch 98/100\n", "227451/227451 [==============================] - 16s 69us/step - loss: 0.7636 - acc: 0.6728 - val_loss: 0.7944 - val_acc: 0.6631\n", "Epoch 99/100\n", "227451/227451 [==============================] - 15s 66us/step - loss: 0.7639 - acc: 0.6716 - val_loss: 0.7963 - val_acc: 0.6682\n", "Epoch 100/100\n", "227451/227451 [==============================] - 15s 68us/step - loss: 0.7633 - acc: 0.6723 - val_loss: 0.7940 - val_acc: 0.6692\n" ] } ], "source": [ "\n", "nb_epoch = 100\n", "batch_size = 32\n", "\n", "autoencoder.compile(optimizer='adam', \n", " loss='mean_squared_error', \n", " metrics=['accuracy'])\n", "\n", "checkpointer = ModelCheckpoint(filepath=\"model.h5\",\n", " verbose=0,\n", " save_best_only=True)\n", "tensorboard = TensorBoard(log_dir='./logs',\n", " histogram_freq=0,\n", " write_graph=True,\n", " write_images=True)\n", "\n", "history = autoencoder.fit(X_train, X_train,\n", " epochs=nb_epoch,\n", " batch_size=batch_size,\n", " shuffle=True,\n", " validation_data=(X_test, X_test),\n", " verbose=1, callbacks=[checkpointer, tensorboard]).history" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "autoencoder = load_model('model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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3y+l0atOmTSooKFBhYaE2bdqkq666Si1btvTbczUEp8Oi0uLyzn0F7rONXA0A\nAACAi/k1SF04uxQREVHhevv27S95rmn48OEaO3asUlJS1KtXL40aNUr9+/fX1KlTJUlBQUF69tln\n9dlnn6lfv37q16+fPv/8c61evVpmc2D30XDazXIV0QIdAAAAMCKrP39YcXGxzGazgoKCKly32Wwq\nKSmpNP7MmTPKzs7WpEmTNHLkSB08eFDPPPOM0tLSNGPGDEnSkSNH1LVrV02fPl1ms1kvvPCCpk2b\npjfffFOhoaGXrSc9Pb3+Hq4eXFyPu8Ss0uLyzn2796fruE40VlkIAEabywgszB/UFnMHdcH8QW0Z\nZe74NUg5HA55PB653W5ZrT/8aJfLpeDg4ErjlyxZIrPZrJSUFElSjx495Ha7NW/ePI0bN05ff/21\nXnjhBX300Ue+Va4VK1boxhtv1MaNGzVu3LjL1hMfH1+PT1c36enpFer524FvlXO6PEi179he8e2M\nUyuM5cdzB6gJ5g9qi7mDumD+oLYaY+5UFdz8uv8tKipKkpSfn1/hel5eXqXtfpK0d+9e33moC/r0\n6aPS0lLl5OToX//6l9q1a1fhu2FhYbr66quVkZHRAE/gP067WWW8lBcAAAAwJL8Gqe7duyskJEQ7\nd+70XcvMzFRWVpb69+9faXxkZKQOHDhQ4dqhQ4dkNpvVsWNHRUZG6sSJEzpx4odtb8XFxcrMzNTV\nV1/dYM/hD067Re5zvJQXAAAAMCK/Bimbzabk5GQtXrxY27Zt0759+zRr1iwlJCSob9++crlcys/P\nl8vlkiSNHz9eH330kVauXKmjR4/qww8/1MKFC5WcnKzQ0FDdeOONioqK0syZM/Xvf/9bBw4cUEpK\niux2u2677TZ/Plq9czouWpEiSAEAAACG4vfWdjNnztSoUaM0e/ZsjR8/Xh06dNALL7wgSdqzZ48G\nDx6sPXv2SJKGDh2qtLQ0bd26VaNHj9Yzzzyju+++W6mpqZKkkJAQvfHGGwoPD9fkyZN13333yev1\nat26dVdsNGF0TrtFHpdDJpl1ppStfQAAAICR+LXZhCRZrValpqb6wtDFEhMTK23lS0pKUlJSUpX3\ni46O9gWxpsTpsEgyya5QnXGzIgUAAAAYSWC/bKkJc9rLfzU2T6jOlJ6W1+tt5IoAAAAAXECQMiin\n3SJJspW1lMvjonMfAAAAYCAEKYMq39on2Vzlrd0zi442ZjkAAAAALkKQMqgLW/ssxReCVGZjlgMA\nAADgIgQpg7qwIqXCdpKkzGJWpAAAAACjIEgZ1IUVqZLCEDktTrb2AQAAAAZCkDKo4PPNJorPeRXj\njFV+SZ7OlZ1r5KoAAAAASARQ31PlAAAgAElEQVQpw7KYTXLYzCoqKVNMcKy88iq7OKuxywIAAAAg\ngpShOe1mFZ3zKMYZK4nOfQAAAIBREKQMzOmw+FakJBpOAAAAAEZBkDIwp708SEU4ImU1WVmRAgAA\nAAyCIGVgTrtZJaVeyWtRlCNK2cVZKvOWNXZZAAAAQLNHkDKwC++SKi4pU7QzVqXeUuWdO9bIVQEA\nAAAgSBnYhXdJFZ4r+6HhBOekAAAAgEZHkDIw5/l3SRWVeH5oOME5KQAAAKDREaQMLOT81r6ic2WK\nccZIIkgBAAAARkCQMrALZ6SKSsoUbHGqja2tMosz5fV6G7kyAAAAoHkjSBnYhTNSRec8kqQYZ6wK\n3Gd1uvR0Y5YFAAAANHsEKQP74YxUectzXswLAAAAGANBysCcjvMrUiUXVqQ4JwUAAAAYAUHKwHwr\nUufOr0jRAh0AAAAwBIKUgV3cbEKSWgeFy2lxKosVKQAAAKBREaQMzNds4vzWPpPJpBhnrPJL8nWu\n7FxjlgYAAAA0awQpA/vx1j6pvOGEV15lF2c1VlkAAABAs0eQMrBg34rURUHqwjkptvcBAAAAjYYg\nZWBBVrNsVpPvPVISLdABAAAAIyBIGZzTYamwIhXhiJTVZGVFCgAAAGhEBCmDc9rNvmYTkmQ1WxXl\niFJ2cZbKvGWX+SYAAACAhkKQMjin3VKh2YQkRTtjVeotVd65Y41UFQAAANC8EaQMLsRhUbHLI3eZ\n13eNF/MCAAAAjYsgZXCtW1glSd8XlPqu+RpOcE4KAAAAaBQEKYNrExYkSTpx+qIg5YyRRJACAAAA\nGgtByuB8QerMD0Eq2OJUG1tbZRZnyuv1VvVVAAAAAA2EIGVwlwpSUvk5qQL3WZ0uPd0YZQEAAADN\nGkHK4MKrClK8mBcAAABoNAQpg2tb5YoU56QAAACAxkKQMrjwFlVv7ZNYkQIAAAAaA0HK4GxBZoU5\nLTpxtmKQah0ULqfFqSxWpAAAAAC/I0gFgDZhQRXan0uSyWRSjDNW+SX5Old2rpEqAwAAAJonvwep\nsrIyLV26VIMHD1ZcXJxmzJih48ePVzl+x44dGjt2rPr27aukpCStXr26Qstvr9er3//+97rxxhvV\nt29f3XPPPfrqq6/88Sh+0yYsSEUlHhWXlFW4HhPcUV559V1RRiNVBgAAADRPfg9Sy5cv18aNG7Vo\n0SKtXbtWubm5mj59+iXHZmRk6MEHH9SwYcO0efNmpaSkaMWKFVq/fr1vzIoVK7R69Wo9/vjjevvt\ntxUREaHJkyeroKDAX4/U4Kpqgd4ltIsk6UjBYb/XBAAAADRnfg1SLpdLa9as0axZszRo0CD17NlT\ny5Yt0+7du7V79+5K47dv3y6Hw6Fp06YpNjZWN998s4YOHart27dLkgoLC/XKK68oNTVVSUlJ6ty5\nsxYsWCCbzab//Oc//ny0BlVVC/TOoddIkr4mSAEAAAB+5dcgtX//fhUWFiohIcF3LSYmRtHR0dq1\na1el8eHh4Tp16pS2bNkij8ejgwcPateuXerVq5ckKT09XSUlJbr55pt93wkNDdUHH3xQ4WcEuqpa\noIcFham9PUJHCr6Wx+tpjNIAAACAZsmvQSo3N1eSFBERUeF6+/btfZ9dbPjw4Ro7dqxSUlLUq1cv\njRo1Sv3799fUqVMlSd9++63Cw8O1d+9e3XXXXRo4cKAmTpyow4eb1gpNVStSktQl9Bqd85xTVnGW\nv8sCAAAAmi2/Bqni4mKZzWYFBQVVuG6z2VRSUlJp/JkzZ5Sdna1JkyZpw4YNWrRokT799FOlpaVJ\nkgoKClRYWKinnnpKDz74oF5++WU5nU7dc889OnnypF+eyR+qWpGSyoOUJH1dcMivNQEAAADNmdWf\nP8zhcMjj8cjtdstq/eFHu1wuBQcHVxq/ZMkSmc1mpaSkSJJ69Oght9utefPmady4cbJarSouLta8\nefM0YMAA33eGDh2qv/zlL7r//vsvW096eno9Pl3dVVVPwTlJsuhwRr7S049V+OycygPorqM71eJo\nywauEEZltLmMwML8QW0xd1AXzB/UllHmjl+DVFRUlCQpPz/f92dJysvLq7TdT5L27t2rpKSkCtf6\n9Omj0tJS5eTk+L7TtWtX3+d2u10xMTHKzMy8Yj3x8fG1eo6GkJ6eXmU9Ho9Xv3tvrzyWEMXHd63w\nmdfr1d++eE8nTSd1/XXXy2Qy+aNcGMjl5g5wJcwf1BZzB3XB/EFtNcbcqSq4+XVrX/fu3RUSEqKd\nO3f6rmVmZiorK0v9+/evND4yMlIHDhyocO3QoUMym83q2LGj7z/il19+6fu8pKRER48eVWxsbAM9\nhf+ZzSaFtwi65NY+k8mka0Kv0enSUzrhqvp9XAAAAADqj1+DlM1mU3JyshYvXqxt27Zp3759mjVr\nlhISEtS3b1+5XC7l5+fL5XJJksaPH6+PPvpIK1eu1NGjR/Xhhx9q4cKFSk5OVmhoqGJiYjR69GjN\nnz9fn376qb7++mv9+te/ltls1ujRo/35aA0uPCxIJ8+65fF4K33WJfRaSbRBBwAAAPzF7y/knTlz\npkaNGqXZs2dr/Pjx6tChg1544QVJ0p49ezR48GDt2bNHkjR06FClpaVp69atGj16tJ555hndfffd\nSk1N9d3vt7/9rUaMGKHZs2fr9ttv14kTJ7RmzRqFh4f7+9EaVNuwILnLvDpTVFbpsy68TwoAAADw\nK7+ekZIkq9Wq1NTUCmHogsTExEpb+ZKSkiqdk7qYzWbTnDlzNGfOnHqv1Uh+aIHuUqvQir+2aGeM\n7Ga7DtO5DwAAAPALv69IoXZ+aIHurvSZxWRRp5DOOnYuV2dLz/q7NAAAAKDZIUgFiDaXeZeU9MM5\nqSOFX/utJgAAAKC5IkgFiIu39l3KNS14MS8AAADgLwSpAHG5rX2SdJWzk8wy03ACAAAA8AOCVIAI\nv8LWPrvFrlhnR31XmCGXp8SfpQEAAADNDkEqQIQ4LAq2masMUpJ0TYtr5ZFH3xZ+48fKAAAAgOaH\nIBVAwsOCLhukeJ8UAAAA4B8EqQDSNixIpwvdKnV7Lvl55xCCFAAAAOAPBKkAcqEF+smzl2440SKo\nhSIckTpS8LXKvGX+LA0AAABoVghSAeRKDSek8u19JZ4SZRVn+qssAAAAoNkhSAWQttUIUtecfzHv\n12fZ3gcAAAA0FIJUAKnuipTEi3kBAACAhkSQCiDVWZFqY2urlkEt9XXBYXm9Xn+VBgAAADQrBKkA\nUp0VKZPJpC6h1+qM+4zyS/L9VRoAAADQrBCkAkh4C6sk6eRlgpTE+6QAAACAhkaQCiBBVrNahlh1\n/IpBqrzhxBGCFAAAANAgCFIBpk1YkE6cKb3s+afo4Gg5zA4dpuEEAAAA0CAIUgGmbViQzrk8Kirx\nVDnGbDKrU2gX5ZUc09nSM36sDgAAAGgeCFIBpjoNJyTpGt85qa8bvCYAAACguSFIBZjqtECXpI7O\nqyVJWcVHG7okAAAAoNkhSAWY6q5IdQiOliRlFWc1eE0AAABAc0OQCjBtzgepK7VAbxnUUiGWEGUT\npAAAAIB6R5AKMG3Cyt8ldaUW6CaTSR2Co3W8JF/nys75ozQAAACg2SBIBZg2YTZJV97aJ0nRzhh5\n5VXOueyGLgsAAABoVghSAaZliEVWi6l6QSo4RpLY3gcAAADUM4JUgDGZTApvYa1WkPI1nCgiSAEA\nAAD1iSAVgNq2tOnk2VJ5PN7LjotydJBJJmUXZ/qpMgAAAKB5IEgFoPAWVnk80qlC92XH2S12tbO3\nU3Zxlrzey4cuAAAAANVHkApA1W2BLpVv7yssK9Tp0lMNXRYAAADQbBCkAtCFIHWlFuiS1OF8w4ks\ntvcBAAAA9YYgFYAuBCk69wEAAACNgyAVgNq2rH6Q8nXuI0gBAAAA9YYgFYDCW1Q/SLW1t5XNbGNF\nCgAAAKhH1poMLiwsVGFhodq3b6/S0lKtW7dOOTk5Gj58uOLj4xuqRvxITZpNmE1mdQiO1tGi7+T2\nuGU11+hXDgAAAOASqr0itXfvXt1444364x//KEl6+umn9eyzz2rjxo0aP368PvjggwYrEhUF2y1y\n2s3VajYhlW/vK/OW6VhJbgNXBgAAADQP1Q5Szz//vDp37qy77rpLxcXF+stf/qLk5GTt3LlTd9xx\nh1566aWGrBM/0qZlULVWpCQaTgAAAAD1rUYrUg899JBiY2P1ySefqKSkRGPGjJEkjRw5UocOHWqw\nIlFZmxZBOlNUJlep54pjfQ0nighSAAAAQH2odpAym82y2+2SpO3btyssLEy9e/eWJBUUFMjhcDRM\nhbgkXwv0szXp3Me7pAAAAID6UO3OA7169dKf//xnORwOvf/++xo2bJhMJpNOnDih1atXq1evXg1Z\nJ37k4ndJRYXbLzs21BqqVkGt2NoHAAAA1JNqr0jNnj1bn376qX72s5/JYrHooYcekiTdeuutysjI\n0KxZsxqsSFRWk5fySuWrUqdKv1ehu7AhywIAAACahWoHqZ49e+rvf/+73nrrLW3dulVXX321JOmp\np57Se++9px49elTrPmVlZVq6dKkGDx6suLg4zZgxQ8ePH69y/I4dOzR27Fj17dtXSUlJWr16tbxe\n7yXHvv/+++rWrZsyM5v+FrY2LavfAl26uOFE0/9vAwAAADS0Gr2QNzQ0VH369JHT6ZQk/ec//5HJ\nZJLNZqv2PZYvX66NGzdq0aJFWrt2rXJzczV9+vRLjs3IyNCDDz6oYcOGafPmzUpJSdGKFSu0fv36\nSmPz8vL05JNP1uRxAlqb8y/lrX4L9PIglcX2PgAAAKDOqh2k8vLydN9992nlypWSpLVr1+qOO+7Q\nww8/rOHDh+vw4cNXvIfL5dKaNWs0a9YsDRo0SD179tSyZcu0e/du7d69u9L47du3y+FwaNq0aYqN\njdXNN9+soUOHavv27ZXGPvbYY+ratWt1Hyfg1XxFqrzhBOekAAAAgLqrdpD63e9+p6+//lrXXXed\nPB6PXn75ZQ0cOFCbNm1S586dtWTJkiveY//+/SosLFRCQoLvWkxMjKKjo7Vr165K48PDw3Xq1Clt\n2bJFHo9HBw8e1K5duyo1tli3bp3y8/M1derU6j5OwGsdGiSTqforUhGOSJllJkgBAAAA9aDaQeqT\nTz7RnDlzNGTIEO3evVvHjx/X+PHj1b17d02aNOmSQejHcnNzJUkREREVrrdv39732cWGDx+usWPH\nKiUlRb169dKoUaPUv3//CoHpm2++0fPPP69FixYpKCiouo8T8KwWk1qFWnXidPWClNVsVWRwlLKK\ns+TxXvndUwAAAACqVu3254WFhYqKipIkbdu2TTabTQMGDJAk2Wy2KhtAXKy4uFhms7lS4LHZbCop\nKak0/syZM8rOztakSZM0cuRIHTx4UM8884zS0tI0Y8YMud1uPfroo5o0aZK6d+9erTB3sfT09BqN\nb2g1rSfYYlb+aWnXrnSZTFce71CwXCrRx7s/Upha1rJKGJHR5jICC/MHtcXcQV0wf1BbRpk71Q5S\nV199tT7//HP16dNHf/vb35SQkOB7Qe8777zj6+J3OQ6HQx6PR263W1brDz/a5XIpODi40vglS5bI\nbDYrJSVFktSjRw+53W7NmzdP48aN07p162Q2mzVp0qTqPkYF8fHxtfpeQ0hPT69xPbH7jij71Bl1\n69lHLYKv/Ks8kZuvI1mHFd6ltfq0iqttqTCY2swd4ALmD2qLuYO6YP6gthpj7lQV3Kq9tW/y5MlK\nS0vTDTfcoKNHj+r++++XJN1555165513qhVmLqxo5efnV7iel5dXabufJO3du7fSeag+ffqotLRU\nOTk5evvtt7Vv3z7169dPcXFxmjhxoqTyd1u9/PLL1X20gHXhXVL5p6r/LilJyirinBQAAABQF9Ve\nkbr11lsVFRWl9PR0JSQkqG/fvpKkxMREPfLIIxo4cOAV79G9e3eFhIRo586dGjNmjCQpMzNTWVlZ\n6t+/f6XxkZGROnDgQIVrhw4dktlsVseOHfXHP/5Rbrfb99m+ffv0yCOPaNWqVc2ig1+nyPJVvENZ\nReocVXlF78eifS3QeZcUAAAAUBfVDlJS+Va4+Ph4FRUVKT8/X61atfJtu6sOm82m5ORkLV68WK1b\nt1abNm00f/58XzBzuVw6ffq0WrZsKZvNpvHjx+uBBx7QypUrNWrUKB0+fFgLFy5UcnKyQkNDFRoa\nWuH+F1a6OnTooFatWtXk0QJS99jy93nt/65II/q1ueL4VkGt5LQ46dwHAAAA1FGNgtRnn32mJUuW\naN++fb7mEr1799bMmTN1ww03VOseM2fOlNvt1uzZs+V2uzVkyBDNnTtXkrRnzx6NHz9ea9asUWJi\nooYOHaq0tDStXLlSq1evVtu2bXX33XfrgQceqOFjNk1XRwbLZjXpwNHCao03mUzqEBytrwsOy+Up\nkc1sb+AKAQAAgKap2kHq888/18SJE9WpUyfNmDFDbdq0UV5ent5//31NnjxZr7/+uvr163flH2i1\nKjU1VampqZU+S0xMrLSVLykpSUlJSdWqsV+/fpW+35RZLSZdG+PUVxmFKi4pU7DdcsXvRAfH6HDB\nIeUU5+iqkKsbvkgAAACgCap2kHrhhRd0ww03aNWqVTJd1Gt76tSpmjJlipYvX6433nijQYpE1brF\nOrXv20IdyipS784trjj+4nNSBCkAAACgdqrdte/f//637rnnngohSirfLnbPPffoyy+/rPficGXd\nY0MkSfuPFlVr/IXOfZyTAgAAAGqv2kEqLCxMRUWX/st6YWGhLJYrbytD/bu44UR1RAV3kETnPgAA\nAKAuqh2kBgwYoOXLl+vYsWMVrh87dkzLly+vdrMJ1K+2LYMU3sKqA0cLfQ1ALsdhcaitvZ2yijKr\nNR4AAABAZdU+I/WrX/1Kd9xxh0aMGKH4+Hi1bdtWx48fV3p6ukJDQzV79uyGrBNVMJlM6t4xRJ/u\nO63jp0vVrpXtit+JDo7W3lP/0hn3abUMqtgmvshdqOzibEUFd1CINaShygYAAAACWrWDVEREhDZu\n3KjXXntN6enpyszMVFhYmJKTk3X//ferXbt2DVknLqNbrFOf7jut/UeLqhWkOgTHaO+pf+nw2UMK\nsYbou6Lv9F1Rho4WZui467gk6frW/TSx85SGLh0AAAAISDV6j1S7du00Z86chqoFtfRDw4lCDbnu\nyi8ijj7fcOK1b1ZXuB5iCVH3Fj/RN4VH9F1RRv0XCgAAADQRlw1SL7/8crVvZDKZeFFuI7k2Olhm\nU/UbTnRt0V2dQ7rIbrEr1nmVOjo7qqPzKoXb2shkMmnZgd/pSMFhlXpKFWQOauDqAQAAgMBz2SD1\n/PPPV/tGBKnGE2y36OpIhw5nFcld5pXVYrrs+BBriH7VveqVxUhHpL4uOKS8kmO+904BAAAA+MFl\ng9T+/fv9VQfqqFtsiI7knNO3ucW6JtpZp3tFOiIlScfO5RKkAAAAgEuodvtzGFu3Gr5P6nIiHFGS\npNzi3DrfCwAAAGiKCFJNxMUNJ+rq4hUpAAAAAJURpJqI2HZ2Oe1mHcis+4pUa1u4gkxByj2XUw+V\nAQAAAE0PQaqJMJtN6hbrVGZ+ic4Wu+t2L5NZEY5IHTuXK4/XU08VAgAAAE0HQaoJ6XZ+e9/Bo3Vf\nlYp0RKnUW6rvXSfrfC8AAACgqSFINSHd67XhRPk5qVzOSQEAAACVEKSakG712XAi+ELDCc5JAQAA\nAD9GkGpCWoVaFdnapgNHi+T1eut0L1akAAAAgKoRpJqY7h2dOltcpuwTrjrdp709QiaZCFIAAADA\nJRCkmpgL2/sO1HF7X5A5SG3sbXmXFAAAAHAJBKkmpj4bTkQ6olTgPqsCd0Gd7wUAAAA0JQSpJqZz\nh2BZLaZ6aThx4ZwUq1IAAABARQSpJsZmNatLVLCO5BSrpLRuL9ON9DWcoHMfAAAAcDGCVBPUvaNT\nZR7p6+ziOt0n0hEliRUpAAAA4McIUk1QfTWc8LVAL2ZFCgAAALgYQaoJ6t6xfhpOhFhD1MLaghUp\nAAAA4EcIUk1QZGubwkIs9dJwItIRpROuE3J56vZeKgAAAKApIUg1QSaTSd1jQ5R3qlQnz5bW6V4R\njkh55VX+ubx6qg4AAAAIfASpJurC+6QOHK3b9r4LDSdy2d4HAAAA+BCkmqgfGk7ULUhF0AIdAAAA\nqIQg1UR1jQmWVPfOfZHBvJQXAAAA+DGCVBMVGmxVTDu7DmYWyePx1vo+rYJay2a2sSIFAAAAXIQg\n1YR1i3GqqMSjzOMltb6H2WRWhCNSeeeOyeP11GN1AAAAQOAiSDVh3eqp4USEI1Kl3lKddJ2sj7IA\nAACAgEeQasK6xpQ3nDiYWT+d+46xvQ8AAACQRJBq0jpFOWS1mOrccOKHzn00nAAAAAAkglSTZrOa\n1aVDsI7kFMtVWvvzTT+sSBGkAAAAAIkg1eR1i3GqzCN9nV1c63u0s7eTSSY69wEAAADnEaSaOF/D\niTqckwoyB6mdvR0rUgAAAMB5fg9SZWVlWrp0qQYPHqy4uDjNmDFDx48fr3L8jh07NHbsWPXt21dJ\nSUlavXq1vN4f3ouUkZGhqVOnKjExUQMGDNCMGTOUnZ3tj0cJCN1iyxtO1Mc5qQJ3gQrcZ+ujLAAA\nACCg+T1ILV++XBs3btSiRYu0du1a5ebmavr06Zccm5GRoQcffFDDhg3T5s2blZKSohUrVmj9+vWS\npKKiIk2cOFEej0dvvPGGXn31VX3//feaPHmyXC6XPx/LsDq0sSk02FIvLdAlGk4AAAAAkp+DlMvl\n0po1azRr1iwNGjRIPXv21LJly7R7927t3r270vjt27fL4XBo2rRpio2N1c0336yhQ4dq+/btkqRP\nPvlEOTk5WrJkibp3766ePXtq8eLFOnz4sPbu3evPRzMsk8mkbjFO5Zx06Uyhu9b38TWcKCZIAQAA\nAH4NUvv371dhYaESEhJ812JiYhQdHa1du3ZVGh8eHq5Tp05py5Yt8ng8OnjwoHbt2qVevXpJknr3\n7q1Vq1YpNDTU9x2zufyRTp8+3cBPEzi61sM5KVakAAAAgB/4NUjl5pb/JTwiIqLC9fbt2/s+u9jw\n4cM1duxYpaSkqFevXho1apT69++vqVOn+u4zaNCgCt9ZtWqVgoODFR8f30BPEXi6nw9SB+uwvS/S\nF6To3AcAAAD4NUgVFxfLbDYrKCiownWbzaaSkpJK48+cOaPs7GxNmjRJGzZs0KJFi/Tpp58qLS3t\nkvdfv3691q5dq5SUFLVu3bpBniEQdY0pbzixvw4NJ5zWELWwhtG5DwAAAJBk9ecPczgc8ng8crvd\nslp/+NEul0vBwcGVxi9ZskRms1kpKSmSpB49esjtdmvevHkaN25chbD00ksv6fnnn9cDDzyge++9\nt1r1pKen1/GJ6ldD1tM6xKz/fHtGu3aly2Sq3T1CFKpcZeuz9M9k9e/UwRUYbS4jsDB/UFvMHdQF\n8we1ZZS549e/DUdFlTcsyM/P9/1ZkvLy8ipt95OkvXv3KikpqcK1Pn36qLS0VDk5OWrdurU8Ho/m\nzZunt956SykpKZo8eXK16zHS9r/09PQGree6g99q2xen1KFTL3VoY6/VPQ5m7Ffu8WxF/6SDYpyx\n9Vwhaquh5w6aNuYPaou5g7pg/qC2GmPuVBXc/Lq1r3v37goJCdHOnTt91zIzM5WVlaX+/ftXGh8Z\nGakDBw5UuHbo0CGZzWZ17NhRkrRgwQJt2LBBCxcurFGIam66xdTDOalgzkkBAAAAkp+DlM1mU3Jy\nshYvXqxt27Zp3759mjVrlhISEtS3b1+5XC7l5+f73gE1fvx4ffTRR1q5cqWOHj2qDz/8UAsXLlRy\ncrJCQ0P18ccf680339RDDz2kIUOGKD8/3/fPpc5cNWfd6qFz34WGE5yTAgAAQHPn94MuM2fOlNvt\n1uzZs+V2uzVkyBDNnTtXkrRnzx6NHz9ea9asUWJiooYOHaq0tDStXLlSq1evVtu2bXX33XfrgQce\nkCS98847kqS0tLRKDSgWL16sMWPG+PfhDKxLB6fMZulAHRpOXHiXFC3QAQAA0Nz5PUhZrValpqYq\nNTW10meJiYmVtvIlJSVVOid1wdKlS7V06dIGqbOpcdjM6hQZrMPZxXKXeWW11LzjRKug1rKZ7coq\nOiqv1ytTbbtWAAAAAAHOr1v70Li6xThV6vbqm9ziWn3fZDKpR1hPHSs5pr2n9tRzdQAAAEDgIEg1\nI10vnJOqQ8OJ0dH/I7PM2pj1v3J73PVVGgAAABBQCFLNSHdfkKr9OakIR4R+2n6Yjpfka1v+h/VV\nGgAAABBQCFLNSEw7h4Jt5jp17pOkW6JuVbDFqfdy3lWBu6CeqgMAAAACB0GqGbGYTeoa41RmfokK\nz5XV+j6h1lCNjPr/VFxWpPdz3q3HCgEAAIDAQJBqZrrGOuX1SgfruCr103Y3qu3/z959x7dVno3/\n/2jLlmzJe+/EiZ29E0JIwgx7zxZaKHsVKLT8ePrwtN/SUmihUPZogTDKCiFNIAQIhOw9vWLHsR3v\nIQ9ZsrXP7w8nJsFx4iReia83L70kpKNzruMcSec6931ftyGKH+q+p9ZV20vRCSGEEEIIcXKQRGqI\nGZHYMU7qRBMprVrL5QlXEiDAosoFvRGaEEIIIYQQJw1JpIaYEb1QcOKAcdYJZJiHs6N5O0Wtu4/+\nBiGEEEIIIU4RkkgNMZEWPRGhOgrK21AU5YTWpVKpuDLxagAWVHxCQAn0RohCCCGEEEIMepJIDUEj\nkoJpavXR0OI94XWlmFKZEj6N8rZ9bGrc0AvRCSGEEEIIMfhJIjUEHRgndaJl0A+4JOEydCod/61c\niCfg7pV1CiGEEEIIMZhJIjUEHRgnVbDvxMdJAYTrIzgz5myavc0sr/2mV9YphBBCCCHEYCaJ1BCU\nmRiMWgX5+3qnRQrg3C/RMpMAACAASURBVNjzCdGGsKx6KTuat/faeoUQQgghhBiMJJEagoIMGlJj\ngyiqbMPr650CEUaNkRtTb0alUvFG8St8V/vNCRezEEIIIYQQYrCSRGqIyk4x4fUpFFe199o6R1lG\n82DmI4TqQllQ8Qkf7vsAv+LrtfULIYQQQggxWEgiNURlJXeMk8rvpXFSBySbUnhk5GMkBCWyuuEH\nXtnzIu3+3utCKIQQQgghxGAgidQQlZ1iAiCvrHcTKYAwfRgPjfgtoy1jyLfn8feCp7C5G3p9O0II\nIYQQQgwUSaSGqJgwPWFmLfn7Tnxi3sMxaozckXEPc6PPosZVzd8KnqTEsbfXtyOEEEIIIcRAkERq\niFKpVGSlmLDZvdT3wsS8h6NWqbkq6VquSboeh8/B84XPkNOys0+2JYQQQgghRH+SRGoIy0ruu+59\nB5sdPZe7ht0LwGt7XmZr05Y+3Z4QQgghhBB9TRKpIezAOKn8Pk6kAEZZxnDP8F+jU+v4997X2WBb\n1+fbFEIIIYQQoq9IIjWEDYsPQqtR9Xrlvu4MD8nk/syHMGqCeLf0bVbXr+yX7R6rVq+djbb1+BX/\nQIcihBBCCCEGKUmkhjC9Ts2w+CCKq9txefonaUg1pfHrzN9g0pr4z773+K72237Zbk+5/C5eKHqO\nd0r/zfd1ywc6HCGEEEIIMUhJIjXEZaeYCASgsKL3JuY9mqTgJB7MfASLzsKCio/5qvqLIy4fUAL9\nEldACTC/9N9UtlcAsKz6S9p8g3sOrFavnV1sxxNwD3QoYr8WbzMuv2ugwxBCCCFEH9MOdABiYGWl\nmGB1Pfn7nIxNN/fbdmOD4nhwxCP8s/AfLK5aRLu/neEhmdjcNho9NmweG437Hzt8DmZEzOS6lBvQ\nqPrukF1StYgdzdvJDBlBZshIllQt4pvaZVyacHmfbfNEKIrCu6Vvk0sO1qowLku8YqBDGvI8AQ9/\nzv0jw0MyuS3jroEORwghhBB9SBKpIe5A5b7+Gid1sChDNA+OeJh/Fv6Db2u/5tvarw95XavSEqYP\nR6/Ws9a2miZvI7em34lRY+z1WDbaNrCsZimRhih+lX4HerWe1fU/8H3tt8yOmoNVH9br2zxRO5q3\nkWvPAeD7um85PeoMIg2RAxzV0FbmLMHpd1JgzyegBFCrpNFfCCGEOFVJIjXERYTqiLbqyC9zoigK\nKpWqX7cfro/gwRGPsKp+BTqVnghDBOH6jluoLhS1So3b7+bfJW+Q07KT53b/jbuG34dFZ+21GEqc\ne3m/7B2MaiN3ZdyLWdvRMndh/CW8XzafL6uXcEPKjb22vd7g8rv4pPwjtCoto5VxbFe28HnlAm5N\nv2OgQxvSih17AHAFXNS5aokNihvgiIQQQgjRV+RyqSA7xYS9zU9lw8CMs7HoLFwUfynnxZ3P5PCp\npJszsOqtnVfzDRoDt2fcxczIWZS3l/P3gqeocVX3yrabPI28vudl/IqfW9JvP+TEd1rEDGKNcaxt\nWN1r2+stX1YvptnbxDmx5zGBSaSa0tjWtIU9jqKBDm1I27M/kYKOBF0IIYQQpy5JpMSAdu/rKY1K\nw/XJP+ei+Eto9Nh4tuBp9jqKT2idbr+b1/a8hN1n54rEqxllGd1lm5ckXI6CwuLKz09oW72psq2C\n72uXE6mP5NzY81Gh4srEawBYUP5xvxXnEIcKKAFKHMXoVDpAEikhhBDiVCeJlPhxYt59g7tCnUql\n4vy4i/h5yi9o97fzz8Jn2dG87bjW1VGh7y3K28s5LfJ05kafddjlxlrGkW7KYHvzNkocx39iHFAC\nfFX9Bd/WfH1CFfYCSoD/7HuPAAGuSb4BvVoPQLo5g8lhU9nXVsamxg3HvX5x/CrbK3EFXEwMm4xO\npaPUWTLQIQkhhBCiD8kYKUFabBAGnZr8ssHbInWwGZEzCdWF8ube13mj+FVGWcagUalRFAUA5aBl\nA4ofv+LHp/jwKT78AR8+xY874KbRY2O4OZNrk27odmyYSqXi0sQr+Mfuv7GwcgEPZj58zOPIAkqA\n98vms962FoDv6r7h/LiLOC1y5jFXIVxnW0OJcy8TrBO7tKBdmng5O5q3sahyIeOtEzFoDMe0bnFi\nivd3qxweMoIGTwN7HXtw+V19UhxFCCGEEANPEimBRqNiRFIwu0ocONp9mIMG/2ExyjKGBzJ/w+vF\nL5PTsrNH79GqtGhUGrQqLVq1lsyQkfwq/Ta06iPv7zDzcMZYxrKrZSe59l2MtoztcZwHJ1HJwSlk\nhWbzfd1yPtz3Pstrv+Hi+EuZEDapR9XdWr2tLKr4DIPawFVJ13Z5PVwfwVkx5/BVzZd8W7uMC+Mv\n6XGc4sTt3T8+KsM8jBpXFcWOIva1lZEZMmKAIxNCCCFEXxj8Z8yiX2Qlm9i518Hu8jYmZYYOdDg9\nkmJK5f+NeRL3IZOfdrQWHWg0UqHuTKBOpCLhJQmXk9Oyi0WVC8kOHd2jxOenSdR9wx8kWBvM7Ogz\n+ar6C1bXr+TfJW+QtH+uqpEh2UeM8fPKBTj9Tq5MvKbbcuznxM5jbcNqvqlZxmmRswgbhGXbT0WK\norDHsYcQbShRhijSTOkAlDr3SiIlhBBCnKJkjJQAIDslGBjcBScOR6PSEKw1HXQLJlgbTJCm42bU\nGNGqtSdc1j0+KIFpETOoaq/s0Rikg5OolODUziQKOqoUXpt8A4+P/n9MDp9Keds+Xix6nr8VPMmX\nVUsoce7tUjBiT2sR621rSQxKYnb03G63a9QYuTjhMryKl/9WLjyhfRY9Z/PYaPE2k2EehkqlInV/\nInUi4+qEEEIIMbhJi5QAYGRSR8GJvLLBXXBiIF0UfwmbGzeyuGoRE8ImolcffgzST5Ooe4c/0JlE\nHSzKEM3Nabdydsx5LKn6nNyWHMraSvmi+r8Ea4IZEZpFdugoMkNG8OG+91Gh4rrkn6FRaY4Y5/SI\n0/ih7ns2Nq5nTvSZpJhSe2P3xREcGB+VYR4GgFVvxaoLo8RZMiDzswkhhBCi70kiJQAINWlJjDJQ\nUO7EH1DQqOXE76fC9OHMjj6T5bVf89vtD5FqSiPDPJx0cwbp5nSCNME9TqIOlhScxF3D7sPpc7K7\ntYB8ey75Lblsa9rCtqYtncvNjJxFmjn9qHGqVWquTLqG5wuf4dPyj3hoxG/lRL6PHZiId5h5eOdz\naaY0tjVvpdFjI8IQOVChCSGEEKKPSCIlOmUlm/hmSyP7al2kxQUNdDiD0gVxF6EoCrtb89njKKLI\nUQiAChXxQQkEaYLY4yjqcRJ1MJPWxMSwSUwMm4SiKNS6a8i355Hfkosr4ObShCt6vK7MkBGMs45n\nR/N21tnWcFrk6ce8r6Lnih170KsNJAQndj6Xak5nW/NWSp0lkkgJIYQQpyBJpESn7JSORCp/n1MS\nqW4YNUauTLoagDZfGyXOYvY6iil27KHUWYJX8R5XEvVTKpWKWGMcsca4bue4OprLE6+isHU3/yl7\nj1CdhdGWMccdj+iew+egxlXNyJCsQ7pdHig4UeLcy6TwKQMVnhBCCCH6iCRSolNW8oFxUk4umCZX\n0I8mWBvMKMsYRu1PUHwBH9WuamKMMZ0T5Q6kKEM0dw67lxcLn+PN4le5d/gDDAsZfvQ3imNyoOx5\n+v7xUQckBSejRi0T8wohhBCnKKnaJzolRRkwGzUnXeW+wUKr1pIUnDQokqgDhpmHc2vGnfgVP6/s\neYHytvKBDumUU3zQ/FEH06v1JAYnUd62D2/AOxChCSGEEKIP9Xsi5ff7eeaZZzj99NOZMGEC999/\nPw0NDd0uv27dOq666irGjx/P2WefzRtvvIGiKJ2vt7e387//+79MmzaNyZMn8/vf/x6nUxKB46FW\nqxiZHEyVzUOzQ078ThWjLWP4RdotuANuXip6jjpX7UCHdEopduxBjZpUU1qX11JNafgUH5XtFQMQ\nmRBCCCH6Ur8nUi+88AILFy7kqaee4r333qOmpob77rvvsMuWlZVx5513MmfOHBYvXszDDz/MSy+9\nxAcffNC5zOOPP86WLVt47bXXePXVV9m4cSOPP/54f+3OKedA976CfVIG/VQyOXwq1yRdT6uvlReK\nnqPZ0zTQIZ0SPAEP+9rKSApOxqgxdnn94HFSQgghhDi19Gsi5fF4mD9/Pg899BAzZ85k1KhRPPvs\ns2zdupWtW7d2WX7VqlUYjUbuvfdekpKSmDdvHrNnz2bVqlUA1NbWsmTJEv7v//6P8ePHM3nyZJ54\n4gm++OILamvlqvvxyErZP05Kuvedcs6InsNF8ZfS6LHxQtFzOHyOgQ6px8qcpbR4WwY6jC7KnKX4\nFX+Xbn0HHJiYt9Qh46SEEEKIU02/JlIFBQU4nU6mTp3a+VxiYiIJCQls3ry5y/Lh4eE0NzezZMkS\nAoEAhYWFbN68mdGjRwOwZcsW1Go1EydO7HzPxIkT0Wg0bNmypcv6xNGNSApGq1GxId9+SBdKcWqY\nF3sBc6PPosZVzctF/6S8rZwyZyl7HEUU2PPY1byTbU1b2GjbQJmz9JjWbffa+bxiAS8U/oMaV3Wv\nxOtX/HxW8SlPF/yFP+U+zubGjb2y3t7y04l4fyrKEIVJY5IWKXHCalzVvFD4D6rbqwY6FCGEEPv1\na9W+mpoaAGJiYg55Pjo6uvO1g5177rlcddVVPPzww/z2t7/F7/dz/vnnc/fddwMdLVLh4eHodLrO\n92i1WsLDw6mu7p0TuaEm2KBh5igLP+xsJn9fG9n7W6jEqUGlUnFF4tW0+dvYYFvHX/P/dMTlU4JT\nmR09l4lhk9GpdYddptnTxLe1X7O6fiVepWNs3d/yn+SW9Ns6KxoeD7vXzr/3vk6Ro5BIfSR2Xytv\nlbxJTssurk2+niDN8ZeX7y3F3VTsO0ClUpFqSiPXnkOr106ILrQ/wxOnkM8rPqOgNZ+Pyz/k/uEP\nDupJtmvaq1lnW8Oc6LMI04cNdDhCiD7i8rtY3bCSyWFTseqtAx3OgOjXRKq9vR21Wn1I4gOg1+tx\nu91dlrfb7VRVVXHrrbdywQUXUFhYyF/+8hdefPFF7r//ftrb2zEYDF3e1936fmqwtVoNlnjSrfAD\nGt5bupsrp0ir1MngWI+dLEbjJ4ATB5rO/7RoUKNBixo1FZRT1lbK/NK3+Lj0P4wgi5FkY8IMgINW\ndrKdQgoIEMCEmclMQ4uWtYFVvLLnRSYzjdGMRcWxnfTVUsP3fEMbbaSQxizPHNpp5we+Y1PjBvIb\nczmDM4kl7pjW25sCBCiiiFAsFO0s6nY5Ax1jp77d+Q3JpPZTdMdmsHz3iMOz0cAudgBQ2FrAf7d+\nTiLJAxxVh4OPHR8+drKNnWwnQID82jzO5YJj/vyLoUO+e05eAQIs52vKKWNTxUbO5fx+3f5gOXb6\nNZEyGo0EAgF8Ph9a7Y+b9ng8BAV1nQD273//O2q1mocffhiA7OxsfD4ff/jDH7jxxhsxGo14PJ4u\n7/N4PAQHH/1q9aRJk05gb3rXli1bBk08EwIKy3Lzyavy8lj2KMxBMt3YYHa8x84Ujj5JrM3dwMr6\nFaxtWM0O/zZ2sYNx1vEYNEY22tYTIECkPpLz4i5gavh0tOqOY2WG8zReK36JTd71qCNUXJ/8825b\ntA6mKAo/1H/P0vLFKChclnAlZ8ec23n1fbZyBkurv+Sr6i9YymLOjZ3HBXEXd263P1W0lePN9zA5\nYgqTUrv/+wfbg9hatBlNrJpJCYPjM36wwfTdIw7v9eKXoRmuSLyahRWfkhO0k4uzLkWtGtgZTA4+\ndnbbC/hw33vUueuw6sII1YWyr60Mbbqa8WETj7ImMRTJd8/JbUH5J5TXlaFCRQX7CB8Z1llgqa8N\nxLHTXeLWr9/CcXEdV4/r6+sPeb6urq5Ldz+AHTt2dI6HOmDcuHF4vV6qq6uJjY2lsbERv9/f+brP\n56OxsZHo6Og+2IOhQa1WMW9KBG6vwvfbpbrbUBZhiOTyxKv489in+FnKTcQFxbOteSvrbWuJMkRx\nU+rNPD76T5wWefohyUyKKZXfjvwfUoJT2WBbx/OFzxy1WITb7+bt0n/xSfmHmLQm7s98kHNizzuk\nC5NGpeWi+Et4aMRvCddHsKxmKc/sfqrXxmQdi+7mj/qpVFMqKlSUyMS84jhUtJWzo3k7aaZ0zow+\nm6kR06lqr2Rj4/qBDg0Ah6+V+aVv8c+iZ6l31zM3+iz+d9Qf+WXar9CoNHxa/hFu/9F7iJyowtbd\nLK1egl/xH31hIcQJWV2/ku/qviHWGMdtGXcB8GXV4gGOamD062XckSNHYjKZ2LhxI5deeikAFRUV\nVFZWMmVK16vjsbGx7N69+5DnioqKUKvVJCcnExERgc/nY9u2bUyePBnoyBgDgYBc5ThBZ08M552v\nq/lyo42LpkcO6v74ou/p1QZOizydGREzKXHupd3fTlZo9hGviFv1Vh4Y8TDvl81nc+NGns7/C3cM\nu5toQwzNniYaPY00eRtp8jTR5Gmk2LGHencdaaZ0bk2/A+sRxlakmzP4/7L/l0/KP2SDbR1/zv0j\np0edwby4C7HoLH3xJ+iip4lUkCaYGGMsZc4SAkpgwFsRxMllafUSAC6IuxiVSsXF8ZeypXETiysX\nMTFs8oBNAK4oCkUU8lHOezj9TpKCkrg+5UZSTKkAGDWxnBVzLl/XLGVZzZdcknB5n8WS07KT14tf\n2Z9EqTg/7sI+25YQQ12BPZ+P9n2AWWvmrmH3EmmIYrg5kzx7LnsdxaSbMwY6xH6l+cMf/vCHftuY\nRkNrayv/+te/GD58OA6Hg8cee4yUlBTuvvtuPB4PjY2N6HQ6NBoNVquVF198EbVaTWxsLFu3buVP\nf/oTl112Geeccw5ms5ni4mI++ugjsrOzqaqq4ve//z1z587lsssuO2Is1dXVxMfH99OeH91giyfI\noKGkpp2de51Mzgwl0jIwP9bi6Prz2FGpVITpw4k2RvcoudaoNIy3TkCn1rGzeTtrGlaxrGYpK+tX\nsKlxA7tadrLHUUhFeznt/jZmR53JzWm3Eqw9epETnVrHOOsEEoMTKWsrI9+ey6r6H/AGvCSbknvU\nlfB4KYrCgoqP0Kl1XJJw+VH/FvvayihrK2VC2ERCB1nBicH23SN+VNlWwScVH5FqSuPS/cdZkCYI\nt99Fnj2HII3xqIl8X1AUhY/KP2BV2wpUKjWXJVzBDak3EaYPP2S5NFM6G23ryW/NY2LYFMxac6/H\nktuyizeKX0WNGpPWTF5LDqOtY7DohubA95OJfPecfGpc1bxY9BwKCncNv4/E4CSgo/fKettamr1N\nTI2Y3udxDMSx0902+31gwQMPPIDP5+ORRx7B5/Mxa9aszgl0t23bxk033cT8+fOZNm0as2fP5sUX\nX+Tll1/mjTfeIDIykmuvvZY77rijc31PPPEETzzxBLfffjtarZbzzjuPxx57rL9365R0/tRIVue0\n8NUmGyOTpXqfOD4qlYpzY88nzhjP1zVfYdAYCdeHE6YPw6rruA/Xh2PVhx3X1fVx1gmMtoxhbcMa\nvqxazFc1X7CqfgXz4i5gVtScPkmoGj02mr3NjLdO6FFCmWZKZ71tLaXOEhKCEns9nt6iKApV7ZXk\ntOwid/+J+pWJ1xBt7Nr1WvS9Lztboy465Dg7N/Z81jasZlnNUmZEnn7UBCWvJZcIQwQxxtheiWtJ\n1X9ZVf8D4UTwQPbDRBgiDrucQWPgyqRreHPva3xS/h/uGfbrXu3dkNeSy+vFr6BCxZ3D7kVB4cWi\n53in5N88mvX7Pr2YAh3TM6hQSSuzOKxmTxP17nqGh2QOdCi9wuFr5ZU9L9Lub+cXqbcwzDy887Xh\nIZlkhowk35435Fql+j2R0mq1PProozz66KNdXps2bVqXrnxnn302Z599drfrM5lMPPnkkzz55JO9\nHutQNz7DTGyYnhU7mrntwgRMRs1AhyROYmOs4xhjHdcn69aotMyKms3U8Ol8X7ecb2q+YkHFJ3xX\nt5xzY+cxKnRMtyd7x+PHbn3Dj7Jkh7SDJuadGTmr1+LoDT685LTsZFfzLnJbdtLk7RgXqUKFgkJh\n624uT7iKWVGzpYtvP6psr2B781ZSglPJDj10rHCwNpjz4i7gs4pPWFa9lCuTrj7sOnwBH5+Uf8jq\nhpXo1XpuSr2ZCWEn1u39+9rlfFXzBVGGaM52n3fUz9V460SyQrPJt+exo3lbrxWeyLfn8lrxS/uT\nqHsYGZoFwBlRc1hZv4LFVZ9zReLh/y4nqtZVww91K9hgW4tOrWd29BxOj5xNiC6kT7YnTj6FrQW8\nWfwaTr+TW9PvOOHP3UDzBry8XvwqDe565sVeeNhWpwvjL6ZwdwFfVP2X+zIfHIAoB0a/du0bTAZb\nk/Jgiwc6WhJcngBbilqJDtOTmTjw8/aIrgbjsTNQtGotw0KGMzNq1v4koIBdLTv4vm45G2zrqGyv\nwOV3Y9KaMWqMx72dlfUr2NdWxsXxl/Zo7gyz1szy2m9wB9ycET3nuLfbW5o8TWxu3MiX1Yv5zv0t\nGxs3sK+tDLVKzTjrBM6Jncf1KT8nOTiFfHsu25q3UuLcS2bICIyarhVWRe/7uPw/1LiquSHl54dt\nSUoKTmZT4wYKWwuYGj6NYO2h3892r51Xil9gR/M2Yo1xuPwuNjVuQK1SM8w8/LiS4o229fxn33tY\ndBZ+nfkwzrq2o373qFQqUkxprG5YSbFjDzMjz+i2ymar186ymi/Z3VqAQWPEorMeNs4Cex6v7XkZ\ngDuH3UNW6KjO14abM9nWtIXclhyGh2QSYYg85v08nIASIKdlF5+U/4cFFR9T1laCSWvGq3jIs+fy\nQ933NHoaiTJEYz5FEipvwMua+lWsrF+BRqUh0hDVqxdT+uO3y6/4qWyvZEfzNrY3byPKENXls9Kb\nFEVhVf0PvFXyJn78aFVacu05TAqb0uPt+hU/efZcQnQhfd6q2hOKovD+vvnsatnBhLBJXJt8/WGP\ng3B9BHsdeyhozWdE6EjC9b138fKnhnTXPnFyOXdSOO9+W83SjTYunNY7P0hC9DWz1swViVcxN/os\ntjdtpbC1gCJHEetsa1hnWwNAtCGG4SGZJAYnkRCUSHxQAkE9TBKKHXvQqw2d/cOPRq1Sk2pKpai1\nkHZ/e4+301sCSoDytn3satlBTvNOytvLO1+zEsaUmGmMtowhzZyORvVjy/Ok8MlkmIfxftl88uw5\nPJH3R65Nvp7JYVNPitYpX8DXmfROi5jByJCsE4rbE/Cw296RnBfY8wjXRzArag7jw8ajUfXez2lV\nexXbm7aSHJzCqNDDT2qtU+u4KP4y3in9F4urFvHLtF91vlbmLOX14pdp9jYzMWwyP0/5BQ3uel4p\nfpElVYuoaa/mZ6k3HVNX2pyWnbxb+jZBmmDuGf5rIg2RlFHWo/fGGGOOWHjCE/Cwom45y6qX4gq4\nAFhWsxSrzspY63jGWScwPGQ4GpWW3fYCXt3zEgoKt2fcfUgSBR3dCW9KvYVndj/Fu6Vv8/9lP97t\n501RFNbb1vFNzVK0ah3h+nDC9RGE6yMI04cTbggnRBvC9uZtrKpbQYOnAYBh5uHMjj6TcdZxeAM+\n1tvW8n3tt6xpWMWahlVkh47mrJizyQwZSbO3iXpXPfXuuv23jscoCufFXcCksCmD7rPkC/hYZ1vD\nsuovO1uo19vWEmOMZW70WUyLmI5e3XUOz8HA4WulxFFCibOYEudeSp2leAI/Vo3cZFvPAyMeJtIQ\n1evbPrgF2KwN4bb0O6lz1/J+2XzeLnmTB0Y8fMj36+EoisIHZe+y3raWCH0kt6TfRqoprddjPZqA\nEmCvs5idzTvY1bydOncdKcGp3JR68xG7sl4YfwkFu/P5omoxv8586IjbaPG2oFGpMWtP7gsPkkiJ\nIwoP1TE9y8La3BaKKtsYniCtUuLkEaYPY27MWcyNOYuAEqCirZzC1t0Uthawx1HEmoZVhywfoY8g\nPiiRhKBEYoyx+BUf7f522v3tuPzttPnbcPnbqXZVMSJk5FF/FA+WakqjsHU3Zc7Szm5Ifcnld1Fg\nzyfXvouc5p3YfXYAtCotWaHZjLaMZYxlLKU5ZUxK7L7biVVv5e5h97GmYRULKj7h7ZJ/saN5O9cl\n/6xPigf0BkVRyLXvYkH5J9S5awHY1LiBNFM68+IuZFTo6B6fvNq99v1dH3eQb8/Dq3gBCNIEYXPY\nKHIUEloeysyoM5gZOYuwI1SbPBAbcMTtf1W9BAWly9ion5ocPoXvar9mU+MGzoo5h6TgZDbY1vFB\n2bv4FT+XJlzBOTEdUwgkBCfyu5GP8XrxK2xu2ki9u447ht3do6IMe1qLeLP4NTQqDXcPu++4xvnN\ni72ATbb1fFv7NdMiTiPGGENACbC5cRP/rVpIk6cRk8bE1UnXEaYPZ0fTNna17GBl/QpW1q8gWBNM\nduhodjRvQ0Hhtoy7GGUZfdhtpZnTOS/2fL6q+ZLPyj/hZ6k3dVmm0WPjg7L3yLfnolPpUKlUVLZX\ndBu/TqVjZuQszoiac8gFFI1Gy5zoMzkjag47m3fwXd235NlzyLPnoEZNgECXdRnVRnyKj7dK3mR1\n/UquTrqOhOCBHzvpV3xssK1nafUXNHps6FQ6zoo5hzGWcayzrWFz40Y+3Pc+iys/Z2bUGcyOmnNI\ndVVPwE2dq45aVy117hoa3A3EGuMYZRlNnDG+TxNGb8DLZxWfsLJ+xSHPxxrjSDdlkGZOp9HTyNLq\nJTy3+++9nky1elt5c++r7HEUkRCUyB0Z9xBhiCDDPIwCez5bmjbxZdUSLk649Ijr+bxyAettawnX\nR9DosfFMwVNcknA5Z8Wc0+dj8TouFOWzo3k7u1p24vC1Ah1VeyeFTeaqpOuOevEl3ZxBVugo8u25\nFLUWHnZ8WEAJ8EP99yyq+AydWsev0u/ol9/EvqJSDnyrDzGDbSK4wRbPwTbttvP423s5f2oE91/e\nsyvwov8M5mNnOpubPAAAIABJREFUMPMrPqraq6hqr6SyvYLKtgoq2ytp3Z9wHImKjkmGZ0b1fLzT\njubtvF78MhfHX8q8PirPXOeq7SgU0bKLIkdh55w6Zm0Ioy1jGGMZy8jQ7EO6NR7L8VPvrmN+yVvs\ndRZj0piYFjGDaREzetwy1x9q2qv5tOJj8u25qFAxK2o2E8ImsaJuOTuatwOQHJzCvLgLGWsZd8jJ\nnaIoNHpslDpLKHWWsNdZTJmzFIWOn8lYYxxjLOMYax1HqimNOnctq+tXst62lnZ/O2rUjLWOY9b+\nk+16Vx117lrqXHXUu2upc9VS565DrVIzMWwy0yNOI82UfkgM1e1V/DnvjyQGJ/G7kf9z1JPPAnse\nLxQ9x4iQkcQHJfB93XKCNEHcnHYroyxdW7O8AS//2fceG2zrsOqs3DHsHpKDU7pdf0VbOc8V/h23\n382dw+45ZJ3H+t2zrWkLb+59jazQbM6LvYCFFZ9S1laKVqVlbvRZnBd3PkGaHy/W+RUfRa1F7Gje\nxs7m7TR7m9GoNNyecRejLWOPuC1fwMffCp6kor2cOzPu6RyfGVACrG5YyecVC3AH3GSFZnN98o2E\n68Nx+p00eRpp9Ng6pmfwNNLsaSY5OIUZkTMx9aCaKHS0CH5ft5w6Vy1RhmiijFEd94ZoogxRmLUh\n2DwNfFr+MbtadqBCxRlRc7ko/uIeVSztbX7Fz6bGDSytWkKDpwHt/jGn58TOO2Q6iRZvC6vqV7Cq\n/gccPgdq1Iy2jMET8FDrrqXJ09jtNqw6K9mho8m2jGJESBbB2uBe++2yuW38a+9rlLWVEmuMY2LY\nJNJMGaSa0rp0p/u6ZimLKhcSpg/ngcyHieyFrp+VbRW8WvwSjR4bE6wTuTH1ZgyaH1vs2v1tPJn3\nBI0eG/dnPkhmyMjDruebmmV8XrmAGEMMD438LZVtlbxd8iZ2n53s0FHclHozIb1c9VVRFErbSlhV\n9wPbmrfgCXgACNGGMtY6jrHW8YwIGXlMXQxLnHv5e8FfGW7O5IERDx/yms1t472ytyls3Y1JY8IV\ncKEoClcmXcPsqLk9TrYHakLew21TEqlBYrDFczB/QOGWv+XR2ubn/cdGEWSQohODyWA+dk5GrV47\nle2V1Llr0an0BGmCOm/Ggx53N86jOy3eFh7b+QjDzZn8LPUmwvURx9SidbCAEqDF27z/BL2OqvYq\n8uw5HV2G9ksKSmKUZSyjLWNIMaV2ezXzWI+fgBJgee03fFPzFU6/E4DEoCSmRcxgSvjUI/7QewNe\nWrwt6NV6TNrgXu0O1+Zz8kX1YlbWrSBAgJEhWVyZdA3xQQmdy1S2VbC05gu2N21FQSEhKJHZ0XOx\ne+2UOksoc5bQuv8qLIAaNRnmYR2FUixju61e6Pa72dy0kZV1K6g4qNvkT+lUOqKM0Th9Tlq8zUBH\nF9OOhHQ6Yfpw3tr7JpubNnJ7xt2Ms47v0b6/WPQ8+fZcoCPZuyPj7iNWWlQUhW9rv2ZR5WdoVVrO\niT2PYI0JtUqNRqXpvFdQ+LziM1p9dn6Z9iumhE87ZD3HeuwoisJLe54n357X+dzksKlcknDZUccy\nHeieqlVre9wiVtVeyVP5fyZIE8T/ZP+Bdn87H5TNp8hRSJAmmCsTr2Z6xGkD2rUutyWHT8s/pM5d\nh1lr5pKEy5kRMbPPWx+8AS+7Wwv2J6k7cPha0aq0zIycxbmx5x9x7Kcn4GFz40a+q/2WalcV0JEo\nRRtjiTbEEGPsuIXpIyhvKyOvJZd8e27n94UaNWnmDCwOK2eP7GhJPd79zW3J4Z2Sf+H0O5kWMYPr\nkm84arfDZdVL+W/ViSdTTZ5GdjRvZ1HlQjwBNxfFX8K82AsPezyVOPfybMHThOhCeSz7f7t0Z1vX\nsIb3yt7BqrPym5G/6xxf1Oq1M7/0LfLsuYRqQ/lF2q96pfXGE3CzuXETq+p/YF9bRxfdKEM0460T\nGWcdf8TfjJ54qeif5Nlz+HXmb8gMGYGiKKyzrWFB+ce4Ai7GWsZxfcqN1LvreKP4FVp9rcyMnMU1\nSdf36LdVEqlBYLCdfA62eH7qg+U1vPttDb++Iol5U/puAKE4doP92BE/+r+c/6HBXQ90zLEVoY8k\n2thxpTraEEOozoJf8eFVvHgDXnyBjse+QEcXwwZ3HXXuOhrc9fgU3yHr1qsNZIVmMcoyhlGhY3pU\nBAOO//jxBXzktOxig20tOS27CBBAjZpRljGMD5uAy+/quLLv7ri63+hp7NLaZ1QbMWlNmLTmjnuN\nmShjVGf3yihDVLc/5n7FR62rloq2Ciray1nfsAan30mUIZorEq9izE9amw5W3V7FVzVfsqVxU2dr\nE0CYPpzU4FRSTemkmtJICk4+5Mry0SiKQolzL2saVuH0OYgyxBBt7Pi3jTZGY9FZUavUBJQAu1vz\nWd+wjh3N2/AqXlSoGB6SSVFrIQlBiTya9fsen+BXtlXw7O6nGRGaxY2pv+zxGLydzdt5u+RfuA8a\nQ3I4Vyddx5zoM7s8fzzHTq2rlr8XPElcUDyXJ17VWdGyr3xb8zULKz8lKSiJGlcNXsXLGMs4rkv+\nWY8/I33NG/Dyfd1yllZ/gSfgJik4mYlhHeMTk4NTeq3ggGv//GPbm7aR27KrczxaiDaUSeGTOTvm\n3C5zgR2Joig0eOoJ0YYetXhPQAmwb39SlWfPodRZ0vnZM2vNjAzJJsuSTVZodo+6mwaUAF9WL+Gr\n6i/QqDRck3Q9p0We3uPPzFfVX7K46nPC9RE8kPmbHhUl8Qa87HEUde5Djasa6Pju/UXqLYwPm3DE\n939d8xWLKj9jjGUsd2Tc0xnrzubtvFH8KkGaIB4c8QhxQYcWNAgoAb6r/ZZFlZ+hoHBu7DwujL/k\nuC7E1bnqWFX/A+tta2jzt6FCxVjrOM6ImsOIExxDerBSZwl/K3iSYebh3JJ+Gx+UvUtOyy6MaiPX\nJF/P1PDpndtq9Nh4fc/LlLeXk2Eexm3pdx615U0SqUFgsJ18DrZ4fqq+xcMvn8pjWEIwz99zasyJ\ncKoY7MeO+FF5Wzm7mndQ567tGHjuquu8SttTRrWRKGM00Qe6Cu0/UU8KPr5JiHvj+Gn12tncuIn1\ntrWHbZHRqrRY9WGE6yOw6qx4Ax6cfidOnxOnz4HT5+wce3QwvVpPnDGehOCOxMqn+Khqq6SivZwa\nV3Vn10Xo+LvMi7uQOdFn9vjvUOuqYVfzTqKM0aSaUgdkEtd2fxtbG7ew3raWvc5iAG7PuItx1iOf\nlP2UL+A75lZS6KjgWNG2D78SIICfgBLAr/jxKx2PIw1R3V4BP95jJ6AE+m3upYAS4LnCZyh2FGHW\nmrk66bpBWeABOuYdWli5gM2NGzuf06q0pJhSSTcNI8M8jHRzRo+7GCqKQpWrkt32fPLt+RS2FnRe\ngInQRzI+bALjrBNIM6X3+1xYTp+TL3d8gTuinTx7bmcLLUBCUCLDzMOJNcYRGxRLjDGWUK2l89+s\n1dvK2yVvUtCaT4Q+glsz7jxi99TuHCmZ8is+GtwN1LpqqHXVUNRaSGHr7s7vKZ1KR2bICLItoxln\nHd+jBDSgBHip6HkKWvO5Kula5kafRVFrIS8VPY9KpeL+4Q+RZu7+wkKps4R/730Dm6dhf6Gb2ZwW\nOfOoxRq8AS87m7eztmE1Ba35AIRoQ5gZOYvTo844puT5WLyy5wVyWnZhUBtwB9yMDMni56m/OOz2\nPAE375a+w9amzYTpw7kj4x6SjtBlXBKpQWCwnXwOtngO5w/z97Ih386L92WSES9FJwaLk+HYEd1z\n+pwdiZWrHofPjlatQ6fSoVVr99/r0Kq0GDQGIvVRmLXmXj0J7O3jp7KtgiJHISHakI4KaIZwQrSh\nRz1R8wQ8OHyt1LpqDxqzVtElYYKOk5j4oITOBCshKIGk4JQTKmk/GNS5amlwN5AVmj0oT/R/6mT5\n7mnxtrCpcQPTwmecFHM9NXuaKXbsodixh73OPVS0lR/ScmrVhRFr7Egwoo0x+x/HYdVZafI2stte\nQEFrPrvtBYe0Ascb4xkXNpFx1gkkBiUO+DF24PhRFIVqVxV59lzyW3LZ4yjq0uIepAki1hhHjDGW\nAns+zd4mRlvG8ovUm09oXNlX1V+wuGoR4foIxlsnUueu6fwc/rRQSJwxnmzLKLJDR5FhHn5cF65a\nvM38Je9PuPzt3JByI5+Uf7h//OG93RZPOVi7v43/Vn7OettaPAEPWpWWSWFTOCN6TpfqfpVtFay1\nrWaTbUPnBbsM8zDOiJrDeOvE47rwciz2Oct4quDP6NV6Lk+8ilmRR56LUFEUltV8yeKqRejVen6R\n+qtuW/kkkRoEBtsPwGCL53A25Lfwh/klXDQ9knsuHfgKQ6LDyXDsiMFrsB8/B7rwVbZXoEZDYnAi\nUYbofr+CLroa7MfOqcLld1Hi3Mtexx72OoqpcdXQvL8s+cF0Kt0hLbsWnYURIVmMCB3JiJCso1aU\n7G/dHT+egIea9mpqXB23WlcNNa6OBCdAABUqLo6/jHNiz+uV74Gl1V+wpGpR5/+bteb93XE7xnpF\nG2JIMaX2WstNbssuXt7zQuf/35x2K5PDpx7TOtp8bWywrWNl/YrOyqTJwSmcETUXr+JhXcOazrFP\nIdoQpkXMYEbkTGKNcb2yDz2111GMRWc96sTdB9vRvJ13Sv6FTq3nr2P/ftjkazAlUlL+XPTY5MxQ\nIkJ1rNjexB0XJaDVDP4rpkKIk5tGpSU+KOGQohFCDCVGjZGs0I7xQwe4/C7q3LWdXc9qXR1VIa36\nMEaGZjEyJItYY9yAtzodD71aT7IphWTTod31DnS306q0vTbRMsD5+6dE8Ct+oo0xPe46ebxGWcZw\nTsx5fFO7jKuTrjvmJAogWBvM3JizmB09l92tBays+55dLTt5r+xtoKOy7GjLWE6LnMloy5heLexz\nLNLNGcf8nnHW8TyW/X+0eu0nxfEriZToMY1GxWmjLCxe10BOqYPxGYO/i4QQQghxqjFqjCQHpxzX\n2KCTlUalJcYY2yfr/mnS1tcuS7ySs2LOPeGupmqVujPJtrltbLCtQ6fWMiV8+qAppnI8Ig2RvVKa\nvj9I3whxTGZkd8wpsT6vZYAjEUIIIYQ4OfX2eL0IQwQXxF/EObHzTuok6mQjiZQ4JmPSzJiMatbl\ntTBEh9cJIYQQQgghiZQ4NlqNiikjLNQ1e9lb3T7Q4QghhBBCCDEgJJESx+y0UR3d+9ZJ9z4hhBBC\nCDFESSIljtmkzBC0GhXr8uxHX1gIIYQQQohTkCRS4pgFGzSMzzCzt7qd2ib3QIcjhBBCCCFEv5NE\nShyXH6v3SauUEEIIIYQYeiSREsdlWpaMkxJCCCGEEEOXJFLiuESE6hiRFMyuUgetbb6BDkcIIYQQ\nQoh+JYmUOG4zsi0EArCxQLr3CSGEEEKIoUUSKXHcDoyTWpcv3fuEEEIIIcTQIomUOG5JUQYSIgxs\nKWzF4w0MdDhCCCGEEEL0G0mkxHFTqVTMGGXB5Qmwvbh1oMMRQgghhBCi30giJU7I9KxQAJmcVwgh\nhBBCDCmSSIkTMjLZhNWsZUN+C4GAMtDhCCGEEEII0S8kkRInRKNWMW1kKE0OH7vL2wY6HCGEEEII\nIfqFJFLihB2o3rdWJucVQgghhBBDhCRS4oSNHxaCQadmvZRBF0IIIYQQQ4QkUuKEGXRqJmWGUFHv\nprzONdDhCCGEEEII0eckkRK9QibnFUIIIYQQQ4kkUqJXTB0RiloN63IlkRJCCCGEEKc+SaRErwg1\naRmTaqagvI3cUsdAhyOEEEIIIUSfkkRK9Jobz4lFpYJ/fFqO2xsY6HCEEEIIIYToM5JIiV4zKtXM\nZadFUWlzM/+b6oEORwghhBBCiD4jiZToVTedG0d8hJ6Fq+vJK3MOdDhCCCGEEEL0CUmkRK8y6tU8\neGUyAP9YsE+6+AkhhBBCiFOSJFKi141OM3PJjEgq6t28923NQIcjhBBCCCFEr5NESvSJX54XR1y4\nns9W1ZG/T7r4CSGEEEKIU0u/J1J+v59nnnmG008/nQkTJnD//ffT0NBw2GVvvPFGRowYcdjbpk2b\nAGhsbOSRRx5h+vTpTJs2jV//+tfU1EgryEAz6jU8eFUyAQX+8ek+PNLFTwghhBBCnEL6PZF64YUX\nWLhwIU899RTvvfceNTU13Hfffd0uu3r16s7bypUryc7OZurUqUyYMAGAhx56iIqKCv7973/z9ttv\nU1dXxz333NOfuyS6MWZ/F7/yejfvLZfkVgghhBBCnDr6NZHyeDzMnz+fhx56iJkzZzJq1CieffZZ\ntm7dytatW7ssb7VaiYqK6rwtWrSI8vJynn32WbRaLQ6Hg/Xr13PbbbeRnZ1NVlYWd955Jzk5OTQ1\nNfXnrolu3DwvjthwPQtW1lEgXfyEEEIIIcQpol8TqYKCApxOJ1OnTu18LjExkYSEBDZv3nzE99bX\n1/PKK6/w4IMPEhUVBYDBYCA4OJjPP/8ch8OB0+nk888/JyUlBYvF0qf7InrGqNfw4JVJBBR4dsE+\nGlu9Ax2SEEIIIYQQJ6xfE6kDY5diYmIOeT46Ovqo45reeOMNIiIiuO666zqf0+l0/PWvf2XDhg1M\nnjyZyZMns2nTJt544w3UaqmjMViMTQ/p6OJX5+bWZ/L5dGUdHp+MmRJCCCGEECevfs022tvbUavV\n6HS6Q57X6/W43e5u3+dwOFiwYAG33norGo3mkNf27t1LZmYm8+fP59133yUtLY17770Xh8PRJ/sg\njs/tFyVw72WJ6DQq/rW0irueK2BDfguKogx0aEIIIYQQQhwzbX9uzGg0EggE8Pl8aLU/btrj8RAU\nFNTt+5YvX47f7+fiiy8+5PnNmzfz/PPPs2LFis5Wrpdeeom5c+eycOFCbrzxxiPGs2XLlhPYm943\n2OLpbbFauPds+C5PxcZiN3+YX8LwGIULxgWICh3o6E5up/qxI/qWHD/ieMmxI06EHD/ieA2WY6df\nE6m4uDigY7zTgccAdXV1Xbr7HWz58uXMmTMHk8l0yPPbt28nKirqkPeGhoaSmppKWVnZUeOZNGnS\nse5Cn9myZcugiqcvzZoBZbXtvLakkm17HLz4rYaLpkcyd3w4GfFBaDWqgQ7xpDKUjh3R++T4EcdL\njh1xIuT4EcdrII6d7hK3fu3aN3LkSEwmExs3bux8rqKigsrKSqZMmdLt+7Zu3cr06dO7PB8bG4vN\nZsNms3U+197eTkVFBampqb0au+hdKTFB/PmWDB6/MY0oq55Faxt44OVCrvrjTn77ehFvL6tiY0EL\nrW2+gQ5VCCGEEEKILvq1RUqv13PDDTfw9NNPExYWRkREBH/84x+ZOnUq48ePx+Px0NLSgsViQa/X\nAx2tVfX19WRmZnZZ39y5c4mLi+OBBx7gd7/7HTqdjn/+858YDAYuu+yy/tw1cRxUKhUzsi1Mygxh\nTU4LuaUO8sqc5JQ62VXyY6n0pGgDZ08I56IZkQQbNEdYoxBCCCGEEP2jXxMpgAceeACfz8cjjzyC\nz+dj1qxZPP744wBs27aNm266ifnz5zNt2jSgoxsgdMwp9VMmk4l33nmHp59+mttuuw1FUZg4cSLv\nv/8+ZrO5/3ZKnBC9Vs3c8WHMHR8GgNPlp2Cfk7wyJ/n7nOTva+OtZdUsWFXHlbOiJaESQgghhBAD\nrt8TKa1Wy6OPPsqjjz7a5bVp06axe/fuQ54bNWpUl+cOlpCQwPPPP9/rcYqBYzJqmJQZyqTMjgoU\nTpefRWvqWbi6nreWVfPpyjquPCOaiyWhEkIIIYQQA0QmWxKDnsmo4YazYnn7d9ncdE4sigJvL6vm\nl0/l8dGKWtrc/oEOUQghhBBCDDGSSImThsmo4fozuyZUN/8tj89W1eH2yiS/QgghhBCif0giJU46\nBydUN54di8+n8MaXVfzq7/l8ubEBn18m+RVCCCGEEH1LEilx0urs8vfbbK6eHY2j3ccLCyu4/dl8\nvt/eRCAgCZUQQgghhOgb/V5sQojeFhKs5ZZ58Vx2WhQffl/L0k02nv6ojI9X1HLh9EhiwvREWnRE\nhuowB2lQqWTCXyGEEEIIcWIkkRKnjPBQHXdfmsgVs6J4f3kN321r4qVFFYcsY9CpiAjVEWnREx9h\nYFpWKBOGhWDQSeOsEEIIIYToOUmkxCknNtzAb65O4do5MeTva8Nm99LQ4tl/76XB7mXnXgc79zr4\napMNo17NpMwQTsu2MHVkKOYg+VgIIYQQQogjkzNGccpKjDKSGGU87GteX4A9le2szWthbW4za3Ja\nWJPTglajYmy6mSkjQgkN1mDQqdHr1Bh0agw6FQadmiCDmiiLHrVauggKIYQQQgxVkkiJIUmnVZOV\nYiIrxcQt8+Ioq3WxNreFtXktbC1qZWtR6xHfbw7SkJ1iYnSqiVGpZoYlBKHXSvdAIYQQQoihQhIp\nMeSpVCpSY4NIjQ3ihrNiqW3ykFfmpN3jx+NV8HgDuLwBPN4Abq9Ca5uP3RVtbCyws7HADoBeqyIz\nKZhRKWaSogyEh+qI2H8LNqilwIUQQgghxClGEikhfiImTE9MmP6oyzW0dCRcOaVOcvffckqcXZYz\n6NREhGoJD9VhNWkJCdYSEqQ56L7jsdmoIdioIdioJtigQSNdB4UQQgghBi1JpIQ4TpEWPWeM1XPG\n2DAAnC4/u8vbqG3y0Gj3Ymv1YrN7Ox/nljpRjmFqK6NejcmoIdigJiRYS2SojkirjiiLniiLjkhL\nx+OAAh7fjy1mbk8At7fj5gsoZCYEo5eqhEIIIYQQvUoSKSF6icmoYeLwkG5f9/sVWtt9tLb5aW33\n09rmw97mx7H/OUe7nzZ3x83pCtDm6nhsb/NTaXMTCHS3Zg0s2NntdkNNGs6fEsmF0yOIshy9pU0I\nIYQQQhydJFJC9BONRoXVrMNq1h3ze/0BhWaHj/pmD/UtHeXc65u91Ld4qKhpJswailGvQq9VY9Af\nqDKoxu0NsGJ7Ex+tqOWTlbWcPsrKJadFkp1iknFbQgghhBAnQBIpIU4CGrWqs3jFyJ+8tmXLFiZN\nyuj2vb86P54V25tYtLaelbuaWbmrmWHxQVxyWhSzx1ql258QQgghxHGQREqIU5xBp+a8KRGcOzmc\nXSVO/ru2nnV5LTz76T5eXVzBrDFWzpwQxuhUs8yNJYQQQgjRQ5JICTFEqFQdkw2PTTdT2+Rh6cYG\nlm9rYtnmRpZtbiTKomPu+DDOnBBOSszhJzIWQgghhBAdJJESYgiKCdPzy/PiuemcOHaVOPhuexOr\ndzXz8Q91fPxDHRnxQZwzKZxzJoUTbNAMdLhCCCGEEIOOJFJCDGFqtYpxGSGMywjh7ksS2VhgZ/m2\nRjbvtvPq4krmf13NuZMjuOS0SOLCDQMdrhBCCCHEoCGJlBAC6BhLNWuMlVljrDQ7fCzd1MCSdQ18\nvqaeRWvrmZ4VyqWnRTE23SwV/4QQQggx5EkiJYTowmrWcv3cWK6aFc3qnBYWralnXZ6ddXl20mKN\nzB4XhsmoIUivxrj/Zth/bzJqiAjVoddKNUAhhBBCnLokkRJCdEunVTN3fBhzx4eRv8/J52vqWZ3T\nzNvLqo/6XqtZS5RFR6RFT5RFR5RVT5RVR3yEgfgIAyZj92OvAgGF8no3BeVOCva1UVjRhkoFESE6\nIiy6zvvwEB2RFh2JkQYp4y6EEEKIfiWJlBCiR7KSTWQlm2ho8bCnsh2XJ4DLG+i49wRwefy4PAEc\n7X4aWjomCy6rdVFU2X7Y9VnNWhIiO5KqxEgDUVY9FfUuCsrb2F3uxOkKdC5r0KkAFcVVh19XWIiW\ney5NZOYoa1/suhBCCCFEF5JICSGOSaRFT6RF36NlFUXB3uanvsVDQ7OX2mYP1TY3lQ0dt/wyJ7ml\nzi7vS4g0MD0rmJHJJrKSg0mNCUKthjZ3gIYWL42t3s77mkY3325t4on3SjljjJW7LknAatb19m4L\nIYQQQhxCEikhRJ9RqVRYTFosJi3D4ru+7vUFqG3yUNHgpq7JQ2y4gZFJwYSaDv/VZDJqMBk1Xea5\nuuL0aJ77rJyVu5rZXtzKHRclMHd8mBTFEEIIIUSfkURKCDFgdFo1iVFGEqNObALgpGgjT98+jCXr\nGnhrWTV/+3gfP+xo5t7LE4nqYeuZEEIIIcSxkERKCHFK0KhVXDoziqlZofzzs3I27rZz5z8KuOS0\nKLRqFS5vALc3gGf/vdsbQK1WMSbVzKTMEBIiDdKC9f+3d+fRVdX33sffZ54yhwyQhDBIGEIRhIBS\nVNSu6mI92PvYq21FqSLetvYq6lXgqnCd8EqVQa0iomv5oG21V4noam1XH6/DA1SmgBUZZDBkkJCQ\nQIYzD/v54yQHckmAgGTQz2uts/Y5v73POb99+JKzv+f7278tIiIiZ0yJlIh8q/TPcPDEbUP565YG\nVv2pmjc+PHzK7dfvaAQgO83GRcNSuGhYMmMvSCLZpT+PIiIi0jkdKYjIt47JZOKakkwuHpnCl1U+\n7DYzDpsZZ+vSbjPhsJvxBWJs39dM2d5myvY185fN9fxlcz1mEwzLdydmE0xM3966PNXU7SIiImcr\nFjPYU+VjaH+XLuvRByiREpFvrbQkGxNHpHa6PtkFV5dkcnVJJtGYwd4qH2V7m9m6t5ndlV72VPo6\nfJ7HaSa/n5PCXCeDcpwU5rgYlOskPcl60vBAwzAIhGI0eiM0eqOtQwrBYjJhNoPZbMJkij+2WEzY\nrSbsVjM2W+vSYsJsjr9mNGbgD0bxBWN4A1F8gfj9QCiG024myWUhyWkhyWXB47LoosgiIn1INGbw\n23cq+cvmBgqyHdx/QyHD8tw93a1TCkVi3+nvGiVSIiLEz7EaMdDDiIEebrwql1AkRn1TmLpjYeqO\nhahrPL48fDTE/kN+9lS1T7RS3BYG5jixWUw0eqM0eiM0+SKEI8Y59c1qMWExmwiGY6ff+AQOm4kk\nl5WB2Q67on73AAAfN0lEQVSGF3gYXuBmRIG70+nhDcOgoTlCRW2AitoAvkAUt8OC22nB4zTjcbbe\nd8STtWS35bTnlRmGQe2xcOI1D9UHyUi2UZDtZGB2/Dpitu/wl/B3TTgS4+DhADnpdpLdOgQRaRON\nGix5q4IPtx+lX6qNytog97zwJT+7MpefTM3Baukd5/BGogY7D3rZvKeJLXuaKD8cYHJxKnf97wJS\nO5lx99vsu7fHIiJnwG410z/DQf8MR4frI1GD6iNBDh72U344wMHDAQ7WBPii3IthgMthJtVjZUiu\nixSPlVSPhRSPFYfNTCwWTzBiRnwYR9syEjMIRwxC4RihiEE4El+GwjGiMQOXw4Lb0ZrQOCy4nWbc\nDgsOuzlxMeQWfxRvIEqLP0KLP0qTL8q2fS1s29eS6HtOuj2RVFVWmvh/5RWJROfECyGfjsUcr/pl\nJFtJS7KRnmwlPcmK026h+kiAitoglbUB/KHOX9Nshv7pDgqyHRRkO0l2WbBZzVgtJmwWEzarCavl\n+M1kMmE2xYdvmky0u28ygYnWxwCtjw0DfMEo/lAMXyCKPxjDF4riC8QnH3HaT/5M3U4zbqeFNI+V\n9CQblrM8iInG4v+OwbBBKBIjHDaIxgwsJ+yT1RyvRtpaH7dVIE8lFjOoaYgn9AcO+Tl4OIDTbqZf\nqo3MFBv9UmyJ++nJNixn8JptAqEYeyq97Cj3UncsFK90uqytSwvJrY8bWkhUQ0+1//u/9vPZ/mY+\n29/CjnJvoio7ZnAS3y9O45LiVDJTzv+136JRg0MNQQ7WBqg4HEjE/OGjIYYOcDNpRAqTRqaS16/j\n//Mi50s4EmPxmwdZv6ORUYUeHr1lCF9W+Vj6VgWv/98aNu1u4r7rB1KQfW4z3J6t+qYwW75sYvOe\nJrbtbcYXjP9Nt1tNDMi0s+GLRnZXeLnvhkLGXZDcI33sKSbDMM7tp9I+auvWrYwfP76nu5HQ2/oj\nfYdip3cJhWNgolcNdWj2R/iy0sfuSh97Kn3sqfTS5Iu228ZihgH9HAzMdjIwy8nAbCfJbgv+0PFh\nhN5AFG8glkjUjjZHONYSoaE5TKiDqpvVYiI/q/U1s50U5jjpn2GnvilCZV2AqrrjyVazP3rS83sL\nkwlS3FYykq1kJMcTxoxkGzGD1s8kitcfpaX1c2oJRAmEYq0J8Nm9V1pS/JaeFE9S05KsuOxmquqC\n7D/k56tD/lMmqCcymyEr1c6ATDsDMuNVwAH94svcDDuBYIwvDnr5ojye6Oyr9nWp30lOCxkpNjJT\nrGSmxJM3l8PCnkov/zjQ0i45H5jtZFShm69qAu2Gzo4ocDO5OJXJxWnkpNtbY6ztFok/DkSJRcHt\nNONyWHA5WpPe1iUmaGgKU98Upr45TP0JF++uawxTfSRIJNo+Th02M/1SbFTXBxNt+VmORFI1aqAH\ni8VELGbQ7I9ytDnM0ZZIYpnktFCU72ZgtvOMk+1AKMqRxnB8FtHIyYl2zDDonxH/cSGpD096EwjF\ncNhMnVat9d0VFwrHWPS7cjbtaWLMkCQenjkYlyN+Hm6LP8KL71Xzwbaj2K0mZl0zgOmX9DujH1u+\nCTvKW3jrk1o27mpKtOWm2ykZkcKEohTGDEnCZjXx9ie1rP7bIaIxuO7SLH7+w/7n9TuwJ2Kns/dU\nItVL9Lb+SN+h2JGuMgyDQw0hvqzycbD8AFMvLmZApv2sh9gZhoEvGONY6wGmLxhlQGa8mncmB5eG\nYdDojVBVF8QXjBKOGISjBpHWWzgSIxxprdwZBkbbMsbx+wYYra91/H789U2meIXQbbccPwi3xw++\n7TYTwbARTxaD8SpVfBlPkI62JopHm+NLf7DzDMNqMbVeNNqM027B0Xqem91mxm41YbOacdjiFadI\n1CAajVcho9H4/kajBsFwjGPeCMeaI7QEOk4uzWYYmOVkcH8XQ/q7GDrARWGOk3DE4EhjiPqmePLQ\ntjzSFKamIUhDc+Sk1zKZjn9OEE+oLxjgpniwh+JCDwVZTnzBeELT7I/S4ovSHIhXO7+qOIzJnhpP\nXJrCtHSQDOdm2Bk7NIkLhyQzZkgSGSdUnuoaQ3y6s5H1XzTy+VctxLqYdHaFy2GO/0CQ4zwhsXeQ\nlWrHbDbR0Bxm8+4mNu5upGxvS2IYbZLLgsNm5mhL+JT9c9hMDB3gZliei6J8N0X5bpJcFirrglTW\nBaisDbb+cBCg9lj4jPudnmSlINtJfpaDgiwnAzIdhCPxGGlsibRfeiNYzKbWimF86O2JlUQjBo2+\nCE2tQ44bvVGaWh+fmJS3/Y9ty38cNjMX5LkYXuBhRIGbYfnueOL6P9QdC/H5Vy18UR6vZlbUBkhL\nslI8yMP3BiVRPNjD4FxXojp6Jt9doXCMr2r87K32s6/ax95qP6FIjO8XpzL1wnQG5brO+LM8nWjU\n4EhTiJqG+O1QQ4jDR4PUNIRIdlsZWehm5EAPw/PdiUTnXAVCUR597Su27WthQlEyD900GEcHE0ys\n33GMZ9+ppMkb5cIhSfzif+UxuH/X9j0Qip9Pm5Z06uQ8FjPYuLuJtz6pZedBLwDDC9xcPiaNkuEp\nnV4q5MsqH7954yDV9UGG9Hcy9yeDKMw5PxU0JVK9QG87+Oxt/ZG+Q7Ej50Lx0zWBUJSGpggNLWEs\nZhNJTks8eXJZsFs7//X9bLQdMMcT1Ai+QJS8LAeF2c6zms3LH4xyqCFE9ZEgh+qDVNcH+fpIEJvV\nRHFh/EB3RIEbp/3MDhL/Z+wEwzEaWitBTb4IQwe4yEk/s2FyTd4IG3c38umuJryBaGLiFM+JE6g4\nLZjNJvzBWOukK63DNIPx5NcwICP5eFUso3WIY0aKrcMD/86EwjG2729h0+5Gtu5tBiAj2ZaoEKYn\n21orhVaOeSPsrfLxZZWP8sOB0yaDGclWCrKc5GbYcdrN2KzxJLst2bbbzBgGHKoPUlEboKouyOFj\nIc7kSC3JaYlPSHOGlUoAl91Miide7YyPg423n/h2zb5IuyTcbIpXFkcMdJOf5eTA1352lLe0SxDb\nkq+ahnhi38btMDOq0MPowUk01VeTX1BINBof7hqJxZfRqMHhoyH2Vvs5eNjfrjpqt8Z/iAi07uPg\nXCdTx6Zz+Zh0ctLbX3w9GI5RXhPgQOvw14raAMFwjFjb+8TiQ7SjsfgPNkebwx1WYs1m2v27mk0w\nKNfFyIFuRhV6GJTrItVjJcVt6dKPUd5AlIf/zwF2lHu5ZFQK83826JRVnKPNYZ4treTT1urQoFwn\nV1yYztSx6WSndXzh+RZ/hE27m1i3o5GtXzYRihhkpti4IM/FsLx44n9BnpuMZBuhSIwPtx/l7U9q\nqayLV2gnjkjh+suzKS70nNHfNn8wykt/quYvmxuwW03cPi2PS4pTqT0a4vDRELXHTlgeC+GyWxg5\n0M2IgR5GFXrISrWd0fsokeoFetvBQ2/rj/Qdih05F4ofOVuKnfYCoRgHDvkTiZUvGCU/Kz6pSn6W\nk4KssxuqFwjF+Lo1sTrUEMRlt5DqaR3+6bHGD+I91sRkBJGokRgS2dw6PLLZF8FsNpHatr3bQorb\nesYJeV1jqHVYsI/dlV72VvnbTX6T4rZQPMhD8aAkRg/yMHSAG6vFhGEY1BwNseOr+LDRHV+18HV9\n6Ize0241MaR/6wF/fvyAf2CWk3DUYPOeJj7cfpTNe5oSwzWLB3kYOzSJQ/Xxcwcr605ObNvOS7SY\n4xMMWVon8rGYTWSm2MhJt5Obbic3I37LSXfQL9VGozfCrgovuyt87DzoZW+1r8NJhNwOc+s5sVZS\n3VaS3ZaTKtJtVeqPth9lT5WPy8akcf8NhWc0mYRhGGzc1cTfyhrYtPv4vo8e5GHq2HQuHZ1GzDD4\n+84m1u84xvb9zYnksCDbQf90B/sP+dsltwCZKTZihsHR5ggWM1wxNp0fX5p91hW/9TuO8cyaylMO\n2U5yWQiEYu2G22am2BgxMF75mzQihfysjitaSqR6gd72BdDb+iN9h2JHzoXiR86WYue7Kxo1KD/s\np+pIkEE5LgqyHGd83k5DU5idFV527jnABUMGJZKZtklXrBYTaR7rGZ131uyPsGFHIx9+dpR/HGhJ\nVO5cdjOD+7sY2jr0dcgA11lXcjsSjsST5p0HvRyqD8Uvb9E6TDI+W2v0pPPxOvKDi9K5+8cDuzQZ\nTJtmf4T1Oxr5cPtRPv8qvu8Wc9tw5/g2Fwxw8f3RaUwuTmXgCRNVNDSF2VvtY1+1n71fx5e+YJRr\nJmTyv6dkkdVJhasrjjSGWP23GgKhGDnpdrLTbK1LO9npdtwOC6FwjL3VPnZV+Nhd4WVnhZejrdXP\nFI+FNx/6Xoev3ZsSqb57FqOIiIiIdDuLJX5O2NABXb/GUUaKjSmj03AFDcaPyzinfiS7rIlrAdY3\nhdlX7SM/Kz6pzfmckMFmNbdeUsLT4fq280Zb/BFCrROJhE6YkTUUieGyWxg7NOms+5nssnJNSSbX\nlGRS1xji48+Ose7zY1itJiaPSuX7o1M7HVqbkWJjUkoqk0Yev86iYRjf6NDkfql27v3ngafcxm4z\nUzwoieJBSYk+1B4Lseugj2R337jwvRIpEREREenT4ufGdX4B9u5kMrVNPNM9yUBWqp1/viybf74s\n+6xf45tMos6lDznpjjM+t7I36D3z84qIiIiIiPQR3Z5IRaNRlixZwpQpUxg3bhx33XUXR44c6XDb\nm2++meHDh3d427x5MxAvA65cuZIrrriCsWPHMmPGDHbt2tWduyQiIiIiIt8x3Z5IPffcc5SWlrJ4\n8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"text/plain": [ "<matplotlib.figure.Figure at 0x1f4015a8588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history['loss'])\n", "plt.plot(history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper right');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reconstruction error on our training and test data seems to converge nicely. Is it low enough? Let's have a closer look at the error distribution:\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "predictions = autoencoder.predict(X_test)\n" ] }, { "cell_type": "code", "execution_count": 36, "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>reconstruction_error</th>\n", " <th>true_class</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>56962.000000</td>\n", " <td>56962.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.790553</td>\n", " <td>0.001720</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>3.461215</td>\n", " <td>0.041443</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.069604</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.258094</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.405381</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>0.646277</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>259.243414</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " reconstruction_error true_class\n", "count 56962.000000 56962.000000\n", "mean 0.790553 0.001720\n", "std 3.461215 0.041443\n", "min 0.069604 0.000000\n", "25% 0.258094 0.000000\n", "50% 0.405381 0.000000\n", "75% 0.646277 0.000000\n", "max 259.243414 1.000000" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n", "error_df = pd.DataFrame({'reconstruction_error': mse,\n", " 'true_class': y_test})\n", "error_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reconstruction error without fraud" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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5PDqwvn37Jkk+8IEPVI516dIlAwYM8C15HNRLL72UXbt2ZciQIZVj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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f400f19c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "normal_error_df = error_df[(error_df['true_class']== 0) & (error_df['reconstruction_error'] < 10)]\n", "_ = ax.hist(normal_error_df.reconstruction_error.values, bins=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reconstruction error with fraud\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1f40160ac88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "fraud_error_df = error_df[error_df['true_class'] == 1]\n", "_ = ax.hist(fraud_error_df.reconstruction_error.values, bins=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model is a bit different this time. It doesn't know how to predict new values. But we don't need that. In order to predict whether or not a new/unseen transaction is normal or fraudulent, we'll calculate the reconstruction error from the transaction data itself. If the error is larger than a predefined threshold, we'll mark it as a fraud (since our model should have a low error on normal transactions). Let's pick that value:\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "threshold = 2.9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And see how well we're dividing the two types of transactions:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ojs6cnQAAAADwzMnUS/UetXPnTs2YMUPdu3eXt7e3zpw5o+vXr2vIkCEKDQ3V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\nG4utW7fCx8cHf/nLX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"text/plain": [ "<matplotlib.figure.Figure at 0x1f410c61d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "groups = error_df.groupby('true_class')\n", "fig, ax = plt.subplots()\n", "\n", "for name, group in groups:\n", " ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',\n", " label= \"Fraud\" if name == 1 else \"Normal\")\n", "ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n", "ax.legend()\n", "plt.title(\"Reconstruction error for different classes\")\n", "plt.ylabel(\"Reconstruction error\")\n", "plt.xlabel(\"Data point index\")\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That chart might be a bit deceiving. Let's have a look at the confusion matrix:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import (confusion_matrix)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAICryeaNcUajEgwAAADTIQkGAADA3xIbG6vatWvn+MTExEiSduzYoSeffFL169dXaGio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\nQxIMAAAA0/lfsE0LrdqB8DMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f410df6f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n", "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n", "\n", "plt.figure(figsize=(12, 12))\n", "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n", "plt.title(\"Confusion matrix\")\n", "plt.ylabel('True class')\n", "plt.xlabel('Predicted class')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "We've created a very simple Deep Autoencoder in Keras that can reconstruct what non fraudulent transactions looks like. Think about it, we gave a lot of one-class examples (normal transactions) to a model and it learned (somewhat) how to discriminate whether or not new examples belong to that same class. Isn't that cool? Our dataset was kind of magical, though. We really don't know what the original features look like.\n", "\n", "Keras gave us very clean and easy to use API to build a non-trivial Deep Autoencoder. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
udibr/seizure-prediction
notebooks/140924-combine-predict.ipynb
1
9263
{ "metadata": { "name": "", "signature": "sha256:9d4b14ba5434b26025528b00c458880e1acd59b82d1228e75a4ff8c7b7593beb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "from matplotlib import pylab as pl\n", "import cPickle as pickle\n", "import pandas as pd\n", "import numpy as np\n", "import os\n", "import random" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "sys.path.append('..')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Read precomputed features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "uncommoent the relevant pipeline in `../seizure_detection.py` and run\n", "```bash\n", "cd ..\n", "./doall data\n", "```\n", "or\n", "```bash\n", "./doall td\n", "./doall tt\n", "```" ] }, { "cell_type": "code", "collapsed": false, "input": [ "FEATURES1 = 'gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "FEATURES2 = 'gen-8_medianwindow-bandstimecorr-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9'" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from common.data import CachedDataLoader\n", "cached_data_loader = CachedDataLoader('../data-cache')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def read_data(target, data_type, features):\n", " fname = 'data_%s_%s_%s'%(data_type,target,features)\n", " print fname\n", " return cached_data_loader.load(fname,None)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Predict" ] }, { "cell_type": "code", "collapsed": false, "input": [ "PWEIGHT=0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.cross_validation import StratifiedKFold\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.linear_model import LogisticRegression as LR\n", "\n", "clf = RandomForestClassifier(n_estimators=3000, min_samples_split=1, bootstrap=False,max_depth=5,\n", " n_jobs=-1)#" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "fpout = open('../submissions/140924-predict.1.csv','w')\n", "print >>fpout,'clip,preictal'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "for target in ['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2']:\n", " pdata1 = read_data(target, 'preictal', FEATURES1) # positive examples\n", " pdata2 = read_data(target, 'preictal', FEATURES2) # positive examples\n", " ndata1 = read_data(target, 'interictal', FEATURES1) # negative examples\n", " ndata2 = read_data(target, 'interictal', FEATURES2) # negative examples\n", " X = np.concatenate((np.hstack((pdata1.X,pdata2.X)), np.hstack((ndata1.X,ndata2.X))))\n", " y = np.zeros(X.shape[0])\n", " y[:pdata.X.shape[0]] = 1\n", " # shuffle\n", " idxs=range(len(y))\n", " random.shuffle(idxs)\n", " X = X[idxs,:]\n", " y = y[idxs]\n", " # model\n", " clf.fit(X,y,sample_weight=PWEIGHT*y+1)\n", " # predict\n", " tdata1 = read_data(target, 'test', FEATURES1) # test examples\n", " tdata2 = read_data(target, 'test', FEATURES2) # test examples\n", " y_proba = clf.predict_proba(np.hstack((tdata1.X,tdata2.X)))[:,1]\n", " # write results\n", " for i,p in enumerate(y_proba):\n", " print >>fpout,'%s_test_segment_%04d.mat,%.15f' % (target, i+1, p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "data_preictal_Dog_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_interictal_Dog_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Dog_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Dog_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Dog_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Dog_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Dog_3_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Dog_3_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Dog_3_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Dog_4_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Dog_4_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Dog_4_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Dog_5_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Dog_5_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Dog_5_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Patient_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Patient_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Patient_1_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_preictal_Patient_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "data_interictal_Patient_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9\n", "data_test_Patient_2_gen-8_medianwindow-bands-usf-w60-b0.2-b4-b8-b12-b30-b70-0.1-0.5-0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fpout.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 } ], "metadata": {} } ] }
mit
eblur/ghost_halos
plot_radprofile.ipynb
1
124510
{ "metadata": { "name": "", "signature": "sha256:f2d1e16a3be5730deafaaa78eae25ada20bdecef459c496854992ca526a488e8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "cd ~/Dropbox/notebooks/ghost_halos/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/Users/lia/Dropbox/notebooks/ghost_halos\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import radprofile as rp\n", "from astropy.io import fits\n", "\n", "from scipy.integrate import trapz\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "## Open function to load the profile object\n", "\n", "def open_profile(fname):\n", " result = rp.Profile()\n", " ffile = fits.open(fname) \n", " pdata = ffile[1].data\n", "\n", " result.rleft = pdata['R'][:,0]\n", " result.rright = pdata['R'][:,1]\n", " try:\n", " result.surbri = pdata['SUR_FLUX'] # photons/cm**2/pixel**2/s\n", " result.surbri_err = pdata['SUR_FLUX_ERR']\n", " except:\n", " print \"Could not find value:\", value\n", " print \"Your options are:\"\n", " print pdata.columns.names\n", "\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Open the flux profile" ] }, { "cell_type": "code", "collapsed": false, "input": [ "SFLUX = 0.0007349181010116529 # 0.8 - 1.2 keV flux [photons cm^-2 s^-1]\n", "\n", "fprofile = open_profile('herx1_c67_1keV_radprofile_flux.fits')\n", "ftotflux = np.sum(fprofile.surbri * fprofile.area)\n", "\n", "print \"Total flux in profile:\", ftotflux\n", "print \"This is \" + np.str((ftotflux/SFLUX)*100.0)[0:4] + \"% of the HETG measured flux\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Total flux in profile: 0.000783782540238\n", "This is 106.% of the HETG measured flux\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.errorbar(fprofile.rmid, fprofile.surbri, yerr=fprofile.surbri_err, \\\n", " capsize=0, lw=2, color='k', marker='', ls=''),\n", "\n", "plt.loglog()\n", "\n", "plt.xlabel('Radius [pixels]')\n", "plt.ylabel('Surface brightness [flux pixels$^{-2}$]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<matplotlib.text.Text at 0x10b70aa90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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tcoGq58+fk/r6emJjY0P4fL6hwwFgHBPb4qpolEQWL168VZVAdu/ePX/+/Pm71c/LZDIe\nI9GoYbPZCg6H0yiXy03kcrkJlc9qOwMTOjaBQEDOnz9Pzp49S8aPH2/ocAAY1/SP66ioKNplaZRE\nunbt+mzFihUbzMzMatPT092fPXvWVdUJo1Ao2KmpqV5paWmetKNohouLy7Xi4uLeHA6n0dXVNYvJ\nsgHokslkJC0tjXC5XLRwAYiGSSQiIiL6zJkzvjdu3BhOCPlLL75CoWBkr/b8/PxBpaWlPRUKBVso\nFIolEkmwXC43CQ8P30alHJFIhMdYoBOVlZVk3LhxxM7OjpSVlRk6HACtMPFYi/IQ39TUVC8vL68/\n3bW4uLh37969i7WKhCEY4gvaGj9+/GsfZ5WVlZGuXbsiiUC7ovNVfJOSkiaq3jdNIIQQop5A1K8F\nAID2TaMkkpmZ6aZpgVSuBQCAtk2jPpHo6OiI2NjYEE2uffHihVVkZCT9rn4GoE8EAKB1BukTMXbo\nEwFtoU8EOhq97mwIAACggiQCAAC0aZVEKisr3zCWzagAAED/KCcRV1fXrEOHDgU+efKk2+DBg2+v\nWLFigwjrjAAAdEiUk8isWbOOBAYGHvr666/XeHt7pyQlJU3s0aPHI10ER5dIJGJscTEAqhYsWECs\nrKzIoUOHDB0KQItSU1O1XmuQchKpr6833bNnz7yTJ09O2bJlyxfV1dWWBw8eDNIqCoaphvgCGEJt\nbS2prKwkMpne9mkDoMXLy0vrJEJ5e1yhUCg+f/78uCtXrozs0qVLWUJCwvQ5c+bs0yoKgDbCwsKC\n7N27F0vEA/wX5STSuXPnqo8++ugnQv7oWO/Xr9/d4cOH32A+NADjY2ZmRj777DOtyqitrSU3b94k\nfD6fODo6MhQZgGFo3bG+cuXK9ehYB9DcgwcPiJubG5k1a5ahQwHQWrvsWAcAAP1olx3rAACgH5ST\nSHh4+DZbW9vnGRkZo7t06VJ2/vz5cehYBwDomCgnkdzcXEeRSCRatmzZJrlcbvLw4cO3HRwc8nQR\nHAAAGDfKo7MiIyOjNm3atOzu3bv9uFxuQ1BQ0EEPD49Lt27dGqKLAOnAUvAAAK1jYil4yi0RX1/f\nMxMnTkyytLSsJuSPTajKy8vttIqCYZhsCNp4++23yaBBg0inTp0MFsPixYuJk5MTVl4AnTLIZEMr\nK6sXixcv3lpZWflGQUHBwF27doUZehMqACb961//MnQI5N69e+TGjRukqqrK0KEAtIhyEgkJCYnN\ny8tzSElJ8ZbL5SbJyck+Tk5Ov+kiOAAAMG60loLv0aPHoylTppycNm3acRsbG+nOnTsXMB0YAAAY\nP8otkcDAwEMnT56cYmdnV646Vl5ebrdgwYKdzIYGAADGjnISKSws7C+VSm24XG6D6tjVq1dHMBvW\n6x09enRmz549S8vLy+0mT558Sl/3BQCAv6L8OGv16tXrCgsL+6sfq6iosGYupJZZWFi8vHjx4gd9\n+vS5r697AlDx9ttvk3fffZdYW+vtxwLAYGgtBV9SUtJL/RiLxVLK5XITbYMRi8VCOzu7coFAcFgq\nldpIJJJgLpfbEBAQ8ENiYuIkLpfbYGlpWT1gwIA7N27cGD506NCb2t4TgGlr1qwha9asee15Ozs7\nEhkZSezsjGpkPAAtlJPI1q1bF0+YMOEsn8+vUx07c+aMLxPBODo65qoSVExMzCKBQHCYx+PJxGKx\nMCoqKpIQQmJjY0P69et3t66uDhs6QJvUpUsXrcfmAxgLyknExcXlmnoCqaiosHZzc8tkIhg2m61Q\nvc/KynJdsmTJZj6fX5edne2sOh4SEhLbWjnqP6CYuQ7tUWFhIdmwYQOxt7cnK1euNHQ40MYwMVNd\nhXISOXLkyKwvvvhii+prCwuLl7NmzTqSkJAwnZGI/ovNZis4HE6jXC43ofqoDH/lQXv39OlTsn//\nfvL+++8jiQBlTf+4joqiP19c4471ffv2zenevfvjpUuXfstmsxWql5mZWW1jYyPlZNQaFxeXa8XF\nxb1LS0t7urq6ZjFdPgAAaE/jX/5z5szZN2HChLOXLl3y8Pf3j9dFMPn5+YNKS0t7KhQKtlAoFEsk\nkmC5XG4SHh6+jUo5WIARAKB1TDzWYimVSsofqq6utqysrHxDoVCwCSEkMTFx0vz583drFQlDWCyW\nks6/CcCYhISEkAsXLpADBw6QcePG/eV8eno68fDwIO+//z5JT083QITQnrBYLKJUKll0Pkv5MVRQ\nUNDBEydOTLWxsZGyWCwlIX/MWDeWJALQHsTGxmpdRlhYGLl58ybZuXMncXR01D4ogGZQTiJ37twZ\nUFZW1sXU1LRedUyfM9YBQDM5OTnk8uXLpLq62tChQDtGa8b6gwcP3lE/JpVKbRiLiAEikQj7MAAA\ntCI1NVU/+4n06tWrpLS0tOfrzjM1Y50pGOILANA61QAknQ/x3bhx4/KamhpzhULBbu51/PjxabQj\nAACANkujJOLv7x+vmqX+3Xfffdb0vLW1dYVYLBampqZ6MRwfAAAYMcp9ImvXrv3Kzs6u3NrauiI4\nOFhSU1Nj7u/vHx8UFHSwW7duT77//vtPdBEoADArPT2deHl5kdWrVxs6FGjDKCcRe3v7ouPHj0/L\nyckZFhYWtis+Pt6/rKysi7W1dcWgQYPymV7+BAB0o7y8nKSlpZGbN7EYNtBHeYjvxx9//KO7u3s6\nIYRwudyGjIyM0WZmZrWq83fv3u3HZIB0YMY6AEDrmJixTjmJmJqa1vft2/eeQqFg19XV8VeuXLm+\nW7duTyQSSfCrV686vfnmm0+1iogBGJ0FANA6JkZnUU4iISEhsb6+vmfu3bvXd+jQoTc7der0Kiws\nbFdpaWnPmJiYRTt27PicdjQAANCmaJREsrOznYcNG5bD4XAa09LSPFXLnWRnZzsrlUrWhQsXxq5b\nt251dHR0hG7DBQAAY6JREomMjIyKiYlZ1KdPn/srVqzYoFQqWer9IEVFRfbr1q3DEA8APXF2diaF\nhYWEz8cGn2BYGiWR06dP+6neb9q0aZmqY13lzp07A5gODABez8zMjNjb2xs6DADqQ3z9/f3jb9++\nPVj92IABA+4wFxIAALQVlJOISCQSqfYRIYQQpVLJOnDgQCizYQGAtsRiMbl48SJxcHAwdCjQjlEe\nnbVu3brVJSUlvdSPsVgsZWho6AHmwgIAbbm4uBg6BOgAaC178urVq07qCzD+9NNPH+kiOLqwFDwA\nQOuYWAqe8va4crnc5NChQ4HZ2dnOdnZ25R999NFPgwcPvq1VFAzC9rgAmjl+/DiZPn06mTp1Kjl+\n/LihwwED0mZ7XMotkdDQ0AOrVq36hsvlNjQ0NHCXL1++UX30FgB0HJmZmWT58uXk6NGjhg4FDIRy\nn0hSUtLEmzdvDlVf3mTDhg0r/Pz8TjMbGgAY2osXL4hCoSBvvPEGMTH5675zubm5ZNOmTeTTTz8l\nM2fONECEYGiUWyKLFi2KkclkPPVjTbfLBYD2wc3Njdja2pK7d+8aOhQwUhq1RNzc3DLLysq6qL4W\ni8VCS0vLakL+GOL7wQcfXNRVgAAAYLw0SiKzZ8+OGz9+/DlTU9P65s7rc+Xeo0ePzjQ1Na03MzOr\nHTt27AV93RegvZk6dSpRKBSGDgPaOI2SiFAoFOs6EE1duXJl5ObNm5esXr16HZIIAH0sFq3BOJRd\nv36dyOVy4uTkRDgcyt2wYOSM6n9ULBYL7ezsygUCwWGpVGojkUiCuVxuQ0BAwA+JiYmTuFxuw5Qp\nU07GxcXNfuutt343dLwA0LqxY8cSqVRKysvLia2traHDAYbRTiIKhYLNZrMZbQs7OjrmqmbDx8TE\nLBIIBId5PJ5MLBYLo6KiIgkhpLS0tGdlZeUbgYGBh5i8NwAAUEc5iWRkZIyeO3fu3oEDBxbEx8f7\n79ix43N3d/d0FxeXa9oGo56UsrKyXJcsWbKZz+fXZWdnO6uO9+zZs7Rnz56lLZWjPgMT2+QCAPwZ\nE9viqlBOIpGRkVEbN25cfu/evb5cLrchKCjooIeHx6Vbt24NYSSi/2Kz2QoOh9Mol8tN5HL5Xweo\ntwDb4wIAvF7TP6612R6X8jwRX1/fM5MmTUpUDfHNzMx0Ky8vt6MdwWu4uLhcKy4u7l1aWtrT1dU1\ni+nyAcA4bNy4kZiYmJAVK1YYOhSggXJLxMrK6sXixYu3VlZWvlFQUDBw165dYZGRkfTTmJr8/PxB\npaWlPRUKBVsoFIolEkmwXC43CQ8P30alHJFIhMdYAG2EUqkkCoWCYM07/WPisRblJBISEhKbl5fn\nkJKS4i2Xy02Sk5N9nJycftMqiv+aN2/eHtV7KyurF1SThwoeZwF0LFeuXCE5OTlkxIgRZPjw4YYO\np81Q/bGtzeMsWqOzHBwc8hwcHPJo3xUAgEHHjh0j3377Ldm4cSOSiJ5R7hO5ePHiBxEREdGEEPLs\n2bOue/funSuVSm2YDw0A4I/HXT///DP5+eefDR0KNINyEtmyZcsXEydOTCKEkK5duz6bOnXqiY8/\n/vhH5kOjD5tSAbQvM2bMIDNmzKD9+draWrJ582ayY8cOBqNq+5jYlIpyEhk/fvy5cePGnVd9XVpa\n2vPatWtGtQ+nqmMdAIAQQl69ekWWLl1KIiMjdXqfixcvktjYWFJSUtLs+ZycHDJy5Egyd+5cncah\nKS8vL62TCOU+EVNT0/qIiIjowYMH3y4sLOy/b9++OXPmzNmnVRQA0CZNnjyZXLlyhXTp0qX1i41c\nXl4eefz4MRkyZAjp0aMHrTK2bdtGTp06RU6cOEF69er1l/PV1dXk6tWrLa4htm7dOlJUVERWrVpF\n+vfvTysOfaLcEpk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"text": [ "<matplotlib.figure.Figure at 0x10a08c390>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "## Convert the profile from pixel units to arcsec\n", "\n", "arcsec_per_pixel = 0.5 # arcsec/pixel\n", "rmid_arcsec = fprofile.rmid * arcsec_per_pixel # arcsec\n", "sbri_arcsec = fprofile.surbri / arcsec_per_pixel**2 # flux per arcsec^2\n", "sbri_err_arcsec = fprofile.surbri_err / arcsec_per_pixel**2\n", "\n", "plt.errorbar(rmid_arcsec, sbri_arcsec, yerr=sbri_err_arcsec, \\\n", " capsize=0, lw=2, color='k', marker='', ls='')\n", "\n", "plt.loglog()\n", "\n", "plt.xlabel('Observation Angle [arcsec]')\n", "plt.ylabel('Surface brightness [flux arcsec$^{-2}$]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "<matplotlib.text.Text at 0x10b903110>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JpKene3A4nAZ/MzMMw/n111//vnbt2tXqBAAAAIarWTWR/v3731QoFNxu3bo9aej1O3fu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\nOW7bti0oPDxc/TQ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"text": [ "<matplotlib.figure.Figure at 0x10b927f90>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit a Beta 1-d plus power law plus background" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from astropy.modeling import models\n", "from scipy.optimize import leastsq" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "XTRANS = 0.5 # pixels, core-wing transition radius, min x val for wing portion\n", "\n", "def wing_model(x, amp, p, xtrans=XTRANS):\n", " wmod = models.PowerLaw1D(amplitude=amp*SFLUX, x_0=1.0, alpha=p)\n", " expmod = np.exp(-xtrans/x)\n", " return wmod(x) * expmod\n", "\n", "def core_model(x, amp, p, g):\n", " cmod = models.Beta1D(amplitude=amp*SFLUX, x_0=0.0, alpha=p, gamma=g)\n", " return cmod(x)\n", "\n", "def psf_model(params, x):\n", " a1, p1, g1, a2, p2, c0 = params\n", " \n", " bkg = models.Const1D(c0)\n", " \n", " result = core_model(x, a1, p1, g1) + wing_model(x, a2, p2) + bkg(x)\n", " return result # arcsec^-2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "def chi(params, x, y, err):\n", " return (y - psf_model(params, x))/err" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_fit(params, x, y, err, comps=False):\n", " plt.errorbar(x, y, yerr=err, \\\n", " capsize=0, lw=2, color='k', marker='o', markersize=3.0, ls='', label='')\n", " plt.loglog()\n", " \n", " rsmooth = np.logspace(-1,np.log10(max(x)),100)\n", " plt.plot(rsmooth, psf_model(params, rsmooth), \\\n", " lw=2, alpha=0.5, color='r')\n", " \n", " if comps:\n", " a1, p1, g1, a2, p2, c0 = params\n", " core = core_model(rsmooth, a1, p1, g1)\n", " wing = wing_model(rsmooth, a2, p2)\n", " bkg = models.Const1D(amplitude=c0)\n", " plt.plot(rsmooth, core, 'k:', label='Core')\n", " plt.plot(rsmooth, wing, 'k--', label='Wings')\n", " plt.plot(rsmooth, bkg(rsmooth), '-', color='0.5', label='Background')\n", " \n", " return" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "## Pick a good value for XTRANS\n", "\n", "atest, ptest, xtest = 1.e-10, 2.0, 0.5\n", "rtest = np.logspace(-1.0,1.0,100)\n", "PLonly = models.PowerLaw1D(amplitude=atest*SFLUX, x_0=1.0, alpha=ptest)\n", "wtest = wing_model(rtest, atest, ptest, xtrans=xtest)\n", "\n", "ax = plt.subplot(211)\n", "ax.plot(rtest, wtest, 'k--', label='wing model')\n", "ax.plot(rtest, PLonly(rtest), 'r-', label='power law')\n", "ax.axvline(3.0, ls=':', color='k')\n", "ax.xaxis.set_ticklabels('')\n", "plt.loglog()\n", "plt.legend(loc='lower left', frameon=False)\n", "\n", "ax2 = plt.subplot(212)\n", "ax2.plot(rtest, (PLonly(rtest)-wtest)/PLonly(rtest) * 100, 'k-')\n", "plt.loglog()\n", "plt.axhline(10.0, ls=':', color='r')\n", "plt.axvline(3.0, ls=':', color='k')\n", "ax2.set_xlabel('Radius')\n", "ax2.set_ylabel('Percent deviation')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "<matplotlib.text.Text at 0x10ceb9510>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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As7MznnvuOcybN6/NIcaEdFR8fDy2b9+OvLw83Lx5EyNHjoSTkxP+/ve/w9vbW/MLqvbV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\nhYWFOHz4MA4fPoz09HRMmjQJwcHBmDV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"text": [ "<matplotlib.figure.Figure at 0x10cf9b9d0>" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "pinit = [0.1, 2.0, 1.0, 1.e-3, 2.0, 1.e-9]\n", "plot_fit(pinit, rmid_arcsec, sbri_arcsec, sbri_err_arcsec, comps=True)\n", "plt.ylim(1.e-10, 1.e-3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "(1e-10, 0.001)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xyiuv0EUG+pil9Px5tAsXKj1uQkJg6FBHhySEy6ptTd5lkvylS5fYsGEDW7Zs4YcffuDt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\nFi3o2bMnq1atqnU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"text": [ "<matplotlib.figure.Figure at 0x10d053490>" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "(pfit, temp) = leastsq(chi, pinit, args=(rmid_arcsec, sbri_arcsec, sbri_err_arcsec))\n", "print pfit" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 5.52265627e-01 2.22329068e+00 5.21205797e-01 5.14941091e-03\n", " 2.42527957e+00 2.00645742e-09]\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_fit(pfit, rmid_arcsec, sbri_arcsec, sbri_err_arcsec, comps=True)\n", "plt.ylim(1.e-10, 1.e-3)\n", "plt.xlim(0.1,300)\n", "\n", "plt.legend(loc='upper right', frameon=False)\n", "plt.xlabel('Observation angle [arcsec]')\n", "plt.ylabel('Surface brightness [photon flux arcsec$^{-2}$]')\n", "\n", "print 'Reduced Chi-squared =', \\\n", " np.sum(chi(pfit,rmid_arcsec,sbri_arcsec,sbri_err_arcsec)**2) / len(pfit)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Reduced Chi-squared = 1.52351405868\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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/Fx0d3VkayR537drlnJSUpOfh4eH25s0bVR8fH3uxWCxnb2/vo6WllVHZ60sH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\nmEzm24iIiM23b9+2qaqq0o6KigpwdnY+s3fv3k0cDocRHx+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"text": [ "<matplotlib.figure.Figure at 0x10d17da10>" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "from astropy.io import ascii\n", "from astropy.table import Table" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "sfilename = 'plot_radprofile_fitparams.txt'\n", "par_table = Table([pfit], names=['params'])\n", "ascii.write(par_table, sfilename)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "print pfit" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 5.52265627e-01 2.22329068e+00 5.21205797e-01 5.14941091e-03\n", " 2.42527957e+00 2.00645742e-09]\n" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What does the integrated intensity add up to?\n", "\n", "Her X-1 has 40% pileup fraction, so we don't expect it to integrate completely to 1.0" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Fraction of flux accounted for by background\n", "bkg_totflux = pfit[-1] * np.pi * (fprofile.rright[-1]*arcsec_per_pixel)**2\n", "print \"Background contributes \" + \\\n", " np.str(bkg_totflux/ftotflux * 100.0)[0:4] + \\\n", " \"% of profile flux\"\n", "print \"This happens to be \" + np.str(bkg_totflux/SFLUX * 100.0)[0:4] + \"% of the source flux\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Background contributes 50.2% of profile flux\n", "This happens to be 53.6% of the source flux\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "xvals = np.logspace(-1,3.0,200)\n", "yvals = core_model(xvals, pfit[0], pfit[1], pfit[2]) + \\\n", " wing_model(xvals, pfit[3], pfit[4])\n", "\n", "int_flux = trapz( yvals*2.0*np.pi*xvals, xvals)\n", "print \"Total flux in PSF model:\", int_flux\n", "print \"This is \" + np.str(int_flux/SFLUX * 100.0)[0:4] + \"% of source flux\"\n", "print \"This is \" + np.str(int_flux/ftotflux * 100.0)[0:4] + \"% of the profile flux\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Total flux in PSF model: 0.000334524716411\n", "This is 45.5% of source flux\n", "This is 42.6% of the profile flux\n" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Source flux (measured from HETG) should be source minus bkg, so only 45.5% of 0th order point source light is accounted for\n", "-- note that pileup is an issue for this observation\n", "\n", "50.2 + 42.6 = 92.8% of the profile flux is accounted for with my PSF model" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
PytLab/catplot
examples/grid_3d_examples/plot_multiple_planes.ipynb
1
69767
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 绘制三维平面" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 创建一个三维平面" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from catplot.grid_components.planes import Plane3D" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p1 = Plane3D([1, 3], [1, 3], 1, color=\"#CD4125\", edgecolor=\"\", alpha=0.5, shade=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "p2 = p1.clone([0.0, 0.0, 1.0])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p3 = Plane3D([1, 3], [1, 3], 1, color=\"#CD4125\", edgecolor=\"\", alpha=0.5, shade=True, axis=\"x\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p4 = p3.clone([1.0, 0.0, 0.0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 创建三维画布" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from catplot.grid_components.grid_canvas import Grid3DCanvas" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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uqKqqokkus9mMycnJFeWWeHJDOYgrdc6Cv1QWEnGF+E3ddNNNOHLkCP7+7/+e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\nj1HX/JycHJSWlqKoqCguidfShRE45wRiMBjQ2to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"text/plain": [ "<matplotlib.figure.Figure at 0x1075affd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "canvas = Grid3DCanvas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘制" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "canvas.add_planes([p1, p2, p3, p4])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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D6O3tRTgc7jizuHZNMXjssceaPkfPPvssnn322fK/+pmdc9+VxC3kC1hZWUF/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\n8ktiY2NJS0tj3rx5/So/a2lpobCwUCkB7IqcBKHVajGbzYq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"text/plain": [ "<matplotlib.figure.Figure at 0x1075affd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "canvas.draw()\n", "canvas.figure" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ES-DOC/esdoc-jupyterhub
notebooks/hammoz-consortium/cmip6/models/sandbox-3/land.ipynb
1
173524
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: HAMMOZ-CONSORTIUM \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:03" ], "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', 'hammoz-consortium', 'sandbox-3', 'land')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. 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.land.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&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.land.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&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.land.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "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", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "*Land surface soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
MaryanMorel/HAR
Human Activity Recognition.ipynb
2
509938
{ "metadata": { "name": "", "signature": "sha256:b2a7ecbe47712f1688854f59761e340783260fa5ccc4a18d4744eba5afc5f446" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.decomposition import PCA\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from sklearn import svm, grid_search, metrics, preprocessing, linear_model, cross_validation\n", "from sklearn.learning_curve import validation_curve, learning_curve\n", "%matplotlib inline\n", "\n", "## Utility functions :\n", "def plot_PCA(X, y, classes, azimuth, elevation):\n", " \"\"\" X numpy array containing data, \n", " y numpy array containing the labels, \n", " classes a list containing the label names\n", " azimuth, elevation float numbers \"\"\"\n", " nClasses = len(classes)\n", " fig = plt.figure(1, figsize=(10, 8))\n", " plt.clf()\n", " ax = Axes3D(fig, axisbg='white', azim=azimuth, elev=elevation)\n", " pca = PCA(n_components=3)\n", " X_redux = pca.fit_transform(X)\n", " print(pca.explained_variance_ratio_)\n", " print(\"Variability of the compressed dataset %.4f\" %sum(pca.explained_variance_ratio_))\n", " cmap = plt.cm.Paired\n", " colors = np.linspace(0, 1, nClasses)\n", " for n in range(nClasses) :\n", " index = np.ravel(y==(n+1))\n", " ax.scatter(X_redux[index, 0], X_redux[index, 1], X_redux[index, 2], c=cmap(colors[n]), label=classes[n])\n", " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " ax.w_xaxis.set_ticklabels([])\n", " ax.w_yaxis.set_ticklabels([])\n", " ax.w_zaxis.set_ticklabels([])\n", " plt.title(\"Data plotted on the three first components\", y=1.03) # y prevent title overlap with axes\n", " plt.show()\n", "\n", "def plot_validation_scores(train_scores, test_scores, param_range):\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " plt.title(\"Validation Curve\")\n", " plt.xlabel(\"$C$\")\n", " plt.ylabel(\"F1 Score\")\n", " #plt.ylim(0.0, 1.1)\n", " plt.semilogx(param_range, train_scores_mean, label=\"Training score\", color=\"r\", linestyle=\"--\")\n", " plt.fill_between(param_range, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.2, color=\"r\")\n", " plt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\",\n", " color=\"g\")\n", " plt.fill_between(param_range, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\n", " plt.legend(loc='lower right')\n", " plt.show()\n", " \n", "def flatten(somelist):\n", " return([item for sublist in somelist for item in sublist])\n", "\n", "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n", " n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n", " \"\"\"\n", " from sklearn examples :\n", " http://scikit-learn.org/stable/auto_examples/plot_learning_curve.html#example-plot-learning-curve-py\n", " \"\"\"\n", " plt.figure()\n", " plt.title(title)\n", " if ylim is not None:\n", " plt.ylim(*ylim)\n", " plt.xlabel(\"Training examples\")\n", " plt.ylabel(\"Score\")\n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " plt.grid()\n", " plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.1,\n", " color=\"r\")\n", " plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n", " plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n", " label=\"Training score\")\n", " plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n", " label=\"Cross-validation score\")\n", " plt.legend(loc='lower right')\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Human Activity Recognition from smartphone sensors data\n", "\n", "# Introduction\n", "\n", "We try to recognize activities of 30 individuals using sensor data gathered from their smartphone. Human activity recognition can be used to measure physical activity of users of a smarphone or some wearable as smartwatches. This may be used to compute how much calories one burns during the day, and to display advices if the user does not take enough exercise to remain healthy.\n", "\n", "## Dataset\n", "We use the Human Activity Recognition Using Smartphones Dataset (Version 1.0) that can be found on [UCI archive](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones).\n", "\n", "It is experimental data : 30 volunteers (19-48 y.o.) performed six activities (walking, walking upstairs, walking downstairs, sitting, standing, laying) wearing a smartphone (Samsung Galaxy S II) on the waist. As the smartphone was worn on the waist, there is no rotation issue as there would be in the case the smartphone is \"worn\" in the pocket.\n", "\n", "Signals come from the accelerometer and the gyroscope of the smartphone (captured at a constant rate of 50Hz). They were filtered to remove noise and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The acceleration signal was separated into body and gravity acceleration (using another filter). \n", "Body acceleration (from accelerometer) and angular velocity (from accelrometer) were derived to obtain Jerk signals.\n", "Magnitude of the three dimensional signals was calculated using the euclidean norm. Fast Fourier Transform was used to compute frequency domain signals.\n", "\n", "At the end, we get a set of 17 signals, and for each of them, several features were created :\n", "```\n", "mean: Mean value\n", "std: Standard deviation\n", "mad: Median absolute deviation \n", "max: Largest value in array\n", "min: Smallest value in array\n", "sma: Signal magnitude area\n", "energy: Energy measure. Sum of the squares divided by the number of values. \n", "iqr: Interquartile range \n", "entropy: Signal entropy\n", "arCoeff: Autorregresion coefficients with Burg order equal to 4\n", "correlation: correlation coefficient between two signals\n", "maxInds: index of the frequency component with largest magnitude\n", "meanFreq: Weighted average of the frequency components to obtain a mean frequency\n", "skewness: skewness of the frequency domain signal \n", "kurtosis: kurtosis of the frequency domain signal \n", "bandsEnergy: Energy of a frequency interval within the 64 bins of the FFT of each window.\n", "angle: Angle between to vectors.\n", "```\n", "\n", "Additional vectors obtained by averaging the signals in a signal window sample. These are used on the `angle` variable. There are 561 features on the dataset :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l ./data/features.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 561 ./data/features.txt\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "!cat data/activity_labels.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 WALKING\r\n", "2 WALKING_UPSTAIRS\r\n", "3 WALKING_DOWNSTAIRS\r\n", "4 SITTING\r\n", "5 STANDING\r\n", "6 LAYING\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "y_train contains one label / line\n", "\n", "X_train contains the 561 features / line\n", "\n", "The whole thing should fit in a pandas dataframe as it is 63Mo :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ls -lh data/train/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "total 256304\r\n", "drwxr-xr-x@ 11 maryanmorel staff 374B Nov 26 22:20 \u001b[1m\u001b[36mInertial Signals\u001b[m\u001b[m\r\n", "-rwxr-xr-x@ 1 maryanmorel staff 63M Nov 26 22:20 \u001b[31mX_train.txt\u001b[m\u001b[m\r\n", "-rw-r--r-- 1 maryanmorel staff 62M Dec 11 21:49 X_train_clean.txt\r\n", "-rwxr-xr-x@ 1 maryanmorel staff 20K Nov 26 22:20 \u001b[31msubject_train.txt\u001b[m\u001b[m\r\n", "-rwxr-xr-x@ 1 maryanmorel staff 14K Nov 26 22:20 \u001b[31my_train.txt\u001b[m\u001b[m\r\n" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk -F' ' '{print NF; exit}' data/train/X_train.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "561\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "561 columns separated by spaces (' ')\n", "\n", "Problem : there is one space in front of positive numbers (in order to get neat columns when you look at the text file), it messes up with pandas parser. We use unix command line utilities to replace `' '` (double spaces) it with `' '` (single space), and remove additional spaces before the first column :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!sed 's/^..//' data/train/X_train.txt > data/train/X_train_buffer.txt\n", "!sed -e 's/ / /g' data/train/X_train_buffer.txt > data/train/X_train_clean.txt\n", "!rm data/train/X_train_buffer.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "!sed 's/^..//' data/test/X_test.txt > data/test/X_test_buffer.txt\n", "!sed -e 's/ / /g' data/test/X_test_buffer.txt > data/test/X_test_clean.txt\n", "!rm data/test/X_test_buffer.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train = pd.read_csv('./data/train/X_train_clean.txt', sep=' ', engine='c', header=None)\n", "feat_names = flatten(pd.read_csv('data/features.txt', sep=' ', header=None, index_col=0).values.tolist())\n", "X_train.columns = feat_names\n", "X_test = pd.read_csv('./data/test/X_test_clean.txt', sep=' ', engine='c', header=None)\n", "X_test.columns = feat_names\n", "X_train.head(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>tBodyAcc-mean()-X</th>\n", " <th>tBodyAcc-mean()-Y</th>\n", " <th>tBodyAcc-mean()-Z</th>\n", " <th>tBodyAcc-std()-X</th>\n", " <th>tBodyAcc-std()-Y</th>\n", " <th>tBodyAcc-std()-Z</th>\n", " <th>tBodyAcc-mad()-X</th>\n", " <th>tBodyAcc-mad()-Y</th>\n", " <th>tBodyAcc-mad()-Z</th>\n", " <th>tBodyAcc-max()-X</th>\n", " <th>...</th>\n", " <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n", " <th>fBodyBodyGyroJerkMag-skewness()</th>\n", " <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n", " <th>angle(tBodyAccMean,gravity)</th>\n", " <th>angle(tBodyAccJerkMean),gravityMean)</th>\n", " <th>angle(tBodyGyroMean,gravityMean)</th>\n", " <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n", " <th>angle(X,gravityMean)</th>\n", " <th>angle(Y,gravityMean)</th>\n", " <th>angle(Z,gravityMean)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0.288585</td>\n", " <td>-0.020294</td>\n", " <td>-0.132905</td>\n", " <td>-0.995279</td>\n", " <td>-0.983111</td>\n", " <td>-0.913526</td>\n", " <td>-0.995112</td>\n", " <td>-0.983185</td>\n", " <td>-0.923527</td>\n", " <td>-0.934724</td>\n", " <td>...</td>\n", " <td>-0.074323</td>\n", " <td>-0.298676</td>\n", " <td>-0.710304</td>\n", " <td>-0.112754</td>\n", " <td> 0.030400</td>\n", " <td>-0.464761</td>\n", " <td>-0.018446</td>\n", " <td>-0.841247</td>\n", " <td> 0.179941</td>\n", " <td>-0.058627</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 0.278419</td>\n", " <td>-0.016411</td>\n", " <td>-0.123520</td>\n", " <td>-0.998245</td>\n", " <td>-0.975300</td>\n", " <td>-0.960322</td>\n", " <td>-0.998807</td>\n", " <td>-0.974914</td>\n", " <td>-0.957686</td>\n", " <td>-0.943068</td>\n", " <td>...</td>\n", " <td> 0.158075</td>\n", " <td>-0.595051</td>\n", " <td>-0.861499</td>\n", " <td> 0.053477</td>\n", " <td>-0.007435</td>\n", " <td>-0.732626</td>\n", " <td> 0.703511</td>\n", " <td>-0.844788</td>\n", " <td> 0.180289</td>\n", " <td>-0.054317</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 0.279653</td>\n", " <td>-0.019467</td>\n", " <td>-0.113462</td>\n", " <td>-0.995380</td>\n", " <td>-0.967187</td>\n", " <td>-0.978944</td>\n", " <td>-0.996520</td>\n", " <td>-0.963668</td>\n", " <td>-0.977469</td>\n", " <td>-0.938692</td>\n", " <td>...</td>\n", " <td> 0.414503</td>\n", " <td>-0.390748</td>\n", " <td>-0.760104</td>\n", " <td>-0.118559</td>\n", " <td> 0.177899</td>\n", " <td> 0.100699</td>\n", " <td> 0.808529</td>\n", " <td>-0.848933</td>\n", " <td> 0.180637</td>\n", " <td>-0.049118</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 0.279174</td>\n", " <td>-0.026201</td>\n", " <td>-0.123283</td>\n", " <td>-0.996091</td>\n", " <td>-0.983403</td>\n", " <td>-0.990675</td>\n", " <td>-0.997099</td>\n", " <td>-0.982750</td>\n", " <td>-0.989302</td>\n", " <td>-0.938692</td>\n", " <td>...</td>\n", " <td> 0.404573</td>\n", " <td>-0.117290</td>\n", " <td>-0.482845</td>\n", " <td>-0.036788</td>\n", " <td>-0.012892</td>\n", " <td> 0.640011</td>\n", " <td>-0.485366</td>\n", " <td>-0.848649</td>\n", " <td> 0.181935</td>\n", " <td>-0.047663</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 0.276629</td>\n", " <td>-0.016570</td>\n", " <td>-0.115362</td>\n", " <td>-0.998139</td>\n", " <td>-0.980817</td>\n", " <td>-0.990482</td>\n", " <td>-0.998321</td>\n", " <td>-0.979672</td>\n", " <td>-0.990441</td>\n", " <td>-0.942469</td>\n", " <td>...</td>\n", " <td> 0.087753</td>\n", " <td>-0.351471</td>\n", " <td>-0.699205</td>\n", " <td> 0.123320</td>\n", " <td> 0.122542</td>\n", " <td> 0.693578</td>\n", " <td>-0.615971</td>\n", " <td>-0.847865</td>\n", " <td> 0.185151</td>\n", " <td>-0.043892</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 561 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ " tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X \\\n", "0 0.288585 -0.020294 -0.132905 -0.995279 \n", "1 0.278419 -0.016411 -0.123520 -0.998245 \n", "2 0.279653 -0.019467 -0.113462 -0.995380 \n", "3 0.279174 -0.026201 -0.123283 -0.996091 \n", "4 0.276629 -0.016570 -0.115362 -0.998139 \n", "\n", " tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X tBodyAcc-mad()-Y \\\n", "0 -0.983111 -0.913526 -0.995112 -0.983185 \n", "1 -0.975300 -0.960322 -0.998807 -0.974914 \n", "2 -0.967187 -0.978944 -0.996520 -0.963668 \n", "3 -0.983403 -0.990675 -0.997099 -0.982750 \n", "4 -0.980817 -0.990482 -0.998321 -0.979672 \n", "\n", " tBodyAcc-mad()-Z tBodyAcc-max()-X ... \\\n", "0 -0.923527 -0.934724 ... \n", "1 -0.957686 -0.943068 ... \n", "2 -0.977469 -0.938692 ... \n", "3 -0.989302 -0.938692 ... \n", "4 -0.990441 -0.942469 ... \n", "\n", " fBodyBodyGyroJerkMag-meanFreq() fBodyBodyGyroJerkMag-skewness() \\\n", "0 -0.074323 -0.298676 \n", "1 0.158075 -0.595051 \n", "2 0.414503 -0.390748 \n", "3 0.404573 -0.117290 \n", "4 0.087753 -0.351471 \n", "\n", " fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) \\\n", "0 -0.710304 -0.112754 \n", "1 -0.861499 0.053477 \n", "2 -0.760104 -0.118559 \n", "3 -0.482845 -0.036788 \n", "4 -0.699205 0.123320 \n", "\n", " angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) \\\n", "0 0.030400 -0.464761 \n", "1 -0.007435 -0.732626 \n", "2 0.177899 0.100699 \n", "3 -0.012892 0.640011 \n", "4 0.122542 0.693578 \n", "\n", " angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) \\\n", "0 -0.018446 -0.841247 \n", "1 0.703511 -0.844788 \n", "2 0.808529 -0.848933 \n", "3 -0.485366 -0.848649 \n", "4 -0.615971 -0.847865 \n", "\n", " angle(Y,gravityMean) angle(Z,gravityMean) \n", "0 0.179941 -0.058627 \n", "1 0.180289 -0.054317 \n", "2 0.180637 -0.049118 \n", "3 0.181935 -0.047663 \n", "4 0.185151 -0.043892 \n", "\n", "[5 rows x 561 columns]" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now read the labels :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "classes = pd.read_csv('data/activity_labels.txt', header=None).values.tolist()\n", "classes = [item for sublist in classes for item in sublist] # flatten list\n", "y_train = pd.read_csv('data/train/y_train.txt', header=None)\n", "y_test = pd.read_csv('data/test/y_test.txt', header=None)\n", "y_train.columns = [\"activity\"]\n", "y_test.columns = [\"activity\"]\n", "y_train.head(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>activity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ " activity\n", "0 5\n", "1 5\n", "2 5\n", "3 5\n", "4 5" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "(7352, 561)" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "X_test.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "(2947, 561)" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Analysis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_train.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>tBodyAcc-mean()-X</th>\n", " <th>tBodyAcc-mean()-Y</th>\n", " <th>tBodyAcc-mean()-Z</th>\n", " <th>tBodyAcc-std()-X</th>\n", " <th>tBodyAcc-std()-Y</th>\n", " <th>tBodyAcc-std()-Z</th>\n", " <th>tBodyAcc-mad()-X</th>\n", " <th>tBodyAcc-mad()-Y</th>\n", " <th>tBodyAcc-mad()-Z</th>\n", " <th>tBodyAcc-max()-X</th>\n", " <th>...</th>\n", " <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n", " <th>fBodyBodyGyroJerkMag-skewness()</th>\n", " <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n", " <th>angle(tBodyAccMean,gravity)</th>\n", " <th>angle(tBodyAccJerkMean),gravityMean)</th>\n", " <th>angle(tBodyGyroMean,gravityMean)</th>\n", " <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n", " <th>angle(X,gravityMean)</th>\n", " <th>angle(Y,gravityMean)</th>\n", " <th>angle(Z,gravityMean)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td>...</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " <td> 7352.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 0.276993</td>\n", " <td> -0.017695</td>\n", " <td> -0.109141</td>\n", " <td> -0.605438</td>\n", " <td> -0.510938</td>\n", " <td> -0.604754</td>\n", " <td> -0.630512</td>\n", " <td> -0.526907</td>\n", " <td> -0.606150</td>\n", " <td> -0.468604</td>\n", " <td>...</td>\n", " <td> 0.125293</td>\n", " <td> -0.307009</td>\n", " <td> -0.625294</td>\n", " <td> 0.008684</td>\n", " <td> 0.002186</td>\n", " <td> 0.008726</td>\n", " <td> -0.005981</td>\n", " <td> -0.489547</td>\n", " <td> 0.058593</td>\n", " <td> -0.056515</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 0.059623</td>\n", " <td> 0.040811</td>\n", " <td> 0.056635</td>\n", " <td> 0.448734</td>\n", " <td> 0.502645</td>\n", " <td> 0.418687</td>\n", " <td> 0.424073</td>\n", " <td> 0.485942</td>\n", " <td> 0.414122</td>\n", " <td> 0.544547</td>\n", " <td>...</td>\n", " <td> 0.250994</td>\n", " <td> 0.321011</td>\n", " <td> 0.307584</td>\n", " <td> 0.336787</td>\n", " <td> 0.448306</td>\n", " <td> 0.608303</td>\n", " <td> 0.477975</td>\n", " <td> 0.511807</td>\n", " <td> 0.297480</td>\n", " <td> 0.279122</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 0.001189</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -0.999873</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td>...</td>\n", " <td> -1.000000</td>\n", " <td> -0.995357</td>\n", " <td> -0.999765</td>\n", " <td> -0.976580</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " <td> -1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 0.263429</td>\n", " <td> -0.024863</td>\n", " <td> -0.120993</td>\n", " <td> -0.992754</td>\n", " <td> -0.978129</td>\n", " <td> -0.980233</td>\n", " <td> -0.993591</td>\n", " <td> -0.978162</td>\n", " <td> -0.980251</td>\n", " <td> -0.936219</td>\n", " <td>...</td>\n", " <td> -0.023692</td>\n", " <td> -0.542602</td>\n", " <td> -0.845573</td>\n", " <td> -0.121527</td>\n", " <td> -0.289549</td>\n", " <td> -0.482273</td>\n", " <td> -0.376341</td>\n", " <td> -0.812065</td>\n", " <td> -0.017885</td>\n", " <td> -0.143414</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 0.277227</td>\n", " <td> -0.017219</td>\n", " <td> -0.108676</td>\n", " <td> -0.946196</td>\n", " <td> -0.851897</td>\n", " <td> -0.859365</td>\n", " <td> -0.950709</td>\n", " <td> -0.857328</td>\n", " <td> -0.857143</td>\n", " <td> -0.881637</td>\n", " <td>...</td>\n", " <td> 0.134000</td>\n", " <td> -0.343685</td>\n", " <td> -0.711692</td>\n", " <td> 0.009509</td>\n", " <td> 0.008943</td>\n", " <td> 0.008735</td>\n", " <td> -0.000368</td>\n", " <td> -0.709417</td>\n", " <td> 0.182071</td>\n", " <td> 0.003181</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 0.288661</td>\n", " <td> -0.010783</td>\n", " <td> -0.097794</td>\n", " <td> -0.242813</td>\n", " <td> -0.034231</td>\n", " <td> -0.262415</td>\n", " <td> -0.292680</td>\n", " <td> -0.066701</td>\n", " <td> -0.265671</td>\n", " <td> -0.017129</td>\n", " <td>...</td>\n", " <td> 0.289096</td>\n", " <td> -0.126979</td>\n", " <td> -0.503878</td>\n", " <td> 0.150865</td>\n", " <td> 0.292861</td>\n", " <td> 0.506187</td>\n", " <td> 0.359368</td>\n", " <td> -0.509079</td>\n", " <td> 0.248353</td>\n", " <td> 0.107659</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.916238</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.967664</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td>...</td>\n", " <td> 0.946700</td>\n", " <td> 0.989538</td>\n", " <td> 0.956845</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.998702</td>\n", " <td> 0.996078</td>\n", " <td> 1.000000</td>\n", " <td> 0.478157</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows \u00d7 561 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ " tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z \\\n", "count 7352.000000 7352.000000 7352.000000 \n", "mean 0.276993 -0.017695 -0.109141 \n", "std 0.059623 0.040811 0.056635 \n", "min 0.001189 -1.000000 -1.000000 \n", "25% 0.263429 -0.024863 -0.120993 \n", "50% 0.277227 -0.017219 -0.108676 \n", "75% 0.288661 -0.010783 -0.097794 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X \\\n", "count 7352.000000 7352.000000 7352.000000 7352.000000 \n", "mean -0.605438 -0.510938 -0.604754 -0.630512 \n", "std 0.448734 0.502645 0.418687 0.424073 \n", "min -1.000000 -0.999873 -1.000000 -1.000000 \n", "25% -0.992754 -0.978129 -0.980233 -0.993591 \n", "50% -0.946196 -0.851897 -0.859365 -0.950709 \n", "75% -0.242813 -0.034231 -0.262415 -0.292680 \n", "max 1.000000 0.916238 1.000000 1.000000 \n", "\n", " tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... \\\n", "count 7352.000000 7352.000000 7352.000000 ... \n", "mean -0.526907 -0.606150 -0.468604 ... \n", "std 0.485942 0.414122 0.544547 ... \n", "min -1.000000 -1.000000 -1.000000 ... \n", "25% -0.978162 -0.980251 -0.936219 ... \n", "50% -0.857328 -0.857143 -0.881637 ... \n", "75% -0.066701 -0.265671 -0.017129 ... \n", "max 0.967664 1.000000 1.000000 ... \n", "\n", " fBodyBodyGyroJerkMag-meanFreq() fBodyBodyGyroJerkMag-skewness() \\\n", "count 7352.000000 7352.000000 \n", "mean 0.125293 -0.307009 \n", "std 0.250994 0.321011 \n", "min -1.000000 -0.995357 \n", "25% -0.023692 -0.542602 \n", "50% 0.134000 -0.343685 \n", "75% 0.289096 -0.126979 \n", "max 0.946700 0.989538 \n", "\n", " fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) \\\n", "count 7352.000000 7352.000000 \n", "mean -0.625294 0.008684 \n", "std 0.307584 0.336787 \n", "min -0.999765 -0.976580 \n", "25% -0.845573 -0.121527 \n", "50% -0.711692 0.009509 \n", "75% -0.503878 0.150865 \n", "max 0.956845 1.000000 \n", "\n", " angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) \\\n", "count 7352.000000 7352.000000 \n", "mean 0.002186 0.008726 \n", "std 0.448306 0.608303 \n", "min -1.000000 -1.000000 \n", "25% -0.289549 -0.482273 \n", "50% 0.008943 0.008735 \n", "75% 0.292861 0.506187 \n", "max 1.000000 0.998702 \n", "\n", " angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) \\\n", "count 7352.000000 7352.000000 \n", "mean -0.005981 -0.489547 \n", "std 0.477975 0.511807 \n", "min -1.000000 -1.000000 \n", "25% -0.376341 -0.812065 \n", "50% -0.000368 -0.709417 \n", "75% 0.359368 -0.509079 \n", "max 0.996078 1.000000 \n", "\n", " angle(Y,gravityMean) angle(Z,gravityMean) \n", "count 7352.000000 7352.000000 \n", "mean 0.058593 -0.056515 \n", "std 0.297480 0.279122 \n", "min -1.000000 -1.000000 \n", "25% -0.017885 -0.143414 \n", "50% 0.182071 0.003181 \n", "75% 0.248353 0.107659 \n", "max 0.478157 1.000000 \n", "\n", "[8 rows x 561 columns]" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is signal data from an experiment : very clean, very regular, no missing values" ] }, { "cell_type": "code", "collapsed": false, "input": [ "y_train.activity.value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "6 1407\n", "5 1374\n", "4 1286\n", "1 1226\n", "2 1073\n", "3 986\n", "dtype: int64" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a slight class imbalance : Class 6 (resp. 2 and 3) is more (resp. less) represented than average representation. This could have a negative impact on our classifiaction, but oversampling seems complicated given our features, and undersampling is costly as we only have 7352 samples.\n", "\n", "There are many variables. We do not hope any correlation plot : it would be useless (too much information in a limited space) plus it would a good way to explode the memory. Let's try a PCA to get a synthetic look at the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pca = PCA(n_components=10)\n", "pca.fit(X_train)\n", "print(pca.explained_variance_ratio_)\n", "print(\"Variability of the compressed dataset %.4f\" %sum(pca.explained_variance_ratio_))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.62555992 0.04913095 0.0412155 0.01874993 0.01694941 0.01272099\n", " 0.01176714 0.01068966 0.00969418 0.00858032]\n", "Variability of the compressed dataset 0.8051\n" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keeping three components seems to be reasonable, let's plot the data according to these three components" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot_PCA(X_train.values, y_train.values, classes, azimuth=59, elevation=-21)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.62555992 0.04913095 0.0412155 ]\n", "Variability of the compressed dataset 0.7159\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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FixejXbt2WLRoEVasWCFORR43bhyWLVuG9PR0jB8/HgAwe/Zs3H333UhLS8OyZctQUlKC\n1157DQ899BDS09PRo0cPvP322wAAvV6PTz75BG+++SYyMjLw4YcfYsSIEW6f81//+lfcddddGDp0\nKPLz8xEfH9+isq3SNrvbD2PGjMGePXuQlpaGP/7xj4qPke4LT9uhRL4fWuuxKv+6VqvFihUrUFlZ\nifz8fGRmZuK+++4Tg+G4ceNwyy234Le//S2Sk5MxePBgbN682ePPd/dvpee2dOlSdO3aFSkpKfjn\nP/+Jd999FwDQu3dvjBo1Cvn5+UhPT1es4rto0SIMGDAAZWVlyMjIwNSpU8HzPHr27ImlS5fi4Ycf\nRmZmJlauXIkvvvgCOp3Oq+et9DyvuuoqdO/eHddddx0mTZokVil+7LHHUFpaioKCAhQUFKC0tBSP\nPfaY2/0h9cQTT6B79+74zW9+I1YPrqqq8up75ceVzWbD1KlTkZmZiQ4dOuD8+fNYuHCh2+8nhBBC\nIpWmlWlP0dFEkBASFFqtFgcOHEB+fn64nwohbXLkyBHk5+eD4ziPI+iEEOID79Z+EEIU0acxIYQQ\nQgghhBBVoIBKCGkzbwsEEaJmdBwTQggh6kFTfAkhhBBCCAkcuutFiB9oBJUQQgghhBBCiCpQQCWE\nEEIIIYQQogoUUAkhhBBCCCGEqAIFVEIIIYQQQgghqkABlRBCCCGEEEKIKlBAJYQQQgghhBCiChRQ\nCSGEEEIIIYSoAgVUQkhEOnfuHHbt2oVWejnj+PHjGDlyJD777LMQPTNCCCGEENJWunA/AUIIkeI4\nDjqd+1MTYwyfffYZZsyYgYSEBGg0GmRnZ6OkpASDBg1CaWkp0tPTYbPZsHjxYjzzzDN48MEHcf31\n14dwKwghhBBCSFtoWhl98Dw0QQghATZ+/HgsX74cpaWlKCsrQ2lpKUpKSpCamor9+/djwoQJyMnJ\nwYwZM5CRkQHGGM6cOYOtW7di27Zt2LZtG/R6PdavX4+cnBxMnz4dv//975GUlASNRhPuzSOEEBL9\n6MOGED9QQCWEqArP8zhw4AC2bNmCLVu24JdffkFlZSWMRiP69OmDIUOG4Oqrr0ZZWRni4+NbfK/T\n6cTf/vY3bNu2DdOmTYPBYMC2bduwfft2NDc3Iz8/Xwy+RUVFMJvNFFoJIYQEGn2wEOIHCqiEENXi\neR7vv/8+nnnmGfzxj39Eamoqtm3bhoqKCsyYMQODBw9GXFwc4uLiwHEcOI7DoUOH0LdvXxiNRpef\ndfDgQWzduhUVFRXYtWsX7HY7evXqJY7UFhQUwGAwUGglhBDiD/oQIcQPFFAJIarDGMP27dsxadIk\n9O/fH1OmTEFycrLLY5xOJ+x2OxwOh/j/Wq0WcXFx0Ol0iIuLg1ardRs4OY7D/v37xdC6Z88eAED/\n/v1RVlaGsrIy9OnTx+OaWEIIIUSGAiohfqCASghRlZqaGsycORMHDhzAggUL0Lt3b8XHOZ1OWCwW\nMMZgNpuh0+nE0Cr9w/O8OMoq/HEXWhljsNvt2LNnjxhaq6qqYDAYUFhYiNLSUgwaNAjdu3eHVktF\n0AkhhCiigEqIHyigEkJUwel04vXXX8drr72Gf/zjHxg+fLhiCOR5HjabDQ6HAyaTCXq93uOU3ECE\nVovFgh07dohFmI4cOYKEhAQUFRWJI62dO3em0EoIIQSggEqIXyigEkLCijGGjRs3YsqUKbjyyivx\nyCOPuBQ/Eh5nt9ths9mg1+thMpnavFZUGlo5joPT6QRjrMXU4Li4OGg0GrehtaGhAZWVleKa2BMn\nTiAtLQ0lJSXiSGv79u1pPSshhMQeOvET4gcKqISQsDlz5gweffRR1NXVoby8HF27dlV8HMdxsFgs\n0Gg0MJvNiIuLC/hz4XneZaQVgOJIqxLGGGpqalBRUSGOtJ47d06xRyuFVkIIiWp0kifEDxRQCSEh\n53A48NJLL+H999/H9OnTce211yqGNp7nYbVawXGcV9N5Ay0QofXMmTPYtm0btm7disrKStTV1SE3\nN1ecGlxSUuJ1j9a1a9eCMYZhw4YFdDsJIYQEFAVUQvxAAZUQEjKMMaxduxaPPfYYbr75ZowdO9al\nHYzwOJvNBrvdDoPBAKPRqIpRR8aYOD1YmBrsdDqh0WgUpwcr4Xkex48fF0dZKysr0dzcjG7durnt\n0Xrw4EFMnDgR27dvx8svv4wbbrghlJtNCCHEN+H/wCIkglFAJYSExLFjxzBp0iTExcVh3rx56NCh\ng+LjHA4HrFYrtFotTCZTUKbzBhJjTHGkVWh3I/3jKbQq9Wjt3bs3Tp8+jZ9//hkTJkzAxIkTYTab\nQ7yFhBBCfEQBlRA/UEAlhASV1WrFokWLsHr1asyePRtDhgxRDGpOpxNWqxU8z4vTeSOVv6GVMYZ3\n3nkHs2bNQnFxMdq1a4cjR44AuNSjVSjCRD1aCSFElSigEuIHCqiEkKBgjGHFihUoLy/H6NGj8Ze/\n/EUxTEmn8xqNRhgMBlVM5w00aWgVpgfzPA+tVttiarCw7vaLL75Az549UVpaKn6/Uo9WvV6PwsJC\ncU1rjx49qN0NIYSEV/R9iBESQhRQCSEBV1VVhYkTJyIrKwszZ85Eu3btXB7DGBOn8+p0OphMppgL\nVvJ2NxzHAQA0Gg10Op0YXH3p0Xr48GEkJiaiqKhIHGmlHq2EEBJSFFAJ8QMFVEKI1+rr6z1WnG1s\nbMT8+fOxceNGlJeXY+DAgW6n81osFjDGYDabY3qaqrwglMFgcJkezPO8YuVgX3q0pqamoqSkBGVl\nZdSjlRBCgotOroT4gQIqIcRrt956K9avX4+SkhKUlpaitLQUJSUlyMnJwUcffYTFixfj3nvvxejR\noxVH7Hieh81mg8PhiOrpvN7wZQRZqXIwY0yxcrC70Eo9WgkhJGToREqIHyigEkJ8Ul1djS1btmDL\nli3YunUrNmzYAJvNhoKCAgwePBhlZWX43e9+B5PJJH6PsH7SZrNBr9fDaDTG9JRTjuNgsVig0Whg\nMpnaNIIcih6txcXFSE5OptBKCCG+oZMmIX6ggEoIaZPa2lrMmjUL+/btw/jx41FbW4tt27Zh+/bt\n+Ne//iW2iNFoNOA4DhqNBvHx8apvGxNMQgEkjuPESsWBCn/SHq1tDa08z+PEiRNiEabKyko0NTUh\nPz8fZWVlKC0tRXFxcYserYQQQlzQCZIQP1BAJYT4xOl04s0338Srr76KRx55BLfccotL4BGCktVq\nFdurCOcab8NSNJGvMzUajSEJeNLQKp0eLBRh8qVHqzA1WOjR2qtXLzG0FhQUeD1dm+M4fPvtt7jh\nhhsCvbmEEKIWFFAJ8QMFVEKIVxhj2Lx5MyZPnowhQ4bgH//4BxISEhQfJ0znlYYx+QifEJg0Gk3U\nhlY1Vir2t0crcClkVlVVYcuWLaisrMSePXvAGPPYo5UxhtWrV2PSpEnIzs7GypUrYTabQ7XZhBAS\nShRQCfEDBVRCSKvOnj2LqVOn4vz585g/fz7y8/MVHyeEMa1WK07x9cRdWIqG0MpxHKxWKwC0eZ1p\nqPgbWoUgvnv3bnGk9ddff4Ver0dBQQFyc3OxfPlyXLhwAU899RRuvvlmmiJMCIlmdIIjxA8UUAkh\nbjkcDrz88st47733MHXqVFx//fWKwYLneVgsFvA8L66tbCtPodXbaanhFMx1pqEkvA7SqcE8z0Or\n1bZ4HTy1uzl8+DBmzZqFdevWoUePHmCMISkpiXq0EkKiXeSd9AlREfXe0ieEhA1jDN9//z2mT5+O\nm266CV9++SWMRqPi46RrK+Pj4/0OY9LRU+nvkYZWh8Ph87TUYJPvC0/9YiOBu9dBOkXbZrMp9mjV\naDSw2+2oq6tDx44dsWPHDqSmpoIxhsbGRlRUVKCiogIfffQRTpw4gbS0NBQXF4vVgzt06BDR+44Q\nQgghbUcjqISQFo4fP45JkyYBAMrLy5GTk+PyGDWsrQzEWspAPY9w74twUlpXzBgTR7yF0VZPI621\ntbWoqKjA1q1bxR6tWVlZKC0tFUMr9WglhEQQOlkR4gcKqIQQAIDNZsPixYuxcuVKzJo1C5dffrli\nIHA6nbBYLGCMwWw2q2ptZahDayStMw02aW9XoTCWdHow4FrBWaPRuA2t7nq0ClODqUcrIUTF6MRE\niB8ooBIS4xhjWLVqFebOnYuRI0dizJgxikGLMQar1QqHwwGj0eh1W5FwC0ZojZZ1poEgtBMS1h/r\ndDq365SpRyshJEbQSYgQP1BAJSSGHThwABMnTkRGRgZmzpyJzMxMl8dIp7Dq9XoYjcaIn8La1tAa\nrn6masTzPGw2W5tvWMjbDrU1tLrr0SpMD/alRyshhAQInXAI8QMFVEJiUGNjIxYuXIiff/4Z8+bN\nQ3FxseIFvHQKq9lsbrVtTCTzVLVW2G6HwwGdTgez2RzxIb2tpCE90DcshNAqfQ0C1aO1X79+4nrW\nvn37xvR0bEJI0FFAJcQPFFAJiSE8z2PZsmV46qmn8Le//Q2jR49WDJ00hfUSxhjsdjtsNhuAS5Vt\npaE1Li4OOp3ObQGgaCIdSY+Li/Oqz22gfm+ge7RWVVVBr9ejsLBQXNPao0cPr4N2fX09du/ejcGD\nBwd6cwkh0SG6PxAICTIKqIREiT179mDz5s0oKSlBnz59XEaIdu3ahQkTJqBnz56YOnUqUlNTXX6G\nNJDp9XqYTKaoD17uuAvpStNSlVqtRFNoVVsxqECEVovFgp07d4prWg8fPoyEhASPPVrtdjuWLFmC\n+fPnY/To0Xj66adDudmEkMgRHSd/QsKEAiohUWLTpk147rnnsHXrVpw8eRIDBgxASUkJevfujX37\n9mH//v1YuHAh+vbtq/j9wuiYVqsN2eiYGrVlnWm0hlahAJLT6VT9SLq/r4G8R+u2bdvEHq1FRUVw\nOp344IMP0Lt3bzz++OMoLCwMw1YSQiKEOk+UhEQICqiERKH6+nps2bIFS5Yswffff4/ExEScOnUK\nffr0wezZs3HllVeKF+vCaJLT6RTbxqg1hASTfAqrv+tMIzm0+lsASS0CEVq//PJLzJkzB3a7Henp\n6WCMISsrCyUlJRg0aBBKS0uRkZERkfuHEBI0dEIgxA8UUAmJMowx/PLLL5g8eTIuu+wyTJw4EQkJ\nCaivr0dlZSU6d+6MrKws8WIdALRaLQwGQ8ysp5QTprAGu7er2kOrfIp3NFRslvP2NRBG0j/88EOY\nTCbcfvvt4v8LPVq3bduGiooK1NbWolOnTtSjlRAioDc/IX6ggEpIFDl37hymTp2Ks2fPYv78+ejW\nrZvLY4QqqRaLBXFxcdDr9S0u2tUUmIJNDcWg1BBaw1UASS3k61k5jgNw6caNTqeDTqcT17O66/F6\n4sQJMbRWVlaiubkZXbt2pR6thMQmeqMT4gcKqIREAY7j8Morr2Dp0qV49NFH8dvf/lbxQtjpdMJi\nsXgcKVRDYAo2tfczlb8GHMeBMRaU1yBUo8dqxxiD1WqFw+GAwWCAXq93Ca6Abz1aDx06JBZh2rlz\nJ/VoJSR20JuaED9QQCUkgjHG8MMPP2DatGn43e9+h4ceeggmk0nxccLFd1vWFLoLrb70p1QD+ehx\nJI0UKlWt9Se0RlIBpGCSTmvW6XQwmUyKoVPo0Sp/DQDvQ6vQo1UIrdSjlZCoFXsnU0ICiAIqIRHq\n5MmTmDRpEpxOJ8rLy9GxY0eXx0inbnq6+G6LSAut3oweRxql0Ar8d2qqdP8Lr4G0AJIaR49DJRDT\nmgPVo3XPnj1iaK2qqoJOp2tzj1ZCiCrE3kmVkACigEpIhLHZbHjmmWfw+eefY+bMmbjyyisVL37D\n0btSjaFVDetMQ8lTaAUuBXWhAFKkjB4HkjCKbrVaodFoAv7eCERotVqt2LFjh7im9fDhw4iPj0dR\nUZE40tqlSxcKrYSoV/R+yBASAhRQCYkQjDGsXr0ac+bMwR133IExY8ZAr9e7PE5tgSxcoVVekdZk\nMkV1MHVHOlIIXAqqQvVmpf0fzftIOoouBNNQbG8gQqtSj9bU1FSUlJSII60dOnTwanuqq6vx008/\n4fbbbw/G5hJCKKAS4hcKqIREgIMHD2LixIlITU3FrFmzkJWV5fKYSApkwQyt0hEyrVYbUetMA81d\nAaRArKcdlSdBAAAgAElEQVSMJNKbNmrp6xqIHq21tbWoqKgQpwefPXvWY4/W2tpaPPHEE3jttdcw\nduxYlJeXh3qzCYkV6vzwJSRCUEAlRMWam5uxcOFCrFu3DvPmzUNpaanb6bwWiyWiA5m/o0xAdK4z\nbYu2jKJHY2iNtPW2gQitSj1au3TpAo7j8N133+EPf/gD5s2bh9zc3DBsISExQ70nGkIiAAVUQlSI\n53l8/PHHePLJJ/HXv/4Vd955p2Lo5HkeFosFTqdTDGRqvgD3lbehVaPRwGazqWqELBzkrVL8DWSR\nGlqlbYSE9bZqen6+8KblkPC+l7/WjDH85z//wfTp0zFgwAB06tQJx44dQ1NTE/Lz88V2N0VFRYiP\nj4/J9wwhQUJvJkL8QAGVEJXZs2cPJkyYgPz8fEybNg1paWkuj1F7H89gkoZWjuPEC3YA0Ov1qqse\nHAretkoJ1O8SQhPHcWJw0mg0YQ+tgajMGwncFcKS7nfhHLFx40ZkZGSgpKSkxfcr9Wjt2bMnysrK\nUFpaisLCwpi90UNIANAbhxA/UEAlJEQuXLgAs9mM+Ph4xa9fvHgRc+bMwY4dO7BgwQL079/f5THy\nPp5mszliR4b8IV9nKgR0f6YHRyK1rLd1N9IdqtAa7Mq8kYDneXAcB4fDAY7jAMCn/e+pR6tQhIl6\ntBLitej5oCEkDCigEhIiixYtwsyZM5Gfn4/i4mIUFxejpKQEBQUFWL58OV588UU89NBDGDFihOJF\npNPphNVqBc/zMb2+Ul6JVamSMRCYNa1qFq6KtN4K1f6XFoJS434IFfl+EG5UyKcHy0Oru/1FPVoJ\n8UvsnYQICSAKqISEkN1ux65du7Bt2zZs3boVP/30E/bt24fc3FwMHDgQAwcOxN13343MzEzxolG6\nrjCW11dKC960dT9EQ2hVY0VabwVy/ws3bJxOpyraKYWLL/sh2D1ahdBKPVoJoYBKiD8ooBISBufP\nn8f06dNx8uRJzJkzBxzHiT0OH3nkEaSkpIhFT4QqnrE4bRFwbZ8T6II3kRJao3Xdsa/7P9Iq8waL\n0+kMSGGwQPVoraysFCsHHz9+HKmpqSguLkZZWRnKysqQk5MTk68TiVl0sBPiBwqohIQQx3FYsmQJ\n3nrrLUyePBk33nijx7YxQrVOoSiNGgrRhEo411d6e9Gu1WqDvv+lhX+CXQBJLTytaRXeEwaDISZH\nTUMR0OX7n+M4lz7FOp3Oqx6twkhraz1aCYkydGAT4gcKqISEAGMMP/30E6ZOnYrrrrsO//u//wuT\nyeTyOE/9K6UXjdKLR8C15YdSy4lIIl1v62mdaSiFuhCQvPCP2WyOyoq0rRFG0IWALr1ho8aR7mAJ\nd+ucQPVoraioENe01tXVITc3V2x3U1JSguTkZI+v34EDBzB37lzMmTMHXbt2DeYmE+KP6DsJERJC\nFFAJCbLq6mpMmTIFFosF8+fPR6dOnVweI5/GajKZvLrI9qVPZSSE1kCsMw2lYIVWeUCPxcI/3gT0\nSJme7Q9pMFXbCLq/oZXneZw8eVIMrJWVlWhsbFTs0Xr8+HHMmzcPn376KcaNG4fx48cjKSkpDFtN\niFci84RDiEpQQCUkSOx2O5599ll8+umnmDlzJoYOHepxOm8gR8mULtqFqZHeXDiGWrDXmYaSP6E1\nkgsgBZI/lXndhaZIC63y3rZGozEiRtDl+1/oU6xUOdhdaD106JA4NfjAgQOwWCzYsGEDrrrqKkyc\nOBFDhw6N2fcGiRh0cBLiBwqohAQYYwxff/01Zs2ahREjRuC+++5TnKIqDSNC25hgXnCpNbQK6yvD\n2ccz2LwJrcLFvMFg8HoEPdoEqzJva6FVmDqshhs2wppjm80WNe8JpWMf8DzLg+d5bN68GSNGjMAd\nd9yBESNG4NChQ6isrMTu3bupRytRu9g7gRMSQBRQCQmgQ4cOYdKkSUhMTMTs2bORnZ3t8hg1VWPl\ned7l4tGXKXr+iPVprEJoFaY0CwWAYqkQliAcI8f+Tk8NxvORTmmO9qrdnkKr8NpotVpcvHgRubm5\nLb5X2qNVGGndv38/9Ho9CgoKxMrBPXv2jPr3DlGt2PkwIyQIKKASEgAWiwWPP/441q5di7lz56Ks\nrMzlolZeldZsNqvy4snThbt8mmRbLtwjbZ1psEindgthRAit8ot3wHW0SY3Hjq/khX/CPXIcrtDq\nz5TmaCGcF+x2u7h/5bMMhJFu6tFKIkBsvYEJCTAKqIT4ged5fPrpp3j88cdxzz334M9//rPidDw1\nVqX1RSDW9UXTOlN/+Dpy7EshrEjZn/L1lWoq/CMXzNAarCnNkcTTWlvq0UoiGB1QhPiBAiohbbR3\n715MmDABXbp0wWOPPYa0tDSXxwh39aNxtFB64S69iFS6eAxXP1M1CeTIcaRWb5b2dI2Li4vYYyEQ\n1WtjvRiWNJj6ciwEIrQq9WjNzMwUKweXlZVRj1biLzp4CPEDBVRCfFRfX4+5c+eioqICCxYsQP/+\n/RWn8woX4mofIQok+cUjx3HgeR4AxCl6Op0OWq02JvYHELr+lWoPrcJNCgBRub7Sm9Cq1Wpht9vF\nYljhXH8eLvKbFEaj0e9jwd15RxpahfOOrz1aS0pKMGjQIK96tBIiQQcKIX6ggErI/+E4Do2NjUhN\nTVX8Os/zWLp0KV544QU8+OCD+J//+R/FoOF0OmGxWABE54W4N6QjxwaDATqdzmXUI9qLAalhtFAN\noVU6pdloNMbUNFZh33McB4fD0eJmjRrbPQWTtDpxKIpABatHa9euXVFWVobS0lIUFxcjPj7ep9fu\n/PnzaNeuXSA3lahTdL+hCQkyCqiE/J+9e/di0KBByMrKQlFREYqLi1FUVISioiJUV1dj0qRJKCoq\nwqRJkxQbxEuncNJ6Ms/rTH3pFaqmKareUvNoYahCK01jVV5rC0CV7Z6CRV6dWBgxDce2BSK0Snu0\n7ty5E3a7HT179hRDa0FBgeLI+J49ezB37lxs3rxZrDhMolrkv3kJCSMKqIRIOJ1OHDhwQLwA2bx5\nMzZt2oTk5GQMGDAAxcXF+MMf/oCCggLx4l0eysJdiTRchNFCjUYDs9ns82ihGkb7/BWpRW8Cue/l\no+exOo3Vl/WVbekTqnZCMLXZbKquTiw97jmOc7lhIK0crPTcOY5DVVUVtm7d2qJHa9++fVFWVoZ2\n7drhgw8+wJo1azBhwgQ88MADSExMDMOWkhBT14FOSIShgEqIAo7j8Nprr+GNN97AhAkT0L9/f3Ga\n1+WXX47BgwcDADQaDXieF0OZGi/Agi2Y/Uy9CU7COrNwjjhFa+scX4ITgIipzBss0mmsWq3Wr/WV\nkRxapW1zInFat7f73t3sEIfDgW+//Rb/+te/sHnzZiQlJaFTp04oLi4W29306NEjIguEEa9FzgFP\niApRQCVEgjGG9evX49FHH8U111yDcePGwWw2uzyO53lYLBZwHCdegAr9K6N1qp5cOCsUK11AhmOa\nZCy2znG374FLN2wMBgP0en3UHvdK5NNYgzWtW+2hVRgxjbQZBN7wdt8LRdH+/e9/4+LFi3jooYeQ\nnJwMq9WKnTt3imtaDx06hPj4eAwcOBBlZWXUozX6RMeBT0iYUEAl5P+cOnUKkydPRnNzM8rLy9Gl\nSxeXx0grsipNX/S0xkneBiFSL9zU2sNSegEp/D1YNwzUUAAp3KShDIC4pi4W1lVKSUcLQz2NVS09\ncqVT26NpBoEn8n0vTA8GAK1WC71e32J6sNL3Cz1ahZY31KM1qtCLRogfKKCSmGe32/H8889j2bJl\nmDFjBq6++mrFCwIhkPjay9NdaPW2b5+acBwHi8XS5nWmoeZvURQl0kAiTOuONUKlak+hTC2j3MGi\n1vXGoQytTqcTNpstpgthSYuB6fV66PV6l2Pf1x6tlZWV4kjrmTNnqEdrZKIXiBA/UEAlMYsxhm++\n+QazZs3Cbbfdhr///e8wGAwuj5OvsQxE9UVPzeblFzNqGJ2UXoxH+lrbtt4wUGsgCSV/K/NGQ2iN\nxOrEgQ6t0n0Qq4WwvN0Hns713obWs2fPioX7KioqUFtbSz1a1Y9eDEL8QAGVxKTDhw9j8uTJMJlM\nmDNnDtq3b+/yGOl03lBciKqx9UqsVGRtLbQKr4vBYIjJKs3SIlCBPg4iJbQGcx+EgxBahampns43\nQmiNtn3QFvJ9YDAYfL6JKD3XC/tfer4Rpga31qNVCK2B6tFKAop2PCF+oIBKYorFYsGTTz6Jb775\nBnPnzsVll13m8gEuXV8Y7jWWrY18SO/CB/ICXk37IFyEC1G73d7iAj0Sp2a3lfQmTSiLQKkptIZr\nH4SDp5tkwteF84Hap/cHWiCCqSeB6NF6+PBhcWrwzp07YbPZ0LNnT3F6sLserSQoaCcT4gcKqCQm\nMMawfPlyLFy4EH/+859x9913K15gSdfWqXl9YTAv4CNtnWmgSYv/yNcbezNdT9r2JlLJb1AYjcaw\nHwdClexArif2RK3FwEJJmEFht9vFfSu8BrFyo0YaTEN9g8Lf0Op0OrF//363PVrLysrQr1+/Vj/n\n9u3bhxdffBGLFy+G0WgM1uZGm+h7MxASQhRQSdTbt28fJk6ciI4dO2LGjBlIT093eUw09LH0t4Kw\n0DonGtaZtpW8Iqs36419mZqt9oATqnYpgRKMIlhUobnlqLFSOPd3XWUkCGcw9USpcrD05qS0crC7\n2UF79uwRpwfv378fer0eBQUFLj1ad+/ejfLycnz33XcYN24cHnnkEcTHx4dpyyNOZB74hKgEBVQS\nterr61FeXo4tW7Zg/vz5KCgoUPzAjuY+lt4UBNJqteA4LubXlQkFTwJRAEl+AS/8HVBPz0q5cLZL\nCaS2hlbh4t1ms0VEOA8G+aixLyPn0RJa1RpMPfG2R6vSdgij5NIerRcuXMCZM2ewf/9+3HrrrXj0\n0UfRv39/1e8HlVHvQU5IBKCASqIOz/N477338Nxzz2Hs2LG4/fbbFT9YY3Uqq3AhKYRS4WJGo9FA\np9O5hNdo1lpf20D/Lm8rqYYytMZCdeLWZhdoNBo4HA7xXBDLwTSQo8aRFFojMZh60pbQ6nQ68dJL\nL+GZZ57B2LFjUVRUhL1794o9WlNSUlBcXIzLLrsMt9xyS9hfM5WjnUOIHyigkohy9OhR7N+/H0VF\nRcjMzGzxNcYYKisrMWnSJBQUFGDKlClISkpy+RmBHi2LRNJwLlyMqq2CcDCpZX2hp9Cq1G4okPs9\n1iuySo8Bxhg0Go0qqwcHUzimM6sttEZbMHVHeq6RVm7WaDTQarXi69LU1ISUlBQkJCS0+H6e51FT\nU4PKykocO3YMDz74YNS+LwKEdg4hfqCASiLKDz/8gFmzZqGiogKJiYkoKipCUVERunfvjg0bNuDw\n4cNYuHAhevXq5fK90gvSWLwgB3wL596M+EkLAkXCvvRUAElNglkEK5aq0rrjbtQ4GGta1Uht05nl\noZXjuKBXzI6VYOqJcAw4nU7xPKhU9E043oW/C8sAIvk9EAK0cwjxAwVUEpEYYzhy5Ai2bt2KN954\nA1u3bgUAWK1W9OvXD8899xy6d+/eYo2l2kNJMAVyKquaWoD4QlqhORLXWPpbxVYto8bhJL1B420x\ntGgKrZFUBMub9fNtCa08z8Nut8f0DRqO48RgKn8fSJeACDcLTCZTi0DK8zwF1NbRziHED+r8ZCLE\nC6dOncJzzz2Hq666Cm+//TbMZjNOnz6NyspKZGZmtij6AgA6nQ46nU4cGYyFD1d5u5DExES/L8aE\nu+jSCrfS8CRc/Egv4qUXlaHe720JJWok7HdpoJBX9JTvd2HfC6NFWq0WCQkJMXeDRj6dOSkpyetj\nQFib7ct+D+fxrkQIpsJ05ki4QdPafnc6nbDb7WJoFarXurtZIA+mgTgXRhrhM1EImPHx8YrHgEaj\nEcO7cDNDzccKIST60AgqiTinT5/GlClT0NDQgPLycuTl5bk8Rj6NUafTuVRVVVrjF00XLMLFCICw\njJQEawTEl98fqgJIaiIPTxzHAfjvBb+aCtMEWyinM4f7eHcnWqozu9PaCLdWq4XT6RSn8sbazAFh\n/wjHgNFoVFzaIdy4BdBiWm9zc7NYPEzA83xMjjz7KHreZISEAQVUEjEcDgdeeOEFfPDBB3jssccw\nbNgwt33ehBFDdxcjvvSujLS7x2ouAuVNgRTputa2/g5vjoFoJl9jqXSDRq3VVANBLdOZwxlapcHU\nXSiJVtIRY2HdPKBcwTZa94l81NzXYCpobm4GALH/qXB+TUhIiLnzqo+i88AiJEQooBLVY4xhzZo1\nmDFjBoYPH46xY8fCYDC4PE66xrAtrSKk4Ul6MQ/AZdqeGi9sInXEMJA3C4RgGmutgwS+TGdWWzXV\nQAhHVVpfBTu0xkLbIE+k50FpL9doWkvsSaCCqUD6mWq1WmG1WqHX65GWlkYB1bPIPYgIUQEKqETV\njh49iokTJ8JgMGDevHlo3769y2OERuMOhyPgawzdVbJVU1GgaBwx9LWCsBDMhLVV0TaNsTWBujnh\nKbQqTYlXi0gq/qMkEKFVGkwjea11W7kLpq19T7SEVm/XGXsbTAUWi0Vc6yudIk1TfFul7gOGEJWj\ngEpUyWq14qmnnsJXX32F2bNnY/DgwR6n84a6GqNa1puFe51pKHm6WQBcCq7CeuNIuKAMhFBMZfVl\nhDvUF6zSi3IAMBqNUXNzwttzDADY7faILwLWVm0Jpq39vEgKrdKbMwACFkyFm35C9fukpCRxv/I8\nD4PBoLrZCSoT/oODkAhGAZWoCmMMX3zxBcrLy3HnnXfinnvuUQxd0g9ktUzlFC5s5BfzwRh9UvM6\n01CQX5Tq9XqXNZby/R1JU1VbE+6prEJolR/rgOs6v2Ct4Y724j9KpDcLhAJYQkXyWCyAJb05428w\n9UQ41oXWK2qYRSPvZevu5kxbg6nQL1woMpWYmNjiMRRQWxXdb0BCgix6h1tIxKmqqsKECRPQvn17\nfPzxx8jIyHB5jJqDmXCRKCUffRLaUQDKU1W96ckoncrpS7uMaOBt2xx5JVu1VFQNBI7jYLFYoNFo\nEB8fH5ZRc+noqdBuSD7CbbfbFUOrNxfJnsTyGksh7AvHtcFgEPe/sN8dDkfEryX2RB5MQ9E2SanF\nk/S8LpyT5KFVCIyB3O/yYBrIEVNhOq/BYEBycjLi4uLE0XlCCAklGkElYdfQ0ID58+dj06ZNmD9/\nPgoLCxU/bIWLEmEdTKRebLVlXWs0rjP1lTSYtWU6s6cRbqUApTZCMBNaPERKMFOaHtyW0ado6Wfb\nVvI+np6WNERrASzhM0CtBbCU9jkQmFkFvo6YCr+jtfeVcF4RgqnZbG5xXAn7PCkpqcV20ghqqyLj\njUWISlFAJWHD8zw++OADPP300/j73/+OkSNHKl5wCcFMq9Wq8qIkUJSmTAqjfsL7NBYvzKXBLNBT\nOSOh3VA0BjOl49zdOj9h1oDD4Yio6tSBEqherpEaWiMhmHrib2hVGjFVeo38CaZGo9HtTU+HwwGL\nxYLk5OQW20QBtVXqeRMREoFoii8Ji+3bt2PixIno168fvvjiixYffgJhylGsVGaVTyMTtp/jOHEa\nnzSsS6cH+7Oudf/+/fjll1+QnZ2Na6+9VjWjhzzPi8EkWMFMGkQF8vWVwZqq2hrp9kfbdG5h3wnH\nNeA6LVs6HV6r1baYzhoL5FNZ3U1n95anY12N04PlwTQUU3mDQelYl+5zT+cX4X2g1Wrdtk6TBlNv\nzklO56V2bMJ5NSUlpdXjSmkgo5XBDUII8QuNoJKAq6mpwZAhQzBw4EAMHDgQRUVFGDhwILKzs1Fb\nW4uZM2eiqqoKCxYsQJ8+fVy+P1L7eQZKa9sfyFG/5cuX48GxD6Nb+wKcuXgMpZcV4p133w5rSA3U\niFGgn1Oo2g3Jp7OrYftDSR5M9Hp9i30vjLTKQ1S0nCNCWfzH3e8P50hrpI+YtoX0/CIUv5LWKpAW\nwBLOBW0ZMRWCqclk8vq8wnEcmpqakJKSIv4fz/NicTriVnSckAgJEwqoJOCcTif27NmDyspKVFRU\noLKyEpWVleB5Hnl5eSgsLMRVV12FoUOHokOHDuKHqlAu32KxxOQ6S3/WmXoKUKdOncK/X30e589U\no29hKcY+/AgSEhLAGENux04YUToBOen5cPIc3vl5Dp596UnccMMNQd5a5W0IZ2XathAu5D1VEPal\nAFakbX8gebv9amnxFGhqfv1DEVrlwVRYYxkr5NsvTKF1Nz04Pj7eq2AqfKYKhQV9rd/gdDrR0NCA\n1NRU8f8ooHolck4+hKhQ7Jz9ScjExcVhwIABGDBgAO68805s2rQJkydPxoABAzBo0CDs3bsXH330\nEQDgxhtvbDGdCbjUNibWPvikLTPaUplVehdduu9qa2sxfcKD+NNADQZcnYSPNq3CjKknMHfBInAc\nh8bGBnRIywMAxGl1yE7ugtOnTwdy07wibRsUrsq0baFUUMndVFV3F/PSPobhrMwbLr5uv1AtW/oY\neQEsNU1VbY10jaFWq1Xl6+/P9ODWio7Jg5katz+YWtt++bIPAGhsbGx1v8qDaWJiouqOfUIIcSd2\nPgVIyJ05cwaPPvooamtr8corr6Br164ujxHWWTocDvHDtrm5OWIqq/or2G1zduzYgV7pDoy6ojsA\noHfHFFz3xCbwPA+z2Yw+vfthfdUXGNLzFpy9eBwHTlWisHAuOI4LyT6PxpYhvgQo6bYaDAax72Cs\nCFQvU29aPLU1QAWLPJi7W2OoVr6EVnfLD6QjxrEeTJXW2Aoz3ISpvNIpvu7eJ8I+Fc6p/gZT4SYa\nIYSEUux8GpCQcTgceOmll/D+++9j+vTpuPbaaxUrDkqnsyYlJbVYW+PLRU4kBppQ9TM1GAxosDjF\nC5xmmxM8u9SiwGg04sNl/8Ht/zMSTyz/FEajEc888zR69+4tXuAEa5/LCyAJ09WilTxACcFcKICl\n0WjE0dZoOs7dkQbTYLXM8TdABSu0yoNpNBWA82af22w2caqq8L7Q6/Uxc2NGeu5318dV2sNU2KfS\nc4AQGqXHjFBtVygqGKgRU3cBlUIrISSYKKCSgGGMYe3atXjsscfw+9//Hl9++SWMRqPL41qbzulL\nZdVgFKkJJuk627i4OL8rc7amuLgYSwztMeXtSgzINeO7Xx249f/dKb4uOTk5uPOu0di8aQt69uqB\nW2+7FWazWXyu0jWW0mqTSqNQ3uxzeQGgYG+/2sgr88qDuad9Hg2hNdwj5t4GKGmBmkDtc2EUPRAj\nxpFEPvInzM4QzkHS0BrKGwWh1tZgKvzd3c8UPk+EWTGhaEMV7ccsIST8qEgSCYhjx45h0qRJ0Gq1\nKC8vR4cOHVweE4zprO6K1Khx7ZlQRZExFrLpfOvWrcNdo++Bw2GFKc4JXmfCRx8vQ0VFBfR6Pb77\nZg32VBxC7/a/wbGaPTCmc/jqm9Ue1wB708NS2P/SAlhqLQATCv5UJg5lBeFgibReru72OdC20BqK\nEWO1Uurj6a5dirfVySMptMqDqVJVZl+DaW1tLcxmM2w2m/h5Esz3VE1NDdLS0lqczzUajeINaCKK\njTc4IUFCAZX4xWq1YvHixVi1ahVmz56NIUOGKE7nlY6a+VpF0Ffy9X7SipNKrSmCfaEY7HWmgoaG\nBhw/fhzt2rVDVlYWqqurcdUV1yJRG49U01kUdIzD+UYO635tRq/OV8Fib8Leo1sx4ZZXoNeZwBiP\nt36chdffeQVDhgzx6Xd7qqyq1WrFi/tYK4Aln8oeyJYh8psy/lQQDhb5iHEkt4xqLbTKexJrNJqw\njxiHk7fBtLWfEamhVZgBEchgKuxToUhSqEZMKaC2SWy80QkJEpriS9qEMYaVK1di7ty5+NOf/oTV\nq1crXnwI04+0Wm3IGq23VjBFaeqk0sWlv+SjZsFaZwoAP/zwA8b/7wSYdIngmQNjH74PK1asQJo+\nF7WNW3Ftz2QAwMlaKxL0DBrejoJOQ/Hr8R3QaC7tK41GC6PeBIfD4fPvVyoMJIwacRwnvu7Nzc0x\nscYyFJV5hfAvDfy+VhAOFvmxHw1Tud1VypavaRVGSgV6vR7x8fGqmMURCvJgajab27zt7qZkS49z\nT9Pgw3HMyYOp0rEvDabSIl2egqndbheXxgifp6G62ae05pUQQoKJRlCJz3799VdMnDgRmZmZmDlz\nJtq1a+fyGKE6r9PpFKezqu3DTX6hozQKJb2w9/b5y9eZBms667lz5/Di8y9j187dWP/zemSldIaT\n52DSJ+Bc/QnYnc1IN6fiT4MsmHFzNix2J5788gwqT1hxfd9s/PPHZpxpYOiS2QtFXYfhyLmdONJQ\ngU2bNyAhIaHNz4sxBqvVqjhqJh8REf4OwGWfq3W6amsCVZk2UJSO8WC1YJHOlojFXsbCekqHwyEW\n/lHr6HagyYOp0Mc0FNvny5TsYB2P0tkC7qbxKwVTT/tHeD9ZLBYx7Ov1etTX1yMhISFkVY/r6uqQ\nlJQkfo4J22AymULy+yNUdLyxCQkTGkElXmtsbMSCBQuwfv16lJeXo6ioSHE6r7Q6rZqrs7obEZFf\n6Ci1p5CGKCnpOtNgtk2wWq0Y9+Aj4C6asH3vTsTrHGDcMTh5Dc43xyPJnIFmux42xwX0zErF+QYb\nHE6Gsjwzdpy0Is1sw5/KeDz7XQ2MmTZsPbsc+fld8dWi1W0Op94UQPJmREQ6ChVJF/Rqnc7pru1N\nIKvZytcYh2q2hFpIp/EbDAYkJycrnhuVRrcj6RhXooaqxErncl9GWv2ZwSEPpt6OmHobTIXRUuk+\npdYvhJBoRwGVtIrneXz44YdYvHgx7r33XkybNk3xAziU1WmDydsLemmlT2GdpdPpFFu4BPMCbe/e\nvais2IVkQzsY0IS7r0zBjYU98esZK2Z/vg/HL5xHdmpnNNv0WLqpFgM7G+HgeLz2Uy0OnbPjlXUX\nUCIv5wcAACAASURBVF3nQFq8Fpy1ARs2bW7zc5FeoLZlKrenqZPCPg/ndNXWRGLLHG+q2XpzQR+K\nqcxqJl9j62kav7vzihqmZLeFGoKpJ8EOrcEKpjabrcW5VGkab6gDKgViQkioxc6VBGmTnTt3YuLE\niejVqxc+//xzpKSkuDxGGDnieT5qL1A9XdALI8bCBY3NZhPXXSpVtA2EjRs3Ij4uFb8tvAtb9i3G\nrcWdUNN4Cl0z4tAzi6GmKRG/6XETNlatwrZjh3HHP0/A4uCh0zKkxcfhL5en4lyDE6/+UIO6w7/i\npZdexn333evzmib5dNZArokSLujcXdDzPO8yui0f5Q6maFtn6et6P+lFq1CZNpK33xfehBNvtBZa\nhX2uptCq9mDqSSBCq/C+D1YwFWYgqK2YHAVUQkgoRV+SIF778ccf8eGHH2LgwIEoLCxE//79xTUl\ntbW1mDVrFvbt24cFCxagb9++Lt8vXWsYCa0jAkk+apiYmNhifY50VMRd6xtpcQxv2Gw2/PDDD2hu\nbkZ9fT06tusGB2dHo82J6trTSDXb0DFVD32cE93aaWG11+Om4r/gvR/noF2iHtlJJhypacCs4dn4\nbd9E8IzB7mR4+ft6LF3yMTZt2Iy33nnDq9cwVJWJ5bwZ3fbUUzEQxZjk6ywjPZh6onRBLxz3wvRU\nAGIBl0ifrtqaUNyU8GVKdihDq/ScByCigqknvoRWIaTFxcUptgtqSzC1Wq1ile/ExESvbvCGYwRV\njgIrISSYKKDGsJycHHTt2hXr1q3D888/j6qqKnTr1g0pKSlITk7GFVdcgXHjxrn0NJW3zojmC3Ql\n0nWmSqOG/q75UwpRNpsNY+97EBeqLUg2p2Hv8S2IYwb01BTiQqMTj68+hlsHJuHweQf6dzTipv56\nzF+9FD/u1cKoc4BnWjh4K05f5FB12oqiXAPmrDyPjYeaodVokWzMwsb1m3DkyBF07drV7bbL1xgH\nszKxt3wZ+fNnXau/U5kjXWtrbCN5umpr5L0sQ33O82ZKdrBmEwjHvdBzM1qCqSfS0CoUumKMwWAw\nIC4uDjzPi/sEgLivha/7GkyTkpJUPfPIXSCmyr6EkGChKr4EwKUPmp9//hnjxo1D165dkZaWhj17\n9mD37t3YuHEjMjIyxIsjoQ2JUJ03VsjXGvo7YixcYMr7/CmFqM8//xzvvLIctwz+C5otFhw4thPL\nN74Ge3MNemdrkWLWonuWAb8fkIzSLka8s/Einv72PAw6DQo6mjDj95lIMMbhu32NeHz1OXA8w/AB\nSbijLBU/H+Tw4vd1MCZk4bOVy9CrVy/F5xoN1VmlFVWlU4XlF/TyEBXMqcxqJx0t9/W4l09XFfZ7\npIRW+XEfyD62wRDIvqGxGEylhNkYQuErpboCwvEtvEdaC5rCZ4jVahV7grflM7SpqQlxcXEhq6Lb\n0NAgvvcFPM8Hvad5hKMdQ4gfYiddELfOnj2LqVOn4vz583jrrbeQn58vfk0oBORwOMS1UMLdVGFK\nX1tasUQSeXXaQI0aSi8aWysOVF1djbT4LJw6dQq1NRfhtBvQ3GhFTrIW1/VJw8Fzjag6Y0fPbBt2\nnrRgyboavDSqA97aUIcLTU4s/voCRg1KwfV9EvFZZT06pOixdn8TxlyZjrsHJ+CLHTVoNJnRrVs3\nl22XrjeL9FFDpWnVntb8CaMnwsVYLE1j96UAkDuhqCAcDNL3fCRVJW5tpFWpB7TSTQIKpv8NpkrH\nvXQqr06ng1arFdvrKBECrPAZkpycHBHHk4CKJBFCQo0CagzjOA4vv/wy3n33XUydOhXXX3+9ywes\nRqNpEc6EO6attWKRh9ZIvLgJ15ROpeJAgwcPxrtvLIPWkoQOafnYsG8l0hOy0Wg7CJ63Y8cJGww6\nhoWrz8Jq59G/oxG/HLnUO2/csAzEaTV4Ye0F9OtgRGGuGR1S4rDhUDMmfHQa44ZlwMbxuOLysha/\nUz6VOVovUpVClDCdVVrsSrjADMa6VjUJ9jrLQFUQDgZ5u5xoKPrm6xR4gcFggMFgiLp1xO74Gkzl\na0yVQpw0mArthwLxGUJVfAkh0S6yP3lJmzDGsG7dOkybNg033XQTvvzySxiNRpfHCW1jlEbOvBkZ\nkV9kytdEqfmiR1qZOJxTOn/88Ues+XYdzGYjcrpkYNWPbyBOw9A9E7i9JB2/HNbj3U21WPrXXDy7\npgYjilPA8QwrdjRg6caLeGdMLrKSdLhocWJgJzPWH2zCnOFZGPN2NcZfm4EUcxxmf3EWZpMB1Ud+\nBeDflM5I56mPr3xKtvT4ViqAFWn7LJzFn3ytIBzo/S0EU2EULBqCqSfSNZY6nU68GQVAXFvsdDrR\n1NQUcf2IfSVdW200GmE2m30Kpkp4nofFYhHPI5E2YkoIIeEWvZ/ARNGJEycwadIk8DyPt99+Gzk5\nOS6PkQYUYZ2pNxcj3lxkqr14SqDXmfrjm6+/wQuLXsPA/KE40VCDn37cALvNgS4ZRjw7sgQ8fwp/\nGdwR975zAit2NaKwkwkPXJWOiuNWdEjRY8Gqs3BwDDmpemQn61DX7ER1HYe/L63G365Iwx+Lk6GL\n0yIrWY/5X9YgJS0DVqvVpQASx3H45ZdfYLFYMHDgQKSnp4dlfwSTN+HM05Rs6UwC4fiWX9Cr4fhW\nIh81VMt0VqXqqoB3+9vbECWfwm42m1X7OgWDdG21UlVawHV/C4+P9NAqD6ZK/Yt9CaZCqJfe4EpJ\nSQnKTR61jKBSkSRCSLBQQI0RNpsNTz/9NL744gvMmjULV1xxheKHcaArtLq7yHTXzzJYrUFaI19n\nqobKxB+8twxXDrgFnbLz8dJ/5iArsQs65HZFTf23uNh0ALo4DTqmGpBq1uLDrXXITTWgwerEdX0S\nUdbFjOxkPR54vxqTftsO5xudWLWrAYY4hrMNPACG/WdsaLAy2Bw8TtRymHv//8LpdLbYdpvNhvvH\n3IW6Y7vRLlmPWeeAfy/9CD169AjrvgmUQIQz4cLV3fHtqdVQOMOQPJxFyqihN/u7tZtgsV4AyJtg\nKlDa30rr5Nt6kyDUhOcbqGAKQDz2mpqaYDKZghZMBRqNRqwPESo0xZcQEkrqvxohfmGMYdWqVZg7\ndy5GjhyJ1atXu0xXFS7WLBYL4uLiQhLOWpsiLC3mEcy79WpvHWJ32LD65w9x7NRhDOh0Bc5ePA4b\nB+yqtqCksxkfbanF9pNW3HdlOnpmG/HN3ga8vaEOPbIMGNYrAdUXOXy9txENFiesdh7dO5pw0WLB\ni2tr8UlFA7KSdNh61ILu/UvRq1cvOJ1ONDY2ivv5xRdfhPZsJT4Zmw+T0YD3N5xB+cwpeOv9T8K9\na/wmncIe6HAWjFZDgSSvShzp4czd/lYqfiXdTuk6y1jgSzD1RGmdvLubBPJZBOG6KROsYGqxWOBw\nOMTziNJymUhHa1AJIaFGATWKHThwABMnTkR6ejo+/PBDZGZmujxGutbSbDaHtX2G0hRhoGVrEE+j\nI0oVWj1RyzpTdwqLB2DxE4+jU3pvOJ0crPZG8M5jGHtVOt5YfwbvbKxDvdWJy7vH49aCJHy7vwlO\nJ8OafU3gnDzGXJGOtzbU4dczdhR1MiHJZMeRC3YwpkVpFzNeHp0DXRzwWWU9nvy6Av/vtt/jy+9+\nQFxcHJxOJ95/7z3866XFeHhoAhrqLsBhjseg/CS8vPG4WDwoEkON/HUPVThra3EgaeExf5+ndNv9\nCSiRQB5ahXDmdDrF97pwPhHOHWqoIBwM0nCm1MM2EHy5SRDKmQTC6y683wMRTIWf6XA4xJ/Z1NQU\nsvdSOKb4hnrElhAS2yigRqHGxkYsXLgQP/30E8rLy1FcXKz4gSx8wIZ7rWVr3LUG8WY0SuliQ03r\nTN1Zu3Ytnly4CO1TuiPJlIGO6d0wuNdwfLd9H/aesiHZqEVhJxO6ZBjw6xkrnlt7AZ3S9Lj3ynSk\nJ+jwxY56HF5+BicvOvDn36Th271N6N3BjHYJGizbVo/SLmZotUBGog43DUjCK+tqUXfyAEaPHI3/\nfPgfTJ0yDR+//zoeuiYdX+1qwD1D0tDc1IQ3frahT79BsFgsAblJEEpqLP7kzbrtQKz7k6+3U8O2\nh4p025XCmfxcItwAA0JbQTgY5MFUKZwFkzehVV4BPhChVfgdrY0WtyWYWiwWcBwHk8mEhIQEj1V8\noxXHceJSIEIICQYKqFGE53ksW7YMTz31FMaMGYMVK1a4jEZK19yFulJnIPnaOkG4mBem9KplnakS\nnucxbfIM9Mwug15nQrwxCTzLxr6TG1HXXIsdJy1otjkx6XftYIjT4oH3qnGmnsPsm7Ow55QNp+s5\nxBu0yE7Wo/oih6qzdvTNMeF4DYc1+5owMNeEb/Y14p4hqahp5PDBlovomxOPc40GrFm7Fv9+/d/Y\nuK4SWclpGHtNDzTbD2DI4wfh4IEu3fvh3RefQlJSkqqmrLa2P/3t5xlKnooDKa3783RhLw/loQ4o\n4SRtG+Jp28NdQTgYwh1MPfFl+ruvoVW+tri1YCq8z3wNpomJiWHdn+EqkiRcOwjvqVgJ5ISQ0KOA\nGiV2796NCRMmoHv37vjss8+Qmprq8hhpK4FIKYjiC08FmYTiT8ClC0zhAkhpbVSoLzwOHz6Mqqoq\nJCUloaysDFarFRdrG9C/22+xoWoVenUowf4Ta3BtnwSMHWrEsRpgxY56PPrJGRR0NOHuwSmY+flZ\nbDtmwRNfncffrkzHny5LwfKKBiSatLjzsnT0yDLg9Z8uYMvRJnA8Q3WtA5c9fhCJRi0SDDrcXpqJ\n1362w2gwYfeuPejZoQS7DpzB17tr8Mj1XTGkRwJmfW3Ha2+9L1bxbctNglBe2Kux8JU/Wlv3Jy82\nJkzL0+v1qltbHUzyUK7UNqQ13lYQVltFW28q06qRN9PfW7sBFoxgKrzGQtD3FEyjdQRVOMc4HI4W\nAZ0q+BJCgim6EkqU+vXXXzFnzhwUFhaKf7KzswEAdXV1mDNnDnbu3ImFCxeiX79+Lt8vHUEK1voj\ntZKvsRXWG7Y2rS9UPVs3btiAT/69GIO6GrG31oH16/qif9FvcLG+Ds32RpR2uw4/7/0cOal23HVZ\nKmqaOPx80I7/vTYDndP02HrMinc21sHhZHj8y3Po3cGEG/olws4xDOxkxmULD6JDahaMBgO6ZDTC\nEKfBlBsycVtRMtbsbcRd/z6BCw0M7/5iREpSJ7TvyqN/QX8s27oaNw5+GAu+ehEzPzsOB4Dn//kW\ncnNzPW5PIEf/2kqtbVOCQT4aJUzdt9vt4mvA8zyampoAwCVEqXH0r61CMVLuSwVh+XkkmOssIzWY\neuJNaBVGiQU6nQ4Gg8FlXXlbgqmwjEENI6ZyoQjDwnnUYrGAMQatVovk5GRxP0RjGCeEqAcF1AiQ\nnp6OYcOGYfv27Vi5ciW2b98Oo9GIrKwsZGdno6SkBPPmzUPPnj1bfJ98BEntUxsDqbU1tt6u/Qv2\nxeayd17FhN/noXNWMhhjePKT3XhiwQ9o///ZO+8wN8pr/39Go15W24vbuldsY2N6J6EklEsPSUho\nCXBJQsoNJLkhHdK5BAgJIUAIvfeOKQaMccW9rbt3vb1pJY00RfP7w79RxrJ2V1u0K63n8zx+7GfX\nu5p5NaM53/ec8z2Fk1i86SUULY6eaMVX4iLgFli1R2HOGDdHT/SiqDqXHRnk2kfD3Pe1UVz5r1oK\nvXZCUoKErlPfqaElYPnOOg4fW8gzKzuw2+D4yT6icoJ51R4OG+Oh0zuTcCjMrPnj+dPtf8Dr9fLR\noo95e82jeAuqcBUX8ujjD1NdXd3v8+wp+2cEnAMtEc7XsSmDQbo5rrma2R5sUoXpUGfKh9McaCQK\n054wPhuMa1RVVQRBwOVyJSsGjM0p2L8hY4jW3oSp2c3e2NDsS6/2UGdQs/VaxmeJ4XDu8XgAkp+r\nQ3EMFhYWFodG9JbnlJSUcPXVVwP7HwjLly/nu9/9LuPGjWP8+PFs3LiRZ555hocffpjJkycngx7D\n+n4kZ5BSGUhZZ08lwunKKNM5f/alRFjXdaLhLqqKxwOQSGgUu3V27tyJiyK0RCNlfoEvLShmc4PM\na+vCaAmdDimBqum4HQIlfpFSn52gW8RmE0joOk8u72BKuYv7PmqjusjOw0va+MOb+8uBZU1nW1Oc\n8SVOOiWVvR0Jnnj070yfPv2AY3vwoftZv349siwza9YsvF5vZm9AHzAC+9Q1STdqCHru/RtpY1My\nJdNscW8lq6kO2UOZ/esvxvUhy3LOlXD3p88ytWqjJw41YWpg3oQC0t7rxmaMcU0ritJjmXdqprCv\nwnQ4yMaxGa0w5pFrxtoqijLor2dhYWHRE0IvO2DW9lgOUVtby3XXXYcgCNx2221MmjTpgEDG/PA2\nduuNuX8jubTPwAjUjV3fbIryVCFlBJ3QNxOVu27/HWXhNcwbLbBi7Xb+vSTMZzWt+FwKMyttqDpU\nBOyMK3ag6zpvbgwzptDB7DFuxpc4eX1dFx1RjdaISkWBg7suq+L5VSHaohqf7Ylyy9nlFPvs/PSF\nRop8DjqjCjtaFI4c72FrU5wOxcPG7bU5fy2Y19i87obxla7rOJ1OXC5XzoiUbJKaLTYC9cH63ebs\nn3mtzdd0JkIqG5h7yu12O263O2/f81TRavzprorA6K891NyYDREZj8eTGdN0wtT429yDHY1GCQaD\n3f5Ow5fBGLPW3/U0BG42NvNS0TSNrq6utF4TfcWoNjKME9ONXFNVlUgkcsA6JhIJ7HZ7zo1nyyFG\n/o1pYZFFrAxqnqDrOq2trUiSxLPPPguALMsHZOyMXXkjUDf3WhpBULrSvuEeoD5QhmOuZboSYej7\nzNaLv3wFV37lS/yloQ6HPUA0UUhCb8YpqjRHRKoKHESVBLtbZaqCdnQdrjuxkA0NCne+2wIInDM7\nwPYWmfV1MTqiGl8/toh7F7Wyu03lR8834RSh2CfitOvUd6rYBIgpCaJxnYICH7FYLFnGlauk9v4Z\ngbqiKMn32wja+pORyieynS3uS/ZvKB2bU8uYR0JlSF+NxmB/2arb7c77c8+EVGHaXcbU+Du1xzTd\nBrxxHRlZ2IEK03zF+Aw1twB1t8k1Ug2gLCwschdLoOYJRtA3fvz4A4J0swA1xqcAyZIcI2B0OBzd\nlq2mlpsNpGx1KMnFWa7dzWztrtfypRdfZvKoEympnkhl0Rj++dptFAcq0bTdnDengB+fVcp7m8P8\n5d1WFm+P4rbDDU/UAwKiANecWEyZ38758wI8+HE7P3y2AQFoDGn8+8oxzK/28FFNhG89Xs+4Igce\np43rTy5E1nRW7YkR9BVgs9m4+YffZ9XSxYwaPYYf/ew3ac22cgFz9swIqlKrCHqbaWkWsMN9vfQF\n80ZMdy6l2aInw5rUzS8Y3FEsZmE60k2v4MBybKMKBkhWB5j7LEdCD3E6UoWpURGTrpQX/nN9pj6r\nzMIqXW/lYN5DRsZ2KBiIYDQLU6fTSUFBwYi+nywsLPITS6DmEZIkHZDpMgJzURRRVTVZcmM8uI0A\n0sB4eJuDn+6yJOb+v9QgyAg+hysISmcIk8sZstReS0VRWLx4MR0dHXz4wceIHeWotnpiXSrxeJSg\n20tCEzl1mo8XVnexsT7OVccX8e6mCJ/ujHLuXD8+l43HlnawoS7OlAqdV9Z2cdxED3PGuHhhdZjZ\nY+ycMs2Pru/vV3WI8MaGLv5x+WiOmuAl4BZpDGmEqo/jpGOPoFRv4hdfKGNr4zouveBs3nz3I8aO\nHTuMq3YgqfN7e+q1zMT8Kl1wP9zXdXekzvPMhY0YOHCtuxvFktrX2peKjUPJjTkV83vekyNxd+7Y\n+Vod019havy7u98Zj8eRJOmg3spDCWODS5ZlnE4nwWCwT/4MVgbVwsJiKLEEah4Ri8Vwu91pv2cu\nb0rFEKlmwWq25jdEqxEIOJ3Og37eHASl9qQN5RxRw2UxX82fVFXlD7/7M827w9gTHpZ9shKfA0YV\nVZAgiCRHaO3qpMibYE1tjCU7ovz1sir2dqgE3Tbiqs4ZM/3sbFU4bbqfH5xegtshcmpznG8/UU91\niYOQpBGVEzSEFJ5a3sk7G8N87ZhCPtke5ekVnRwxzkNLWEXVBTZt2kjtnt0s/M1UygJ2Tpqis6q2\nkXfeeSdpzDWcDIYzbybmV+mu69Ry7KHGPM8zW2NTskFvo1h6c7UFDhAph5Ibc6bC1KC32bjZdBAe\nTFKz5MZIsNT/0x9havzt8/my2i85lCKuL69lfH4alUZ9Eaa9vZ4lWi0sLLLFofHUzzNUVeW0005j\n5syZybmns2fPRpIkXC5Xn3+f+WFkFnRGibC5VM9sMGEWrj1lW1NLKQezvM/A7Fppnmeab6xbt44d\nG/cxf+IpbNy0kYmlhRw3oZXyQBur9uxiV6OIPxDnzFkBnl3Rgc1m4431Xby4ugtVhy5J446FLcwa\n5Sah62yqlzmy2o3fZSPoESgP2FlfF6PQY+OUP21HSQi89K1xFHpEvnVqCWfduYt/f9rBhzUSoViC\nuLYRhyjQFdUoC9hxiAJhSc0J44tDtddyuMemZIO+rLWB0ZZgGGHl4/2eKX0Vpj2RbQfhwSRVmKbb\njOivMDXcaAEKCgqyeBbDR0/3hbGZq6oqbrcbr9c7qO/tSL4fLSwshh9LoOYot956K2vWrGHlypX8\n61//YsOGDQQCAaZOncqtt97KYYcdxnHHHUdZWVm/HxS9ZVuNwDzTbGtqKWUmJkG9kdpnmu/jFNau\nXcu+3U3Yw2vZUbeZuaOdTK8sREkILBgbY+mOVr4428dv/qucSDzBpfft4S/vtfHHiyoYW+Tgr++3\n8cqaEJICx0708NSKTjbVx9ASOjbBhtsucNMZpTzyaSdOOyR0KPPb0XQQbQKlfpG/LGzh+lMq+OqJ\nM6hr2svtb2mcc89u/veLZaytjbF4l8YdZ589bGtk3owwHCWHu9dyKGaIpvbXjgRh2hPmzS9BSD/T\n0tzXmk5I5fNnAQxdlrynHmLzJuNQbcb0VZianxc9CVOzG61x/3R2dg7acffEUGdQ05E6y9XtduP3\n+wftvUsVxFYG1cLCIltYAjUHsdvtnHTSSZx00knJr2maxh133MHevXuRZZmHH34YURQ59dRTDwhi\njV3xwRCtvWVbDVL7WlN70noyCerOuCa153AkBOtdXV08/cRz7KrfTSwq47J7qW2rpyUUISprqIkE\ngqAypsjO1sY4NkFgQqkLpygwpdyFaIMrjyvktXVdKFqC7c0yJ0z2cO+H7YgCFPvt3HFpFevrY7zw\nWRf3f30UNz3XyMtrujh2oofXdkbZ3apwzEQfc0Y7icWjiILGqdN8LNsZ5acvNhKSEtxxzz8pLi4e\n8vUxZw5zaTOiuxLh7vr/+lpKORLdaTMlkyx5pj3E+SRac6F8uy+bMTA4JmOZGF6lE6a93T+GME11\nozX/rpFMtme55sM9ZWFhMbKwBGqeIIoiRUVFOJ1OrrrqquTXjWBCUZRkgG/MPjUHEsZDPpvZ1lQH\nQ+M1u+uTSg06zQG+cQ5DnUHLFpFIhJ//9NfE2xwU+0ZRFhiLQ3SyemeUEydDkddOa1TF7xL5uCZK\necBOWUAkpiSIxPePhfE6bYRiCaqCdr7/+RL+saiNhz7ppCzgoNwvsqkhzuIdUew2gWK/SMAjcudl\nlfzm1Wb++FYzakLn9xdUsGpPjLW1USqCraDrNIRU/nRxFUdN8HD5A7Us+WQxl1122ZCtTb5mDnvq\n/+tpM8YsCgyBdqiZAJmFaW+OxJn2EPd3g2AoyfXy7XRrPRgbBNkQpsZaGsI0F9xoh1IMG1UGxr0E\n2R2ZY2wcWxlUCwuLocASqHlEunmVRr+RpmmIoojT6Twgy2mMJDAyoKmiNZvZVnNPq/l4zcLVnG01\nhqprmpb8mnksQLoS4VwJPHtj6dKlEPFSFhjFvlgtbeEGfK4AdptARFb59mllTCh28MDidu5+r5Vd\nbQoTSxxEZI1drSq/e6OFyeVOPq6JcP1JxVx8RCFLdkjsaJa55YtlOO0Cq/bE+O3rzfzs7DJ2tyo8\nvaKT8+YWcMxEL8t3SXicAtuaZBAEnv8sxMZ6ma2NMaZVOPnh50t4ZX2YDimBDZ1oNJr1AD/f3Jgz\nIdWxGQ52xzZKKQ3Mw+4PhV7LwSrf7q7XMt0GQbrxWUN5reW6MO2JvlYQpK6xoijJecWDJUzN8zsz\nEaZDcV8N5X1rrJcx93koZrlaTr4WFhZDiSVQ8whJkjIye0hXugXpgzcjU5lOtA5UuKaKVuNvo9fM\nfLyqqibL3YzRAubjTjdv0byDn+vjFCKRCBu3rCIe2o4cb2H6aD+iWMCmvTEmlXqYPcpNh6Ry2Gg3\nY4sdeBxQGrAz3e+i2CdTGbSzplaiJawxd6ybzqjGvg6FqRVOqgoduOwCWgKau1Rufq4RLaHz4OJ2\nbn+7BZdd4MtHBtnbofDXD9q4ZEEhT147jrp2lS/ft4suSePYP+5kVKEd0eHh3PMvTG56ZMMkyDxK\nwhj7MNyZj2xiXjvjOrfZbMley3QBfrpZxPmMWZhms3y7tw2C7mbjZmvTyyxM86k6IBN6cxBONc6D\n/Zlz43Pb+P/m35WpMM10fudQ3zfZFnDmkTm6ruP1evtlnGhhYWGR61gCNY+QJKnbMTOZ0FPwli7b\nmipaByPbmhqcGa+lqmqy/8zYcTf/TLreVrNoTR2nkC7AH64gX1VVXnnxNeTOrcwb62VmRREnT/Ox\nrUkmJntYWxfn050RorKOx7Hf6CgcT9AYUtncEOe28ysYXeigK67x6Ked/GVhK7NGu/l0R5TtzXYu\nXRBkbJGDpbui+F02Zla5WLgpwgXzChAE+KgmgqTqOEQBmwC3nF2OaBNZvUfiiAk+jpvk58VVHexs\n1bj+uzdy2mmnHXD8mWwQZNoDaC7tTDdKYqTSU+awp3EsueK22l9SZ7gOR1/xcBhfjWRh2hPGkTkE\nnAAAIABJREFUZ7ksy9jt9qRzbDoHYVEUk+vSmzCVJCk5vzMXSnnTkc3r2lzObLfbCQQCRCKRIV0H\nK4NqYWExlBwa0eEIobc5qP0JgLrLtppFqznbmipYByvbajgCG8GMEUCaZ7ca/y/VlKmncQpmk49s\njL/JhPXr17Nl7S7mjqsmGmukQxJYXxelwO1m7lgHf3u/kzveaWVcsYM9bQqhWIJin0hNUxwBKPTY\nkBSdioADuwhvbuhiyY4IDnG/GP3W4/Voif2Bg8susGxXlOtPLqIhpCHJCY6e4GHTvhiFPjuCYOP4\nP+xk9oQSlm9t4qnrJnLy9ADfO72cOb/ewWGz5yTfZ/OaG2vWV5Mg49+GkclwOPMOJ30VaJmMCEmX\nARyIcU02yAUToJ7IlvHVoSpMDcdlQ5imVkUY16b5ug6Hwz0KLGNTxxCm/ZnfCel7J7PFYAu4/pQz\nZxNLoFpYWAwVlkDNIwaaQe0L6cbApMu2po6A6G+21RxA9GbIZPzb3MdnLitOHX9j/LxR3tybmBrs\ngDIejxOXNNbubqK6qBOn3UZjyMYHW5oJx3QEdHY0y+xsVYjGE3zntGKOneglIif489st3PhUPTec\nUkI4nuD5VSFuv6SSvW0Ke9tVbjqjlHZJQ9N0Lr1vDzYBErrAMytD3HhaCVVBB396u5nN9XG+fVoJ\nn58R4M732gjFBdxuD9/49x7OmBVg4cYwJcWTePCeJ1ixbBW3/Px/u12HRCLBzp07URSF6urqA/qi\ne5ptaRYDxtrnkmgZTFJ7Dgc617K3DKDZZCx1E2YoS4TzudcSei9bTZfVNtbXKN8+1IRpb4I8XY9p\nT0LHLExdLle/helQM5j3WKo4TydMhzqj2Z351Ujvm7ewsBgeLIGaR6QzSTIYKhOI7gLlTLOt/Q00\nejNkyiTbmpoBTCemzJmpVPHa337L1avXUN+6h+piN8dNclIWsKFqCdqjGntbFb44p4CL5xWwtSnO\nPR+0M7XChaTsL/c9cYqPfyxq5fa3Wyj127lofgFzx3jQdYFOKYGagOpiJ5sb4oDADScX8caGCEdU\ne7jq+CKW7ogi2sDpEOiUNG48LcCMKhc3PtXA7ReW8ce3mllbKxHV/Nx06R+w2UReWvwgn3zyCSec\ncMJB56MoCnfe8Vd2bNqL0+HG5lH5yS03UVZWllxzY72Ma8LpdOJ0Og+4PlJdQHO9hzhThiqDlqmz\nrbEpk656YDCPayRnDnvKahvlrMbnhuGsKstyTrQXZIv+CtOeHGA1TUOSpOSYqcESpkMp5Ab6Otla\ng8EgdR2tkl8LC4tsYgnUPGIoM6iZYg6UzXSXbTWX5g5UtEL/sq3GMafLtqZmpnoq8+vp2M0Otc89\n/RwFnkKi8XrcDjfzxpYQlcMs2hpB02XmjXXjdtoo8Iioms7uVoXZY9yEpAQfb4vy5aP2O/bqQETW\n0XQ4eoKHh5a047K3c0S1h79/2Mb0ShclAQdnzvLTKSVYsUvihsfr+e0F5ZQHHDywuI2fv9zI5ccU\noqgaZ8/288DHrTz1zbGc+pcm9jRuZ9KYGZQEqmhtbQVg5cqV/PbWPxDqDHHmF05n9pzDqN/WwdnH\nX47NZmPtlmU89siTfO8H3znovFOdedNtEJgzU+Ye4nSiNZeD/FwZlZNJifBgGl/lynkPNcZ1bgg0\nw/Cquxmiw9VeMNgMhjBNxehLVxQFt9ud7FvNNwbyfvZnDSyBaGFhMZKxBGoekYsCtTsyzbbKspx8\nyBqurkORbTWOwcg8m/+k67fMZL6lccxGECcIAp2dnXS0RJlTfTL1ratZsXszs0Z18umOCBv2Sdht\nAi1dGg4RnlsV4qL5Bby1IUxNk8zKPRKNnTLTypycMzvAij1Rnl/VSXtEZWyxk2hcZ3VtjJfWdFHm\nF3GIML3CyfyxHi68dw972xXOOszPcZN8qAmdX5xTzkl/2smK3TFCksa/Pm5lWoULmwCxeBSAjq5W\naltrmDLlcrZu3cpXL/sax0+5iAmlpTz76EusmLaSeRNOS67x6PLxrKtblHTm7ctMz0zElDnIH6ys\n9mCSKshz0ZE4GyZBZmE6UkYEZYK51zKdQOuurzXdhlc+VQ9kQ5jC/jEpqqridrvx+XxZc3bO1Qyq\n8ZmpqioejydrazAYWILYwsJiKLEEah4Rj8eHtcR3oHSXbTXmn4qimAwAjfE33Zky9Yfesq2G4DSP\nwTFer7vetJ4yJgCPP/4EDs2PXbDTIUVZuCnEqt1h/C4bJ03xc+IUL//6pIPWqMbeNoX/PrmYL87x\ns6NJwSnCG+tVluySmBFPsKNZQdMSjCmy8+yqDsIxnboOUDQd9AQFHpG/LWrHJsCJk728s7GLIyf6\n0BI6DptATUOciJzg2IkeEgk3v3q1iT9cWMVPXmikviPOW8sfx+f38a0br2PmzJncddddTK04ijkT\njgPgTPfXeezj31HqH8v0ibNx2J1s2b2WCdOrCYfDCIKA1+sdkDNvJmKqP+Y1g01/BHku0V+TIGMD\nxhAq+Xbe/aU3YdoTxmdHX6oHhvp67o5sZUwlSQJIbm7k+rMrEzI9B/NnRyKRwO1292sNhqMH1RKo\nFhYWQ4UlUPMISZJG5Myz7sZtdJdtTRWsAwniesu29lQibA7GjOM0HGoTiQSvvvIabzz3PlI8SmtX\nPUJiH8dMDGIXZOKazg2nFlPgFlET8PAn7bRFVR5b2sH8cR4+N93H85+FGF3k5I5LKvj7onZsgoDL\nKbJ0p8SCcR4SCBw/yYuiJnh6ZYgtDXE2NcjYbQJepw0lAZ/URLjt9WYmlDr550dtaAmdD7dGOH2m\nH7fDxr+XtDO/2ofXaefue+9g5syZSYHpcrmQVSl53nFl/xzeo0+Zw0tv/wtBsDF2fCXnX3g5brc7\nOSZosMmk3zK1jHygpavdYYzRiMVigyLIc42eTILi8TjxeDz5dfNMy3wpxe4r2coUD3Updl9JdWEe\nDGFqiDLDydsoZ81Hd93eXqs7QyFFUZLi3O1243Q6B2SelguCMR82xy0sLPKPkRNZHQJIkoTX6037\nvVx4UPWXdA+4THtbu8u2DjSIM/dNGhhi1ewIbJR4iqKIy+U6wKHyH/f8EzUqEomHqGlYjdeRYFyR\nQGXQw8JNEbSETk1TnHHFDqZXuagPqSgabGyIc//H7YwtsjNnjJvnPuvC7bTxxo3VrK6NcetrzcRV\nndlj3FSXOLj97VZKfHZq2xX8bhuPXTOWyeUuXvwsxK2vNbFyd5R3NoY5cYqX5bskYorOg4vbKfDY\nuPTIQrY0aMQTdrq6ug4ImM8//3z+9td7eW/t0wQ9ZXy2eyE3/fR7fPXyL3PmWacjSRIVFRXJ/jsD\nWZZ55OFHWb1qLaWlJXzjuqsZPXp0v96HnugtyDeuj8Gab2me4ZpNQZ5LpMsU2+32btcZRka/5XCU\nMHdXPdDdOmej5N08FmkwhKmxoSNJ0kHZQkmShszcbyjoaQ1kWUaSJARBwOPx5OWYrVwRxBYWFocG\nlkDNI1RV7TFbk28PvP6QSW+r4ayZrWyrOWAzgkbDpVZVVQD+ds+9tNRKFPsq6RLX4nHEUTWBFXs0\nfnFOGUu2R/npi00sqHZjtwk8t6qTc+cGuXBeAZKSYGq5kw9rIizeJlHkE/nVuWVEZB2f08Y5c/zc\n/1E7o4sc/OT5BsaXOBlX4mBqhYOtjQplATudUY2TpvrQX4P2aILlP52EaBNo7lI4+c+7UDSd1i6V\nn73YjNdbxAlHXEx9ff0B51tRUcGbb7/OP+69j472Tv7wvVs59dRTCYfDFBQUUFZWlnYt7/i/O9m8\nYi9zJh1LY30dP/juzfzj/nsoLCzs17r3hXTXB2Q2Zqg74ytj5MOhNMPVEKZGL3Vqpri3dU7Xb5m6\n1rm4hqnCdLhLmDMpeU/nit1X0ZoqTNONRRqIMPV4PAPKFuYjxrUUi8Ww2WzJzZ3BWgNLMFpYWIxk\nLIGaRxxKD/e+kEm21QiYs5FtTQ0ia2tref/NTygPjEFTVnLzGUEOH+vg2VXtPLS4hese2UepX6Qr\nptHQqdDUpVLqd3DCZC9HT/RQ26ZS16GwrjZGVNaxiwKvrw/zhVkBdF1nS6OMrCV47NMOinx26jpU\nCr0iKxrjdMV12iMqowudrN8XQ9F0Cjw24qqOTdAZXeikKmjHKUJDl5/DZ53MuadczpvLHmXcuHEH\nnVtVVRW/+OXPk0E70GM2SVVVPlj4IZef/kMcdiejy8fTsqyO1atXc8opp/RrfQeDdMKzt5JKo3Rb\n0zRcLhder3fE34OpJcx9zRT31m9pLsXOJbfmXBOmPZFJyXumfdrZEKZGGauu6zkhTIdSyBmfGbIs\nE4vFktn3kdAGkG4dR/rnoYWFxfCR/5+aFkB+94Fk69jTOfKas61GIJcu2zqQkrm2tjb27N6DqDu5\n6HAHE8pECjwJxpeIuB02JpQ6kRSdYyd5qGuXqeuAWaNdvLUxzNETvHhcAgs3RfA4bRR6BS5bEOSF\n1V2sq4vhc9pojWgUe+3YBIF/XTkGmwC3vd6M22FjV2ucL923l2kVLtbUxrjl7DLufLeVx5d1cPH8\nIE8s29+HmkBkxsT5yHGFB1++jRNPPYZnHrqHx/5xO4cffTJfvfIbOByOPhsB2Ww2EARUVcFh3z++\nR1HjBwTSuUJ32SkjaFcUJXkNGEF8qvNqvt5zqRjCNB6PD3oJc3/cms1rnM0S4XwSpr3R3Tp35z5u\nfL87syvjszK13z4dqf2VmZSxjrQMoNHyEQqFcDgcBAKBrArT4TBJMs8Zh/xuLbKwsMhtLIGaJ1gP\ngsHBnH1IFzD3lG01B8s9BV6vvfIGAXcJxd4qWiLL6JJivN+k8OyqTj433U97VOXI8R7cdoGGzv3i\nuEvSmFHl5vrH6ghJCVojKidP8bKtWeaxZZ2Azqigh1On+Tmi2s27myM8tbyToyfs70m+7qRivv90\nPSdP8bF0V5SPava76o4vcfLIVWO47J97ue31ZqqLHXgcNoKlE/nFzbcRCnXy0vuPYuus4YfnTSXo\nc/Hv9xbx5GMOLrj4sj4bAdlsNi657AJef/Expo2ZT3PHPhwFGvPnzx/Q+zYUpM70DAQCyaxranbK\nCPrTZQHzbdyKubfW5XINSQlzpqWr2RrJkg/jgQYDQ7QaaJqGJElompZ8nzVNIxKJAP/pazWEbibC\n1MgWQmbCdKjJtpAzDKUMAzGfz3fAbO2RjhWbWFhYZANLoOYRPbkDWgyMTLKtiqIkd8lTAzfjvZFl\nmQ1rNjNz7AIiUYmPtoZAT9ApqcysdNES0agucTK90sVjyzopcImML3ViswlsbZRx2wU2dcioms7K\nPTGau1ROmuqnK64xrtjJpDInTruN9oiGrOoomo4gQGNIxWUXOGdugM2NcWQVXHb4+oO1TC534rIL\nBAWB7c0yXbEEY6pkHnz8r5z/xcuIxcKcPMfHqNL9JcTnHjmaO99fypcvv7JfWbSrrr6SMWNGs2bN\nOsaXTuPiiy/sdjxSLpCJIU4mWUBDTEFuzmxNxciY5kpvbSalq92NZDFvFPTEoSJMUzG/1y6X66B5\nm8a1rKpq8k+q+ZkZs/GPzWbrlzDN9wyq0ZtueBAEg0FCodCQXU/pMprZfr18fr8sLCzyC0ugjiBy\nLQDOlFwtT+4t22oEzLquE4lEaG9v5x9/u5+aLTuYWFyExxEggZsl21oJegQiMQ2P08aZs/ys3B1j\neqWTaFxnRpWLsw7zs3pvjJiis6khThyNyqCDYp+dn32xlJV7Y9z1bhstYQ3QeeTTDgB+9UoTTrvA\n48s6uen0Ep5a3klbWOWX51Vw/uEFdERVvnTfXi4/Jsif32nj6uOLcDts/H3RDlasCrFuyyqOOv4I\nGjpbURQVHZ361gjBotJ+l+UKgsAZZ57BGWeeMRhvQ9YYqFjpTxYwF4yCzKZP+dBbO5CRLObeY+O9\nzse5tf0lVZime6+Na9ZmsyVFqVE9kkqqMB1s459sMNjCyqg4UBQFl8tFMBhMXmMjXcSN5HOzsLDI\nLSyBmifkqog7FDEHwUbmzOPxcNtvfk+kTmRC5WHU1++gK7qSL852sLdVZG+7ipoQqSq0U9+p8tle\niR+fVcbH26I0hlQicoJX1nYRcNsQbQJBr52vHBngd2+2ctNzjQiCwNmH+Xh0aQeSomND56xZBSzf\nFWVXi4LXBc9/FmJPm4ykJLhsQRDRJiBg55Rpfv6+qJ3vnFrC/5xRiqrpTKlw8tDiTra3dDJj5lfY\nXredv762ab/Z0t4EX//WdYTD4UEtq8wVzA61RpA9WGIl0yyg2SgonWjNxjqbDXHyQZj2RCabA+YR\nQ8bPOJ1OHA5H3pVh95XehKmxJul6TLsTpqmOtLnYV55NDFdiVVVxu90HZaGHmuHoQTVjXGOH2nVg\nYWExNFgCNU/obkcbLPGaC2zdupX33v6Q02ZdRoFdZF3n63z1aCfzx3q4e3eUH5xeiprYP390UpkL\nmwBbGuK0hFVW75V4f0uYrxxVyIlTfKyvi/F/C1v47RutnDnLzyULggRcNv73hUZkDW48rYSorLOu\nLs6R4z2UBews2hqhpUsCBMYUu1i5W+JzM/xE5ASLtkQIxRJUBu1o/78kuCJgx+MU+NV5pby69CP+\nfv/DrFmzhng8zo9nzqSiomLYBdVgk+pQ6/F4hsxds7csYDZniRo9cj05tY4EUisejPU0l+1rmkY0\nGu3V3TZfMa5vY+ZoX4RpOgxhKknSoDvS5kO2sac5rukYasfgoRao5rnIxkZXrr+HFhYW+YklUPME\nSZJwuVzDfRhZIZ8fcEag9/orb1LkL6Ut1Eyhv5gir4zb4WFNbZTzDy/ghCk+FFWnyGPjZy83EZUT\nbKyPc+ZMP2fOCnDPB62MLXLQ3KXy7yUdXHJEkLc2hDl8jJvmLo2uWIJ549yoiQQ7WxUCbhvjS+18\nsl3i2hOLmFTmZFerQktY5bw5AX74bAOTy51sbZRpj6iUB2z8/o1mppQ7cTsEbn6ugTNm+PbPVvXv\nd5s84ogjksGqEcBnIqgGMn9xqDAbAQ2mQ+1ASJcFhIHNbE39PYYb8UgWpmbMZds9lfL25G6bTrTm\n8roZ59KT0VVfhKlx/0uSlByVkm1H2mzSHyGXi+NyhhNDqKuqSiQSSQr1fH52W1hY5Db5+cQ5BJEk\nKaeNZgZKvj/4FUWlqKiUFdveIaFCwK6yao9EeUBkYpmT7U1xCr0iS3ZKlPntfGG2H79L5P0tET4/\nw8VR4z1saYyzs0XmmhOKOHysm6aQSkzVEQBFTbCuLs6yXTEaQwkK3DYSOnxumo/ygIMpFS6eXtGJ\nxyHwyrowv7uggg37YqzeG8MuCigqfGlBAf/7QiNdMQ1F1XlzQxetKyKcebbIPf/3WyZOO4wzzjr7\noMDVCGjN2SmzMEp1P04nqAZj3mx/MPdb5oIRUCZ0N7M1E0Fl7rdUFAWHw9Hj3NqRQqow7c15OtXd\n1vgd5ms5WxntwSJ1NFB3wtRc4pypK6+iKAiCkNfCtD8M1JV4pGVQzethbG4Eg8EDzAEtLCwsssGh\n8+TJc2KxGG63O+33rBLf4UcQFWKtH/HF6U421YepaRQZHbSzu03hiWWdXDS/AFnTeXlNFzedUUp5\nwE5FgZ0Ct43msMpXjy7kxifrcTkEjp/swybAZUcV8qtXmvC7BLriCZyiwJqfTWZtXYw/vtlCfaeC\n1yVQXiBS16Gws0XmjJl+lrzbys3PN1IRELHbBAR0fnB6KcdP8vGzc8rZUB/n2ofrOHWaj4piH5tr\nP6G0dBq7F6/nieZmrvrm9QBJh0gjk5cqWs0llb0ZSRkOyDA0Drcjqd8SMhdUqqoe8P9FUUy2B+Tz\n+XdHX4VpT6Rz8obuM9rDZXrVH2FqZJG7Oz4j2270mBobG9lkqMRcJq+T2mObi+NyhpLU9TA+P8Ph\n8CG7JhYWFkOLJVDzhGg02q1AzXfyfRe2pqaGd557lG8eU4JD0FlQXcSjn4ZYsnMf44tFTpjs5YOt\nEbY3xfE6BcaXOKgMOtjbprC3XeGNdV00hzViSoKWLo3Hl3VQ5hdxOwW6Yhof1UiML3Xy4BVjSOiw\noNrL+FIHcTXBk8tDBD12GkIqNU0ymxpaKfWLTKt04neJbNjXSYnPxmvrwkyrcFFeYOdfi9upKLBz\n0oxKKgttXHx8IfcvbuaWK07hWw+8xVevuBqn05nMuqVm34xRO8bf5oDF/DOGaDVmAqaa2AykfLU7\nDqWyVkN42Gy25Jo6HI5kX1h3M1v7MpYlVzH3wg1UmPZGTxltY22Hokfb3EMNpC1V748wNWZ4OhwO\nCgoKkps7hwKpPbYDNX/K9wxq6vVg7jk2qgksLCwshgJLoOYJkiSN6Axqvh1/LBbjgw8WUV/byFtv\nvUXA4WXyqMlEpQitoUY8TpldbR7CUog97TIxRacrluCc2X7u/bCd8w8vYG+7wr2L2hhXZKc8YOfU\naT7W1kqs3B3lgcU2bILAmbP8eJ02GkIqcTWBrOnsbovzyXaJcEzDJsCd77Vw9fFF/OXSKm57vRmH\nXeCVNWEULcEjV4/lsOoi7l7YxLWP1mOzCYQknWtOGY8/UIDTKeMQbSR0HR1A6P29MIJ1c2+fIVbN\nWT2D1GxraoYqk/JVs6DqyWVUluVDsqw13Zicvo5l6W2Nc4GhFKY9kcnoG3OJ8ECqBrItTJ1OJwUF\nBcmfGUohMlQZ1NR5oeaMcb732A4GqTNdzdeDQT6YWllYWIwcDt1P5DyjpxJfi6FF0zQeeuBhIs0C\nFcWjkDsctIYFPty8m2J3Jx1RB9uaVA4bpXHh4cV0SgmW7pTY3RqnQ0pw0lQfe9pkVu2OURYQ0XSB\n35xXzowqNxFZ4/L7a7lwXgGKpoMA2lad9qjKb19vZlqli9V7Y5w4xcv0She/f7OZu84vZ9YoL6v2\nSmxrjhNXdYIeER2R7zzVwOUnOoioNmx2J9f/z8/pbO9i5Ycvs0AspKmliY8/2sWkCdXc+cpmjjzp\nrH5lENKJQSMoNAJ3Y+1Sf8YI2FOD/UzmiRrZQ1mWky6jh5IwzXSmZyZjWVLXOJdGDJlHAwmCMGzC\ntCcGe41Tz3mwhKkkST0KkaFiOK6ndBnjwTz/fMug9jTTdaiOwcLCwiIdufWEt+iWnjKoFkNLQ0MD\nDXvb+dyR/0VMlhhVPobPNn3Kfe83U+L34XIEsds0LjzcR1kgyrlzvMwd7eKXrzby2Z4Y4bhOVdDO\npHInVUEbCzdHmVm136E5HEswoczBT19s5IhqDzrQ0KkypcLFpQsK+OXLzdx7+SimV7mJKQlW75W4\n49025oyReHJ5O6omcPJUH+OKHbyytgtRgO11LVx7cjkTSh0s//BNbv/bQ7R96SJeefZxImII+2SF\nWEkR4ybP4POnn4kkSclSxd5MVXrCHOh0l201i1fIPNtqzgIaP2cIt3xxX+0rqdnDgc5vNa9zrs1s\nNR+LWaQN5WigwSDTNZZlOVmGbdw3Roa7N2GayT1qzpBlIkRGmujQdZ1IJJITwny4MWfk+zLTtTsx\nOtKuFQsLi9wgf570hzgj2SQp345f13UEBOJyjGdfuJtArIZzD4N1+7z4vAuYVDGHjXueR0m0ICBQ\n6LWxbl8Mu83GxEoHPrfA144JMq7Ywb+XdFLfoXLZ/XsJekTK/Xb2tCqMLnLgcQgcN8nHjEoXb27o\nYm+bSqFvvyuwTYASv51IXGdXi0xjSEXX4axZfh66an+v6qnTfHznyX1MKHbwWa3MJacvIPJRE2vX\nruXEE0/kmhu+d4ARSmqJrhE467p+kGDtb48o9C/bag70jeOy2WxJkZbOfTV1/I3ZSTifGOrsYV/L\nV7PhcJvvwrQ30q2xMbpJluXk/zEynqm92ZluHvVVmBqvOxQMRfbNbB7mdrv7nCHsK7mcQU0dndPb\nTNeefk++fYZaWFjkHyPniT9CaG9vZ968ecyZM4c5c+Ywe/Zs5syZQ1dXl5VBzRGqqqooHVXASwuf\noETdwkkzSmkOScwaHeCF1XuxCfPpiLRS0yihKHYeXary1sYI88d5CMc12sIqP36+kURCp7ZDoTIo\ncu7sAiqDdt7fEqE8YOecuQXc92Eb25tl1tbGGF3o4L6P2hBtAtc+uo/rTixmR0ucT7ZHQUjwt4tH\n8bV/1TKjykUknsAuCkytdCEpOs9+FqK6OMbd777BsYdP5li7/aCeLDhQBJoxi1YjiDacYfsaMHdH\nJtlWw0XVGJ9iCFOzEE11XzWPv0k1C0otrcy1oMuc6RhukZZJ+WpPpleZbgykE6b5uKnQF9JtQBjn\nbKyxcf0aZd29CQtN05Akqd+lm/mOqqpIkoSqqtjtdhwOB16vd7gPa1DJVKCaHXmNe6o/DsUj+R60\nsLDIPSyBmmMEg0Heeecd1q1bx9q1a3nqqae45ZZb2LNnDxMnTmTdunXMmjWL8847j/Hjx2Oz2awd\nzSFGFEWu/uYV/PmPf0Tp6EC0leN2eLAJIl6Xk5ZQHQ5biPnjCtnZovDQkhY8TgGnQ6CtPUF5wAGC\nRk1DjLiic9wkH1WFIlPKnZw42ceNT+3DYYPZY9z86rwKfvJCA08u78BlFyj1iVx/UhFra2OoCR3Q\nufG0Uu79sI3PT/fzxPJOzp0bIOgR+dmLjdhtAjeeVsIjn3bgtCV4dfFmIsJvuTMaYfqcI/ifH/+c\ngoKCA86vpqaG7du3EwwGOfLII5PjStKJk0yyrQMRGKlBtaqqJBIJHA5HUhwJgnBQb2um429ycdZl\nJqY4uUAm5atmh1uzyE3dGMik33KkYS7ZNkabdLcBYbhhq6qKLMvdrou5p9DtduP1evslTPO1bNNY\nT1VV8Xg8+P3+5HU1FORST6au68RisaQRlM/nG/A9ZZxfarm5hYWFxWAj9PLhYn3y5Ah33303bW1t\nFBYWsn79ei666CLmzp2bfFAIgoDD4RhQFmu4MHrqBmLvPxzU1NRw89UXcMncKkQUPtlwSTu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2XNxi14vH5Q4vz3tdcc5DrcVzZu3MjMBccxuno8ACd/4b9Y+PRD+Hy+ZFattraWZcuWoQNHHXkk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JpxUXs3t0lOU9PaRyOVRSKTlBYEZeHnMdDupNZjpDQdqDQbx6PYraWn54++1857vfJ69+BmKR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\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olPZxMNLyOLIpyFBGTSLqKhCkFkmSBjjIZkK9TSWm+Mqp0MFgEK1Wi9FoRKfTjfp+JiKo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\nALIgoAIAAJAFARUAAIAsCKg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"text": [ "<matplotlib.figure.Figure at 0x110999dd8>" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_PCA(X_train.values, y_train.values, classes, azimuth=-93, elevation=-7)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.62555992 0.04913095 0.0412155 ]\n", "Variability of the compressed dataset 0.7159\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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PSj3mRETUO9a8ayDLM9L8d+VwaL22eoBq9fqnRsIVq56j0TNl5Xl4kiTB5XJZ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\nkUiE1atX1/47kUjUbieR7MVu7MIcqZ4Glsry9nzdqSyzPTjQAibtPUDvW2Z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J5duquGvW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\nEEK0C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"text": [ "<matplotlib.figure.Figure at 0x11296d4e0>" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two big clusters that can be separated by an hyperplane. Each of these big clusters are compound of three smaller overlapping clusters. The first cluster contains activities involving walking, while the second contains activities for which the subject stays still.\n", "\n", "According to the frist three components of the PCA, each of the three walking activities seems to be well expressed by the data. Laying seems to be well separated from other still positions (as the gravity effect is measured on a different axis : the smartphone is horizontal while it is vertical for sitting and standing activities.). However, sitting and standing seems to be harder to seperate. This is quite intuitive, as they are quite similar activities from an accelerometer or gyroscope perspective.\n", "\n", "Nonetheless, these figures gives us great hope of finding some good classifier to separate our data (remember we are only using the three first principal components, accounting for approximately 71.6% of the variability).\n", "\n", "Having one component explaining 65.5% of the variability also accounts for some high correlation in X_train.\n", "\n", "#### What are our options ?\n", "1. Use principal components as features\n", "2. Use Support Vector Classifier. As $n << d$ and the PCA shows easy-separated data, the linear kernel may be powerful enough to find some good separating hyperplane.\n", "3. Use regularized logistic regression (LASSO or Ridge)\n", "4. If we're still stuck, try SVM with non-linear kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modelisation\n", "\n", "We use cross validation on 5-Folds. Parameters are selected using F1-score as this performance measure avoid giving good scores to naive models in case of class imbalance. Performance of each model is evaluated on test set (train test consists of 70% of the data) looking at precision, recall and F1-score. For more information on cross validation on K-Folds, cf. _The Elements of Statistical Learning_ (T. Hastie & al.). For more information about the performance measure we use, cf. :\n", "- Precision and recall : https://en.wikipedia.org/wiki/Precision_and_recall\n", "- F1 score : https://en.wikipedia.org/wiki/F1_score" ] }, { "cell_type": "code", "collapsed": false, "input": [ "K_folds = 5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Logistic regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### On the compressed dataset" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pca = PCA(n_components=50)\n", "X_redux = pca.fit_transform(X_train)\n", "print(\"\\n Variability of the compressed dataset: %.4f of the total variability \\n\" %sum(pca.explained_variance_ratio_))\n", "logit = linear_model.LogisticRegression()\n", "logit.fit(X_redux, np.ravel(y_train))\n", "y_pred = logit.predict(X_redux) ## No fit_predict method\n", "print(\"\\n performance on train dataset : \\n\")\n", "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n", "print(\"\\n\\n performance on test dataset : \\n\")\n", "y_pred = logit.predict(pca.transform(X_test))\n", "print(metrics.classification_report(y_test, y_pred, target_names=classes))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Variability of the compressed dataset: 0.9309 of the total variability \n", "\n", "\n", " performance on train dataset : \n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 0.98 0.99 0.98 1226\n", " 2 WALKING_UPSTAIRS 0.98 0.98 0.98 1073\n", "3 WALKING_DOWNSTAIRS 0.99 0.99 0.99 986\n", " 4 SITTING 0.90 0.87 0.88 1286\n", " 5 STANDING 0.88 0.91 0.90 1374\n", " 6 LAYING 1.00 1.00 1.00 1407\n", "\n", " avg / total 0.95 0.95 0.95 7352\n", "\n", "\n", "\n", " performance on test dataset : \n", "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 0.89 0.98 0.93 496\n", " 2 WALKING_UPSTAIRS 0.92 0.86 0.89 471\n", "3 WALKING_DOWNSTAIRS 0.93 0.92 0.93 420\n", " 4 SITTING 0.94 0.85 0.89 491\n", " 5 STANDING 0.88 0.94 0.91 532\n", " 6 LAYING 1.00 1.00 1.00 537\n", "\n", " avg / total 0.93 0.93 0.93 2947\n", "\n" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### On the original dataset :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "logit = linear_model.LogisticRegression()\n", "logit.fit(X_train, np.ravel(y_train))\n", "y_pred = logit.predict(X_train) ## No fit_predict method\n", "print(\"\\n performance on train dataset : \\n\")\n", "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n", "print(\"\\n\\n performance on test dataset : \\n\")\n", "y_pred = logit.predict(X_test)\n", "print(metrics.classification_report(y_test, y_pred, target_names=classes))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", " performance on train dataset : \n", "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 1.00 1.00 1.00 1226\n", " 2 WALKING_UPSTAIRS 1.00 1.00 1.00 1073\n", "3 WALKING_DOWNSTAIRS 1.00 1.00 1.00 986\n", " 4 SITTING 0.97 0.97 0.97 1286\n", " 5 STANDING 0.98 0.98 0.98 1374\n", " 6 LAYING 1.00 1.00 1.00 1407\n", "\n", " avg / total 0.99 0.99 0.99 7352\n", "\n", "\n", "\n", " performance on test dataset : \n", "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 0.94 1.00 0.97 496\n", " 2 WALKING_UPSTAIRS 0.97 0.95 0.96 471\n", "3 WALKING_DOWNSTAIRS 1.00 0.97 0.98 420\n", " 4 SITTING 0.97 0.88 0.92 491\n", " 5 STANDING 0.90 0.97 0.94 532\n", " 6 LAYING 1.00 1.00 1.00 537\n", "\n", " avg / total 0.96 0.96 0.96 2947\n", "\n" ] } ], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model performs better without dimentionality reduction. As the time-complexity in the number of variable is reasonable considering the number of variables in our dataset, we decide to keep the original feature set. \n", "\n", "Regarding performance, sitting and standing activities are harder to seperate as seen in the 3D plots. As sitting has a poor recall and standing a poor precision (compared to the other classes), we can deduce that lots of activites labeled as sitting are identified as standing. Resolving this issue may be hard.\n", "\n", "There is some overfitting, thus we try a L1-regularized variant of logistic regression to prevent this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### L1 regularization\n", "To prevent overfitting, we use L1 regularization (L2 regularization gives very similar results in our case). For more information about regularization and the influence of the parameter $C$, cf. http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression, or _The Elements of Statistical Learning_ (T. Hastie & al.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "logit = linear_model.LogisticRegression()\n", "C = np.logspace(-2, 4, 15) # Exponential exploration of the parameter space\n", "params = {'penalty':['l1'], 'C':C} # L1 Penalty\n", "train_scores, valid_scores = validation_curve(logit, X_train, np.ravel(y_train), \\\n", " \"C\", C, cv=K_folds, verbose=0, scoring=\"f1\", n_jobs=-1)\n", "plot_validation_scores(train_scores, valid_scores, C)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Efaie8vxy65BnGEafpjM12LOqejDwt0wb0y3efRcqKnbt4AdQWel6hQ/Ozkiv\noWiItdVr+azuMzePhXkZhmH0A3YrHKqqIvKOiExT1bf2lVFdoqQEfD6X4xg6tGXMqaoqGDnSDSmy\nj1FVtjVs46PKjxAVhhQMsTyGYRj9hs50m54OvCEi60RkRWL5INOGdZq8PNi2zbWe+vhjd6yhwSXH\nv/CFfZ4Mrw/V8+bmN3l/x/uU5JRQll9momEYRr+iM6GqrybWydEEe1ct2NgI114LF10ERx3lEuGR\nCEyb5jyRfUQkFmFtzVrW16ynwF9g06sahtFv2WPNqqobRORQ4FiceLyiqu9n3LLO8pOfuDGnLrrI\ndQCsrYXp0920sPsAVWV743Y+2vkRMY1ZWMowjH7PHkNVInINrqf4EGAY8IiI/GumDes0kQhcf70L\nSVVWuvBUefk+eXRjuJFlW5ex/PPlFPgLrE+GYRgDgj3OxyEiK4DpqtqU2C8E3lTVqfvAvt0iIqov\nv+zGnUomww8+OOPPjcajrK9Zz5rqNeT78inO7X0d6Q3DMNpjX87HEe9gO/sUF0N9vVtPnpzxx+1s\n2smHOz8kEoswuGCwTb1qGMaAozPC8QCwVET+gkuMnwksyqhVXSEYdB39Dj20/b4cPURzpJlPKj9h\ne+N2SnNLKcmxMaUMwxiYdCY5fqeILAGOwSXHL1PV5Rm3rLPU17vWVPn5Gbl9LB5jU90mPqn8hFxf\nrnXiMwxjwNOZYdWnAx+r6juJ/RIROVJVl3bi2pOBuwAv8FtV/UWb82U472UCEAQuV9WP0s57gWXA\nZlU9rd2HTJ0KgwbtyZRuUR2oZsX2FQQiAcrzy/F6MufRGIZh9BU6E6C/DzdOVZKmxLHdkqj07wFO\nBqYAc0SkbRLiRuBdVT0EuBS4u835a4CPaelDsiujR+/JlC4TiUX4cMeHvLnpTXweH0MKh5hoGIZh\nJOhUclzTml6paiwhCntiGrBGVTcAiMgTwBnAyrQyk4HbEvddJSLjRWSIqu4UkdHAqcC/A9/vjJ09\nQVVzFR9s/4BoPMqwot4z4ZPR+whGgzSGG6kP1dMQbqAh1JBaN4YbyfHmUJxbTHFOYsktTs3kWJRT\nZC8jRp+lM8KxPtFv415ccvxfgHWduG4UsCltfzNwZJsy7wNnA6+KyDRgHDAa2An8EvghsE+y0JFY\nhNXVq9lQs4HSvFKbUGkAEI1HaQw3dlj5J9f14XoaQ400hBuoD9XTGHbbqkpJbsku4pAUhtpgLWtr\n1rrybe7+HbTcAAAgAElEQVTTFGki35fvhCS3mJKcEopyi9oVmdR9055TmFNoLfqMrNEZ4fg28Cvg\n3xL7/wf8v05ct/sOIo7bgLtFZDmwAlgOxEVkNrBDVZeLyMzd3WDevHmp7ZkzZzJz5m6Lt0t1oJoP\ntn9AOBZmaOFQ68TXhwnHwlQHqqkKVFHVXEV1oJrK5kqqA9UtxwPueCASoDCnMFUxp1feJbklFOUU\nMaRgSGq7JLeklTjk+nK7bWdc4zRHmlsEqh3R+rzxc1aHVqfON4YaqQ/X0xBqIBANkOPNIc+X13rx\ntmzn+/Nbncv15u5avs2S78tvdb2JU/9g2evLeOeNdwDnKe8te+wA2O0bu6T6PFU9ObF/AxBvmyBv\nc8164IvADcAlQBTIw3kdf1bVS9uU172xPxqPsqZ6Detq1lGaW0qeL6/b9zIyR1IMqgPVVDVXtar8\n049VB6oJRANU5FdQnl9OeX45FfkVVBRUtDo2uGAw5fnlFOUU9dmKMRaPEY6FCUQDBKPBLi3p14Si\noV3LxIIEIq5Mri+XAn8B+b58t/bnU+AroCCngAJfYj/t/O7KFPgLyPPl2Xw0WaYnOgB2KByJ2f4W\nq+qniQmdFgHnABtwTXLf3e2NRXzAKuBEYCvwFjBHVVemlSkFAqoaFpFvAUer6mVt7nM8cF17rar2\nRjhqAjV8sP0DQrEQ5Xnl5mXsIyKxCHWhOuqCddSH6qkL1VEbrE0dqwsljgfrqA46YWiONFOWV0ZF\nQUWLGCSEICkKyf2S3BL7W/YQcY0TioZoijQRiARojja7daSZ5kgzgWiApnATgag7ljyXvt/etTne\nHApzChmUN4jyvHLK8ssozy9P/Y3L8spSIl+eX06+L9/+pj1IpnuOX4Pr/AcwBzgE2A84DNf66djd\n3VhVoyJyNfACrjnuQlVdKSJXJs4vwLW2elBEFPgQuKKj23Xu4+yZWDzG2uq1rKleQ0luCRX5FT11\n6wGFqtIYbtyl4u9QDILueDAapCS3hNK8UkpzS1PrQXmDKMktYXTJ6NSxZEVSklvSZz2DvoxHPOT7\n88n391wfKVUlFAvRGG6kNlib8hqrA9XUBGv4cMeHLfuBGqoCVQBOWPLLKM9rEZRWgpNfQVl+GYPy\nBplHsw/Yncfxnqoemth+DHhLVe9K7C9X1cP2nZnt01WPoy5Yx/ufv58KZ9hbTOcIx8Ksq1nHp1Wf\ntizVn6KqrSr/Pa0H5Q2i0F9o37vRJQKRAFWBKmoCNVQHnaCkBCdtvyZYQ12wjqKcIgblDSLXl0uu\nN5ccbw653lz8Xn9qO8ebQ67PrdPPJ8+lyvnaP5fMB/XFPFCmPY64iIwEqnHhplvSzmWmm3aGiMVj\nrKtZx+rq1RTnFDO4IDtTyfYF6kP1rQWi6lM+q/uMUSWjOLDiQCZVTOLYcccyqXwSZfll2TbXGADk\n+/MZ7R/N6JI999mKxWMpjzccC6eWUCxEOJpYpx1PnmsIN7jtaOJ8PEw4mnZt2joSixCMBmmONBOM\nBsnz5bXkdvwur9Mq3+NP2/flU5hT2Con1HbJ9eb2+per3QnHT4G3E2WeUtUPARKtnNZm3rSeoT5U\nzwfbP6Ap3MSQgiF97u0gU6gq2xq38WnVp6yqWpUSibpQHQeUH8CkikkcOvxQzvvCeUwom2ANB4w+\ngdfjTYWy9gVxjadEJJX7aZPbSR5vCjexo2lHq5xPqlza9ZF4pFXrtlxfbqvWcrm+XVvHdabFXHLJ\neKsqEfEDxapanXasMHFd414/fS/ZXagqrnHW16zn0+pPKfQVUphTuI+t6z1EYhHW1baEmlZVrWJ1\n1Wry/flMKp/EpAq3HFhxIKNKRpm4GkYWicVjqZZvqVZvsV1bx7XbIi6tZVxH5wPRAM0/ac5Mq6q+\nQEfC0RBqYMWOFdQH66koqBhwFWFDqIElny1h2dZlLaGm4lEpgUgu++qtzDCM3sO+nI+jTxDXOJ/V\nfsYnlZ9Q4C9gSOGQbJu0z2gMN/KPz/7B39f9nXe3vcsRI4/g6DFHc+4XzmX/sv0t1GQYRo/Rb4Sj\nMdzIiu0rqAvVUZFfMSDGAWqONPOPz/7Bi+te5O2tb/OlEV/ipAkn8bMTfkZRTlG2zTMMo5/SLeEQ\nkYNU9ZOeNqY7xDXOprpNrKxcSb4vnyEF/dvLCEQCvLrxVf6+/u8s3byUQ4YfwlcmfIW5x8+1KWwN\nw9gndNfj+BswticN6S5vb3mb6kB1v/YygtEgr216jRfXvcjrm15n6tCpnLT/SfzkmJ9QmleabfMM\nwxhgdCgcIvLr3VzXaxrwByKBfjkrXyga4o3Nb/D3dX/ntU2vMWXwFGZNmMX1R1/PoLzMTFxlGIbR\nGXbncVwGXAeEaD3khwAXZtCmLtGfwjPhWJg3N7/J39f9nVc3vsqkikmcNOEkfnDUD6wFlGEYvYbd\nCccy4ENVfa3tCRGZlzGLBhiRWISlW5by4roX+cfGf7B/2f7MmjCLa468xnq4G4bRK9mdcJyDmwd8\nF1R1fEasGUBsqN3AQ+8/xJLPljBu0DhOmnAS3/nyd/pl2M0wjP7F7oSjKL3HuNEzxDXO7z/6PQuX\nL+SiqRfx6NmPMrxoeLbNMgzD6DS7E44ncUOoIyJ/VtVz9o1J/ZfPGz9n/pL5BKNBHjj9AcaUjsm2\nSYZhGF2ms81xJ2TUin6OqvLcmuf45Zu/ZM7Bc7j0kEttzgDDMPosVntlmNpgLbe+eivra9bz61N+\nzUGDD8q2SYZhGHvF7kb/+6KINIhIAzA1uZ1Y6veVgX2Z1za9xpw/z2F40XAePuthEw3DMPoFHXoc\nqto/u2HvA5ojzdz15l28sfkNfnbCzzhi5BHZNskwDKPHGFjjje8DPtj+ARf95SLCsTCPn/O4iYZh\nGP2OjOc4RORk4C7AC/xWVX/R5nwZsAiXgA8Cl6vqRyIyBngIGIrruX6/qv4q0/Z2l0gswv3v3s9T\nq57ix0f/mBP2OyHbJhmGYWSEjAqHiHiBe4BZwBbgbRF5SlVXphW7EXhXVc8SkQOB/0qUjwDXqup7\nIlIEvCMif29zba9gbfVafrr4pwwtHMqjZz9qPb4Nw+jXZNrjmAasUdUNACLyBHAGkF75TwZuA1DV\nVSIyXkSGqOrnwOeJ440ishIY2ebarBLXOI+teIwH33+Qq798NWcceEavn2TeMAxjb8m0cIwCNqXt\nbwaObFPmfeBs4FURmQaMA0YDO5MFRGQ8rjPi0gza2iW2NWxj3pJ5RONRHjzjQUaXjM62SYZhGPuE\nTAtHZyY0vw24W0SWAyuA5UAseTIRpvoTcI2qNra9eMEdC1Lbhx91OEfMyGwyWlV5ZvUz3LX0Li6e\nejGXfPGSfjsPiGEY/YNlry/jnTfeAdz8PnuLqHambu/mzUWmA/NU9eTE/g1AvG2CvM0164GpifCU\nH3gaeE5V72qnrC7bsixD1u9KTaCGW169hU31m5g/cz6TKibts2cbhmH0BLXBWmbtPwtV7XZcPdPN\ncZcBByTyFjnA+cBT6QVEpDRxDhH5FrAkIRoCLAQ+bk809jWvfPYKc/4yh9Elo3nozIdMNAzDGLBk\nNFSlqlERuRp4Adccd6GqrhSRKxPnFwBTgAdFRIEPgSsSlx8NXAx8kAhjAdygqs9n0ua2NIWb+OWb\nv+StLW9xyz/dwpdGfGlfPt4wDKPXkdFQVabJdKjqvc/fY+7iuRw+4nC+f9T3KcopytizDMMw9gU9\nEaqyQQ7bIRwLs+CdBTz96dPccMwNzBw/M9smGYZh9BpMONqwpnoNN718EyOLR/L4OY/bXN+GYRht\nMOFIEIvHeHTFozz0wUNcc+Q1zD5gtnXmMwzDaAcTDmBL/RbmLZmHIDx05kOMLB6ZbZMMwzB6LQNa\nOFSVJ1c9yT1v38Nlh1zGhVMvxCM2YHA2UFViGiMajxKLu7Um+o+qKoqm1p29X2/zGD3iwSMevOJ1\na4+31bZh9BUGrHBUNVfx81d+zvbG7dz3tfuYWD4x2yb1K+IaTwlAuiCkxABFaKnYRYRcby55vjyK\ncorI9eXiEx8ejwdB8IgHEcGDJ3UsKQzJ7eT9ktudOQZOZLpKZwQsFo8R01hqHY6GCcfDRGIRwjG3\njsQjBKIBwrFwp57bkeh4xdvrhNLovwxI4Vi8YTG3vnorpx94OrfPuh2/159tk3o1SW+gbUUYjUc7\nvMbn8ZHnyyPPn0euN5dcnxOFHG8Ofo8fn8fXarE37haxbftdp8Q3FiUcD6dEJxwLE4qFCEaDhGNh\n4hrf7f3TPR6vx4mOz+NLHTeMzjKghKMx3Mgdb9zB8s+Xc/tJt3PIsEOybVJWSL7970kE0t/Ic325\n5HpzKfQXkufNSwmB37urCPg9fnv77QYe8eDxevDTvRcZVd3Fw0vfTgpNJBYhFA2lRCgUDRHT2C5e\nYDo+jy8lOOlr+zsPTAaMcCzbuoybl9zM9NHTeezsxyjwF2TbpIwR1zj1ofoOPYIcbw65vlwKfYUp\nQehIBMwb6DuICH6vv1vC0zbHlNyOxqNEYhGC0SChaIhgzK0D0QChaMiF+dK0IxUGRPB6vO0KjtH3\n6ffCEYqG+O9l/83f1v6Nnxz7E44Ze0y2TcoYgUiAxkgjXvEyumQ0QwqHtCsEhtEWEcEnXf99JENp\nbZeU2MRCKcFpijS1yuUkPZzks/1eP36P30LHfYB+XYt8UvkJP138U8YPGs/j5zzOoLxB2Tapx4nG\nozSEG4jGowzKHcRhww+joqDCBMLYJ3g9zovIJbfT1ySFJRJ3IbNQNERjpJHmcDNNkSbqQnVAmrCk\neS9JYbGcTHbpl7VLNB7lofcf4vEPH+fa6ddyysRT+l0stjHcSHO0Gb/Hz7jScYwoHmFjaRl9gqTn\nm08+7emNqhKJu+R/MgfTFG6iKeKWumDdLjkZj3hSorInzzq9FV1667jk8Y5azLXX+m53Lfv6M/1O\nODbVbWLu4rnk+nJ5+KyHGV40PNsm9RiRWIT6cD2xeIwhBUOYMmQK5fnlFjc2+hUiQo43hxxvTodl\novFoK2EJRAJOWBIC01Hz5lYVfXpT7bTj6WV3EYS0ZuBxjRPXeKqPUVzjbk28W028u0J7DRnSbW1P\n0JLHOtv0e3f0G+FQVf7yyV+4d9m9XHHYFZz/hfP7hTurqjSGGwnEAuR585hUPolhRcPI9+dn2zTD\nyBpJr6KjRi7Jijvbb/5tO6+md2Lt6Fx31kkRi2ucmMaIx+PEibeIWdr5ktySvf5c/UI4Kpsrmf+P\n+dQEavjN7N+wX9l+2TZprwlFQzSEGwAYXjScL5Z+kbK8sqz/RzCMvkBv+X+S8lZ6hzk9Rp8XjhfX\nvcjtr9/O2QedzTe/9M0+nRRONqMNx8IU+guZMmQKQwuHkuvrfOLRMAwj0/T5iZzG3jmW+SfM5+Ch\nB2fbnG4TjAZpCDXg8XgYXTKaUcWjKMkt6TVvTYZh9C9EZK8mcurzwvHKhlf6ZLw/3bsoyS1hQtkE\nBhcMtjbshmFknL0Vjr4b10nQF0UjFA1RF6pjv7L9GFU8iuLc4mybZBiG0Wky2uxIRE4WkU9EZLWI\nXN/O+TIR+auIvC8iS0XkC529tq/SEG6gKdrEtFHTOGjwQSYahmH0OTImHCLiBe4BTgamAHNEZHKb\nYjcC76rqIcClwN1duLZPoapUNVeR683lmDHHUFFQkW2TDMMwukUmPY5pwBpV3aCqEeAJ4Iw2ZSYD\nLwOo6ipgvIgM7eS1fYZoPMqOph2MLhnNtFHT+mR4zTAMI0kmhWMUsCltf3PiWDrvA2cDiMg0YBww\nupPX9gmawk3UBGo4bMRhTBk6xXp5G4bR58lkcrwzzbVuA+4WkeXACmA5EOvktQAsuGNBavvwow7n\niBlHdNHMzFETqMHv9XP02KMtl2EYRtZYvHgxixcv7rH7Zaw5rohMB+ap6smJ/RuAuKr+YjfXrAem\nAgd35loR0WVblmXE/r0hFo9RFahieNFwDh56sDWxNQyjV7G3zXEzGapaBhwgIuNFJAc4H3gqvYCI\nlCbOISLfApaoamNnru2tBKNBKgOVTB48mUOHH2qiYRhGvyNjoSpVjYrI1cALgBdYqKorReTKxPkF\nuBZTD4qIAh8CV+zu2kzZ2lPUheqIa5zpo6dTnl+ebXMMwzAyQp/vOd4bQlWqSmVzJWX5ZRwy/BDy\nfHnZNskwDKNDBnzP8WwTiUWoDlYzoWwCkyom9Yuh3A3DMHaHCcde0BhuJBgNcviIwxlWNCzb5hiG\nYewTTDi6SVWgigJ/AUePPdqmbDUMY0BhwtFFkk1tR5eMZvKQyX16/g+jc9jw9kZfJhN5bKv1ukAw\nGqQ+XM/BQw9mTOmYbJtj7EP6ciMSY+CSqZceE45OUhuqRVQ4avRRDMoblG1zDMMwsoYJxx6Ia5zK\n5kqGFA5h6tCpNo2rYRgDHhOO3RCOhakN1jKxfCL7l+9vTW0NwzAw4eiQhnADkViEI0YewZDCIdk2\nxzAMo9dgr9BtiGucHU07yPflc/TYo000jAHBqaeeysMPP9zjZY3+iQ05kkYgEqAh3MCBgw9k/KDx\nFpoygNTwDNk2YxeKiopSrWaamprIy8vD63Xzvdx///3MmTMnm+YZvYCOfrs25EgPoKpUB6rJ9eUy\nY8wMSvNKs22SYeyRxsbG1PZ+++3HwoUL+ad/+qddykWjUXw++69u30PPMeBfqcOxMDua3bSuJhpG\nf2Dx4sWMHj2a22+/nREjRnDFFVdQW1vL7NmzGTp0KOXl5Zx22mls2bIldc3MmTNZuHAhAA8++CDH\nHHMMP/zhDykvL2fChAk8//zz3Sq7fv16jjvuOEpKSjjppJO46qqruOSSS9q1u7KyktmzZ1NWVkZF\nRQXHHXdc6m1506ZNnH322QwdOpTBgwfz3e9+F4B4PM7Pf/5zxo8fz7Bhw/jGN75BfX09ABs2bMDj\n8bBo0SLGjRvHrFmzAFi0aBFTpkyhvLyck08+mY0bN/bUVz9gGNDCUReqozHSyOEjDmfK0Ck2d4bR\nb9i+fTs1NTVs3LiRBQsWEI/HueKKK9i4cSMbN24kPz+fq6++OlVeRFp1Fnvrrbc46KCDqKqq4kc/\n+hFXXHFFt8peeOGFTJ8+nerqaubNm8cjjzzSYae0O+64gzFjxlBZWcmOHTu49dZbERFisRizZ89m\nv/3247PPPmPLli2pMNyDDz7I7373OxYvXsy6detobGxs9bkA/vGPf/DJJ5/w/PPP8+STT3Lrrbfy\n17/+lcrKSo499lgL6XWDASkcsXiMHc07KMkt4dixx9oAhcbeMW8eiOy6zJvX+fIdle0mHo+Hm2++\nGb/fT15eHuXl5Zx11lnk5eVRVFTEjTfeyJIlSzq8fty4cVxxxRWICJdeeinbtm1jx44dXSq7ceNG\nli1bxvz58/H5fBx99NGcfvrpHeaLcnJy2LZtGxs2bMDr9XL00UcDTpi2bdvGf/zHf5Cfn09ubi4z\nZswA4NFHH+UHP/gB48ePp7CwkFtvvZUnnniCeDyeuu+8efPIz88nLy+P++67jxtuuIEDDzwQj8fD\nDTfcwHvvvcemTZu6+1UPSAaccDSFm6gKVDFl8BQOH3G4zZ1h7D3z5oHqrsvuhKOzZbvJkCFDyMnJ\nSe03Nzdz5ZVXMn78eEpLSzn++OOpq6vrsBIfPnx4arugoABonVPpTNmtW7dSXl5OXl7L/7ExYzoe\nqueHP/whEydO5Ctf+Qr7778/v/iFmyl606ZNjBs3Do9n1+pq27ZtjBs3LrU/duxYotEo27dvb/eZ\nn332Gddccw1lZWWpkBjQKmxn7JkBIxzJyZY8Hg/HjD2GcYPG2eB1Rr+l7W/7jjvu4NNPP+Wtt96i\nrq6OJUuWoKoZbS02YsQIqqurCQQCqWO7yycUFRXxn//5n6xdu5annnqKO++8k5deeomxY8eyceNG\nYrHYLteMHDmSDRs2tLq/z+dj2LCWKEL6dzF27Fjuv/9+ampqUktTUxPTp0/fy087sBgQwhGKhtjR\nvINxg8Zx1OijKM4tzrZJhrFPaWxsJD8/n9LSUqqrq7n55psz/sxx48ZxxBFHMG/ePCKRCG+88QZP\nP/10hy9szzzzDGvWrEFVKSkpwev14vV6mTZtGiNGjODHP/4xzc3NBINBXn/9dQDmzJnDL3/5SzZs\n2EBjYyM33ngjF1xwQbveCcC3v/1tbrnlFj7++GMA6urq+OMf/5iZL6Afk1HhEJGTReQTEVktIte3\nc36wiDwvIu+JyIciclnauRtE5CMRWSEij4lItwaJqgnWEIgGOHLUkRw0+CC8Hu9efCLD6Bu0rZy/\n973vEQgEGDx4MDNmzOCUU07psAJvm/xu736dLfvoo4/yxhtvUFFRwU033cT555/fKoSWzurVqznp\npJMoLi5mxowZXHXVVRx//PF4PB7+93//lzVr1jB27FjGjBnDH/7wBwAuv/xyLrnkEo477jgmTJhA\nQUEBv/71rzu0+8wzz+T666/nggsuoLS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"text": [ "<matplotlib.figure.Figure at 0x11288fba8>" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model still exhibits overfitting that cannot be reduced by penalizing the coefficients without decreasing the performance on cross validation samples. The cross-validation score is quite variable (+/- 2%) depending on the 5-Folds sampling." ] }, { "cell_type": "code", "collapsed": false, "input": [ "logit = linear_model.LogisticRegression(penalty='l1', C=C[np.where(train_scores == np.max(train_scores))[0][0]])\n", "print(\"Optimal C parameter : %.4f \\n\" %logit.C)\n", "logit.fit(X_train, np.ravel(y_train))\n", "y_pred = logit.predict(X_train) ## No fit_predict method\n", "print(\"\\n performance on train dataset : \\n\")\n", "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n", "print(\"\\n\\n performance on test dataset : \\n\")\n", "y_pred = logit.predict(X_test)\n", "print(metrics.classification_report(y_test, y_pred, target_names=classes))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Optimal C parameter : 193.0698 \n", "\n", "\n", " performance on train dataset : \n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 1.00 1.00 1.00 1226\n", " 2 WALKING_UPSTAIRS 1.00 1.00 1.00 1073\n", "3 WALKING_DOWNSTAIRS 1.00 1.00 1.00 986\n", " 4 SITTING 1.00 1.00 1.00 1286\n", " 5 STANDING 1.00 1.00 1.00 1374\n", " 6 LAYING 1.00 1.00 1.00 1407\n", "\n", " avg / total 1.00 1.00 1.00 7352\n", "\n", "\n", "\n", " performance on test dataset : \n", "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 0.94 1.00 0.96 496\n", " 2 WALKING_UPSTAIRS 0.98 0.93 0.96 471\n", "3 WALKING_DOWNSTAIRS 0.99 0.98 0.98 420\n", " 4 SITTING 0.96 0.87 0.91 491\n", " 5 STANDING 0.90 0.97 0.93 532\n", " 6 LAYING 0.99 1.00 1.00 537\n", "\n", " avg / total 0.96 0.96 0.96 2947\n", "\n" ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen previously, the sitting and standing activities are note well separated. There is a still a huge overfit despite the regularization.\n", "\n", "We try to improve these results by using a SVM classifier, as the hyperplane found by this model is often better at classification than the one found by logistic regression thanks to the margin maximization (for more precision, cf. _The Elements of Statistical Learning_ T. Hastie & al.)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Support Vector Classifier\n", "\n", "As the data is likely to be separable by an hyperplane in the original feature space, no need for nonlinear kernel here. We user `linearSVC` (SVC with linear kernel implementation of liblinear. Complexity is $O(np)$, i.e. this model is less complex than logistic regression that is $O(np^2)$. For more information on SVM and the influence of the parameter $C$, cf. http://scikit-learn.org/stable/modules/svm.html, or _The Elements of Statistical Learning_ (T. Hastie & al.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "linSVC = svm.LinearSVC(dual=True, tol=0.0001, multi_class='ovr', fit_intercept=True, \\\n", " intercept_scaling=1, verbose=0, random_state=42)\n", "C = np.logspace(-3, 3, 15) # Exponential exploration of the parameter space\n", "params = {'C':C}\n", "train_scores, valid_scores = validation_curve(linSVC, X_train, np.ravel(y_train), \\\n", " \"C\", C, cv=K_folds, verbose=0, scoring=\"f1\", n_jobs=-1)\n", "plot_validation_scores(train_scores, valid_scores, C)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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6MgwjexnTwXFVjYnIF4FncN1x71XVDSJyRTL/LuBQ4AERUeAN4LKMS3wJeEhE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\nnHnmmezYsSN9Tl1dHffeey8ADzzwACe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"text": [ "<matplotlib.figure.Figure at 0x10aaa88d0>" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "linSVC.C = C[np.where(train_scores == np.max(train_scores))[0][0]]\n", "print(\"Optimal C parameter : %.4f \\n\" %linSVC.C)\n", "linSVC.fit(X_train, np.ravel(y_train))\n", "y_pred = linSVC.predict(X_train)\n", "print(\"\\n performance on train dataset : \\n\")\n", "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n", "print(\"\\n\\n performance on test dataset : \\n\")\n", "y_pred = logit.predict(X_test)\n", "print(metrics.classification_report(y_test, y_pred, target_names=classes))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Optimal C parameter : 19.3070 \n", "\n", "\n", " performance on train dataset : \n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 1.00 1.00 1.00 1226\n", " 2 WALKING_UPSTAIRS 1.00 1.00 1.00 1073\n", "3 WALKING_DOWNSTAIRS 1.00 1.00 1.00 986\n", " 4 SITTING 0.98 0.99 0.99 1286\n", " 5 STANDING 0.99 0.99 0.99 1374\n", " 6 LAYING 1.00 1.00 1.00 1407\n", "\n", " avg / total 1.00 1.00 1.00 7352\n", "\n", "\n", "\n", " performance on test dataset : \n", "\n", " precision recall f1-score support\n", "\n", " 1 WALKING 0.94 1.00 0.96 496\n", " 2 WALKING_UPSTAIRS 0.98 0.93 0.96 471\n", "3 WALKING_DOWNSTAIRS 0.99 0.98 0.98 420\n", " 4 SITTING 0.96 0.87 0.91 491\n", " 5 STANDING 0.90 0.97 0.93 532\n", " 6 LAYING 0.99 1.00 1.00 537\n", "\n", " avg / total 0.96 0.96 0.96 2947\n", "\n" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This linear SVC performs better than the logit, and is faster to compute.It still has a hard time to separate sitting and standing activities. In order to address this issue, we could try to :\n", "1. Try to obtain more data\n", "2. Improve the data representation of the signal contained in the dataset\n", "3. Improve the model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot_learning_curve(linSVC, \"Learning Curve (SVC)\", X_train, np.ravel(y_train), cv=5, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Te7FSr2TykMnMHDlTBgp2UwLLtwZCTIGlXEPXBFdKYVZmbGZbcAsY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\nREU8ESHucXvdOL3OhOqaKwhC5JDBhnF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"text": [ "<matplotlib.figure.Figure at 0x111805cc0>" ] } ], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen in the learning curves, the performance in terms of training example starts to level off at 5000+ training examples. Obtaining more data would help to reduce variance of our score, but may not be sufficient to improve the model performance dramatically as we would still have the standing/sitting representation overlap in our data.\n", "\n", "Trying a nonlinear SVM (e.g. using a RBF kernel) would be very costly in terms of time-complexity as this model would be $O(n^3p)$ and would imply at least one more tuning parameter, making the cross-validation step way harder.\n", "\n", "Further work on signal representation seems to be a more efficient way to improve the model, as we saw that these two classes seems to present serious overlapping with our features (cf. 3D plots). If we manage to find a representation in which these two signals would be easier to separate, this would solve our problem. This could by done by using spherical wavelets rather than Fourier transform in a scattering network fashion to perform the classification (cf. Stephane Mallat & al. work http://www.di.ens.fr/data/software/scatnet/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "Our best model is a linear Support Vector Classifier. This model can be seen as a first step toward an health monitoring application for smartphone or wearable. There are still some issues to face in order to launch a proper product :\n", "- In our dataset, the smartphone is fixed on the waist. In a real application, it would be in the pocket, and thus its position with respect to the body would vary. We should try to detect the position of the smartphone before the feature computation step in order to take accound of an eventual rotation. \n", "- It would be useful to detect if the smartphone is carried by the user, in order to avoid activity quantification when the smartphone is simply carried by a car.\n", "- We should try to recognize other activities that are quite common such as running or biking. These activities would be relevant for our health application. \n", "\n", "Adressing these issues would add complexity to our task. In this fashion, scattering networks with spherical wavelet discussed above would be of some help.\n", "\n", "The model we developped is time stable, thus it would not be useful to refresh it regularly. However, we should try to obtain a broader dataset in term of individuals in order to take account of the variety of human bodies & movements. As we are using smartphones to collect the data, obtaining more data points would not be very costly in term of aquisition or storage. If some retraining would be necessary, it could be done automatically as the whole parameter selection steps are done by our code without human intervention. Some checks should be programmed in order to verify that our exploration of the parameter set is wide enough, and to send alerts if there is some drop in performance.\n", "\n", "As we use a SVM, the model is not very scalable, but as seen previously, this is not an issue as the learning curves show that increase in performance from additional data would not be very significant.\n", " \n", "Such application could be thought as a smartphone app financed by advertising revenues, or as a SAAS (software as a service). In the SAAS fashion, the app would be accessed by an API to allow developpers to integrate some health analytics in their application. Pricing would be set according to the transactions volume (e.g. number of API requests)." ] } ], "metadata": {} } ] }
mit
bioinf-jku/SNNs
Pytorch/SelfNormalizingNetworks_MLP_MNIST.ipynb
1
75529
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "pycharm": { "name": "#%% md\n" } }, "source": [ "# Tutorial on self-normalizing networks on the MNIST data set: multi-layer perceptrons\n", "\n", "*Author:* Kajetan Schweighofer, 2021" ] }, { "cell_type": "code", "execution_count": 1, "outputs": [], "source": [ "import os\n", "import copy\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.data import DataLoader, Subset\n", "import torchvision\n", "from torchvision import transforms\n", "\n", "from sklearn.metrics import accuracy_score\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 2, "outputs": [], "source": [ "# load data and preprocess\n", "\n", "path = os.path.join(\".\", \"dataset\", \"mnist\")\n", "os.makedirs(path, exist_ok=True)\n", "\n", "# convert PIL image to tensor and normalize\n", "transform = transform=transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,))\n", "])\n", "\n", "train = torchvision.datasets.MNIST(path, download=True, train=True, transform=transform)\n", "test = torchvision.datasets.MNIST(path, download=True, train=False, transform=transform)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "## Functions" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "### Model" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "class MLP(nn.Module):\n", "\n", " def __init__(self, in_features: int, out_features: int, p_drop=0.2, use_selu: bool=False):\n", " super(MLP, self).__init__()\n", "\n", " activation = nn.SELU() if use_selu else nn.ReLU()\n", " dropout = nn.AlphaDropout(p=p_drop) if use_selu else nn.Dropout(p=p_drop)\n", "\n", " self.net = nn.Sequential(\n", " nn.Flatten(), # flatten input image from batchx1x28x28 to batchx784\n", " nn.Linear(in_features=in_features, out_features=512),\n", " activation,\n", " dropout,\n", " nn.Linear(in_features=512, out_features=256),\n", " activation,\n", " dropout,\n", " nn.Linear(in_features=256, out_features=out_features)\n", " )\n", "\n", " if use_selu:\n", " for param in self.net.parameters():\n", " # biases zero\n", " if len(param.shape) == 1:\n", " nn.init.constant_(param, 0)\n", " # others using lecun-normal initialization\n", " else:\n", " nn.init.kaiming_normal_(param, mode='fan_in', nonlinearity='linear')\n", "\n", " def forward(self, x):\n", " return self.net(x)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Accuracy metric" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 4, "outputs": [], "source": [ "class Accuracy(nn.Module):\n", "\n", " def forward(self, x, y):\n", "\n", " y_pred = F.softmax(x, dim=1).argmax(dim=1).cpu().numpy()\n", " y = y.cpu().numpy()\n", "\n", " return accuracy_score(y_true=y, y_pred=y_pred)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Training / Evaluation methods" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 5, "outputs": [], "source": [ "def _forward(network: nn.Module, data: DataLoader, metric: callable):\n", "\n", " for x, y in data:\n", " x = x.to(next(network.parameters()).device)\n", "\n", " y_hat = network.forward(x).cpu()\n", " loss = metric(y_hat, y)\n", " yield loss\n", "\n", "@torch.enable_grad()\n", "def update(network: nn.Module, data: DataLoader, loss: nn.Module,\n", " opt: torch.optim.Optimizer) -> list:\n", "\n", " network.train()\n", "\n", " errs = []\n", " for err in _forward(network, data, loss):\n", " errs.append(err.item())\n", " opt.zero_grad()\n", " try:\n", " err.backward()\n", " opt.step()\n", " except:\n", " print('error in update step')\n", " return errs\n", "\n", "@torch.no_grad()\n", "def evaluate(network: nn.Module, data: DataLoader, metric: callable) -> float:\n", "\n", " network.eval()\n", "\n", " performance = []\n", " for p in _forward(network, data, metric):\n", " performance.append(p.item())\n", " return np.mean(performance).item()\n", "\n", "\n", "def fit(network: nn.Module, trainloader: DataLoader, valloader: DataLoader,\n", " testloader: DataLoader, epochs: int, lr: float):\n", " optimizer = torch.optim.SGD(params=network.parameters(), lr=lr)\n", " ce = nn.CrossEntropyLoss()\n", " accuracy = Accuracy()\n", "\n", " train_losses, val_losses, accuracies = [], [], []\n", "\n", " # performance before training\n", " val_losses.append(evaluate(network=network, data=valloader, metric=ce))\n", "\n", " pbar = tqdm(range(epochs))\n", " for ep in pbar:\n", " # update network\n", " tl = update(network=network, data=trainloader, loss=ce, opt=optimizer)\n", " train_losses.extend(tl)\n", " vl = evaluate(network=network, data=valloader, metric=ce)\n", " val_losses.append(vl)\n", " ac = evaluate(network=network, data=valloader, metric=accuracy)\n", "\n", " if len(accuracies) == 0 or ac > max(accuracies):\n", " # here we would store the model on disc for early stopping\n", " best_model = copy.deepcopy(network)\n", "\n", " accuracies.append(ac)\n", "\n", " print(f\"train loss: {round(np.mean(tl), 4):.4f}, \"\n", " f\"val loss: {round(vl, 4):.4f}, \"\n", " f\"accuracy: {round(ac * 100, 2):.2f}%\")\n", "\n", " pbar.set_description_str(desc=f\"Epoch {ep+1}\")\n", "\n", " # evaluate on best model obtained throughout training\n", " acc = evaluate(network=best_model, data=testloader, metric=accuracy)\n", "\n", " print(f\"Final accuracy on testset: {round(acc*100, 2):.2f}%\")\n", "\n", " return train_losses, val_losses, accuracies, acc" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "## Training\n", "\n", "### Hyperparameters" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 6, "outputs": [], "source": [ "epochs = 20\n", "lr = 1e-3\n", "batch_size = 128\n", "num_workers = 4\n", "p_drop = 0.05" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Create Dataloaders" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 7, "outputs": [], "source": [ "# obtain validation set (1/6 of train data to be equal to size of test data)\n", "rng = np.random.default_rng(seed=42)\n", "val_inds = rng.choice(np.arange(len(train)), size=len(train)//6, replace=False)\n", "train_inds = np.delete(np.arange(len(train)), val_inds)\n", "\n", "trainloader = DataLoader(Subset(train, indices=train_inds),\n", " batch_size=batch_size, drop_last=True, shuffle=True, num_workers=num_workers)\n", "valloader = DataLoader(Subset(train, indices=val_inds),\n", " batch_size=batch_size, drop_last=True, shuffle=True, num_workers=num_workers)\n", "testloader = DataLoader(test, batch_size=batch_size, drop_last=False, shuffle=False, num_workers=num_workers)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Train Networks" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 8, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Epoch 20: 100%|██████████| 20/20 [04:17<00:00, 12.88s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train loss: 2.2289, val loss: 2.1446, accuracy: 54.21%\n", "train loss: 2.0434, val loss: 1.9147, accuracy: 67.01%\n", "train loss: 1.7677, val loss: 1.5914, accuracy: 71.63%\n", "train loss: 1.4285, val loss: 1.2525, accuracy: 75.86%\n", "train loss: 1.1241, val loss: 0.9904, accuracy: 79.36%\n", "train loss: 0.9095, val loss: 0.8160, accuracy: 82.39%\n", "train loss: 0.7681, val loss: 0.7009, accuracy: 84.16%\n", "train loss: 0.6725, val loss: 0.6219, accuracy: 85.42%\n", "train loss: 0.6051, val loss: 0.5652, accuracy: 86.22%\n", "train loss: 0.5560, val loss: 0.5216, accuracy: 87.00%\n", "train loss: 0.5176, val loss: 0.4883, accuracy: 87.64%\n", "train loss: 0.4883, val loss: 0.4622, accuracy: 88.20%\n", "train loss: 0.4633, val loss: 0.4400, accuracy: 88.41%\n", "train loss: 0.4424, val loss: 0.4229, accuracy: 88.78%\n", "train loss: 0.4272, val loss: 0.4079, accuracy: 88.98%\n", "train loss: 0.4127, val loss: 0.3949, accuracy: 89.13%\n", "train loss: 0.4001, val loss: 0.3840, accuracy: 89.35%\n", "train loss: 0.3901, val loss: 0.3744, accuracy: 89.57%\n", "train loss: 0.3799, val loss: 0.3667, accuracy: 89.84%\n", "train loss: 0.3722, val loss: 0.3584, accuracy: 89.98%\n", "Final accuracy on testset: 90.76%\n" ] } ], "source": [ "# ReLU training\n", "\n", "# 28x28 = 784 input images for 10 classes\n", "network = MLP(in_features=784, out_features=10, p_drop=p_drop, use_selu=False).to(device)\n", "rtl, rvl, raccs, racc = fit(network, trainloader, valloader, testloader, epochs, lr)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 9, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Epoch 20: 100%|██████████| 20/20 [04:09<00:00, 12.48s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train loss: 1.3306, val loss: 0.6669, accuracy: 80.90%\n", "train loss: 0.7245, val loss: 0.5074, accuracy: 85.03%\n", "train loss: 0.5906, val loss: 0.4466, accuracy: 86.72%\n", "train loss: 0.5260, val loss: 0.4126, accuracy: 87.70%\n", "train loss: 0.4874, val loss: 0.3906, accuracy: 88.40%\n", "train loss: 0.4577, val loss: 0.3744, accuracy: 88.74%\n", "train loss: 0.4374, val loss: 0.3625, accuracy: 89.11%\n", "train loss: 0.4202, val loss: 0.3527, accuracy: 89.34%\n", "train loss: 0.4073, val loss: 0.3451, accuracy: 89.52%\n", "train loss: 0.3966, val loss: 0.3377, accuracy: 89.89%\n", "train loss: 0.3869, val loss: 0.3317, accuracy: 90.16%\n", "train loss: 0.3763, val loss: 0.3260, accuracy: 90.22%\n", "train loss: 0.3694, val loss: 0.3220, accuracy: 90.33%\n", "train loss: 0.3649, val loss: 0.3173, accuracy: 90.56%\n", "train loss: 0.3575, val loss: 0.3130, accuracy: 90.66%\n", "train loss: 0.3485, val loss: 0.3080, accuracy: 90.85%\n", "train loss: 0.3480, val loss: 0.3054, accuracy: 90.95%\n", "train loss: 0.3404, val loss: 0.3021, accuracy: 91.06%\n", "train loss: 0.3368, val loss: 0.2993, accuracy: 91.14%\n", "train loss: 0.3339, val loss: 0.2957, accuracy: 91.24%\n", "Final accuracy on testset: 92.04%\n" ] } ], "source": [ "# SELU training\n", "network = MLP(in_features=784, out_features=10, p_drop=p_drop, use_selu=True).to(device)\n", "stl, svl, saccs, sacc = fit(network, trainloader, valloader, testloader, epochs, lr)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Plot results" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 10, "outputs": [ { "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(np.asarray(range(len(rtl))) / len(trainloader), rtl, alpha=0.4, label=\"ReLU train loss\")\n", "plt.plot(np.asarray(range(len(stl))) / len(trainloader), stl, alpha=0.4, label=\"SELU train loss\")\n", "plt.plot(range(len(rvl)), rvl, color=\"C0\", label=\"ReLU val loss\")\n", "plt.plot(range(len(svl)), svl, color=\"C1\", label=\"SELU val loss\")\n", "plt.title(\"Train and Validation loss over Epochs\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"CELoss\")\n", "plt.yscale(\"log\")\n", "plt.legend()\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 11, "outputs": [ { "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(range(1, len(raccs)+1), [a*100 for a in raccs], label=\"ReLU val accuracy\")\n", "plt.hlines(y=racc*100, xmin=0, xmax=len(raccs), colors=\"C0\", linestyles=\"dashed\", label=\"ReLU test accuracy\")\n", "plt.plot(range(1, len(saccs)+1), [a*100 for a in saccs], label=\"SELU val accuracy\")\n", "plt.hlines(y=sacc*100, xmin=0, xmax=len(saccs), colors=\"C1\", linestyles=\"dashed\", label=\"SELU test accuracy\")\n", "plt.title(\"Validation Accuracy\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Accuracy\")\n", "\n", "plt.legend()\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 11, "outputs": [], "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "name": "pycharm-91bfdd0f", "language": "python", "display_name": "PyCharm (SNNs)" }, "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-3.0
QuantumTechDevStudio/RUDNEVGAUSS
rms/parser_test.ipynb
1
149913
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas.io.json import json_normalize #package for flattening json in pandas df\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import json\n", "import os\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"./runs/lagaris/1d_trapz/\")\n", "origin_path = os.getcwd() \n", "runs_id = os.listdir(\"./\")\n", "runs_id = [int(item) for item in runs_id]\n", "runs_id = sorted(runs_id)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_list = []\n", "for run_id in runs_id:\n", " os.chdir(\"./\"+str(run_id))\n", " f_in = open('out.json', 'r')\n", " run_info = json.load(f_in)\n", " f_in.close()\n", " a = json_normalize(run_info)\n", " #a.set_index(pd.Index([run_id]))\n", " df_list.append(a)\n", " #a = pd.concat(a,b)\n", " os.chdir(origin_path)\n", "res1 = pd.concat(df_list,ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x23b27b48c18>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res = res1[res1['Model info.m_train'] == 40]\n", "#res = res1\n", "plt.figure(figsize=(15,10))\n", "plt.grid(True)\n", "plt.title('MSE vs m_trapz', fontsize=26)\n", "plt.xlabel('Number of integration points', fontsize=20)\n", "plt.ylabel('MSE', fontsize=20)\n", "res.plot(\n", " x='Model info.m_trapz',\n", " y='Out info.MSE',\n", " logy=True,\n", " #logx=True,\n", " ax = plt.gca(),\n", " style = 'ro--',\n", ")\n", "\n", "plt.gca().legend(\n", " loc='best',\n", " fontsize=26\n", ")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x23b27a89438>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res2 = res1[res1['Out info.MSE'] > 10e-5]\n", "plt.figure(figsize=(15,10))\n", "plt.title('MSE vs m_trapz', fontsize=26)\n", "res2.plot.scatter(\n", " x='Model info.m_trapz',\n", " y='Out info.MSE',\n", " logy=True,\n", " ax = plt.gca(),\n", ")" ] }, { "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>Model info.a</th>\n", " <th>Model info.b</th>\n", " <th>Model info.m_train</th>\n", " <th>Model info.m_trapz</th>\n", " <th>Model info.n_sig</th>\n", " <th>Out info.MSE</th>\n", " <th>Out info.Std</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1600</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>44</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>1.565983</td>\n", " <td>1.252018</td>\n", " </tr>\n", " <tr>\n", " <th>1601</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>44</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0.099898</td>\n", " <td>0.316224</td>\n", " </tr>\n", " <tr>\n", " <th>1602</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>44</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>0.016354</td>\n", " <td>0.127948</td>\n", " </tr>\n", " <tr>\n", " <th>1603</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>44</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>0.113917</td>\n", " <td>0.337685</td>\n", " </tr>\n", " <tr>\n", " <th>1604</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>44</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>0.002545</td>\n", " <td>0.050472</td>\n", " 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</tr>\n", " <tr>\n", " <th>3041</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>188</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0.099342</td>\n", " <td>0.315343</td>\n", " </tr>\n", " <tr>\n", " <th>3042</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>188</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>0.016354</td>\n", " <td>0.127948</td>\n", " </tr>\n", " <tr>\n", " <th>3043</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>188</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>0.113921</td>\n", " <td>0.337691</td>\n", " </tr>\n", " <tr>\n", " <th>3044</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>188</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>0.002545</td>\n", " <td>0.050472</td>\n", " </tr>\n", " <tr>\n", " <th>3045</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>188</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>0.000946</td>\n", " <td>0.030774</td>\n", " </tr>\n", " <tr>\n", " <th>3080</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>192</td>\n", " 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<th>3120</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>0.068231</td>\n", " <td>0.261341</td>\n", " </tr>\n", " <tr>\n", " <th>3121</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0.099760</td>\n", " <td>0.316005</td>\n", " </tr>\n", " <tr>\n", " <th>3122</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>0.016354</td>\n", " <td>0.127949</td>\n", " </tr>\n", " <tr>\n", " <th>3123</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>0.036926</td>\n", " <td>0.192257</td>\n", " </tr>\n", " <tr>\n", " <th>3124</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>0.002545</td>\n", " <td>0.050471</td>\n", " </tr>\n", " <tr>\n", " <th>3125</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>196</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>0.000946</td>\n", " <td>0.030774</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>266 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " Model info.a Model info.b Model info.m_train Model info.m_trapz \\\n", "1600 -5 5 44 1 \n", "1601 -5 5 44 2 \n", "1602 -5 5 44 3 \n", "1603 -5 5 44 4 \n", "1604 -5 5 44 5 \n", "1605 -5 5 44 6 \n", "1621 -5 5 44 22 \n", "1622 -5 5 44 23 \n", "1639 -5 5 44 85 \n", "1640 -5 5 48 1 \n", "1641 -5 5 48 2 \n", "1642 -5 5 48 3 \n", "1643 -5 5 48 4 \n", "1644 -5 5 48 5 \n", "1645 -5 5 48 6 \n", "1680 -5 5 52 1 \n", "1681 -5 5 52 2 \n", "1682 -5 5 52 3 \n", "1683 -5 5 52 4 \n", "1684 -5 5 52 5 \n", "1685 -5 5 52 6 \n", "1694 -5 5 52 15 \n", "1720 -5 5 56 1 \n", "1721 -5 5 56 2 \n", "1722 -5 5 56 3 \n", "1723 -5 5 56 4 \n", "1724 -5 5 56 5 \n", "1725 -5 5 56 6 \n", "1760 -5 5 60 1 \n", "1761 -5 5 60 2 \n", "... ... ... ... ... \n", "2961 -5 5 180 2 \n", "2962 -5 5 180 3 \n", "2963 -5 5 180 4 \n", "2964 -5 5 180 5 \n", "2965 -5 5 180 6 \n", "3000 -5 5 184 1 \n", "3001 -5 5 184 2 \n", "3002 -5 5 184 3 \n", "3003 -5 5 184 4 \n", "3004 -5 5 184 5 \n", "3005 -5 5 184 6 \n", "3025 -5 5 184 29 \n", "3040 -5 5 188 1 \n", "3041 -5 5 188 2 \n", "3042 -5 5 188 3 \n", "3043 -5 5 188 4 \n", "3044 -5 5 188 5 \n", "3045 -5 5 188 6 \n", "3080 -5 5 192 1 \n", "3081 -5 5 192 2 \n", "3082 -5 5 192 3 \n", "3083 -5 5 192 4 \n", "3084 -5 5 192 5 \n", "3085 -5 5 192 6 \n", "3120 -5 5 196 1 \n", "3121 -5 5 196 2 \n", "3122 -5 5 196 3 \n", "3123 -5 5 196 4 \n", "3124 -5 5 196 5 \n", "3125 -5 5 196 6 \n", "\n", " Model info.n_sig Out info.MSE Out info.Std \n", "1600 5 1.565983 1.252018 \n", "1601 5 0.099898 0.316224 \n", "1602 5 0.016354 0.127948 \n", "1603 5 0.113917 0.337685 \n", "1604 5 0.002545 0.050472 \n", "1605 5 0.000946 0.030774 \n", "1621 5 0.040381 0.201052 \n", "1622 5 0.040382 0.201053 \n", "1639 5 0.040380 0.201048 \n", "1640 5 0.646883 0.804693 \n", "1641 5 0.099899 0.316226 \n", "1642 5 0.016355 0.127950 \n", "1643 5 0.113911 0.337677 \n", "1644 5 0.002545 0.050474 \n", "1645 5 0.000946 0.030774 \n", "1680 5 0.038037 0.195127 \n", "1681 5 0.099890 0.316212 \n", "1682 5 0.016355 0.127950 \n", "1683 5 0.113918 0.337686 \n", "1684 5 0.002545 0.050473 \n", "1685 5 0.000946 0.030774 \n", "1694 5 0.040381 0.201050 \n", "1720 5 1.308294 1.144379 \n", "1721 5 0.099883 0.316200 \n", "1722 5 0.016355 0.127949 \n", "1723 5 0.113915 0.337682 \n", "1724 5 0.002545 0.050473 \n", "1725 5 0.000946 0.030772 \n", "1760 5 0.504081 0.710342 \n", "1761 5 0.099433 0.315488 \n", "... ... ... ... \n", "2961 5 0.099844 0.316139 \n", "2962 5 0.016354 0.127949 \n", "2963 5 0.113919 0.337687 \n", "2964 5 0.002545 0.050473 \n", "2965 5 0.000946 0.030773 \n", "3000 5 0.749179 0.865985 \n", "3001 5 0.099872 0.316184 \n", "3002 5 0.016354 0.127947 \n", "3003 5 0.036925 0.192256 \n", "3004 5 0.002545 0.050473 \n", "3005 5 0.000946 0.030773 \n", "3025 5 0.040375 0.201036 \n", "3040 5 0.009601 0.098035 \n", "3041 5 0.099342 0.315343 \n", "3042 5 0.016354 0.127948 \n", "3043 5 0.113921 0.337691 \n", "3044 5 0.002545 0.050472 \n", "3045 5 0.000946 0.030774 \n", "3080 5 0.756748 0.870348 \n", "3081 5 0.100542 0.317242 \n", "3082 5 0.016354 0.127948 \n", "3083 5 0.036929 0.192266 \n", "3084 5 0.002545 0.050473 \n", "3085 5 0.000946 0.030773 \n", "3120 5 0.068231 0.261341 \n", "3121 5 0.099760 0.316005 \n", "3122 5 0.016354 0.127949 \n", "3123 5 0.036926 0.192257 \n", "3124 5 0.002545 0.050471 \n", "3125 5 0.000946 0.030774 \n", "\n", "[266 rows x 7 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res1[(res1['Out info.MSE'] > 10e-5) & (res1['Model info.m_train'] == 40)]" ] }, { "cell_type": "code", "execution_count": 11, "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 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<td>0.040380</td>\n", " <td>0.201048</td>\n", " </tr>\n", " <tr>\n", " <th>2234</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>104</td>\n", " <td>65</td>\n", " <td>5</td>\n", " <td>0.040380</td>\n", " <td>0.201049</td>\n", " </tr>\n", " <tr>\n", " <th>2310</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>112</td>\n", " <td>49</td>\n", " <td>5</td>\n", " <td>0.040382</td>\n", " <td>0.201053</td>\n", " </tr>\n", " <tr>\n", " <th>2350</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>116</td>\n", " <td>49</td>\n", " <td>5</td>\n", " <td>0.040380</td>\n", " <td>0.201049</td>\n", " </tr>\n", " <tr>\n", " <th>2398</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>120</td>\n", " <td>81</td>\n", " <td>5</td>\n", " <td>0.040384</td>\n", " <td>0.201057</td>\n", " </tr>\n", " <tr>\n", " <th>2751</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>156</td>\n", " <td>53</td>\n", " <td>5</td>\n", " <td>0.040382</td>\n", " <td>0.201053</td>\n", " </tr>\n", " <tr>\n", " <th>2752</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>156</td>\n", " <td>57</td>\n", " <td>5</td>\n", " <td>0.040379</td>\n", " <td>0.201045</td>\n", " </tr>\n", " <tr>\n", " <th>2876</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>168</td>\n", " <td>73</td>\n", " <td>5</td>\n", " <td>0.040378</td>\n", " <td>0.201043</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>79 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " Model info.a Model info.b Model info.m_train Model info.m_trapz \\\n", "28 -5 5 1 41 \n", "29 -5 5 1 45 \n", "30 -5 5 1 49 \n", "31 -5 5 1 53 \n", "32 -5 5 1 57 \n", "33 -5 5 1 61 \n", "34 -5 5 1 65 \n", "35 -5 5 1 69 \n", "36 -5 5 1 73 \n", "37 -5 5 1 77 \n", "38 -5 5 1 81 \n", "39 -5 5 1 85 \n", "68 -5 5 2 41 \n", "69 -5 5 2 45 \n", "70 -5 5 2 49 \n", "71 -5 5 2 53 \n", "72 -5 5 2 57 \n", "73 -5 5 2 61 \n", "74 -5 5 2 65 \n", "75 -5 5 2 69 \n", "76 -5 5 2 73 \n", "77 -5 5 2 77 \n", "78 -5 5 2 81 \n", "79 -5 5 2 85 \n", "108 -5 5 3 41 \n", "110 -5 5 3 49 \n", "111 -5 5 3 53 \n", "112 -5 5 3 57 \n", "113 -5 5 3 61 \n", "114 -5 5 3 65 \n", "... ... ... ... ... \n", "194 -5 5 5 65 \n", "195 -5 5 5 69 \n", "196 -5 5 5 73 \n", "197 -5 5 5 77 \n", "198 -5 5 5 81 \n", "199 -5 5 5 85 \n", "230 -5 5 6 49 \n", "234 -5 5 6 65 \n", "269 -5 5 7 45 \n", "271 -5 5 7 53 \n", "355 -5 5 9 69 \n", "516 -5 5 13 73 \n", "878 -5 5 22 81 \n", "1079 -5 5 27 85 \n", "1229 -5 5 31 45 \n", "1518 -5 5 38 81 \n", "1558 -5 5 39 81 \n", "1588 -5 5 40 41 \n", "1639 -5 5 44 85 \n", "1913 -5 5 72 61 \n", "1992 -5 5 80 57 \n", "2068 -5 5 88 41 \n", "2069 -5 5 88 45 \n", "2234 -5 5 104 65 \n", "2310 -5 5 112 49 \n", "2350 -5 5 116 49 \n", "2398 -5 5 120 81 \n", "2751 -5 5 156 53 \n", "2752 -5 5 156 57 \n", "2876 -5 5 168 73 \n", "\n", " Model info.n_sig Out info.MSE Out info.Std \n", "28 5 0.020602 0.143607 \n", "29 5 0.016742 0.129457 \n", "30 5 0.023801 0.154354 \n", "31 5 0.002786 0.052808 \n", "32 5 0.020919 0.144705 \n", "33 5 0.020933 0.144755 \n", "34 5 0.048123 0.219480 \n", "35 5 0.013348 0.115590 \n", "36 5 0.014256 0.119456 \n", "37 5 0.002534 0.050361 \n", "38 5 0.021469 0.146595 \n", "39 5 0.018435 0.135845 \n", "68 5 0.002216 0.047096 \n", "69 5 0.003521 0.059368 \n", "70 5 0.011947 0.109356 \n", "71 5 0.029590 0.172104 \n", "72 5 0.033579 0.183337 \n", "73 5 0.009101 0.095446 \n", "74 5 0.021763 0.147598 \n", "75 5 0.018085 0.134546 \n", "76 5 0.014408 0.120095 \n", "77 5 0.030586 0.174976 \n", "78 5 0.015163 0.123198 \n", "79 5 0.035955 0.189713 \n", "108 5 0.000227 0.015078 \n", "110 5 0.008134 0.090237 \n", "111 5 0.000310 0.017620 \n", "112 5 0.002974 0.054563 \n", "113 5 0.001763 0.042009 \n", "114 5 0.000481 0.021950 \n", "... ... ... ... \n", "194 5 0.023198 0.152384 \n", "195 5 0.039177 0.198030 \n", "196 5 0.000918 0.030312 \n", "197 5 0.033485 0.183081 \n", "198 5 0.002806 0.053001 \n", "199 5 0.000687 0.026231 \n", "230 5 0.039949 0.199972 \n", "234 5 0.037382 0.193442 \n", "269 5 0.001368 0.037006 \n", "271 5 0.053074 0.230494 \n", "355 5 0.005545 0.074505 \n", "516 5 0.040381 0.201051 \n", "878 5 0.040382 0.201052 \n", "1079 5 0.040345 0.200962 \n", "1229 5 0.040379 0.201046 \n", "1518 5 0.040389 0.201070 \n", "1558 5 0.040379 0.201046 \n", "1588 5 0.040382 0.201052 \n", "1639 5 0.040380 0.201048 \n", "1913 5 0.040382 0.201052 \n", "1992 5 0.040381 0.201052 \n", "2068 5 0.040382 0.201052 \n", "2069 5 0.040380 0.201048 \n", "2234 5 0.040380 0.201049 \n", "2310 5 0.040382 0.201053 \n", "2350 5 0.040380 0.201049 \n", "2398 5 0.040384 0.201057 \n", "2751 5 0.040382 0.201053 \n", "2752 5 0.040379 0.201045 \n", "2876 5 0.040378 0.201043 \n", "\n", "[79 rows x 7 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res1[(res1['Out info.MSE'] > 10e-5) & (res1['Model info.m_trapz'] > 40)]" ] }, { "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": "3.6.8" }, "varInspector": { "cols": { "lenName": 16, 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gpl-3.0
CyberCRI/dataanalysis-herocoli-redmetrics
v1.52.2/Data mining/ruleAssociationMining.ipynb
1
114026
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rmdfTestUsers read_csv success (1/3)\n", "rmdf1522 read_csv success (2/3)\n", "rmdf160 read_csv success (3/3)\n", "gform read_csv success\n", "gformFR read_csv success\n", "temporalities set (user answer method)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "857b740567f344aaa7cb210ec2636432", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, max=1708)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8f4ad17c5dcc49b2b07d28b6a0af8dd8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, max=854)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "profile info set\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "97bda55dc0a64e74b298c78fef985d37", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatProgress(value=0.0, max=126.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "dataFormating.ipynb:16: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n", " \"metadata\": {},\n", "dataFormating.ipynb:16: FutureWarning: using a dict on a Series for aggregation\n", "is deprecated and will be removed in a future version\n", " \"metadata\": {},\n" ] } ], "source": [ "%run dataFormating.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What subsets of scientific questions tend to be answered correctly by the same subjects?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mining" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from orangecontrib.associate.fpgrowth import * \n", "import pandas as pd\n", "from numpy import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "questions = correctedScientific.columns\n", "correctedScientificText = [[] for _ in range(correctedScientific.shape[0])]\n", "for q in questions:\n", " for index in range(correctedScientific.shape[0]):\n", " r = correctedScientific.index[index]\n", " if correctedScientific.loc[r, q]:\n", " correctedScientificText[index].append(q)\n", "#correctedScientificText" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "252" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(correctedScientificText)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Get frequent itemsets with support > 25%\n", "# run time < 1 min\n", "support = 0.20\n", "itemsets = frequent_itemsets(correctedScientificText, math.floor(len(correctedScientificText) * support))\n", "#dict(itemsets)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Generate rules according to confidence, confidence > 85 %\n", "# run time < 5 min\n", "confidence = 0.80\n", "rules = association_rules(dict(itemsets), confidence)\n", "#list(rules)" ] }, { "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>antecedants</th>\n", " <th>consequents</th>\n", " <th>support</th>\n", " <th>confidence</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>(QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>0.962963</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>(QGenotypePhenotype, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>52</td>\n", " <td>0.866667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT...</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>52</td>\n", " <td>0.881356</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.896552</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " antecedants \\\n", "0 (QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer... \n", "1 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "2 (QGenotypePhenotype, QDeviceRbsPconsAmprTer) \n", "3 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT... \n", "4 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... \n", "\n", " consequents support confidence \n", "0 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "1 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 52 0.962963 \n", "2 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 52 0.866667 \n", "3 (QDeviceGfpRbsPconsTer) 52 0.881356 \n", "4 (QGenotypePhenotype) 52 0.896552 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Transform rules generator into a Dataframe\n", "rulesDataframe = pd.DataFrame([(ant, cons, supp, conf) for ant, cons, supp, conf in rules])\n", "rulesDataframe.rename(columns = {0:\"antecedants\", 1:\"consequents\", 2:\"support\", 3:\"confidence\"}, inplace=True)\n", "rulesDataframe.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Save the mined rules to file\n", "rulesDataframe.to_csv(\"results/associationRulesMiningSupport\"+str(support)+\"percentsConfidence\"+str(confidence)+\"percents.csv\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Search for interesting rules\n", "Interesting rules are more likely to be the ones with highest confidence, the highest lift or with a bigger consequent set. Pairs can also be especially interesting" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>antecedants</th>\n", " <th>consequents</th>\n", " <th>support</th>\n", " <th>confidence</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>62</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>56</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>55</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>54</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>(QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>(QDeviceRbsPconsAmprTer, QAmpicillin)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGreenFluorescence)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>(QDeviceGfpRbsPconsTer, QAmpicillin)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>50</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>65</td>\n", " <td>0.984848</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>59</td>\n", " <td>0.983333</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.983051</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.982143</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.982143</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.982143</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>(QGreenFluorescence, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>50</td>\n", " <td>0.980392</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>59</td>\n", " <td>0.967213</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>0.962963</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>0.962963</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>0.962963</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.950820</td>\n", " </tr>\n", " <tr>\n", " <th>110</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.950820</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.949153</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.935484</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.935484</td>\n", " </tr>\n", " <tr>\n", " <th>103</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.935484</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.932203</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>(QBBFunctionTER)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>50</td>\n", " <td>0.925926</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.918033</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.918033</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>60</td>\n", " <td>0.909091</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>(QBioBricksDevicesComposition)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>69</td>\n", " <td>0.907895</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>59</td>\n", " <td>0.907692</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.903226</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>56</td>\n", " <td>0.903226</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.903226</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>55</td>\n", " <td>0.901639</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.901639</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " antecedants \\\n", "100 (QDeviceGfpRbsPconsTer) \n", "61 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "79 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "71 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "11 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer... \n", "50 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "0 (QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer... \n", "24 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe... \n", "26 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "32 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... \n", "75 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "92 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "48 (QDeviceRbsPconsAmprTer, QAmpicillin) \n", "53 (QDeviceGfpRbsPconsTer, QGreenFluorescence) \n", "66 (QDeviceGfpRbsPconsTer, QAmpicillin) \n", "97 (QDeviceRbsPconsAmprTer) \n", "42 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) \n", "82 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) \n", "15 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "20 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... \n", "86 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "46 (QGreenFluorescence, QDeviceRbsPconsAmprTer) \n", "105 (QDeviceAmprRbsPconsTer) \n", "1 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "9 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... \n", "60 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "81 (QDeviceAmprRbsPconsTer) \n", "110 (QDeviceAmprRbsPconsTer) \n", "74 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) \n", "10 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... \n", "12 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "13 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "18 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... \n", "83 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "84 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "63 (QDeviceGfpRbsPconsTer) \n", "65 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) \n", "103 (QDeviceGfpRbsPconsTer) \n", "22 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) \n", "112 (QBBFunctionTER) \n", "73 (QDeviceAmprRbsPconsTer) \n", "107 (QDeviceAmprRbsPconsTer) \n", "98 (QDeviceRbsPconsAmprTer) \n", "96 (QBioBricksDevicesComposition) \n", "43 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) \n", "70 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) \n", "72 (QDeviceGfpRbsPconsTer) \n", "106 (QDeviceGfpRbsPconsTer) \n", "17 (QDeviceAmprRbsPconsTer) \n", "88 (QDeviceAmprRbsPconsTer) \n", "\n", " consequents support confidence \n", "100 (QDeviceRbsPconsFlhdcTer) 62 1.000000 \n", "61 (QDeviceRbsPconsFlhdcTer) 58 1.000000 \n", "79 (QDeviceRbsPconsFlhdcTer) 58 1.000000 \n", "71 (QDeviceRbsPconsFlhdcTer) 56 1.000000 \n", "11 (QDeviceRbsPconsFlhdcTer) 55 1.000000 \n", "50 (QDeviceRbsPconsFlhdcTer) 54 1.000000 \n", "0 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "24 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "26 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 52 1.000000 \n", "32 (QDeviceRbsPconsAmprTer) 52 1.000000 \n", "75 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "92 (QDeviceRbsPconsAmprTer) 52 1.000000 \n", "48 (QDeviceRbsPconsFlhdcTer) 51 1.000000 \n", "53 (QDeviceRbsPconsFlhdcTer) 51 1.000000 \n", "66 (QDeviceRbsPconsFlhdcTer) 50 1.000000 \n", "97 (QDeviceRbsPconsFlhdcTer) 65 0.984848 \n", "42 (QDeviceRbsPconsFlhdcTer) 59 0.983333 \n", "82 (QDeviceRbsPconsAmprTer) 58 0.983051 \n", "15 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 55 0.982143 \n", "20 (QDeviceRbsPconsAmprTer) 55 0.982143 \n", "86 (QDeviceRbsPconsAmprTer) 55 0.982143 \n", "46 (QDeviceRbsPconsFlhdcTer) 50 0.980392 \n", "105 (QDeviceRbsPconsFlhdcTer) 59 0.967213 \n", "1 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 52 0.962963 \n", "9 (QDeviceRbsPconsAmprTer) 52 0.962963 \n", "60 (QDeviceRbsPconsAmprTer) 52 0.962963 \n", "81 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 58 0.950820 \n", "110 (QDeviceRbsPconsAmprTer) 58 0.950820 \n", "74 (QDeviceGfpRbsPconsTer) 56 0.949153 \n", "10 (QDeviceAmprRbsPconsTer) 55 0.948276 \n", "12 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 55 0.948276 \n", "13 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 55 0.948276 \n", "18 (QDeviceGfpRbsPconsTer) 55 0.948276 \n", "83 (QDeviceAmprRbsPconsTer) 55 0.948276 \n", "84 (QDeviceGfpRbsPconsTer) 55 0.948276 \n", "63 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 58 0.935484 \n", "65 (QDeviceRbsPconsAmprTer) 58 0.935484 \n", "103 (QDeviceRbsPconsAmprTer) 58 0.935484 \n", "22 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.932203 \n", "112 (QGenotypePhenotype) 50 0.925926 \n", "73 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 56 0.918033 \n", "107 (QDeviceGfpRbsPconsTer) 56 0.918033 \n", "98 (QGenotypePhenotype) 60 0.909091 \n", "96 (QGenotypePhenotype) 69 0.907895 \n", "43 (QGenotypePhenotype) 59 0.907692 \n", "70 (QDeviceAmprRbsPconsTer) 56 0.903226 \n", "72 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 56 0.903226 \n", "106 (QDeviceAmprRbsPconsTer) 56 0.903226 \n", "17 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 55 0.901639 \n", "88 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.901639 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sort rules by confidence\n", "confidenceSortedRules = rulesDataframe.sort_values(by = [\"confidence\", \"support\"], ascending=[False, False])\n", "confidenceSortedRules.head(50)" ] }, { "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>antecedants</th>\n", " <th>consequents</th>\n", " <th>support</th>\n", " <th>confidence</th>\n", " <th>consequentSize</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>17</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>55</td>\n", " <td>0.901639</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>55</td>\n", " <td>0.887097</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT...</td>\n", " <td>52</td>\n", " <td>0.852459</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT...</td>\n", " <td>52</td>\n", " <td>0.838710</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>55</td>\n", " <td>0.833333</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.982143</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>0.962963</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.950820</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.948276</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.935484</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.932203</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.918033</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>56</td>\n", " <td>0.903226</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.901639</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.896552</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.896552</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>59</td>\n", " <td>0.893939</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.887097</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>55</td>\n", " <td>0.887097</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.881356</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>58</td>\n", " <td>0.878788</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>0.878788</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>54</td>\n", " <td>0.870968</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>(QGenotypePhenotype, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>52</td>\n", " <td>0.866667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>(QGenotypePhenotype, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>0.866667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.852459</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.852459</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.846154</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.838710</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.838710</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>55</td>\n", " <td>0.833333</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGreenFluorescence)</td>\n", " <td>51</td>\n", " <td>0.822581</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QAmpicillin)</td>\n", " <td>50</td>\n", " <td>0.806452</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.800000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.800000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>62</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>56</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>55</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>54</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>(QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe...</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>(QDeviceRbsPconsAmprTer, QAmpicillin)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGreenFluorescence)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " antecedants \\\n", "17 (QDeviceAmprRbsPconsTer) \n", "16 (QDeviceGfpRbsPconsTer) \n", "30 (QDeviceAmprRbsPconsTer) \n", "6 (QDeviceGfpRbsPconsTer) \n", "14 (QDeviceRbsPconsAmprTer) \n", "26 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "15 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "1 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "81 (QDeviceAmprRbsPconsTer) \n", "12 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "13 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "63 (QDeviceGfpRbsPconsTer) \n", "22 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) \n", "73 (QDeviceAmprRbsPconsTer) \n", "72 (QDeviceGfpRbsPconsTer) \n", "88 (QDeviceAmprRbsPconsTer) \n", "5 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "29 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "44 (QDeviceRbsPconsAmprTer) \n", "21 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) \n", "87 (QDeviceGfpRbsPconsTer) \n", "31 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) \n", "62 (QDeviceRbsPconsAmprTer) \n", "80 (QDeviceRbsPconsAmprTer) \n", "52 (QDeviceGfpRbsPconsTer) \n", "2 (QGenotypePhenotype, QDeviceRbsPconsAmprTer) \n", "25 (QGenotypePhenotype, QDeviceRbsPconsAmprTer) \n", "77 (QDeviceAmprRbsPconsTer) \n", "91 (QDeviceAmprRbsPconsTer) \n", "19 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) \n", "8 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) \n", "59 (QDeviceGfpRbsPconsTer) \n", "85 (QDeviceRbsPconsAmprTer) \n", "56 (QDeviceGfpRbsPconsTer) \n", "67 (QDeviceGfpRbsPconsTer) \n", "7 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) \n", "28 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) \n", "100 (QDeviceGfpRbsPconsTer) \n", "61 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "79 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "71 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "11 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer... \n", "50 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "0 (QDeviceRbsPconsAmprTer, QDeviceGfpRbsPconsTer... \n", "24 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe... \n", "32 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... \n", "75 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "92 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "48 (QDeviceRbsPconsAmprTer, QAmpicillin) \n", "53 (QDeviceGfpRbsPconsTer, QGreenFluorescence) \n", "\n", " consequents support confidence \\\n", "17 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 55 0.901639 \n", "16 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... 55 0.887097 \n", "30 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT... 52 0.852459 \n", "6 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT... 52 0.838710 \n", "14 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... 55 0.833333 \n", "26 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 52 1.000000 \n", "15 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 55 0.982143 \n", "1 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 52 0.962963 \n", "81 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 58 0.950820 \n", "12 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 55 0.948276 \n", "13 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 55 0.948276 \n", "63 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 58 0.935484 \n", "22 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.932203 \n", "73 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 56 0.918033 \n", "72 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 56 0.903226 \n", "88 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.901639 \n", "5 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 52 0.896552 \n", "29 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 52 0.896552 \n", "44 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 59 0.893939 \n", "21 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.887097 \n", "87 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) 55 0.887097 \n", "31 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) 52 0.881356 \n", "62 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 58 0.878788 \n", "80 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 58 0.878788 \n", "52 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 54 0.870968 \n", "2 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 52 0.866667 \n", "25 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 52 0.866667 \n", "77 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 52 0.852459 \n", "91 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) 52 0.852459 \n", "19 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) 55 0.846154 \n", "8 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) 52 0.838710 \n", "59 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) 52 0.838710 \n", "85 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) 55 0.833333 \n", "56 (QDeviceRbsPconsFlhdcTer, QGreenFluorescence) 51 0.822581 \n", "67 (QDeviceRbsPconsFlhdcTer, QAmpicillin) 50 0.806452 \n", "7 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) 52 0.800000 \n", "28 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) 52 0.800000 \n", "100 (QDeviceRbsPconsFlhdcTer) 62 1.000000 \n", "61 (QDeviceRbsPconsFlhdcTer) 58 1.000000 \n", "79 (QDeviceRbsPconsFlhdcTer) 58 1.000000 \n", "71 (QDeviceRbsPconsFlhdcTer) 56 1.000000 \n", "11 (QDeviceRbsPconsFlhdcTer) 55 1.000000 \n", "50 (QDeviceRbsPconsFlhdcTer) 54 1.000000 \n", "0 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "24 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "32 (QDeviceRbsPconsAmprTer) 52 1.000000 \n", "75 (QDeviceRbsPconsFlhdcTer) 52 1.000000 \n", "92 (QDeviceRbsPconsAmprTer) 52 1.000000 \n", "48 (QDeviceRbsPconsFlhdcTer) 51 1.000000 \n", "53 (QDeviceRbsPconsFlhdcTer) 51 1.000000 \n", "\n", " consequentSize \n", "17 3 \n", "16 3 \n", "30 3 \n", "6 3 \n", "14 3 \n", "26 2 \n", "15 2 \n", "1 2 \n", "81 2 \n", "12 2 \n", "13 2 \n", "63 2 \n", "22 2 \n", "73 2 \n", "72 2 \n", "88 2 \n", "5 2 \n", "29 2 \n", "44 2 \n", "21 2 \n", "87 2 \n", "31 2 \n", "62 2 \n", "80 2 \n", "52 2 \n", "2 2 \n", "25 2 \n", "77 2 \n", "91 2 \n", "19 2 \n", "8 2 \n", "59 2 \n", "85 2 \n", "56 2 \n", "67 2 \n", "7 2 \n", "28 2 \n", "100 1 \n", "61 1 \n", "79 1 \n", "71 1 \n", "11 1 \n", "50 1 \n", "0 1 \n", "24 1 \n", "32 1 \n", "75 1 \n", "92 1 \n", "48 1 \n", "53 1 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sort rules by size of consequent set\n", "rulesDataframe[\"consequentSize\"] = rulesDataframe[\"consequents\"].apply(lambda x: len(x))\n", "consequentSortedRules = rulesDataframe.sort_values(by = [\"consequentSize\", \"confidence\", \"support\"], ascending=[False, False, False])\n", "consequentSortedRules.head(50)" ] }, { "cell_type": "code", "execution_count": 11, "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>antecedants</th>\n", " <th>consequents</th>\n", " <th>support</th>\n", " <th>confidence</th>\n", " <th>consequentSize</th>\n", " <th>fusedRule</th>\n", " <th>ruleSize</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>62</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>65</td>\n", " <td>0.984848</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>59</td>\n", " <td>0.967213</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>110</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.950820</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>103</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>58</td>\n", " <td>0.935484</td>\n", " <td>1</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>(QBBFunctionTER)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>50</td>\n", " <td>0.925926</td>\n", " <td>1</td>\n", " <td>(QBBFunctionTER, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.918033</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>60</td>\n", " <td>0.909091</td>\n", " <td>1</td>\n", " <td>(QGenotypePhenotype, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>(QBioBricksDevicesComposition)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>69</td>\n", " <td>0.907895</td>\n", " <td>1</td>\n", " <td>(QBioBricksDevicesComposition, QGenotypePhenot...</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>56</td>\n", " <td>0.903226</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>102</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>58</td>\n", " <td>0.878788</td>\n", " <td>1</td>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>58</td>\n", " <td>0.878788</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>54</td>\n", " <td>0.870968</td>\n", " <td>1</td>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>(QUnequipDevice)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>66</td>\n", " <td>0.868421</td>\n", " <td>1</td>\n", " <td>(QUnequipDevice, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>71</td>\n", " <td>0.865854</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>108</th>\n", " <td>(QDeviceAmprRbsPconsTer)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>52</td>\n", " <td>0.852459</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>111</th>\n", " <td>(QBBFunctionRBS)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>50</td>\n", " <td>0.847458</td>\n", " <td>1</td>\n", " <td>(QBBFunctionRBS, QDeviceRbsPconsFlhdcTer)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>101</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QGreenFluorescence)</td>\n", " <td>51</td>\n", " <td>0.822581</td>\n", " <td>1</td>\n", " <td>(QDeviceGfpRbsPconsTer, QGreenFluorescence)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>(QGreenFluorescence)</td>\n", " <td>(QGenotypePhenotype)</td>\n", " <td>69</td>\n", " <td>0.811765</td>\n", " <td>1</td>\n", " <td>(QGreenFluorescence, QGenotypePhenotype)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>104</th>\n", " <td>(QDeviceGfpRbsPconsTer)</td>\n", " <td>(QAmpicillin)</td>\n", " <td>50</td>\n", " <td>0.806452</td>\n", " <td>1</td>\n", " <td>(QDeviceGfpRbsPconsTer, QAmpicillin)</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>(QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>58</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>56</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>54</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>(QDeviceAmprRbsPconsTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsAmprTer)</td>\n", " <td>52</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>(QDeviceRbsPconsAmprTer, QAmpicillin)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>(QDeviceGfpRbsPconsTer, QGreenFluorescence)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>51</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>(QDeviceGfpRbsPconsTer, QAmpicillin)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>50</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>(QDeviceRbsPconsAmprTer, QGenotypePhenotype)</td>\n", " <td>(QDeviceRbsPconsFlhdcTer)</td>\n", " <td>59</td>\n", " <td>0.983333</td>\n", " <td>1</td>\n", " <td>(QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT...</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " antecedants \\\n", "100 (QDeviceGfpRbsPconsTer) \n", "97 (QDeviceRbsPconsAmprTer) \n", "105 (QDeviceAmprRbsPconsTer) \n", "110 (QDeviceAmprRbsPconsTer) \n", "103 (QDeviceGfpRbsPconsTer) \n", "112 (QBBFunctionTER) \n", "107 (QDeviceAmprRbsPconsTer) \n", "98 (QDeviceRbsPconsAmprTer) \n", "96 (QBioBricksDevicesComposition) \n", "106 (QDeviceGfpRbsPconsTer) \n", "102 (QDeviceRbsPconsAmprTer) \n", "109 (QDeviceRbsPconsAmprTer) \n", "99 (QDeviceGfpRbsPconsTer) \n", "95 (QUnequipDevice) \n", "94 (QDeviceRbsPconsFlhdcTer) \n", "108 (QDeviceAmprRbsPconsTer) \n", "111 (QBBFunctionRBS) \n", "101 (QDeviceGfpRbsPconsTer) \n", "93 (QGreenFluorescence) \n", "104 (QDeviceGfpRbsPconsTer) \n", "61 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "79 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) \n", "71 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) \n", "50 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) \n", "75 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "92 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) \n", "48 (QDeviceRbsPconsAmprTer, QAmpicillin) \n", "53 (QDeviceGfpRbsPconsTer, QGreenFluorescence) \n", "66 (QDeviceGfpRbsPconsTer, QAmpicillin) \n", "42 (QDeviceRbsPconsAmprTer, QGenotypePhenotype) \n", "\n", " consequents support confidence consequentSize \\\n", "100 (QDeviceRbsPconsFlhdcTer) 62 1.000000 1 \n", "97 (QDeviceRbsPconsFlhdcTer) 65 0.984848 1 \n", "105 (QDeviceRbsPconsFlhdcTer) 59 0.967213 1 \n", "110 (QDeviceRbsPconsAmprTer) 58 0.950820 1 \n", "103 (QDeviceRbsPconsAmprTer) 58 0.935484 1 \n", "112 (QGenotypePhenotype) 50 0.925926 1 \n", "107 (QDeviceGfpRbsPconsTer) 56 0.918033 1 \n", "98 (QGenotypePhenotype) 60 0.909091 1 \n", "96 (QGenotypePhenotype) 69 0.907895 1 \n", "106 (QDeviceAmprRbsPconsTer) 56 0.903226 1 \n", "102 (QDeviceGfpRbsPconsTer) 58 0.878788 1 \n", "109 (QDeviceAmprRbsPconsTer) 58 0.878788 1 \n", "99 (QGenotypePhenotype) 54 0.870968 1 \n", "95 (QGenotypePhenotype) 66 0.868421 1 \n", "94 (QGenotypePhenotype) 71 0.865854 1 \n", "108 (QGenotypePhenotype) 52 0.852459 1 \n", "111 (QDeviceRbsPconsFlhdcTer) 50 0.847458 1 \n", "101 (QGreenFluorescence) 51 0.822581 1 \n", "93 (QGenotypePhenotype) 69 0.811765 1 \n", "104 (QAmpicillin) 50 0.806452 1 \n", "61 (QDeviceRbsPconsFlhdcTer) 58 1.000000 1 \n", "79 (QDeviceRbsPconsFlhdcTer) 58 1.000000 1 \n", "71 (QDeviceRbsPconsFlhdcTer) 56 1.000000 1 \n", "50 (QDeviceRbsPconsFlhdcTer) 54 1.000000 1 \n", "75 (QDeviceRbsPconsFlhdcTer) 52 1.000000 1 \n", "92 (QDeviceRbsPconsAmprTer) 52 1.000000 1 \n", "48 (QDeviceRbsPconsFlhdcTer) 51 1.000000 1 \n", "53 (QDeviceRbsPconsFlhdcTer) 51 1.000000 1 \n", "66 (QDeviceRbsPconsFlhdcTer) 50 1.000000 1 \n", "42 (QDeviceRbsPconsFlhdcTer) 59 0.983333 1 \n", "\n", " fusedRule ruleSize \n", "100 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTer) 2 \n", "97 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprTer) 2 \n", "105 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcTer) 2 \n", "110 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) 2 \n", "103 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 2 \n", "112 (QBBFunctionTER, QGenotypePhenotype) 2 \n", "107 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) 2 \n", "98 (QGenotypePhenotype, QDeviceRbsPconsAmprTer) 2 \n", "96 (QBioBricksDevicesComposition, QGenotypePhenot... 2 \n", "106 (QDeviceAmprRbsPconsTer, QDeviceGfpRbsPconsTer) 2 \n", "102 (QDeviceGfpRbsPconsTer, QDeviceRbsPconsAmprTer) 2 \n", "109 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTer) 2 \n", "99 (QDeviceGfpRbsPconsTer, QGenotypePhenotype) 2 \n", "95 (QUnequipDevice, QGenotypePhenotype) 2 \n", "94 (QDeviceRbsPconsFlhdcTer, QGenotypePhenotype) 2 \n", "108 (QDeviceAmprRbsPconsTer, QGenotypePhenotype) 2 \n", "111 (QBBFunctionRBS, QDeviceRbsPconsFlhdcTer) 2 \n", "101 (QDeviceGfpRbsPconsTer, QGreenFluorescence) 2 \n", "93 (QGreenFluorescence, QGenotypePhenotype) 2 \n", "104 (QDeviceGfpRbsPconsTer, QAmpicillin) 2 \n", "61 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 3 \n", "79 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... 3 \n", "71 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... 3 \n", "50 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 3 \n", "75 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsFlhdcT... 3 \n", "92 (QDeviceAmprRbsPconsTer, QDeviceRbsPconsAmprTe... 3 \n", "48 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT... 3 \n", "53 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 3 \n", "66 (QDeviceRbsPconsFlhdcTer, QDeviceGfpRbsPconsTe... 3 \n", "42 (QDeviceRbsPconsFlhdcTer, QDeviceRbsPconsAmprT... 3 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select only pairs (rules with antecedent and consequent of size one)\n", "# Sort pairs according to confidence\n", "rulesDataframe[\"fusedRule\"] = rulesDataframe[[\"antecedants\", \"consequents\"]].apply(lambda x: frozenset().union(*x), axis=1)\n", "rulesDataframe[\"ruleSize\"] = rulesDataframe[\"fusedRule\"].apply(lambda x: len(x))\n", "pairRules = rulesDataframe.sort_values(by=[\"ruleSize\", \"confidence\", \"support\"], ascending=[True, False, False])\n", "pairRules.head(30)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['QGenotypePhenotype', 'QBioBricksDevicesComposition', 'QAmpicillin',\n", " 'QBBNamePlasmid', 'QBBFunctionTER', 'QBBNamePromoter',\n", " 'QBBFunctionGameCDS', 'QBBNameTerminator', 'QBBFunctionBiologyCDS',\n", " 'QBBNameRBS', 'QBBExampleCDS', 'QBBNameCDS', 'QBBFunctionPR',\n", " 'QBBFunctionRBS', 'QBBFunctionPlasmid', 'QBBNameOperator',\n", " 'QDeviceRbsPconsFlhdcTer', 'QDevicePconsRbsFlhdcTer',\n", " 'QDevicePbadRbsGfpTer', 'QDevicePbadGfpRbsTer', 'QDeviceGfpRbsPconsTer',\n", " 'QDevicePconsGfpRbsTer', 'QDeviceAmprRbsPconsTer',\n", " 'QDeviceRbsPconsAmprTer', 'QGreenFluorescence', 'QUnequipDevice',\n", " 'QDevicePbadRbsAraTer'],\n", " dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correctedScientific.columns" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "QDeviceRbsPconsFlhdcTerc 43\n", "QGenotypePhenotypec 33\n", "QDeviceRbsPconsAmprTerc 30\n", "QDeviceGfpRbsPconsTerc 21\n", "QDeviceAmprRbsPconsTerc 19\n", "QGreenFluorescencec 5\n", "QAmpicillinc 4\n", "QBBExampleCDSc 0\n", "QBioBricksDevicesCompositionc 0\n", "QBBNamePlasmidc 0\n", "QBBFunctionTERc 0\n", "QBBNamePromoterc 0\n", "QBBFunctionGameCDSc 0\n", "QBBNameTerminatorc 0\n", "QBBFunctionBiologyCDSc 0\n", "QBBNameRBSc 0\n", "QDevicePbadRbsAraTerc 0\n", "QBBNameCDSc 0\n", "QBBFunctionPRc 0\n", "QUnequipDevicec 0\n", "QBBFunctionPlasmidc 0\n", "QBBNameOperatorc 0\n", "QDevicePconsRbsFlhdcTerc 0\n", "QDevicePbadRbsGfpTerc 0\n", "QDevicePbadGfpRbsTerc 0\n", "QDevicePconsGfpRbsTerc 0\n", "QBBFunctionRBSc 0\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sort questions by number of apparition in consequents\n", "for q in scientificQuestions:\n", " rulesDataframe[q+\"c\"] = rulesDataframe[\"consequents\"].apply(lambda x: 1 if q in x else 0)\n", "occurenceInConsequents = rulesDataframe.loc[:,scientificQuestions[0]+\"c\":scientificQuestions[-1]+\"c\"].sum(axis=0)\n", "\n", "occurenceInConsequents.sort_values(inplace=True, ascending=False)\n", "occurenceInConsequents" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "QDeviceRbsPconsAmprTera 41\n", "QDeviceGfpRbsPconsTera 41\n", "QDeviceRbsPconsFlhdcTera 37\n", "QDeviceAmprRbsPconsTera 33\n", "QGenotypePhenotypea 19\n", "QGreenFluorescencea 10\n", "QAmpicillina 9\n", "QUnequipDevicea 4\n", "QBioBricksDevicesCompositiona 1\n", "QBBFunctionTERa 1\n", "QBBFunctionRBSa 1\n", "QBBNameOperatora 0\n", "QBBFunctionPlasmida 0\n", "QDevicePconsRbsFlhdcTera 0\n", "QBBFunctionPRa 0\n", "QBBNameCDSa 0\n", "QBBExampleCDSa 0\n", "QBBNameRBSa 0\n", "QBBFunctionBiologyCDSa 0\n", "QBBNameTerminatora 0\n", "QBBFunctionGameCDSa 0\n", "QBBNamePromotera 0\n", "QDevicePbadRbsGfpTera 0\n", "QBBNamePlasmida 0\n", "QDevicePbadGfpRbsTera 0\n", "QDevicePconsGfpRbsTera 0\n", "QDevicePbadRbsAraTera 0\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sort questions by number of apparition in antecedants\n", "for q in scientificQuestions:\n", " rulesDataframe[q+\"a\"] = rulesDataframe[\"antecedants\"].apply(lambda x: 1 if q in x else 0)\n", "occurenceInAntecedants = rulesDataframe.loc[:,scientificQuestions[0]+\"a\":scientificQuestions[-1]+\"a\"].sum(axis=0)\n", "occurenceInAntecedants.sort_values(inplace=True, ascending=False)\n", "occurenceInAntecedants" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name: Operator XXX</th>\n", " <th>Device: PBAD:RBS:ARA:TER</th>\n", " <th>Name: RBS</th>\n", " <th>Name: CDS</th>\n", " <th>Device: PBAD:RBS:GFP:TER</th>\n", " <th>Function - game: CDS</th>\n", " <th>Function - biology: CDS</th>\n", " <th>Name: PR</th>\n", " <th>Function: PR</th>\n", " <th>Device: PCONS:GFP:RBS:TER XXX</th>\n", " <th>...</th>\n", " <th>Interested in video games</th>\n", " <th>Interested in biology</th>\n", " <th>Studied biology</th>\n", " <th>Play video games</th>\n", " <th>Heard about Synthetic biology or BioBricks</th>\n", " <th>Volunteered to answer more questions</th>\n", " <th>Language</th>\n", " <th>Enjoyed playing</th>\n", " <th>Played Hero.Coli</th>\n", " <th>Temporality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>pretest</th>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>6.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " <td>...</td>\n", " <td>77.0</td>\n", " <td>74.0</td>\n", " <td>6.0</td>\n", " <td>74.0</td>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>posttest</th>\n", " <td>4.0</td>\n", " <td>15.0</td>\n", " <td>13.0</td>\n", " <td>21.0</td>\n", " <td>20.0</td>\n", " <td>18.0</td>\n", " <td>22.0</td>\n", " <td>25.0</td>\n", " <td>21.0</td>\n", " <td>32.0</td>\n", " <td>...</td>\n", " <td>77.0</td>\n", " <td>74.0</td>\n", " <td>6.0</td>\n", " <td>74.0</td>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>6.0</td>\n", " <td>80.0</td>\n", " <td>100.0</td>\n", " <td>100.0</td>\n", " </tr>\n", " <tr>\n", " <th>progression</th>\n", " <td>4.0</td>\n", " <td>11.0</td>\n", " <td>13.0</td>\n", " <td>14.0</td>\n", " <td>17.0</td>\n", " <td>17.0</td>\n", " <td>18.0</td>\n", " <td>21.0</td>\n", " <td>21.0</td>\n", " <td>30.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>73.0</td>\n", " <td>100.0</td>\n", " <td>100.0</td>\n", " </tr>\n", " <tr>\n", " <th>occ_ant</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>occ_csq</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 43 columns</p>\n", "</div>" ], "text/plain": [ " Name: Operator XXX Device: PBAD:RBS:ARA:TER Name: RBS \\\n", "pretest 0.0 4.0 0.0 \n", "posttest 4.0 15.0 13.0 \n", "progression 4.0 11.0 13.0 \n", "occ_ant 0.0 0.0 0.0 \n", "occ_csq 0.0 0.0 0.0 \n", "\n", " Name: CDS Device: PBAD:RBS:GFP:TER Function - game: CDS \\\n", "pretest 6.0 2.0 1.0 \n", "posttest 21.0 20.0 18.0 \n", "progression 14.0 17.0 17.0 \n", "occ_ant 0.0 0.0 0.0 \n", "occ_csq 0.0 0.0 0.0 \n", "\n", " Function - biology: CDS Name: PR Function: PR \\\n", "pretest 3.0 4.0 0.0 \n", "posttest 22.0 25.0 21.0 \n", "progression 18.0 21.0 21.0 \n", "occ_ant 0.0 0.0 0.0 \n", "occ_csq 0.0 0.0 0.0 \n", "\n", " Device: PCONS:GFP:RBS:TER XXX ... \\\n", "pretest 2.0 ... \n", "posttest 32.0 ... \n", "progression 30.0 ... \n", "occ_ant 0.0 ... \n", "occ_csq 0.0 ... \n", "\n", " Interested in video games Interested in biology \\\n", "pretest 77.0 74.0 \n", "posttest 77.0 74.0 \n", "progression 0.0 0.0 \n", "occ_ant 0.0 0.0 \n", "occ_csq 0.0 0.0 \n", "\n", " Studied biology Play video games \\\n", "pretest 6.0 74.0 \n", "posttest 6.0 74.0 \n", "progression 0.0 0.0 \n", "occ_ant 0.0 0.0 \n", "occ_csq 0.0 0.0 \n", "\n", " Heard about Synthetic biology or BioBricks \\\n", "pretest 0.0 \n", "posttest 0.0 \n", "progression 0.0 \n", "occ_ant 0.0 \n", "occ_csq 0.0 \n", "\n", " Volunteered to answer more questions Language Enjoyed playing \\\n", "pretest 100.0 6.0 6.0 \n", "posttest 100.0 6.0 80.0 \n", "progression 0.0 0.0 73.0 \n", "occ_ant 0.0 0.0 0.0 \n", "occ_csq 0.0 0.0 0.0 \n", "\n", " Played Hero.Coli Temporality \n", "pretest 0.0 0.0 \n", "posttest 100.0 100.0 \n", "progression 100.0 100.0 \n", "occ_ant 0.0 0.0 \n", "occ_csq 0.0 0.0 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sortedPrePostProgression = pd.read_csv(\"../../data/sortedPrePostProgression.csv\")\n", "sortedPrePostProgression.index = sortedPrePostProgression.iloc[:,0]\n", "sortedPrePostProgression = sortedPrePostProgression.drop(sortedPrePostProgression.columns[0], axis = 1)\n", "del sortedPrePostProgression.index.name\n", "sortedPrePostProgression.loc['occ_ant',:] = 0\n", "sortedPrePostProgression.loc['occ_csq',:] = 0\n", "sortedPrePostProgression" ] }, { "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", 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</tr>\n", " <tr>\n", " <th>Play video games</th>\n", " <td>74.0</td>\n", " <td>74.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Heard about Synthetic biology or BioBricks</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Volunteered to answer more questions</th>\n", " <td>100.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Language</th>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Enjoyed playing</th>\n", " <td>6.0</td>\n", " <td>80.0</td>\n", " <td>73.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Played Hero.Coli</th>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>Temporality</th>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pretest posttest progression \\\n", "Name: Operator XXX 0.0 4.0 4.0 \n", "Device: PBAD:RBS:ARA:TER 4.0 15.0 11.0 \n", "Name: RBS 0.0 13.0 13.0 \n", "Name: CDS 6.0 21.0 14.0 \n", "Device: PBAD:RBS:GFP:TER 2.0 20.0 17.0 \n", "Function - game: CDS 1.0 18.0 17.0 \n", "Function - biology: CDS 3.0 22.0 18.0 \n", "Name: PR 4.0 25.0 21.0 \n", "Function: PR 0.0 21.0 21.0 \n", "Device: PCONS:GFP:RBS:TER XXX 2.0 32.0 30.0 \n", "Example: CDS 0.0 30.0 30.0 \n", "Name: Plasmid 1.0 37.0 36.0 \n", "Device: PBAD:GFP:RBS:TER XXX 0.0 37.0 37.0 \n", "Device: PCONS:RBS:FLHDC:TER 0.0 37.0 37.0 \n", "Function: RBS 7.0 50.0 42.0 \n", "Name: TER 11.0 53.0 42.0 \n", "Ampicillin antibiotic 26.0 70.0 43.0 \n", "Function: Plasmid 0.0 44.0 44.0 \n", "Function: TER 2.0 47.0 45.0 \n", "BioBricks and devices composition 10.0 61.0 51.0 \n", "Unequip the movement device: effect 8.0 61.0 52.0 \n", "Device: AMPR:RBS:PCONS:TER XXX 2.0 54.0 52.0 \n", "Genotype and phenotype 28.0 82.0 53.0 \n", "Device: GFP:RBS:PCONS:TER XXX 0.0 56.0 56.0 \n", "Green fluorescence 7.0 65.0 57.0 \n", "Device: RBS:PCONS:AMPR:TER XXX 0.0 62.0 62.0 \n", "Device: RBS:PCONS:FLHDC:TER XXX 4.0 70.0 65.0 \n", "Want to learn more about Engineering 85.0 76.0 -8.0 \n", "Want to learn more about Synthetic biology 80.0 76.0 -3.0 \n", "Want to learn more about Video games 84.0 81.0 -3.0 \n", "Want to learn more about Biology 85.0 83.0 -2.0 \n", "Age 32.0 32.0 0.0 \n", "Gender 26.0 26.0 0.0 \n", "Interested in video games 77.0 77.0 0.0 \n", "Interested in biology 74.0 74.0 0.0 \n", "Studied biology 6.0 6.0 0.0 \n", "Play video games 74.0 74.0 0.0 \n", "Heard about Synthetic biology or BioBricks 0.0 0.0 0.0 \n", "Volunteered to answer more questions 100.0 100.0 0.0 \n", "Language 6.0 6.0 0.0 \n", "Enjoyed playing 6.0 80.0 73.0 \n", "Played Hero.Coli 0.0 100.0 100.0 \n", "Temporality 0.0 100.0 100.0 \n", "\n", " occ_ant occ_csq \n", "Name: Operator XXX 0.0 0.0 \n", "Device: PBAD:RBS:ARA:TER 0.0 0.0 \n", "Name: RBS 0.0 0.0 \n", "Name: CDS 0.0 0.0 \n", "Device: PBAD:RBS:GFP:TER 0.0 0.0 \n", "Function - game: CDS 0.0 0.0 \n", "Function - biology: CDS 0.0 0.0 \n", "Name: PR 0.0 0.0 \n", "Function: PR 0.0 0.0 \n", "Device: PCONS:GFP:RBS:TER XXX 0.0 0.0 \n", "Example: CDS 0.0 0.0 \n", "Name: Plasmid 0.0 0.0 \n", "Device: PBAD:GFP:RBS:TER XXX 0.0 0.0 \n", "Device: PCONS:RBS:FLHDC:TER 0.0 0.0 \n", "Function: RBS 1.0 0.0 \n", "Name: TER 0.0 0.0 \n", "Ampicillin antibiotic 9.0 4.0 \n", "Function: Plasmid 0.0 0.0 \n", "Function: TER 1.0 0.0 \n", "BioBricks and devices composition 1.0 0.0 \n", "Unequip the movement device: effect 4.0 0.0 \n", "Device: AMPR:RBS:PCONS:TER XXX 33.0 19.0 \n", "Genotype and phenotype 19.0 33.0 \n", "Device: GFP:RBS:PCONS:TER XXX 41.0 21.0 \n", "Green fluorescence 10.0 5.0 \n", "Device: RBS:PCONS:AMPR:TER XXX 41.0 30.0 \n", "Device: RBS:PCONS:FLHDC:TER XXX 37.0 43.0 \n", "Want to learn more about Engineering 0.0 0.0 \n", "Want to learn more about Synthetic biology 0.0 0.0 \n", "Want to learn more about Video games 0.0 0.0 \n", "Want to learn more about Biology 0.0 0.0 \n", "Age 0.0 0.0 \n", "Gender 0.0 0.0 \n", "Interested in video games 0.0 0.0 \n", "Interested in biology 0.0 0.0 \n", "Studied biology 0.0 0.0 \n", "Play video games 0.0 0.0 \n", "Heard about Synthetic biology or BioBricks 0.0 0.0 \n", "Volunteered to answer more questions 0.0 0.0 \n", "Language 0.0 0.0 \n", "Enjoyed playing 0.0 0.0 \n", "Played Hero.Coli 0.0 0.0 \n", "Temporality 0.0 0.0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for questionA, occsA in enumerate(occurenceInAntecedants):\n", " questionVariableName = occurenceInAntecedants.index[questionA][:-1]\n", " question = globals()[questionVariableName]\n", " questionC = questionVariableName + \"c\"\n", " sortedPrePostProgression.loc['occ_ant',question] = occsA\n", " occsC = occurenceInConsequents.loc[questionC]\n", " sortedPrePostProgression.loc['occ_csq',question] = occsC\n", " #print(questionVariableName+\"='\"+question+\"'\")\n", " #print(\"\\t\"+questionVariableName+\"a=\"+str(occsA)+\",\"+questionC+\"=\"+str(occsC))\n", " #print()\n", "sortedPrePostProgression.T" ] }, { "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 }
cc0-1.0
raoyvn/deep-learning
first-neural-network/Your_first_neural_network.ipynb
1
328844
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Load and prepare the data\n", "\n", "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "data_path = 'Bike-Sharing-Dataset/hour.csv'\n", "\n", "rides = pd.read_csv(data_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>instant</th>\n", " <th>dteday</th>\n", " <th>season</th>\n", " <th>yr</th>\n", " <th>mnth</th>\n", " <th>hr</th>\n", " <th>holiday</th>\n", " <th>weekday</th>\n", " <th>workingday</th>\n", " <th>weathersit</th>\n", " <th>temp</th>\n", " <th>atemp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " instant dteday season yr mnth hr holiday weekday workingday \\\n", "0 1 2011-01-01 1 0 1 0 0 6 0 \n", "1 2 2011-01-01 1 0 1 1 0 6 0 \n", "2 3 2011-01-01 1 0 1 2 0 6 0 \n", "3 4 2011-01-01 1 0 1 3 0 6 0 \n", "4 5 2011-01-01 1 0 1 4 0 6 0 \n", "\n", " weathersit temp atemp hum windspeed casual registered cnt \n", "0 1 0.24 0.2879 0.81 0.0 3 13 16 \n", "1 1 0.22 0.2727 0.80 0.0 8 32 40 \n", "2 1 0.22 0.2727 0.80 0.0 5 27 32 \n", "3 1 0.24 0.2879 0.75 0.0 3 10 13 \n", "4 1 0.24 0.2879 0.75 0.0 0 1 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rides.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Checking out the data\n", "\n", "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n", "\n", "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10dff8748>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ffapq86tvD37PeA3MPoSUuDz4PvcDevBJ3Ykx6Co7Dz6bbAkhdcUsPOLk4I8q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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu\nGJroAr/8+p1bdOSvya9aS2w6h8XbdJyk6AB8Fr6O9Bz8mFwF320fhuwFnk4sGkFnBT/ujICUFae7\nOHbhCnyXItCy7voNAKwG58eWUODzjbbBvieaoSq0V/vecICiMu3sdCWq5ODTXfnu1jguWdljfK77\n8JOk58JxDn5cjoKfpwW+YDsaEMwC/8sAbkapyG8DcCGALwJYDeC/GWPbyWO75v9fKQZD/7r4DjqJ\ncJNsPSr4YRp2xSn4ggubuhT4NsaKVyriL1npzyErc4fHSQ6+GdkefADYtZ768EeF/3ynGeir6qDg\nN0lU8ItFjd+CrqJQmSfZ+pGBr1MpiljE7BBZU1kvX9MLoJTnvXm4w9XP4OaD+DwAsBpzEnPwAVMW\nfggabVPZPD7xs2fxxn/7Fe4/dN74utcG/KGu4ERl0v6fiik6MWuLjqZpmCIzLLpa4thBCnxdwfdm\n0ZFzn6TXR6tp3l4JXKSKpmkfNX3pGQBvZ4wlAXwAwF8BeJWE37vT6uvzKv4O0b+vEoWixo1Md5OQ\nQKGr/GTAh11xCr7gwoa+j/Vosq20YKl006cXKJm0SezRsGvPsErRkRmTqUMV/L3HJ5HOFoR6fmlx\nZ0fBb2uKYaCjiZvmK92DL1HBp1Ma41FWNZHG3GA27JNFBygp4MfGFha4tCnPLZmcHIvOn//aFly+\nuhfblnVxirQTVvQEU8HnB12J1yDp+yVil0Y23919Ep//xZEFX/eq4FMPft0VfBszUyo12aayBaNQ\nbo5H0ByPYvNwB5rjEczlihiZTOPc9By3Q+3JopMTc8xomma6Ri4OBb8SX5j//3Xka7pC3wVr9K+L\nN9hKYjqdM6ZfdjTHPP/RaQEn2mMtGj7jV56C71eT7aytJtv6KvhUIRbhOaZQdcKJRaenNe6LPWmw\nsxnr54deZQtFbiiKCJym6AALffjSJ9maJimLxMnrNzeY+RGRqVPJw54MsEWnNRHDbZcsM45fN/AK\nfn092BSZg66A+vRjeeHI+dkFX+ttS+Cy+V0ct1Ab3Kk6e/DtKPiVBl1x/vv5RKl4NIKLlhEf/vFJ\nLhY1CE22haIGjUw6j0pQ8MNU4Ot7U9So+tz8/zeaH8wYiwFYAyAP4Hm5T00c4wL994ApJjPgTbaz\nmcby4KftNNlaFLKDHU2cP1YmfIyqYIuOyyZbP+w5OlTFl1rg2yzuzD586ZNsyfMSnYPPR2RWf/3m\nFB0/LTp0Jga9TojY8cw63MXxk+V0wnegFHzSZCthB4trsg2BRYcqz2++ejW+8pbL8cCHbvQ802aY\nWHROjKfqmqRErz3NFXZtKuXgV3I8UJFs7/EJrv5xPMlWQoGfL9IEHTnXhmBdcapz5fz/abF+7/z/\nX2Lx+OsAtAJ4WNO0+oa8OoAerF7994BcC4ZoGi0H384FJR5lMC/cL1nZ7UtEIMDHqIru0bBd4Mfq\nV+BvHCr7l0VP9OV7EOwdz34r+FyKjmAF3+4ODrAwI3+40z+LzmsvW4HO5hjW9Lfh965dY3xdiEVH\nkoIvghW9fIEXFGhKiYydvLA12dL79o6VPbh+44CQ3pzlPS3GrInRZBYnJ+qn4s/ZsLJVmmRL42zp\nfZ422j5waBSF+YI6EYs4XmxTIUxUio7sKbZAwAp8xtgWxtiCKAnG2GoA/zD/6VfJt74DYBTA6xhj\nl5LHNwP42Pynn5fyZCUxPitmiq2OTAuGaFISU3TqsS3L5TlX2HZkjC3YkvTLfw/w77PIHg1N0/gL\nWJUGy4UFvn+ptsvJTonobWo3xZ1ZwZfRZEhp8knBr3VDpcdHayLKNULK5vI1vXj8z2/FvR+4nnv/\nRaRKyYrJFAG16JycSAcmC58ehzIU/LA12XrxjlcjEmHYvoKo3Cfq52TmbVnW1wrahJovasaQMj4i\ns3yfp1GZehY+AFcD9HiLjpg6SvYUWyBgBT6A1wI4wxj7CWPsnxhjn2CMfQfAAQDrAfwUgDHNVtO0\naQC/ByAK4BeMsS8xxj4J4AkAV6G0APim3y/CCyKn2AK8ApgKepOtxBx8Ou3RNwWfNtlWeT3mAv8S\nHwt8WT0avDpRvcHSrO76FZEI8HnrpybFNpplXFh0VpMCszURrfq+iaBZpge/YC8mFeBz8P0ackVJ\nxCJgjJliYwWn6ARMwe9sjhuKZyZf5Jq764kdYcQLvIIf/AKf3kecxjvWwiovvh7YWQgzxnibzvw9\nhovIJPf5wc5m3L5z+YKf4+Y9pMehKAVf9hRbIHgF/v8C+DGAdQBeD+D9AK4H8CCANwJ4uaZpWfoP\nNE37wfxj7gfwGwD+EEBu/t++TguKLGGTcc6i4y1BB+BXnkG26BSKmpGewJi1N90LdNrjZDpb5ZHi\nSHETGStfVKgPOhZhuHBZpZ5x8fA7POIWgE4aLM3Fr+wpthSaJDEymRY6ut5Nk+3agTajyLTKxReN\nzBQdJwscukXtp//eDL35JwWou1yKjoREGK9wNp2A+PDTNtRcL9Dd3DCk6IjMvjdjlRdfD+wo+IB1\noy29n5tTB//Pr2/FpiV8jKyb95BT8AVdJ+1O+fZCoGIyNU27D8B9Lv7dQwBeJv4Z+Q8dclUpn9kJ\n7SGx6NCLemtcvHJZlxz8bO2YTIBXUbcMd0rZlq5EG9dkK+744PzXNYq7eir4Hc1xdDbHMD2XRzZf\nxNhsFgMdYixCTnPwgdKC67O/eTF+8tQZ/M5Vq4Q8j2rInGTr5Big2+9DPvrvzXDXSyEKfnAtOgCw\noqcVz4yU7AsnxtPYKf+Qq4n0HHyq4C9iiw4AXEwsOvtPTWEuV5CSXFQL7jyp8vuplUW/vnAefFOB\n35qI4fNv2IFX/sNDxvvoRsGXkYPPZeAvEovOoodadISk6DSJPzBlQO0hojPwgVKBrcdQzeWKwv3G\nVtiJyQT4C9oOn+IxdWR4CwFn/ut6NtkCvE1nZFKcD99Nig4A3LR5CT71mu2cP1YWvEVHoge/hkK1\njexaXeEx/s8Loic7B9miAwRz2JWdyEQvhDlFR7R1tactgbX9pZ3CXEHDvlOVZobKZc7GJFvAutG2\nkkVHZ+1AO/7ujouMzzcPOR8M1xyPQHcNZvNFo2HXC3kfmmwDpeAr+CZbMR788gVBxA1LFnwxLP6i\nzhhDV0sc4/MLqKl0DoMdcpWKlI2YTIAf5uKn/x4QX9DocBGJDi06fjbZAqU0iWfPzAAATk2mOVXL\nC9SDHsTiDjA32crLwa91DOxa14cvvGEn0rk8XnHRUqHPwwn0fJgRYdHJB9yiE8BhV/7m4Af3ngiU\nwgr4Xi7xJdslK3vw/Ggpa3/PsUnsXOX/Atvuos4qKrOaRUfnJduG8dW3XoGDZ2fwustXOH5+jDG0\nxKOGSJrK5tHhcsCcDu1TkzHFFlAKfuCYEOzBpxeEIDfZyszA1+n2OUnHbuzntRsGAJR2bK7fOCD9\neVFk7fBwDZYOFPxohKGv3d8Cn1PwBUbFUf910DLQdejNNJXNC1GmdDiPaY3XzxjDS7YN4VWXLJfW\ncGYHc0+K1xYuWZNsRbE8gMOuuEm2MlJ0QqTgZ/JFIy89FmFShAIuL/5EfRpt7e500Z1A6ybbyjXT\nNRv68ZZr1riuL6hIJ6LRNu/AwugWpeAHjAnBg644j3WAPfgyp9jqdPo8zZYuqKo12b73lg24ZkM/\nVvW2Cum7cEKbpCbsjKMppuXvD7Q3SZnoV41lsiw6PjRReYUWUKPJLH7j8w/jk7dfhI1LnG9jm3GT\nIlRv9IxsfRt+Llf0VGQG3qITsGFXmqbxTbYS3jOqvM7M5aFpmu+pTXYxJ+jIeJ40lnnPsfo02mZs\nKvhWFh3aU2f24ItE9LCrnFLwFx9cTKYQD344UnTo4kOGBx/wv9HWbpMtYwyXre71NT1Gp01wU6EO\nbSCqVdjQ7/ttzwH88uAHT70FSufEtRv6jc+fODGJX/vsA/iffWc8/+wwLHCsoDnZMx6HXQW9yXY5\nseicnprjfMH1IFsoQt80iUeZlN2cRCxiNO8Wilqge9NkJujobFzSbqjTZ6bncFrwPBA78JNsHVp0\nqIIvwNZcida42N3uXEFNsl1UFIoaP7ShynaTXdolxSCKJpWRr+D7WeBrGn/jkGU78oqsHR4nDab0\nokwtA36xrIdm4de/ydZv/vWNl+F9t2w0EipyBQ1fffSY55+bC8nrNyNy0Rv0Y6A5HsXgfGpUoajh\n9JTYWRBOmcvKbbDVCUujLV1gik7Q0YlFI9i+nObh+6/i85NsqzXZ0hQdCw++gJqpElTBT+e83ytz\ni22S7WJnKp0z1IvO5pgQ9aIpFkGEdH/n6qzQVGJW4hRbHT8L/GyB904G8eYO8Ds8KQGeYx0nhc2u\ndX24Zcsg1g204W3XrRXy+52wrFtOgZ8p2Ltp1ZtELIL33LIBX3rjZcbXxpLeZ0WEVcHnGs895qS7\nmWbsN0FK0klLjsjUCUujrczhjxTqw3/82Li031OJOZsKPi2Es3kNc7mCsTiIR5mUgA6dVsEWnXyR\nHwYpg2BecQLIVDqHn+8/KyRZoRLjgiMygZL9IwyNtimJUWA6tMNetgc/LTkVSBTxaMQowAtFTViS\nCm2yraVOxKMRfOmNl+GeD9yAi5b7GxMKlHz/+gV2IpUTFhcahiZbyhoyWEvEAphL0YkF0+NsBc3J\n9m7RIR78AKboAMFK0pGdoKMTlkZb2Qk6OpetLifnfG/PiO/vScamgk+vo7lCkffftySk9lKILvCz\neZqDrxT8uvLaLz6C3/33x/HOr+2R9jsmBQ+50glDo63dzHgv+Kngz3IJOsG05+jIaLR1M8W1XkQi\nDMNd4lX8rIMUmSAg+vzgj4HgLnLNiBx2FXQPPmBW8OubpFMfBT+4BT6NLu5wMaDJLtds6MeqvtJx\nMJXO4V8feEHa77LCroLPpejkiyb/vTx7DsAHZQhJ0SnKv0cG/64TAPJFzcjJfuDQqLTikFPwBTaL\nuG201TQNP3xiBF95+KjwIThm7DakesHPi3qavB4/J9O6QcaUvrAlqCztLjc4nxQUlZml6m3AFzlA\nqYDQBbBkJu+54TJsCxwdfjaERwXfpjJZT4KUpMMl6Ei8boZlmi2n4Evs44pHI3jvLRuMz//1wRe4\nwA/Z8Ck69gddUVFUpv8eAFrjElN0lEWnfhRN2dD7T01L+T00IlNkN7jbRtuHDo/hPd94Ah/50T58\n/ZfHhT0fK6hS1ggpOn6kH4hCxrCrnA8ZvyJZ1l0uck5Nimk0DHqDpZlIhJkKH2/HQthevw616CQ9\nK/jBjskEgOW95d2rA6enhfXhuGFOckSmDlXDZzwe5zJJ+mTRAYBXbl+G9YPtxu/94v3PS/19Opqm\ncQp+tZ2uOE3RKRT5UBLpCr7Yqe8qRScgmIe/yBrnTKfY9raJO1g575iDAu6XL4wZHz8zIneEtR8K\nfrevFp0QKfhNYi9cAF/cBbWwoSwjCv7IpBgVM1sIvj3DjMhFMN9kGyIPvqwmW4mWEy9cMNxpLMAO\nnk3ingPn6vZc5iQPudIJo0VHVoqOTjTC8L5bNhqff+Xhozg/k5H6O4GF0ajV5qAkOAVfw1SK9+DL\nRPSgKz5FRyn4daOgmQt8OQq+Hx58Jwrt0bFyoTPpo2ddmoJPm2ylW3Tkx36Kgj8+xFh0qD1Fljoh\nEj4qc3Eq+IDYAj+sMZlCLTohWOh2tybwhitWGZ9/6u6DC3at/SJNYjKlevAF7lTJxK8mW52XbhvC\nluFOACW71A+fGJH+O530qSRIs342X+QjMiUr+OYp117JKwU/GPin4Mvx4NMD81cvjOPln3sAr/+X\nR/Hkiep5t8fGZo2P6eJDBlyKTgPk4PuxYBEFTS1yssNTjbBFJHLDrgR58MPWhwDw54jXcz5sx4CO\nqCbbfKFo3DsiTN60ShG844Z1RkF94PQ0fiZg0JkbfEvRaSEpOqZ7wX/+6jiu/pt78fd3H5T2++1C\nBZd2iU22OpEIw20XLzU+Fzn4rxL839z+1POSB1/s3KBq9BHR9dyMdxGIn2SrCsBbD3IAACAASURB\nVPy6YS7wD59LCtmiMSPLg08LuC89+AKeGZnGw0fG8Kp/eggf/8l+y9eiaRpeGCUFvp8FcQOk6NBC\nuTWgW/M6fMqSKAU/XMWtjGm2YXsPAH6Xy7NFh4vJDMfrB/gC34s/26xMyozw88pARxPeuGu18fmd\ndx9ccN/zg7RvMZnWTbbFooa//skBjEym8Zl7DmHP8Qlpz8EOs5xFx5/7yEBHeZq4iHkYteAb0au/\nxgUFvo8e/KGuso3zjICBcJwHX1KMcHiuunXEfKErasCzZ8TbdGTk4AOVC+aiBvzLAy/gtn98aMHN\nfDKV425uU5Jz4zkPvqQLWUs8anjdsvmi1GSgVIhiMt2mLFUjG7om23KBf2Z6znOCDBCuqFCdLoHe\n5NAq+M1iLDph28F523VrjcXN4XNJ/PipU74/BydqrhcqNdmenEhjhlwD77yrviq+Xyk6lL52UuDP\nyvfgO5kVQc+jTL7Ie/AFiqJW0ChlMQU+EUCUgl8/zB58QI4Pn2439QhcjZoLTMaAi1eUBwo9d3YG\nH/z2k1x6wlFizwFKCr7MdAU6gEuWgs8Y4xpxZKr4KW4yb8AVfM5bKGjIE7loh6G4aY5HjS3YQlHD\nOQHNZVyjcUCHHJkRucsVtgJXR5RFJ2yN5j1tCbz56tXG5/cfHPX9OczVOQf/ubMz3OMePDyKR46M\noV7M+Nhkq0OtKH4o+HM0ItOxgu9fTCZV8E9PzXmuh/Jck60q8OuGVcORDB8+v90kssmWP2nee/NG\nfP+du/DRV241vnbX/rP4ZxKLdWyMTxIpFDVhEYpWzPqg4ANAF/FeypxmmwrJJFuAV4ZETTqmP8ev\nG5NX+EZb7zadTAgVbJEFfqoOxYkI6IJ3xsM1LwxTbM3QSdITkvuurPBt0FWFJtuDpgIfAO68+7m6\nRYf63WQLAP1EwR/1w6LjRMEnaTNmD36X5AK/szlm1FLpXEFAyhidZKssOnXDyosoWsEvFjWuqU3k\nwapPqAOAGzYN4A9vWg/GGN64azWn2HziZ88aaoVZwQckF8Q+KPiAfz78lA89BaJok5CDT29MQX/9\nOks6ywqNVwVf07TQW3S8nu+zPp3ToqH2jaSHIUhhmGJrhu4c16XAJyk69Wiyfe7MwgL/saMTuP+Q\n/7sZgMmD70OTLcDbg8dnM9ITlZwo+HQnMFfQfJ1kyxhboOJ7Ie+DABSOu06dsSrwnz09w3movJLM\n5qH/mrZEVOiW9i1bluBDL9mEt1+/Dp/7zUsQIWkOf/LSLdi5qgdAyZP/x999CpqmLVDwAXkFsaZp\nnIIvU/H2q8DnC9xg39zbBA/wAEw7MgF//Tqy/OfxKOPOuSAj8vzwa1dONG2CLDphmGJrhhZJMgWd\nStCBR35OstUVeqrgX7S8y/j4C784Iu25VCNZh53QRCyCzvnFRFGTH7BBFXwnKTrZfJG7RnVLzsEH\nxPrw1STbgGDlwc8Wijh8Linsd0yl5NhzACAWjeCdN6zHh1+6GR3N/Co3EYvgH1+/Ax3zF4/j4ykc\nPpf0VcHP5IvG4iYRi0jNTfdNwc+FJyazVXC+L2Ca5Bvw168jcnx9GNV7QOwwuHrYC0QgarIzN+wu\n4ElaOvTeUw8Ffy7rj0WnOR41zstcQcNcrohcoYgj58v39L959UXGx7KisWtRr3OI2nTGknIbbedc\npuiksnnj/GSM33mThUgFP1dUOfiBgCr46wbajI9F2nT89JKZGepqxtXr+43PHz4yZqng04YWkcz6\nkIGvQ29gUgt8H1+TV9olpOiEUb0VOsU1pA2mnUIVfDrsLZwF/mw279qiUA97hVfMCzy/ozLTPqXo\nALxNZ2Yuh6Ojs0Z04bLuFmwZ7jCew/RcXnq0splCUePeDz/jlvvay/dJ2T58J8lJVCyhz6urJe7L\nLulSrsD31qdFBwGqSbZ1hF7kdq0rF8LPjIhb1fs5kc2KXev7jI/v2n+Gi+zUkaXg++lX5woYiQoV\nLW5kjlwXQavkJtuw+K95X663hU6YejAoIm1K3CI3JIs8AIhGmKEeaxq/G+eEZAibjGPRiKGEalqp\n8PUTv1J0gIU7djRBZ+OSdjDGsLyn3L92Ynyh6CWTpEkk8tPm19fmX1Sms0m25ZL1POmTkp2gozNE\nLDqePfhKwQ8G1KJz1bpyIWzVkOMWP5tFrNhFXtdDh61jwWQpGH6qvdVU2mJRE9ZQlA6Rekmfn6gm\n2zAWNyIV/DC+foAfdOXFe1ssar4Mr5MFl4XvcthVWI+BHs6m42+B71eKDgB0cOd7HgfJ/XzjUAcA\nYAVJ1jo54W+BX88dIKrgy47KdDvJlk6TlZ2BrzMscNhVlvPgqwK/fszXfM3xCFb3lS06owK9afRm\n2uVDs4iZdQPt3AQ7K7yOrq+En2kblYq4Z89M44r/ew9uufM+IZ7DMFlU6PMT1WQbpjkAOiI9+LMh\nVa/bEzHoQmEqW3AdJGC2WkRD0mSs0yHAh08XBmEq8LvrmKSTpn5s6Qo+2bEzKfiblswX+L1UwRcz\n4dou9exh6fPRg08V/FrJSdTKQi06/in44iw6fIqOsujUnc7mOLeytbKxuIXaReqh4DPGOBVfh97o\nZCn4fkyx1alU4H/5waM4P5PB86Oz+OnTpz3/njRn0Qn2zb1NRpNtiCb56lD1WqSCH5bXDwCRCBOy\nk0EXuGEqbnVERMcmQ+jBB/g+JVmiTiUy9bLopHM4eLbcYLtRL/CpRcdnBb+eO0D91IMvsM6xgt4r\nay3qaEgItU77VTMtNVl0vMxHyNEcfDXJtv50tcRN25dZYZYOzqLjc5OtjlWBfyGJCpPlwfdTwe+u\nUMQ9S9Sb8wK2JP1sHPYKVdhFNNnmCkWjyTQaYaGJCDTf8L0QVnsGIMaqFNYMfB0uSWfRWXSIgj9b\nR4uO5Osm7bk5MZ4ykuMYA9YPtgMAVvS2cI/xEy6JzOdziPPgS1bw6XnSUeM82bmqB9dvHFjwdb9q\nps6WmLHwTGUL3JA0p9Dd0bike2Q47rwBobMljkSs3IRU1MSp2vwU23oV+P0LvrZ9RXmyoaw83JSP\nmel0DPeJidIWW7Go4RAp8L0235Zy/cNT4HBNttmC50Ur32AbBWPhsGdwDaYeLtxAeCMiAVEFfvgs\nWhTOg7/ILDpUxJKdgW6GqrmyU3So/eaz9xyGLsau7mszrCJck+2EvxadZKb83vtv0fHPg8+dJzV2\nuhKxCL78psvw16+6kFsMrOlvq/KvxMEYE+bD5wp8SRZGVeA7QL/x0SJxTND2FR+T6b8HHyhd8JaT\npiIA2E5Gl0/JUvBpMSz5Qraqr81YgZ+fyeDczBxGJtNc6onXm9pcrmhsHzbFIoGPSaSpIQCvorkh\nGVJ7BlX0vFt0/B9QIwo+acrd+0DPp7C9fkBMFn54LTp02JW/Fh0/U3Red9lKDM1Pr6YNjxuXtBsf\n00XAyYmUJ0uGU+g1xI+Mdwq16IiqcSrhdKcrEmF4/RUrcff7r8ebr16NN+1ajdsvXSHzKXKI8uHn\niUVHKfgBQG/K6WkT78OfqnNMpg616cQiDFuXdhqfy8rB9zMzPhph2DLcYXy+79T0gjQkr1YkGi3n\n94XZLW0Cs/BTIVVvW+JRo4krmy9yxYZTZkNqzwAkKPghe/2A2aLj7j0Iax+G2YbqJ3ToUa2GS6/0\ntiXwj7+1AzGTeqo32AKlc0G/78/lijgv2a5CqWejPrXoiAwTsWLG5UJ4qKsZH3nFVvzVK7f6eo0V\nNeyKm2SrFPz6Y6XgjwvKiK13TKYOtems6G3ltuqkefB9trNsXVruK9h/appLTwC8q7fU3mGeHBxU\nRDbahrHBFihtv4ry4YfZolOpT8UJfJNteBZ5OrTQcHs+OPEWBwk+Rcc/i06hqBlKOmPwpXdn56oe\n/NmvbeG+pkdk6tQrSaeeC8SulriRfDUzl0cmLyZ8wQq6gA7DeWJutHULbbJVOfgBQN+67uUKfAke\n/DpZdADghk0DxgLm5s2DaCEjvTMeVc1K+D0QZ9uy8q7EMyNTOCi4wOdu7CFR8OnCyquCzzcYh+P1\n6/A+fPfHAadKhazAFaHgh3HQGYUqgjMu+zHCOMkWqF+KDpeHHvOvd+dNu1bj1ZcsA1BqML7a1ItG\nk3T8zMLnjh+fz6FIhJnqHHnHQdisbEOcB9/9go/z4Esq8IP/bgaILqPAL29fiVDwNU3jvK71VPC7\nWxP46XuuxeFzSVy5tg+MMXS1xo2pcZOpHIa6xBYs9VTw952aXmAj8XpToxadsNgz2gQm6YQ1Ax4w\nD79ZnAo+LfDd7tqZp3CGDXreuj0faPNgmBa6Pa3e//5u8DNBh8IYw6desx2vvWwFVvW1cRZcoH5J\nOvW+hvS1JYz7/lgyi+Gulhr/wh1ha0YflmDRiUvKwQ/+uxkg9O170U226VzB2JpsikWkew9rsaSz\nGUs6ywdxdwsp8NNZbgUrAj9z8AFgw5J2xCIM+aKG4+OpBf63qXQOxaLmejT4zFz4FHx6A0l5tOik\nQpQgZIZT8NPuFzr1vjl7QYiCnw3v6wfE5ODPhHAnDzCl6PhY4PMKvr/mAsYYrli7MCYaqJ9FZ6bO\nfTz97U0ASrvbMn343P2yKfiWVlEefHptlWXlVRYdB+gWHdFNtkHx31eiW7Ki43dmdlMsagwyAYC8\nKRayqPEXV6fwikTw/p5WcE22HqfZhrW5EOCnW3pR8MPqvwbEFPg0ASRsxwDg3aKjaVpoF3mVJtme\nm5njhguJhivwA7TrYzXsaiqdkzb4UafeFi8/ojKLRY1PXQvBQph68N3GZBaLGnf8yKr7VIHvAD1G\nr09mgV9H/30laGynjAKfz8H35wSn6UBWeIkEnQ5hio5ID76fcw1EI8qDPxviArezxXujMT0GwpSk\npEPPWzfnQzpXgF4LN8Ui0jy2Mmhvihm7mqlsAZl8AV95+Cgu//g9eOln7pfWcJnOkgSdWHCOGc6i\nM5HCI0fGcNnHf45r/uZeHD43U+VfeqPe1xBu2JWgMBEzqVzBmD/Qmogajb1Bprs1bjSAJzN5zpJr\nl5m5vHF96GiKqSbbINBl2WQroMAn8ZNddZpiWw0+VUP8Sp7Pwffnwr5tWVfV73uJBKWKX2dICnze\nc+wxBz/j31wD0YjIgAfCbdGhIoOISbZhe/2Ad4tOMoQ2PR3G2IJd26/98hgA4ODZJB59flzK753L\n18eDXws67OrU5Bw+8qNnkM0XMZPJ4ydPnZH2e5N1btT3Q8EPm/8eEDPsiu6MdbfJq/lUge8A3YMv\nusCnhURXEC06ApruqpGqQ+pKLQXfy+sMWyoAwKusInPww5wgIy5FJxzHgA69Brld6IY5SQnwPugq\nzDY1gE/SGU1mcHS03Fx66Kwc1ZpOsZU95MoJzfEoBjpKanahqOHg2aTxvVOT8jz59RYJ6LCrUVkF\nPpnWG5Z7JcD78EdcHANcgS/RtaEKfAfo6l6facqb1+l2fERmAAt87oYvw6JDPfj+XNi3DHfCnMK2\nkjRTeXmd/KCr4P09rZCVgx+2Jls+B19Uk21wihU7CBl0RS06IXv9AK+6ey3ww7bAA/gknX0j09yk\nV/NgQFHQFJ3meLBKkxU91gkyboo7uyTrvEj2w6LDN9iG5zxZ3ddmfPzMyJTjf+9X32WwzqIAw1j5\nAGyJRw0PVjZf9FwQBb3JtktyqsJsHRI32ppiWNNfPkkZAy5d1WN8PuUhKjOUKTpkYZXy2GS72Ivb\nYlHjFq1hU7DbiBd2Lld05bmmrz+MBS69Dk2nc45FnLAX+FTBf/wYb8kxzw0RBddkGyAFH+CTdCgy\nFfx6z1PxxaITwt1uALhiba/x8cNHxhz/e6rg09Qq0agC3yYdTTEjNpExxjfaejz46TZ4t8Q/tlu6\nW+R68PmhOP5d2Gke/qreVm7bTZhFJyQ391YBsYA69WiaFoXeSA+4t+jMmpqM3cat1gvGmOeFDl3k\nhbHJti0RNVTsTL7I2TLsEEZvMYUq+I8fm+C+d/BsEkUJaTq0wA+SRQcAlldR8L3u4FsRhBSmUkxm\niTFJMZlhPU+uWlsehvb4sQnHA0DphOgepeDXH7M3vpesbsc9DkbiPPhBt+gIVvCz+aKx/RvxaTy5\nzjbiw9+wpEOYFWmaU/CD9/e0gl5cUx6bbMPcYCnEnhLi16/T7TFJhy5ywnTj1mGM4ap15Vz0h4+M\nOvr3YVUmdajQ9Pz5We576VwBJyfEK9fUgx80BZ/aN3ta48aOZyZfFDILx8xcrmikrCTqlMJEew1H\nBViRrZgJYaQ0UPLgrx0oOQCy+SL2HJ+o8S94qENApqirCnybdJoKNZHTbP3IQ/VCt8SYzLTJyuDX\neHIAePn2pcaF+vady4W9TurBD0uKDtdk69WiE+IhR5wH36WCzzWOhez163R6bKxP+TzbQgZXrSur\ndE634WdDuItHqXUfek6CTWcuX/b5BylFBwBu3DRo2GT+5KVbOMuODJtOEHaBWxNRoxcimy963tm1\nIqzD4ADgKjIY7RGH1wel4AcMs7LOTbP1atEJeA4+H5MptsCvZzPesu4WPPKnN+OhD9+EF28d4nZp\nvFiRuG3HkFy0uCZbjxdyPkElWDfqWnQJiMkM+5AnwPtORjLEfRg6u4iC/+jzY46GPIU5RQmo7QuW\n4cMPsoI/2NmM+z94I+7/4I14zWUrsKy7bNkZkbCbEYQ+ppIVmdp0xO9UhDlOdpcHAWBCKfjBwqzg\n0wug16jMyYAr+Fxsnkc7kpl6+7U7m+PGxVpUHOhMCC069L1PeU3RCXEOPr3JzGTyrrzGQbg5e8VL\ngZ8vFJGZV2MZC56f2i5r+9uwpLNU4MzM5bHvlP20jLB6i3VqqYoyCvy5AKfoAKUJ9iv7Ssr9Ulrg\nS1fw63cP6ecSA8X78MO823klabR98sSkI2FMpegEjAUKfru4Ap/6sYLowe9oihmpGrPZArJkK9Ur\n9ECvt9pNV9JuPfi5QtGIe2MsPAo2bS49N5Px5Lfk/Nchs2fEohHjRqNpvBJrl6DcnL3gpcCfraPt\nTiSMMW4b3olKN9tAHnydGGkWlxGVeXSs7PUPujCyrMfPAr9+95DBznLwxIHT4v/mYe5V6Wtvwuah\nDgBAvqjhsaP2B8CpFJ2AQQsgQOywq6Ar+CJSNSpBm7Xotmc9ENFMbPbehqW4WdrVYhS247NZnJ12\np9ZoGh8RGTQvrR1o34SrBtOA3Jy94OVcoLtyYUzQobjdhp+pcwKKV6yKjl3ry+/F8+dnkSuIE3rm\ncgXcf7DcyEwXVkGEKvgyPPj1TtDRoTa1u/efFf7zZ0K+00WvD058+JOcB18V+HXHrKyLKvAz+YJR\nEEUjLLAHuayozBPj5QmJlbKG/aLL9BrdqNj0gmW2dQWZSIThguFyqpCb4R1AKVVC9yonohEkfExF\nEkWnUP95MM/nWtCIvBMTqSqPXAi1aAX1emYXmqTz2AvjtncvaYEWpgE+OlZC08XLu7B0Pko4Wyji\n2Njsgse45aHDo8bO59r+NqwfbBf2s2WwrNvbJNNanJmeMz6WWQDW4tYLlhgfP3JkTHijbb2z/r2y\na527HT5qde5uUxadutNZrcnWQ4E/ZZpiG1TFt0tSVCYtHipNC/SL5ng5NSBX0Fx50ae5KbbhumBt\nXVYu8Pedmnb1MxrBf07PdTdJOkFIwPDKFrLY2+/wWOAy8EN6DOis6G3Fit7SdSmdK+Cpk5O2/l2Y\nrQeAdYG/brAdG+ctCQDw3BlnswGqQdVhWlQGlWXdNEVnrsoj3XGQWKA2LKnfYmd5T6txLcgWirjv\nufNCf34ypDGZOpev7YXuXHvm1JStYAY6HDUaYVIFAFXg20SWgs9l4AfQnqNDFfy//ukBvPNru/GT\np057/rknxsvqx/I6K/iAKRLUjXob4i1HOvjrGQcNhRS6KAprPGIXlwHvXLEKyva6Fy4gMyIOnUs6\nGuQyG+JBZ1bsWuvcpkOvA2E8Bppi0QX2qnUD7di0hBT4ghpti0UNPz9wzvg8DAX+QEeT0ZMwPpvl\nEoBEQN9b+p7XA/r3uHv/GaE/O+wWnc7mOC5c3g2g1LP16Au1rw+cei9Z1FUFvk0W5uCLKfAnTQp+\nUKFNV3uOT+KnT5/Bu7+xFycdbt+b4RX8ABT4HhODZkIc+7V1qXvVVqcR4hG5LHxXHvzwx2S2N8Ww\npr80yKVQ1Bw1VaYa4PVTdq3n4zLtkAy5RQdYaA1Z09+GjaTYPCio0XbviUmMzk9K7WtL4JKVPUJ+\nrkyiEYZhSTYdTePPt411LvBfRAr8e589J7T3IuwWHYC36djx4U/4lKADqALfNuYm287muJEsk8zk\nkcm7W8HzcUnBy8DXsVJVCkUNu485m+BGyReKOD1V3t6sNA7cT7zmoM9kqEUnuAs2K9YPthue+ZHJ\nNCZcLFxTIR5ypdMl0KIT1uIO4Bd8Tixbsw3UZAsAF6/oNj4+dM6eLSXsFh2ALz6Gu5rR1hTDJmLR\nuffZc7jmE/fi1//hQfzS5sLHCmrPuXnLoHFfDTpLu+Q02o4ms0YR2JaI1j18YuvSTqP3Ynouj8de\nsJ8WU4uZEFtadXY5nHjtV4IOoAp825gtOpEI4/44E7PufOnm7Zqg8rILh3H3+67DF96wE6/cvtT4\nuluvNgCcnpozGjIHO5oCMdyEU/C9WnRCdsGKRyNG7Bfg7m/LDXkKqT2DLubdNNk2gkUHcG/ZaqQm\nW6DkQ05ES7fK8zMZW4u+RtjFodfCtQOl3Zz1g+2G5zhbKOLkRBpPnpzCx396wPXvuYvYPm69YMj1\nz/EbWVGZdMbAhiUdiNR5wcMYwy1E4LtLUJqOpmkNEUhw6apexKOlv9HBs0mcn6meQDfp05ArQBX4\ntrFKROEbbd3FCtICIsgefKB0sXnJtiG87MLyRdht2goQrAQdHerBd1PcTYfYogPwRZ2TwT46qUz4\n1Vveg784p7gCwDaXTdd8TGb4zgEz0Qgz7EpAKSKyGsWiqXAJ6XtAi491A6VGz+Z4FG/atWbBYw+c\nnnZl3ThyPmm8n83xCK4hUZxBZ5mkqExqz6m3/16H9+Gf9TQnRSedK0CfI9gcjyAeDWc52pKIcray\nR2rsZvERmcqiU3fWD7ZznnsdET58zqLTElyLDoUvAqddn+xBStDR8ZqFH9aYTB1qy3jGhYJPhxyF\nVb2lfzevMZlhfQ8A/jx/9vQ08jYLuEZZ4FB0BRsAjtSw6ZgtSmGxnJjRbRkAOGvOX77iAjz+57fg\ngQ/diOH5x+QKGo6OOo/NpKks124YCNXcDFrgj0zIUvCDERd6xZo+w244Mpnm5te4JewJOhQnPnzq\nwe+xqCtFogp8GzTHo4hZrC6FFPhpul0TjoN8eU+LMQxoKp1zvT1JE3SCouBzcaAu8v7DPHobALYt\n86bgN0JEIh+TuThTdIDS9U0v8jL5Io7UUK51aJJSmF8/RVewAeD50RoFfoNYlN5w5SpcvKIb128c\nwKsuWcZ9r7+9CSt6WzlLn5tUHZpKdN3GAfdPtg7QYVciLTpcgs5QMBT8RCzCxSgfFJCgNNMADbY6\n/MCr6j58atExW79Fowp8D4go8A8TNWigo6nKI4MDY4z354648+EHLUEHMFl0PCr4YbxobR7qMBTH\nF0ZnuWLVDo0Qkeh1anOjFHgAcAF3nttb8HELnBApstXgFfzqCx1ukR/Ca4DOqr42/OBdV+Mrb7m8\notWK5uI7TdXJF4pccy5VQcMA9eCfmhJT4Guaxr2PQbHoAHyaj4iI1DBHSpu5eEW3MUPn6Fiq6oJP\nNdmGBK8F/lyugD3HyoNTLl0d/HgwHT5S0Z0Pn3rwl/c2nkUnbCk6QGm3at18MaNpJW+tExohIpE2\n2Xr14If9xuXGh8/t4oR0kWfGiYIf9mxvJ3jJxd93atpQcZd0NmEt6XMIAzRF5/RkOTDCCyOTacPm\n2N0aD5ToJzoitZHOk0QsgstW9xqfV7PpTCgPfjgY7CyffDTu0S67j00gO+9rXT/YjsGO5hr/IjhQ\nK4cbrzYAnCA+vuAo+B4tOg1w0dpm6rFwQrLBmmydKviNkgyh4yZJZ7YBLTpUwT86mqraj9BIOzi1\n4Iq+s84m21J7zq51/YGd4l6JlkTUCNrIF7Wa6Sl2oNaXjUs6AvWebOLsWN6nGDfKTpfOVTbjMlWK\nTkhYTopSqkbbhR4EYdue5DOynSv4c7mCcUGMRpjRrFVvujwq+NMNkOtLp5g+7TAlqRFy8LlBVw5z\n8DP5oqHkJaIRY65AWKEK/oFT0yjaUClnG7DJtqM5jsF5NVWPh6xE2PtwnEBjM4+OzTqaeEzvf1eF\n7P6nQ3347/r6Hhw+503Zfu5MuXAOkj0HADYOlp/PkXNJ2033leB2uxvgPKE+/EerKPhcik6bUvAD\nC01+cdNVzisY4brArR1oNzxnZ6czjtULOgF3aXezZRNzPaArajf+67Cn6ADARcvLg32eODFZ5ZEL\naYT879ZE1BhDP5crOhpi12gJMkOdzYYVcSaTx3EbQkYjKviAfZtOchEp+M3xKFb1lS19h20OAsvm\ni3j8aHlIYtjufzp0YbL72ARe9pkH8Z3dJ13/PE7BD0iDrU5XaxxDnSUhLlso4uiYtyn2jTDFlrJt\naach6JyamqvYw8dbdJSCH1iW9bRA30E7PZV2lAM8M5fDUydL6ihjpRiqMBGNMGwZdq/icwk6AbHn\nACaLjgsFvxEmWF64rMsocA+fSzpa6PBNtuEscBljnE3n7JT9xWujJOjolBrqne3opBogA94Ku422\nybnGsh7UYiOJcnzOpjf7yZOTSM+r/St7W7nd8DDxwRdvwrtv3mBcL7OFIv7s+0+7EoeAYGbgU7im\nao+NtmEeCmlFLBrBhsHyuXDQYjdH0zSVohMWmmJRLJn3zRc1Z8MuHjs6bmzlXzDcKT0PVQZevNpB\nTNABSuqtPpUunSs42nI2+6/Dqkq0JKLc4s2Jit8ITbYAb1P6xcFztv9dMVeVSgAAIABJREFUIzXY\n6mx3uKMz2wB9GFbYV/Ab7xioxqYlzou+hw+Hd/eaEo9G8P5bN+LH776Gi5R90uHOJ1BKFTp8vnxc\nbQxIBj5lk4vFXCX48yScu91muKQhi/cnmckjP1/3tcSjaI7LvT6qAt8jK0j6C1Wla9EIFzgvPnx+\nim0wEnQAXb0tL7acpKiksgVj0RbmyXwAcMnKclG39/hElUfyNMIETwB4kWlyo10ascGSHgt7bBwL\njTDszArbCn6DLHLtstFFFn4j+O8pm4c6cQu5Ztg5T8zsOT6JbL7kAljS2SS9AdMNG10s5iox0wC7\n3WZqvT9+TrEFVIHvGao+U1W6FuYEgTBCEzaeHplyNNE2iEOudGhU5n0Hz1d5JE8jKRI7yOjtvccd\nKPh0imeIPej0Zv3IkTHbW+60wbJRiruLV5QL/H0j0zV7EhrlGDBjX8EPf6O9EzY5jE9MZwvcNaUR\nCnzALIo4V/C/9fgJ4+ObNg8KeU6i2eRxsBkl2WBNtgCwaaj6DseEjwk6gCrwPbO813mSzsRsFgfO\nlCwt0QjDZWt6a/yLYLJxqN1oKjkxnsaDh6tPcKPQxVDQ/Jc00eeD33kK7/zabltNxDPEe9sZ8hu7\nWcG3k54CNI56O9zVggvno2DzRQ2/eM6eTacRGyz72puwuq90jmYLReyvYsfL5AvIFUrHSizCkAjx\nLpaZZd0taJq/3o0ms5yXltIIUblOWN3fZtgaT03N1UyeevLkZGjjoavBiyL2r5lA6d7xk6dOG5+/\n5tIVQp+bKNYPtht9h0dHnaUmmaH3y0Y5T8wKvln0nPAxQQdQBb5naJLOCZtJOo8+Pwb9737R8q7Q\nHtxNsSju2Lnc+Pzv7jpoS8XXNC2wFh0AeN+tG7kBIz99+gze8KVf1mying75FFvKyt5WI+N5ei6P\n50erT+/UaST/9a0ubDqNGBEJAJeQ4mVPFXXS3IMRpBxvr0QiDGvIMKYj563PicVm0YlHI9zuxqEa\nyi61r1y6KjzDHWuxsrfVSJwqXTPtZ8X/+KnTRtPxxiXt3K5ZkGhNxLByXtQsasCR8+7z8BshkMLM\nsu4WI1xiIpXD+SQvDPqZgQ+oAt8zK1wo+Pc8W1YDrw6pPUfnD25ab6j4T56YxD0HaiudJZWndHK3\nN8Uw0B6caX1ASYn5+fuux2uJivLc2Rl8+/Hq8WeNlArAGHPsvS4WNaSIgh/2KaYv2lou8O977rzh\nj61Go6Xo6Oyw2ZPRCClK1VhHUjKer1DccBadBjoGqsE3F1Yv+qh9hV5jwg5jjDtPqi2EzXzzsbI9\n5zWXrgj0wliUD7+RJtnqMMb4pCHTuaA8+CFjOZeFX7vALxQ13EsK/Ju3BNNrZ5fhrhb81hUrjc/v\nvPtgza1JWiBsX9EVyItZV2scn7j9IvzRizYaX/vcvYeqbknygzvC7cEHeNXWjqc0Rd6blngU0Ujw\n/q5O2LSkw9hdmsnk8ejzlYeX6HApSg1y0wLsHwuNMAehGuuIgl8p851rtA75Qt8u1Jv97JnKFi5N\n07jrP7W1NAKXmGw6djh4dsZIp4pHGV69Y3mNf1FfNjlYzFWjERLnrODeH9MCiPPgtygFP/AMd7UY\nGbijySzXYGbF7mMTGJ8t/ZEHO5q4CLqw8o4b1qFlPu5p/+lp/GzfmaqP33OMKDgrgn2Bf8s1a9A/\nv8NwemoO3/jV8YqPnWmAKbYUp0k6qQazpzDGcOuWIePzv/jhM/itLz2K935jL0YqROLSXZxGKnA3\nD3UYg+1GJtM4Oz1n+bhZrsG2cV6/Do2PfezouOVjkg26i1MNmqj22NHK14qTE2mMJkv3v47mGGft\naQTcNNp+i6j3t16wxLD5BBWqUO8/7Swem8IX+OEXxHQ2Vmk6pwp+t1Lwg080wrhx1bUm2t69v1z8\n3nLBEkRCrnICwGBHM964a7Xx+Z13HzTiIq3Ye4IoOKuCvcBpTcTwzhvWGZ//w/8eQTprreI3mqdw\n+/JuYwz9c2dnuNdnxWwD2XN0qA//2FgKDx0eww+eOIW/+MEzlo/nLCoNVNzFohFuwnGlBR/nwW9A\ni84Va8uJL0+enLI8J2YaMB2kFpeu7jWErgOnpw0Rywy1+l28orsh7n8U8zVzpkbDcaGo4QdPjBif\nB7W5lkIV6vsPnse7vrbH8SR7TdNMYkjjXCuqJQ0dIsOvZE+xBVSBLwQ+C7+yTUfTNNxFmvVo8RB2\n3nbdWsNHd/hcEj96csTycZl8AftGyqv+iwOu4APA669YaYzoHk1m8O+PHLV8HN9kG35Foq0phk1D\nJWVO01BzeEsj+s8vW92Dbcs6F3z9F8+dw+mphYv5RkzR0bETndro6nVvW8JQ8QtFzVLFb8TzoBbt\nTTFsJ42hup1tz/EJvPxzD+AjP3wGhaJm8t8H/9rvFPM1U59WX4m9xyeMHY3+9iZcu2FA+nP0yvrB\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ\nmZmViAt8MzMzM7MScYFvZmZmZlYiTRf4kvaSdKakOyT9QtIGSeskLZJ0hqTCMSRNlnS3pJfyNo9L\nmiNpVEHfCZIulXRrHmOrpJB0cD9xfVDSlZJ+IOmF3H9Vk6/1nZK+ImmZpI2S1kj6F0mH1+l/mqS5\nkh6U9Oscw01NxjBB0o2SnpP0hqSVkq6WNK6g7xhJF0iaJ2mJpE05hjObiaEZzpeuzpf9JV0r6eH8\nHryRt3tQ0mxJY5qJpcH4nS/dmy89ecx6jwXNxNJg/M6X7s2X+dvJl5B0XzPxNBC/86VL8yX3303S\nFZKW5phflnSPpOObiWPEiIimHsDngQCeA/4JuBK4EXglt99GvqFW1TanAJuB14DvAH8DLM39by0Y\nY2ZetxVYAbycnx/cT1xX5z6bgCX536uaeJ07AYvyfh4B/hq4GXgTeB04umCbyrivAj/P/76piRgO\nAlbn/XwfuApYmJ8vBfaq6b9HXhfAC8Az+d9nNvtzd76UMl+mAuuAHwHXAV8Drq/Km4XAaOeL8yX3\n78nrlgC9BY/ThjJXnC9dny8z6+RJb34fA7jY+eJ8yf3HAU/k9f+b35MbgLW57YyhzJXh+GjFB2Q6\ncDKwQ037PmwrDD5Z1b47sAZ4A5hU1f4OYHHu/4c1+5oAHAfsnp/3DeAD8n7gA8CO+XmzH5AvVT7A\n1a81f9gjJ2LtezANOAQQqXhq9gNyT97HeTXtf5/br6tp3xE4Cdg3P++l8wW+86W782WHgv2MAe7P\n23zK+eJ8ye09uX3+UOaE82V45ks/+9kDWJ9/BuOdL86X3H5Nbr+dqgNLwN75Z7MemDCU+TLcHu3d\nOVySf0Bzq9o+m9v+saD/9LzuJ9vZ73Y/IAXbNPwByQn+dN7HAQXrH8jrpvWzj6Y+IKRvvwE8VfBB\n3I10NOF1YJd+9tFLhwt858vwyZeabS7I+7u003nifOmOfKELC3znS/fmSz/7Oi/v65ZO54jzpXvy\nhW1fsH6zYH9z8rrLOp0n3fxo90m2b+bl5qq26Xn5w4L+D5C+lU2WtFM7Axukg4CJwJMR8VTB+h/k\n5fSCda0yLS9/FBFbq1dExKvAQ8DOwDFtjKHdnC+t07J8yfNKP5qfPt7KIJvkfGmdZvLl3ZLOlnRJ\nXh7Zxjib4XxpnVb+f/S5vPxW68JrCedL6zSSL/vk5S8L9ldp81z8frStwJc0GviT/LT6w/CevHyy\ndpuI2Ez6hjcaOLBdsTWgbszZ8rw8tOQxtI3zpXtikDReUm8+Ieta0vzIGcDNEXFX60MdPOdLV8Xw\ne6RzNq7Iy8ck3S9pYmtDbJzzpTtjkHQs8D5S8Xl/i2JrmvOlK2J4MS8PKOhfeX/fU7DOsnYewb8K\neC9wd0TcU9U+Ni/X1dmu0r5HuwJrQDfE3A0xtJPzpXtiGA9cDlwGnEM6AvS3wKwWxtcs50vnY1gP\nfBX4bdIJceOAj5DO15gK3Cdpl5ZH2hjnS3fGcFZefrvpiFrL+dL5GP49L79SfXUiSb8BXJifFl59\nx5LR7dippPOBi0hH/k5vxxitJqm3oHl+RKwcovF7KCigIqJ3KMbvJOdLQ+P30KZ8iYilaQiNAvYD\nTgX+EviQpI9FxEvNjtEM50tD4/fQ4nyJiDWkL4HVHpA0g3TFjqOBM0kny3WM86Wh8Xto8/9HksYC\nnyJdKWZ+q/bbLOdLQ+P30Pp8uQw4ATgNWJIvoboL6cTgZ0nTjrbW39xaXuBLOpf0C/1nwPEFxUDl\nm9pYilXaX2l1bNtxeUFbH7CSoYm5p04MvXnZre9bU5wvDeupE0NvXjYdQ0RsIZ3odI2k1cAtpEL/\n3EHG2jLOl4b11ImhNy9bFkNEbJZ0A6nA/zAdLPCdLw3rqRNDb162IoZPk+ZdL4iIF/vpN2ScLw3r\nqRNDb14OOoaIeF7S7wBfBj4OfIE0beefST+j5aQrGlkdLS3wJc0B/oF0zdLj8xGeWsuASaS5Vv9Z\ns/1o0nyrzRSfWNE2EaF+Vi/Ly3pz1A7Jy3rzywYyfh/pbPeOxTDUnC/DKl8qJ2JNHWD/lnO+DKt8\nWZuXHZui43zp+nypnFx7/cAjax/nS/flS0SsJh1QestBJUmVE4IfGVSgI0zL5uBL+nPSh2MJ6XJL\n9b5ZLczLEwvWfZj0jX5xRLzRqthaYAXpSOahkopO+DgpLxcWrGuVyglIM2rvridpN2AKaU7sT9sY\nQ8s4X4DhlS/75eXmfnu1ifMFGF75UrkaxpAWOhXOF6CL80XS0cBvkU6u7WtjnAPifAG6OF8KVE6A\nvrk14ZVUK661SfoTSgCPAntup+/upKM7A75RRME++hjC68jm7Qd9o4ia7afS4RuL0CXXwXe+dGe+\nAEcBowr2sytwb97mCueL86UqX4pujHY8sDFvM9n54nwp2PY7uc9FQ50fzpfhkS+kA9C7FuzndNLc\n+4f6i9mPSLdgboakz5BOkNkCzKX4LOmVETG/apuZpFtAbwQWAC8BnyBd8ug20t0y3xKYpPlVT08E\n3gV8j3QbZYAbImJRVf/DgC9WbfMZ0jfEW6vaLo4Bzv3L17VdCEwm/SK4j3SSxx+QThKaHhEP12wz\nk3SbakjXdD2BdETrwdz2YkRcPJDx8/4OIv0S2Ru4k3T76KNJ15h9kvSf6a9qtvkicFh++n7SUZPF\nbLss1aKIuGGgMTTL+dK9+SLp+6QjKYvZdqfA/UlHePbI7SdExGsDjaFZzpeuzpc+0p/WFwOrcvOR\nbLue9pcj4q8GOn4rOF+6N1+qttsdeI40RXjCQF9zOzhfujdfJO0KrCYdXFpBKuqnAMfmbX83Ip4b\n6PgjUrPfENh2VLi/R1/BdlOAu4GXgQ3A/5AuffS2I4i5//bGmFXTf+oAtukZ5GvdmXSS4XLSN/i1\npA/cEQ2+NysbeL/3B+YBz5M+mE8DVwPj6vTv204M89vxzdH5MvzyBfgYcBPpl+060o1e1gA/Jl3O\nbvRgx3e+lDpfzgD+jXQi32s55mdIJ8EdN9S54nzp7nyp2uacPF7H71zrfOnefAHGkP7Ss4x0l9vX\nSVOoLgF27nTuDIdH00fwzczMzMyse7TzRldmZmZmZjbEXOCbmZmZmZWIC3wzMzMzsxJxgW9mZmZm\nViIu8M3MzMzMSsQFvpmZmZlZibjANzMzMzMrERf4ZmZmZmYl4gLfzMzMzKxEXOCbmZmZmZWIC3wz\nMzMzsxJxgW9mNsJIWilp5Ugd38ys7Fzgm5mNcJJmSQpJszodi5mZNc8FvpmZmZlZibjANzMzMzMr\nERf4ZmYlpORcSU9I2ijpWUnfkDS2pl8fMC8/nZen6lQePVX9Rkv6gqSfSvq1pPWS/juP8bb/SwY6\nflX/sZL+TNJCSaskbZK0VtK/Sjq2pu+4PP4KSaqzv7vya5g0qDfOzKwEFBGdjsHMzFpM0jXA+cDz\nwG3Am8ApwMvAfsCmiOjJ8+5n5nV3AkuqdnN1RLwiaQxwF3ACsAzoAzYC04AjgZsi4vRGxq/qfwzw\nQH6syP0mAp8AdgJOjogfVvW/EZgNzIiIe2vG3h94ClgSES7wzWwMVTl8AAADxElEQVTEcYFvZlYy\nkiYDD5EK5Q9GxEu5/R3A/cAxwNOVAjsX+fOA2RExv2B/vcDlwDeAORGxJbePAr4FfBaYGRF3NjJ+\nXjcWGBMRL9aMPQH4D2BdRBxe1T4JeAS4PSJOqxPvWRHx7QG/cWZmJeEpOmZm5TM7L6+oFNcAEbER\n+NJgdpSn35wHvABcWCnu8/62ABcBAfxxM+NHxLra4j63ryL9BeAwSROr2h8FHgVOkbRPVbyjgDOA\nV4FbBvNazczKYnSnAzAzs5Y7Ki9/UrBuEbCloL2eQ4E9geXAX9SZ8r4BOLzqeUPjS5oCXAAcC+wN\n7FjTZT/gmarn1wI3kv6C8LXc9lFgAvDNiHit8BWZmZWcC3wzs/KpnMi6unZFRGyW9LYj5f3YKy8P\nIU17qWfXZsaXdCrpSP1G4F7S9J7Xga3AVOAjpLn41RYAfwd8TtJVEbEVOCuvu76fWM3MSs0FvplZ\n+azLy3cBv6xeIWk0MB5YNch93RERv9/G8b8KbAImRcTPa7a5nlTgv0VEbJA0H7gQmCHpCeAk4OGI\neGyAsZqZlY7n4JuZlc9/5eXbimLgQ8ComrbKlJnadoClwCvAMflqOu0YH+Bg4GcFxf0OeZt6vkk6\nB+Bs0tz7UfjovZmNcC7wzczKZ35eXippz0pjvorNlQX9f5WXE2tXRMRmYC6wL/B1Se+s7SNpX0lH\nNDE+wErgEEnvruovoBc4os42RMRy4D7g48DnSV9GFtTrb2Y2EvgymWZmJSTp66Sr32z3OvSSxpGm\nzGwGvku6Yg7A3IhYl4/c30a6Jv2zwMK83Js0N38KcGlEXNXI+Ln/2cB1wBrg9tx/Cqm4/zFwMjAt\nIvoKXuupwPeqYj5/8O+YmVl5uMA3MyuhfPT7T/PjQNJR+juAS4DHAGoK7BNJJ9G+D9glNx8QESur\n9vdpYBbwAdJJtWtJN5S6G/huRPxfo+PnbWYBc0hfGjYADwKXAZ/MsdUr8EeRvpSMB94bEU8M+I0y\nMyshF/hmZjasSToQ+AXwUEQc1+l4zMw6zXPwzcxsuLsYEOlOu2ZmI56P4JuZ2bCT72r7R6TpPLOB\nx4Gj8rXwzcxGNF8H38zMhqMDSVfkWU+6MdY5Lu7NzBIfwTczMzMzKxHPwTczMzMzKxEX+GZmZmZm\nJeIC38zMzMysRFzgm5mZmZmViAt8MzMzM7MScYFvZmZmZlYiLvDNzMzMzErEBb6ZmZmZWYm4wDcz\nMzMzKxEX+GZmZmZmJeIC38zMzMysRFzgm5mZmZmViAt8MzMzM7MS+X/IzNoeXik6hgAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10dff89e8>" ] }, "metadata": { "image/png": { "height": 263, "width": 380 } }, "output_type": "display_data" } ], "source": [ "rides[:24*10].plot(x='dteday', y='cnt')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Dummy variables\n", "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>yr</th>\n", " <th>holiday</th>\n", " <th>temp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " <th>season_1</th>\n", " <th>season_2</th>\n", " <th>...</th>\n", " <th>hr_21</th>\n", " <th>hr_22</th>\n", " <th>hr_23</th>\n", " <th>weekday_0</th>\n", " <th>weekday_1</th>\n", " <th>weekday_2</th>\n", " <th>weekday_3</th>\n", " <th>weekday_4</th>\n", " <th>weekday_5</th>\n", " <th>weekday_6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 59 columns</p>\n", "</div>" ], "text/plain": [ " yr holiday temp hum windspeed casual registered cnt season_1 \\\n", "0 0 0 0.24 0.81 0.0 3 13 16 1 \n", "1 0 0 0.22 0.80 0.0 8 32 40 1 \n", "2 0 0 0.22 0.80 0.0 5 27 32 1 \n", "3 0 0 0.24 0.75 0.0 3 10 13 1 \n", "4 0 0 0.24 0.75 0.0 0 1 1 1 \n", "\n", " season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n", "0 0 ... 0 0 0 0 0 0 \n", "1 0 ... 0 0 0 0 0 0 \n", "2 0 ... 0 0 0 0 0 0 \n", "3 0 ... 0 0 0 0 0 0 \n", "4 0 ... 0 0 0 0 0 0 \n", "\n", " weekday_3 weekday_4 weekday_5 weekday_6 \n", "0 0 0 0 1 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 0 1 \n", "\n", "[5 rows x 59 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n", "for each in dummy_fields:\n", " dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n", " rides = pd.concat([rides, dummies], axis=1)\n", "\n", "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n", " 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n", "data = rides.drop(fields_to_drop, axis=1)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Scaling target variables\n", "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n", "\n", "The scaling factors are saved so we can go backwards when we use the network for predictions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n", "# Store scalings in a dictionary so we can convert back later\n", "scaled_features = {}\n", "for each in quant_features:\n", " mean, std = data[each].mean(), data[each].std()\n", " scaled_features[each] = [mean, std]\n", " data.loc[:, each] = (data[each] - mean)/std" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Splitting the data into training, testing, and validation sets\n", "\n", "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Save data for approximately the last 21 days \n", "test_data = data[-21*24:]\n", "\n", "# Now remove the test data from the data set \n", "data = data[:-21*24]\n", "\n", "# Separate the data into features and targets\n", "target_fields = ['cnt', 'casual', 'registered']\n", "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n", "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Hold out the last 60 days or so of the remaining data as a validation set\n", "train_features, train_targets = features[:-60*24], targets[:-60*24]\n", "val_features, val_targets = features[-60*24:], targets[-60*24:]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Time to build the network\n", "\n", "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n", "\n", "<img src=\"assets/neural_network.png\" width=300px>\n", "\n", "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n", "\n", "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n", "\n", "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n", "\n", "Below, you have these tasks:\n", "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n", "2. Implement the forward pass in the `train` method.\n", "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n", "4. Implement the forward pass in the `run` method.\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "class NeuralNetwork(object):\n", " def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n", " (self.input_nodes, self.hidden_nodes))\n", "\n", " self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " self.lr = learning_rate\n", " \n", " #### TODO: Set self.activation_function to your implemented sigmoid function ####\n", " #\n", " # Note: in Python, you can define a function with a lambda expression,\n", " # as shown below.\n", " # self.activation_function = sigmoid # Replace 0 with your sigmoid calculation.\n", " \n", " ### If the lambda code above is not something you're familiar with,\n", " # You can uncomment out the following three lines and put your \n", " # implementation there instead.\n", " #\n", " def sigmoid(x):\n", " return 1/(1+np.exp(-x)) # Replace 0 with your sigmoid calculation here\n", " self.activation_function = sigmoid\n", " \n", " def train(self, features, targets):\n", " ''' Train the network on batch of features and targets. \n", " \n", " Arguments\n", " ---------\n", " \n", " features: 2D array, each row is one data record, each column is a feature\n", " targets: 1D array of target values\n", " \n", " '''\n", " learnrate = self.lr\n", " n_records = features.shape[0]\n", " delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n", " delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n", " for X, y in zip(features, targets):\n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", " # TODO: Hidden layer - Replace these values with your calculations.\n", " hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", "\n", " # TODO: Output layer - Replace these values with your calculations.\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer, since activation function is f(x) = x\n", " \n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error - Replace this value with your calculations.\n", " error = y-final_outputs # Output layer error is the difference between desired target and actual output.\n", " \n", " # TODO: Backpropagated error terms - Replace these values with your calculations.\n", " output_error_term = error # since derivative for identity function is 1\n", " \n", " # TODO: Calculate the hidden layer's contribution to the error\n", " hidden_error = np.dot( self.weights_hidden_to_output,output_error_term)\n", "\n", " hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)\n", "\n", " # Weight step (input to hidden)\n", " delta_weights_i_h += hidden_error_term * X[:, None]\n", " # Weight step (hidden to output)\n", " delta_weights_h_o += output_error_term * hidden_outputs[: , None]\n", "\n", " # TODO: Update the weights - Replace these values with your calculations.\n", " self.weights_hidden_to_output += learnrate * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n", " self.weights_input_to_hidden += learnrate * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n", " \n", " def run(self, features):\n", " ''' Run a forward pass through the network with input features \n", " \n", " Arguments\n", " ---------\n", " features: 1D array of feature values\n", " '''\n", " \n", " #### Implement the forward pass here ####\n", " # TODO: Hidden layer - replace these values with the appropriate calculations.\n", " hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", " \n", " # TODO: Output layer - Replace these values with the appropriate calculations.\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer \n", " \n", " return final_outputs" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def MSE(y, Y):\n", " return np.mean((y-Y)**2)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Unit tests\n", "\n", "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".....\n", "----------------------------------------------------------------------\n", "Ran 5 tests in 0.007s\n", "\n", "OK\n" ] }, { "data": { "text/plain": [ "<unittest.runner.TextTestResult run=5 errors=0 failures=0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import unittest\n", "\n", "inputs = np.array([[0.5, -0.2, 0.1]])\n", "targets = np.array([[0.4]])\n", "test_w_i_h = np.array([[0.1, -0.2],\n", " [0.4, 0.5],\n", " [-0.3, 0.2]])\n", "test_w_h_o = np.array([[0.3],\n", " [-0.1]])\n", "\n", "class TestMethods(unittest.TestCase):\n", " \n", " ##########\n", " # Unit tests for data loading\n", " ##########\n", " \n", " def test_data_path(self):\n", " # Test that file path to dataset has been unaltered\n", " self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n", " \n", " def test_data_loaded(self):\n", " # Test that data frame loaded\n", " self.assertTrue(isinstance(rides, pd.DataFrame))\n", " \n", " ##########\n", " # Unit tests for network functionality\n", " ##########\n", "\n", " def test_activation(self):\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " # Test that the activation function is a sigmoid\n", " self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n", "\n", " def test_train(self):\n", " # Test that weights are updated correctly on training\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy()\n", " \n", " network.train(inputs, targets)\n", " self.assertTrue(np.allclose(network.weights_hidden_to_output, \n", " np.array([[ 0.37275328], \n", " [-0.03172939]])))\n", " self.assertTrue(np.allclose(network.weights_input_to_hidden,\n", " np.array([[ 0.10562014, -0.20185996], \n", " [0.39775194, 0.50074398], \n", " [-0.29887597, 0.19962801]])))\n", "\n", " def test_run(self):\n", " # Test correctness of run method\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy()\n", "\n", " self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n", "\n", "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n", "unittest.TextTestRunner().run(suite)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training the network\n", "\n", "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n", "\n", "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n", "\n", "### Choose the number of iterations\n", "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.\n", "\n", "### Choose the learning rate\n", "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n", "\n", "### Choose the number of hidden nodes\n", "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress: 100.0% ... Training loss: 0.067 ... Validation loss: 0.154" ] } ], "source": [ "import sys\n", "\n", "### Set the hyperparameters here ###\n", "iterations = 10000\n", "learning_rate = 0.3\n", "hidden_nodes = 12\n", "output_nodes = 1\n", "\n", "N_i = train_features.shape[1]\n", "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n", "\n", "losses = {'train':[], 'validation':[]}\n", "for ii in range(iterations):\n", " # Go through a random batch of 128 records from the training data set\n", " batch = np.random.choice(train_features.index, size=128)\n", " X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n", " \n", " network.train(X, y)\n", " \n", " # Printing out the training progress\n", " train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n", " val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n", " sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n", " + \"% ... Training loss: \" + str(train_loss)[:5] \\\n", " + \" ... Validation loss: \" + str(val_loss)[:5])\n", " sys.stdout.flush()\n", " \n", " losses['train'].append(train_loss)\n", " losses['validation'].append(val_loss)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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N0sAqtfSl5enSr1+5XQUAoApr0KCBDh48qEOHDmnTJrYIBipDzZo11aBBA8eu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"text/plain": [ "<matplotlib.figure.Figure at 0x1069bf4e0>" ] }, "metadata": { "image/png": { "height": 250, "width": 373 } }, "output_type": "display_data" } ], "source": [ "plt.plot(losses['train'], label='Training loss')\n", "plt.plot(losses['validation'], label='Validation loss')\n", "plt.legend()\n", "_ = plt.ylim()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Check out your predictions\n", "\n", "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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QQghpBqyKdiFEF4AvoFSA7mchu/2pfHu85rEjAYwH8ISUcijiMR9T9iFj0SzV410XeONx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K6004LA6temtu9CwBpV2kWIoMN4rVgqVugQwkVI3AdRuFiqVC91PoHjIil4XHvnG5hSCD25XSPt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xo2NIOzWiUwYK5+RCLh2jzpBLvW5MmyNJ9lGKGs/JznI8UFsQO2ga3If6boCGmNK1M6Ohx+e4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MzjjBKlFJ8+VDfa5qTGcWrrt3gt5a+ZPWjcYZE4TR5Wz6PtU3ShCTrXwp/9ULEPUoOoqYx2\nib+Jw3pRX13Gj9pOZ5uT1oFQ1hNO+mE0YtkwRwzNOZEVtcEstVFz2NX+lII2Y93zMJfb6YRXR11O\nq6Ofkw3rX+P+ptkclTSWYTokRqPsoOc7jjo6+3v7nhb+PM1c7H08SwZkXoctlKPOukfP744CpeDo\nb/k/m0m/hh8Go46MxslstE67zJ9vvXk5/OkC+NtnMr83YH8dTh4Gx/0vnHGHH9K/cbGOVFj/oc7t\n40UqDD889zrTM67X99OsH8Bnn4MTb4GjvrH708rqhgTqiL1alzMr+RJXqrupfejTelrO4hf8rPTJ\nsug7t6ob4GO/9etoy1/VURTmqHWY00e6glkve+Hn8MvD4cVf+PvCmFbSBUb368k7A2azydHRIWUt\nfqTcK3XHUD484jwB+5ziv/7HF/UUVtDRNoddE41TiFjRaAe8LsbHHMdJmW84jrMZeAaoAg5K/2JR\noFSgd3mUkTRrab8jdGUkTiilG+gjZ8Iwo/ex0HPLukPv0cHecdAP3REzIpDpBKWgrCpqi64z/PDM\nAn+/0+NTWQaO27c/w3vr/r8N9ORzpd/krdGX4ky/koppWSp/ITCqbw/WUMcf22dkvNfuKP6ROoja\niBvtNRWl/P2y6dSPmswJrd/lmXY3AqhHfzj/wfgkBYLs03TGnqwrw3EJjwc9remsu/2QX9ARC8f/\nX3ROBg1D9uKu9uyN4K2TPhtdJuTOOPseOPrbOKVGH39pFRxyZe7vRMCWI75Lk+OPdC5IDeDlmb/T\nOQKiZNg0mHBO5v6jvxWLaCnKewQHKt5/2J+GBXo+7qQL4Ox7g439QqIU7DcbZho5fP71VfjxATq3\nj0dnnddV9bqRfuBn8j/SOfywYMi5x9wH4Ka94bcn+vv2OTUe0y1HHA6zjAipVW/pJIQA/cbF6zkO\nut4z0ujUMjuL9zkVDrwkfKcczDpoPP/bFrzHW5xSdhz+5YiMDMaelH3/wZfHJy9WAQnpibXbeMMK\nuRa5/AA9Er8XkGU9Eh+l1Cs53gpx0cECMOZ4HV5jLCmyxqml8rQfxaMgzcXH7oDHvqp7l83wrzgw\n4zp4417YsRVQcNQ3ozayH6Xg5Ft1aNtzt+rKcpyiF4CSZILff2oq9764hJF9qzl+3ADKS07c+RcL\nyLAG3Wj7atsFlA8Yy+yNv4PWLSyvGsNnm87lDWcUR0bcaAcYUl/F7y6cynMLRqI4Deo365C2OCzl\naNJ/vF4Lffmren7m8TfC6BwjsFEzYLyOSnr+Nh3WP+UiKIl2KoTHyD7VXNR2KrOSLzLAXdov5Shu\nbDuTS4761k6+HRHJUph+BWrS/8DqeYCjy84YdRwCDG8czumtX+YzJQ/yZmoEbww8k/sOO3znXwyD\nU27Vjbz/fBtaNulVBbI1+qLigE/Cc7fBOiNbe3ktfOpR6Ds29/cKzaTz9X1srlbhUdYD9o6onFEK\njvwa3HW6Djv2no3gJ/kCPTc/fR58lEz9tP73n1+iI1qhdgic9rPIlHJSUqY7iv5+mZ7e5jFgfzj1\ntljV00/efyC/fuoUnml6nulJvSzqH5jFJyZ0kvw4LGoH68hib2Un0GV4zOqRhcKWRruXJjRL2s3A\n/hjFV4dMIgFH3AD3fRKA93sfxabDvsnk/kMjFtsJPfrC7F/s/HNR0KMvnHUXPHurzujbf7+ojfYM\nEgk49ju6AlPWI3c4YIQM7lXF1cfGIwQZYFiDHhVso4Q7nROYfeWXoGkhN81xeGO9XjEi6vB4D6UU\nB4/yRmJiMCKTjUQCLnxEh9b12w9Kynf+nSgZemDnSaUiYkTvHjRRw3Et32Ny6Ue0p9p5r30IK2jg\ni2Uxr15U1MbymHpUlCYpGTSBK5Y2klDw4GkHoOJSsU8k4cCLdbm4boGeXx4nkiVw3PfgD2cAjg6V\nnvWD6EdfS8r06PA9Z+tpd+U1eh5+aZVuDEeZ82PkEXD5y/p1/Qh48Vcw52Y/+eWgSTokP271oKmf\n1g25F36hOzWnX6HzlsSRkjI4/Vd6msOmZXqqwZCpsevULi9Jcuu5Ezn7lsv5Gr9ji1PJwv0/R2ky\nJsHZUy7yG+0TztFRPnFJOlhgYl6q5h/HcbJmxXFH4GOacruL7HMKXPwkJErYq/+4qG32DEYeEc8Q\nzz2BkNc7t5nGBj88etG6Zj0qWFXPxu1+b3PU4fHWUVIeXN5R6Da9qsuory5j/dYePL7DL3PKShKU\nxKWCZzE/OGMCdz77ETPH9GXfgTtZ4iwKKuvilWjQZPRRcIm7VOKgifEZydzrWPjss3rpt4EHxCZq\nBghm/j/wYt04WvmGXva28dDoV1bIxZhZerOFfvvEK6lxFkb368lXzzqcq/5UR2kywf2HRhihks74\nj+sE2+U9YpPfJSxsabR7I+m5Si1v/4YQXOJNnELUBEHIC316llNZmmTbjnY2btvBhuZW6qrK2Lht\nR8dnaiqk0S6Ez4je1azf2hrYV10Wk9VKLGdM/5787+wYhKTaStxGhT36WjIbM5GQOmURc8r+gzhw\neAMVpQnqIl6dJoBS8e0sLDC2NNrdRQ3ZK8f73pBdrjnvgiAI1qKUYlhDFfNW6nXZH5+7mpKkYuUm\nP6O4jLQLUTCiTzUvL2oK7KuKe2i8IAiCsFP610a8tKgQwJaS1Vu/7BilVMLMIK+U6glMB5qB56OQ\nEwRBKDRmo/2qP72R8X5tlTTahfAZ0SdzHm51uYy0C4IgCEI+iekElSCO4ywAHgMagcvS3v4mUA38\n3nGcrSGrCYIghEJjQ+fJdWSkXYiCEb0zr8tKGWkXBEEQhLxiU8l6KfAs8GOl1JHAXOBA9Bru7wNf\nidBNEAShoAzrpNGeTCiZRyxEwtgBNRn75FoUBEEQhPxixUg7dIy2TwbuRDfWrwJGAj8CDnIcZ110\ndoIgCIVlmJFBPp2aipL4LAclFBVD6quYMDiYI1bmtAuCIAhCfrGqZHUcZwlwQdQegiAIYdNZo11C\n44UoOfWAQbyxdGPHzzKnXRAEQRDyizUj7YIgCMXMgNrKnO9Jo12IkhPHDwz83NS8I8cnBUEQBEHY\nFaTRLgiCYAHJhOKU/XXjqHeP8sB7PWWNdiFC+vQMXo+O40RkIgiCIAh7JtJoFwRBsISbz9yfBy6f\nzlPXzAjsX7e1NRohQXC54/wpHa/PmTo0QhNBEARB2POwak67IAhCMZNMKMYPrsvYv2idrHYpRMvM\nvfvyp0umsaMtxbSRDVHrCIIgCMIehYy0C4IgWIgXKg/wsUmDIzQRBM2UxnoOHtVbVjIQBEEQhDwj\nI+2CIAgW8s2T92XFhu3sSKW4ZMbIqHUEQRAEQRCEAiGNdkEQBAupqyrjvkumRa0hCIIgCIIgFBgJ\njxcEQRAEQRAEQRCEmCKNdkEQBEEQBEEQBEGIKdJoFwRBEARBEARBEISYIo12QRAEQRAEQRAEQYgp\n0mgXBEEQBEEQBEEQhJgijXZBEARBEARBEARBiCnSaBcEQRAEQRAEQRCEmCKNdkEQBEEQBEEQBEGI\nKdJoFwRBEARBEARBEISYIo12QRAEQRAEQRAEQYgp0mgXBEEQBEEQBEEQhJgijXZBEARBEARBEARB\niCnSaBcEQRAEQRAEQRCEmCKNdkEQBEEQBEEQBEGIKdJoFwRBEARBEARBEISYIo12QRAEQRAEQRAE\nQYgpynGcqB1igVJqXWVlZf3YsWOjVhEEQRAEQRAEQRDyyNy5c9m2bdt6x3EaonbpLtJod1FKfQTU\nAAsjVomKvd1/50Vq0Tk2OIIdnjY4gh2e4pg/bPC0wRHs8LTBEezwFMf8YYOnDY5gh6cNjmCHpw2O\nE4B2x3HKoxbpLiVRC8QFx3GGR+0QJUqpVwAcx5kUtUsubHAEOzxtcAQ7PMUxf9jgaYMj2OFpgyPY\n4SmO+cMGTxscwQ5PGxzBDk+bHG1E5rQLgiAIgiAIgiAIQkyRRrsgCIIgCIIgCIIgxBRptAuCIAiC\nIAiCIAhCTJFGuyAIgiAIgiAIgiDEFGm0C4IgCIIgCIIgCEJMkSXfBEEQBEEQBEEQBCGmyEi7IAiC\nIAiCIAiCIMQUabQLgiAIgiAIgiAIQkyRRrsgCIIgCIIgCIIgxBRptAuCIAiCIAiCIAhCTJFGuyAI\ngiAIgiAIgiDEFGm0C4IgCIIgCIIgCEJMkUa7IAiCIAiCIAiCIMQUabQLgiAIgiAIgiAIQkyRRrsg\nCHs8SikVtcPOsMSxX9QOgiAIthD353rc/Tyk7BEEabQLghXEsWBVStVE7bAzlFJnAjiO40Tt0hlK\nqVOA45RS1VG75EIp9QDwiFKqLmqXnaGUKldKJd3XUs7lCTmWxYWUO7uODWWPDeUO2FP2SLlTGORY\n+pRELSDsWSilVFwLKaXUXsBQoA54CmhyHGdHtFaZKKUOAQ4ARgBPAE87jtMUp2OrlPobsEAp9X3H\ncdZE7ZMNpdTDwHil1EeO47wUtU8ulFK/AU4H5gCvAFujNcrErTSdCCwBGoHX43Q9eiilzgcOBsYA\nbyml/s9xnEVxclVKjQUGAJXAC8AWx3G2K6USjuOkorXzUUodjz7XfYCXgJdifK/H5vymI+VO/rCh\n3AE7yh4byh2wo+yxodwBO8oeKXd2guM4ssm2WxvwXeAC42cVtVMWx5uBhUDK3V4DLgGqo3ZL8/wp\nsMrwbHKPb2w8gW8bft8BekftlMXxn8B24AtAz6h9OvG8H9gE3AKMcvcp999E1H6uxyNAK/Cse85/\nGrVTDs/fAxuAZve+SQGPAvVRuxmOP0NXPr3750Pg18CwmJ3zu4CNhmcKmAscBZRH7ec6SrmTP08p\nd/LnGfuyx4Zyx3WJfdljQ7njesa+7JFypwt/P+oDIJvdG/An98Z6HjjD2B+bChTwgFuIPgd8A/iP\n+5D9AJgatZ/h+Xf3of9H4BjgU8A89+E6JGo/1zEB/BxoB56OYwUKeBjY5laaao39sbkmXZ+vuwXU\ndZ0V8FF6G8fys8BUYB2wAjgg6uOX5nk3sBm4CZgADAMeB1qAcVH7uY5/cyt2fwU+4d43r7j30BJg\nStSOruc9wBb3Pj8OONd9hqbcY3w10D9iRyl38ucp5U7+PGNf9thQ7qQdy9iWPTaUO65n7MseKXe6\n6BD1iZLN3g24yr2A57k321vAx4z3Iy+ogB+7FZLrgT7uvv7A913326J2dJ1+7j6YrjU8k8D3XM9D\n0z4fWa8ocAawzC1M33D9/l8cKlDAg+gwv6uAXmnvjQb2B2qBqog9a9GjB08Bfd19FcBw4FvAT4Af\nAROjOtfoEaNtwBe9Y+k6pYCLoj7XhuclboXkm2Yl1C34VwAHuj+XuP+G/lxy7+sUuvHm3d8lwN7u\nNZAC1gMz3feiOucnuPfPTVnunxuAle418TXvuo3AUcqd/HlKuZM/v9iXPTaUO65T7MseG8od9+/G\nvuyRcqcbHlH852WzfwMOA+YDy4GDgCvdm+7NuFSggOPdm/1Or2AHku6/I9wb72lARex5EbDULTAb\n0t671S0AJgLnuQ+3Qe57UVXsj0SHrI1wX7+GP/IxwP1MDW7YXYheT3gexr4ewAx0OOB246F7JxGO\nIqHnjrYAlxvH6yLgfYKhYVvdQndABMfSGzGqMfafjh9a1xjV8UtzvRNYk+Xe+Yp7nX4R+A3wKyIY\n4XSfL/9wn5UN7r6E9y/wOfdYe5Wnvb3vReDqVUwOM/xKjPcvBha51+Vnzf9LSH5S7uTPU8qd/LlZ\nUfYQ83LHOJaxL3uIebnjulhR9iDlTtddoriQZLN/cx/0KeBE9+eBwJejupCz+CXQvXY7gDGmB7qX\nsQR4G91zX4NbqYrQc1N6QYQOVVyJHrFZYBSo84G9Ijy2/YDVwPnuz6cCr7pu16NHFBag5/7Uheh1\nv+vwOG4YFXpUZgU6LPVpdNIdb17XM0RXeZqErjxd5v58oltoPgt8DJgO/NDdtxW4wrteQnA7Fd2L\nfA1upcn8u8Cf0SMMx7k/R3XvKHSymgXufdzbeG+me39vA97Br5hsAs4N8Vgm3Gfjeve+rTLe8xpy\nB7peXtjvU6RVBEM8pje4Dkd7xzjL+b/U9d2AG6oa1nMIKXfy7SnlTn7crCh7iHG5Y5zTWJc9WFDu\neH8HS8oepNzpukvYJ0e2PWdDjyj0NH7u18mFXBKyW5lbkH/Z/TnjQQn8G1gUg+NYR2YFbyZ6DmQL\n8Hl0j30jOlFHCnid6MKESoF3gduNfaegs5F6SYy2EVIYW9qD/U7X4TH03Mzl6ArSSLcQKwWm4IeF\n/ZAIEpwA+6LDUv/iXqv/RId8lqV97jL3WDYR0ggSujFxANAj7Zr0eugvdo/dP6O4/rL4/tH1uRmd\nvfdT7rXYCnwcHfpZih/y24Tb+AjR8Wl05ckLmfSOpReK/Bo6o+8j7j1/pHnsQ/T8tHuM/kzmCJJ5\nn/3A/dzDhJxsCyl38uUq5c7uO1lV9hDjcsf9u9aUPVhQ7riesS97kHKn6x5hX0Cy2b/RSe9mtgvZ\n/Dy6UhBKyJVbADRm2e8VBI+ge0qTaY5jSJtXE5Kv56XQ2XxT3gM07XP/dQve0BOyGA/8PwL/Na8H\n4AL3oZ9Ch2SFVrlLO3+/xR8deh6oMI+v+3q6W5C9QEQZkt1jtB6dGGYhcIO7vyTt//Mb9/9yXljX\n4E4+Uwu8hw75PLqr3yvgtXgo/oibuc02P+e+/r373lUhOSp0xe0m/JGM/YBS9/1z0aGpj6Ir9se5\nn7sxomuyp3vPrAXOIrMy7x1zha7sfYg7TzIEt1iXO8bzW8qd/DvGstxx/74Zxhv7socYljvmOd7J\nZyIve7Cg3PGOCzEve4xnT5zLnZwNcCIod2TBeqHbOI7T3sl7q9AP+++ge5i/CpwEoJT6BHAHcKNS\nqiQEz02O4yzM8lbS/TeFflhVef8npdRxwG3AtUqpZJbvFgzHvcvdf7+Ezuj5uFIq4bpVuR99B6hG\nr/0bKo6/luer6HVohzmO066U6o9OZNOCnic5C/iMUmpASF7t3vlyHOd/0FldW9FhgN46pI7xlQ/Q\nD9qxhHwcvfOJvtZjzEgAABSLSURBVE+S6GRKg9AFFkC7+/8pd39+3P23ttBuaccoA6VU0nGcjegE\nVmXokbidfq8QGNfic+gkVTegC9DLgH8BD3vrzyqlKtzPPub+WxmSo+PoNblvRoegHoJOWPW4Uuq/\nwO2uy6fd/898dAhgrzD8TNz7Zxt6VLUKfTynmc9B91iWuef7DfQo7D5h+Ln3RNY6SxzKHeP5bUW5\no5RSEO9yx3OIa7njurV5z+o4lz1KqVL3ZezKHfDPca57PC5ljw3ljusZ+7LHcRzHfSbHudxpy/VM\njqTcCaOnQrY9Y6MbPZroHqivoJPuvIlOdrMC/VDYN0pHgiMeS439x6ArBduBfaI6lgR76lT659EF\nwAIKvPzFThzPRldM6oEG9MjROuBC96H1HLpyegMFnMOV7ph27M4jbdSFYI/tR+jCrKxQfp0dS3R4\n6s/Qc7RSrkuj+16p8bkb0ZXSQ6I631k+e5DrtA2YXOjj1x1P93gtwZ8TaV4TP0LPNz4+LEfjmhuM\nHomb657vD9BhqoOMz9agwyh/UWC/vYBj3Wfe3mnv1eOPsr2OXiO3Mst1eQ+w2PQPw7Gz5wkhlzvd\ncSTCcqcrnkRc7nTRMfJypxPPcuN1pGXPTu7v2JQ7u3iPh1r2dOKYXveItNzZyTmPRdkDHIzu3Pgy\n8PG09+JS7mR13Mk1GVq5U9CLXTb7N3QW2XONn7tTsa9DrwW6GT875X5xcUTPLZzrvvYqThuB8XE6\nlgQrLZ9AJ2L5Le68rygc0T2dy9C99Yvcc3up8f4ZwJMUoBK6M0dyhNGmHcdL3Wvy+2aBEJYnfqW4\nv1sIbXTvkx8Cg43PnYoOBXuJAoR97ub97c0vuyj9+Ebpia48bUYnWao09p+MLuxfAvqFfL69CnsP\ndBKjw9EFfXXa7/g8ehTuzO6ej2543oiutHnhnK8Dn0v7TD/0iGEKHY56OUaYHzqb+DL03MLaKBw7\n+W5Y5c4uORJ+ubOrnmGWO506Gs/LRiIqd7romTWUlhDLni7e35GWO7tzXbrfDaXs6cL5TqR9NvRy\npxvnPNKyB10+LjMcUxirLbifibrc2aljJ98Np9wpxAUk256x4SfaeB842di/s5Eu80F2BdCG7g0v\nRAOu247462b+xy2YZqMzlm6icBWnXT2WZm+t57kEGBGlI9AX3ZOYQs+LuyT9c+mFQoyO42noHuf5\nwLCozjd+Q64fel1nr7B4DR1m9Qf3XK+Ny71jvu8WoCn06FvB5uJ21dPwOhc9qvE6etmiqcDX0ZWA\n9cDYiM53tvvIfFae5N7fr1Og+dfA39EjlS+jR38eRY/wrgROcD/jPR/7oUcMVqOf4a+hRx9+457z\ntaSN6ITlmON7YZY73XYkmnJnV49lmOVOlx2JqNzJ47EsaNnTxfvbywUQSbmzm8cytLKnq45EWO50\n45xni/wJrewB/oZuzN6N7sT4GLqDYC3+ihRmfSiKcmenjjm+F1q54zjSaJctxwZcjd/blUL3FJ5i\nvN+VMPTzgVXuA6sQoYm75IhfaM1xb8rX3Ju1UBWnfBzLa9ANglXAuCgdjUJqNjqZztXGvkRX/j8R\nHscr0WvlrqYAvaC7cCy9gqoWXXD+Db+Hdx06u28hCqjdPpbu515Fj8AVqpHZbU902Oxd+MvteNvb\ncXoOGe+Xoit5c93rsiDTh9AVoQ3o0QBv/fC+6LC+wIhC2nV5KvCAcRw3oUczC9H50WXHTn7H+RS2\n3NklR8Ivd/JxLAtd7nTnmoyk3MnjsSxo2bMb93do5U6+jqX7nYKVPbviSMjlTj6OJSGUPcAv0REd\n1wP1xv7rXceMxJboUeswy51uOZK9E+R8CljudPydQv1i2ezd0AkrFro38QjgKvfCXUQXK6Po5S4e\ncR8ohSjs8+Hora26jsJVnHbLE51R+C/oHtxnKUwDbpcc0clsRmBUnOJ6TQJ7u8exHXilEA/+XfXM\nclxHAxPRoWyFCEXNx73jjRrOAkbH7Vi6x+4z6FGje9AV5rzPgcvTsfy8+505BbwuT0AvQXUHmUvq\nHIiu/L6NTvCUyOaMzjw8DZ08qxChid12zPI7Cl3u5MMxjHJntzwJp9zpjqM5Wh1auZOnY1nwsidP\n93dBy518HEv3cwUte3bnWBJSuZPHY1nQsgfdkF2K7lyoT3vv5+hn4Fh0R9wpZJnaSOHLnXw4FrTc\nCfytQv5y2ezb0D3Wn0H3GJ1i7Psq3a+Mng6MjJsjOhFMGTqUaC6FCwHb7WOJ7nG8wv09eU8AlK/z\nnatQiIsjOsnJt9AJigbH0ZMclak4OWb5fYWKqthlz0Jei4U6lsDRFG7uaBKdeCqF+zxOP17o5XY+\nIssc2zCO5+46pv2uQpU7u30cCafc2e1jSeHLnbyc70Jfm3k6lgUte/J1f3f2fIqDZ5bfV4h8H7vs\nGMZzshDHkgKVPe6z7k50OdiY9t4x6CkYG9Ah795o+pO4HZmEkyB4dx3NzsSClDsZzmFdZLLZswG9\n0T1KFWkPglyV0fSHVxg32245uvsaKFBikDx7BtbzjZtjId3yfBzLCn1tFsOxDMMxT55lxutCdS7s\nrmNFCMcxiW58fTfb+UOHSD4JLMl1vAincbS7jgVJKJlPR3dfQcudPHoWrNyx4ZrM87EsWNlTTMfS\ntudQtmshRp7lhXBL+xuDg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"text/plain": [ "<matplotlib.figure.Figure at 0x1128c07f0>" ] }, "metadata": { "image/png": { "height": 272, "width": 502 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,4))\n", "\n", "mean, std = scaled_features['cnt']\n", "predictions = network.run(test_features).T*std + mean\n", "ax.plot(predictions[0], label='Prediction')\n", "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n", "ax.set_xlim(right=len(predictions))\n", "ax.legend()\n", "\n", "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n", "dates = dates.apply(lambda d: d.strftime('%b %d'))\n", "ax.set_xticks(np.arange(len(dates))[12::24])\n", "_ = ax.set_xticklabels(dates[12::24], rotation=45)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n", " \n", "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n", "\n", "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n", "\n", "#### Your answer below\n", "Model is failing to predict accurately around holidays" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
CoreSecurity/pysap
docs/fileformats/SAPCAR.ipynb
1
255160
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": "# SAP CAR" }, { "cell_type": "markdown", "metadata": {}, "source": "The following subsections show a graphical representation of the file format portions and how to generate them.\n\nFirst we need to perform some setup to import the packet classes:" }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": "from pysap.SAPCAR import *\nfrom IPython.display import display" }, { "cell_type": "markdown", "metadata": {}, "source": "## SAPCAR Archive version 2.00\n\nWe first create a temporary file and compress it inside an archive file:" }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": "with open(\"some_file\", \"w\") as fd:\n fd.write(\"Some string to compress\")\n\nf0 = SAPCARArchive(\"archive_file.car\", mode=\"wb\", version=SAPCAR_VERSION_200)\nf0.add_file(\"some_file\")" }, { "cell_type": "markdown", "metadata": {}, "source": "The file is comprised of the following main structures:" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Archive Header" }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451eec9370>" }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": "f0._sapcar.canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Entry Header" }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451eec2dc0>" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": "f0._sapcar.files0[0].canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Data Block" }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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raGuTVWFHRgqVo0SrsYcCdmRE1abcq+5SlYMcCsBxbDmdfsRxudVV7/XrS6kUrNPCBbVms/6TolKCannV7/2eloQFjrsdCl28elW6na9aqKTR6ZT6xC2l060dHaqFxqvrlO+5Avpw8Xhef5E90aHk3aiDwTthT2hyOsHXgRAS6u6+ePWq9MBCXidNnfYaaYXWV0+6wZFXB6K4bfRHQX7C4s2+vlqz+eWxMf/Q0KmGBpl37oNo9N6dOzJ309lodC2bhbMcpxoaBi9fhqcHVW6FQuzoaIvbfbGnp8XtlrZNVbhMflNLSzgY1JGvJUQfmN1lhQvJpL5zcl50Gnso0HKrUe2u9ycn9V3iDcpBypzzXu8rY2Pwz9Xe7u3vh1k/l82+f/MmXLMmilw8rvX4oiVBq1yVRDR6OxTqHhhweTwLHAe7vKqFqtRaLI62NvihWOC4R8mky+NRLTReXad8zxXQJ7e6asQiL16HknejFsbvhD1h4NIlmK3vsazJYmlyOuGhHyZs+r3QQqu9Bluh9dUrSAeiuG30R2HHqsMCx1GXTkKI8hH5Lst2DwzcDoVkPiO1ZjP9mTBZLLdDIdWFykQ0+iAa/eY779DzqVJVtITL5Dc6HK+98IKWy6uOkELR2ak1gn5jEeSw80Zf33I6zcXjF9Lp815vk9N5vrNz4NIlWLa92NOTd+VTJkG1nNTXKysucFw4GGx0ONjRUULIcjrtam83WSzKQh0dvP39b/b1cbFYThT9Q0M6hcarFy/BeHVCSCIavcuyjziOHR01SZxLltJpg8vOxetQ8m5UdoLq7aHTIUoJUAIPNgvJ5AWvN88ilsMBbpUmiwW0hTXmwcuXTWZzo9NJCHmjr09nTlG213grtL56heqgvG10RuHY1tbWsWPHxmdnCSFLqdRrL7xw49YtuvSxQ24qFQ4GB27fHrh06WJPD/2qv9HX1+RwUIMDhIBAGaHubldHh+qivZbwguRrCQEHFrAKaeAU8AGBnnqjr+9iT4/JYsmJIu078C+ttVjoCyjnYrFGpxPKc6IIrwtqLFVA6r8KSkI5jcUBBqxUn5woLm8PsFa7qEpLqRTc/TolylbAjleT09nb2qraz9BdcJvS7uptbX357bepYrLWSVst63baWNABfB3u/eAHqv2GlJC+yUkjgajXMplaq3VvPxoCUY+kUpMfffQfCNnDLS7pOTr9QuPVi5dgvLoqa6JoJAjSHupQ8m4sOUuplMliKajblRTZ3n26bWRiv0dIWrbqcKqh4StXrgxevny2vb2ppUUWvuoey7o6Ogghrvb2uVhM67n/vYmJc52dqn96lExqOf3qC1/LZmGtZjmdvsuysiMreYWwo6OtHs97ExPgusGOjIATLIgFo+S9iYkmhyMxM9PocPQODxNCBi9fhrkQdq3gsqVU6vbgYOidd26FQsvpdJPDcXtwEE6+Gm8sOzKykEw2ORxcPH5h+9AwOzra5HAQQhIzMydtNpPZfMpmAz3ho8H2NJnNj5JJk9n88tiYrF0ylZbTaXZ0FPauZqPRywMDEOFEWqJsxa1QKBmPu9rb39Nd4ILuWkgm2dHRgdu387YOypXdDkAQFfTqKnMcagsAKtjtsoJ/+ugjYW3ti1/4wolf//W9V6s4VH9qjf/+al1ZpIQi58tCJ7DidSh5N5acPfHZKrK9+3TbqIqVu0l6r193ut1cLHaPZd+/efPlsTFajR4OOe/1vvbCC0up1OcBaGdmIBYVLKp85erVQpXWEU62T6yuZbMPolH/0JDWHKMjZC4WAzeWBY5LzMzcuHVL1k2tHg+c7n3thRdkYl0dHeFgEKa6eyzram9f4LjldBpWfppaWu6xrPEjKlwsxsXjMN2uieLg5cvUVRAChYIOYLU0tbTclTh2wLoZ1HoQjcraJVOJEOJsa5MqBhFOaImyiqujIxmPQ+esieKDmRmtVtAVvDf6+qSO9Fqt0+p2sm03FL/BhOw313flshfj+ZcmJwkhX/qt3xp4/vm9VgpBkBKgEk0SHBK916/fCoXeu3kTHsET0SjZPlVFCDGZzYmZGboaT5+M9TGZzaqOh/rCCSF0w2I5ndZyrNMXcmF7bktEIq72duUEBsfAVC3HJqfTZDZzsZjT7Qbr5B7LLqfTqpFY8jYW5m94DX4oVE+pDmAemcxmmUyoBeeGZe2SqfSrbap43NnW9pWrV2stFldHh7REWYUQQjtH/8GFKtbkcEgd6bVap9Xt7Ojoo2Tym3/zNzqfhRxqJu7fhxfRhw/RdECQo4FeIOpWj4fdPgk6F4vJfDTusWyhRw3Pejx3WVbpb2Jc+MWennAweNbjUU5CBoWYnn56F1FWLni9s9EoIYTu3+e1lrQaK0MZdmPXKFX65jvvJOPx2UgkHAy+MjbW5HRKS5ocDlkVdmSkoBwEeaGt0+r2Rqfzgtd7e3CwSKdU5ABgl5ZSm5sFVclubAiNjf/2i18U19eXP/nkz/7xH+vr6natQHJnHiwEQUqF4oSFxE8QPNcIIWui+CAafUPiudbodL4/OQkP4sY/zNvfP3j58i3J8Qf4COPCnW73WY+HHR2VrW8b19DV3j54+TKNV2GwCWc9Hjij0urxEELOe71vvvQSPMoTQmTbKzqNhRWdcDAIdeHEi5HweQAsY0Bkse4bN6QJS5QqEUJONTS4PB5HW9trly5RPWlJ940bsiqwLOHt7wd3ReOa0HKt1ml1O3TOXCwGHhsG+wEpCVFBiO/CsoQFqqoqUlcX2dwkH32054ohCHLA7DAdTGbz7VAoNzgInvyEkJfHxggh9+7cObvz6bnWYjnX2TkbjRZkOtRaLDdu3bodCsExkpwomiwWsAaMC/f297926ZIs1YdxDU81NHj7+9986SU4IKB/aEcqzdHW9iAahamOHnqBVqiepNBqrNPtBqsCFPD29xv3r5mNROAQ0fnOTqfbLQ1foVSp1mymzQTvk2Q8Li1RbcX5zs7XLl1qdDrBZ9OgJrRcq3X63d49MPDapUvOtrZD5BVVsfwux83tzM972u22F+7iui6KD+7c2VhdrW9paSnk3PKpQj9JF1nib+NpjvXTW6tmcDaiAKCVwVmGfqplgzrQJqume86b87rQTOuq1ekDzK5RdmOhebeVEgoaheKboNSh+LvRoIRd5GrfcTgTgPN70mOB+0GhKxblqYBBIaqXFXqQBo4yNjqdOVHU+TmQfZbsZKlqiayKwVNGa6Koo4lW6/L2GB7OLFv6Pvggns02TkxwsRi1LN+fnPQPDRUaswTOUTvb2pxu95svvaR1IPwACHV3Nzqd9FEk1N0NWoHD78tjY1pfhPe0+2EplRq8fFnHoVtHAULIrVAoJ4oXvF7wDdfxIH6jr++kzVZrNhPFtG1QB9WBKGh0dHQwAuRn6r5xo8hfY2U3Gh9KVQnGR2GvmqDUofi70aAEnZtZicrhTOBUQ8MBxAYu+Um8PVHAoBCtOB67+MTafJO67LOUn6IsUS7MFK+JVutKPu5IkRSa1lmV24OD9OBuZ39/3lwV+4QswXdByZq1+kErg7MRBUi+DM5KVFMtG9dBdSAKHR3jmdaVuDyeu9rRgQ2i7MZC827LJBQ0CnvSBKUOxd+NxiXs4kut5yaJlBVNDofB3yMEOQCUaZ0XOI5GvpMFdpOyJorL6bSjrY0dGZEldjEogRByKxSCVDs5UeweGICfddVCLZQJvgtN1qzaDzqJsPMqQLQzOGsJUU21bFAH1YHQGR3jOhgfR+Auy7Kjo8vp9PnOTpjqjA+lajcWNJRKCYWOgmoTCuqE/bgbdyHB+JdansMCKVu816+jKwBSPrCjo7J55XYodMHrfWVs7MatW+9NTGgdZXrEcTlRvD04SAj5kONC3d2FSiCEPOt0+oeGXhkbu3j1Kg1fplqohTLBtzLNcd45j6j1g0FUM4zTDM5UB/2f+7ssCzn2jBgrMlQHQmd0jOtgfBwpA7dvv/GDHywkk3DA3vhQqnZjQUOplFDoKKg2oaBO2I+7cRcSjH+pcdUBQZCCUebnhQw4C3Nz4DSnjM4ixWSx0OO4NEdwQRLOejz37tyRnQNSLVRFJ3U4KSRZ867TW2spoJrBWUuIwSznOigH4qTNpjo6xnVodDqNjyNA49Nc8HpnI5HzXq/BodQfR2JgKLUkGB8F1SYU1An7fTcalGDwS016egg1HXZhsSLIfpATxcZnnim1FkgetNI6098dp9vdqLFIJiuXpec2IgEClV7s6em+ceNUQwP8fKkW6qOa+LugZM0G01sXpIAyg7Nq3UKznCtRHQj5UTVdsao6gFgj46gEYs0VOpRaCdyND6VSgsFR0GoCUFAn7NPdaFyCwS/13xFCwHSI7DyVgZQt3/ve9+rr68+ePQuvM5mM9K8ul+vs2bOy8t///d9/7rnnVCWULQzDlFoFJA/KtM4QdHV5OwfmrVAol82q/lrVWiwnbTY4awM5gmGp1riE5XTaZDbDZfCopFWoxfmd2VXoUbRENPrezZuwuS6Lc2OwHwyipQAhZODSJUgoQzM4q390Nvv+zZvUQYF2o3FUB0JrdIzrYHwcKXdZFpr/3sREq8djfCh1utHgUGpJMDgKWk0oqBP2724sSILBLzXxeAgcztTvEaR8CAQChJDh4WFCiMfjsdvtXV1d9K92u91ut0vLeZ4fHx/3eDxQRSYBQQoCDmf6CSGEjAcCF7xe2c/QmiiGg0EICdPodOqcZ4OEwrAafN7rpQu5xiVANjUISZLLZk1mc/fAQCISURbq/+hDgm+T2XzB6210OgcvX27c9keGACT6QWOV/UAzOMtW/o0oAD/Q44EAhNuHDM4655hoJ8hc2IzroDoQWqNjXAfj4wjtzWWzMGSfZ/tTG1/9M6LSblzguEKHUibB+ChoNaGgTlDqUPzdWGgnGPxSw+FMuemAU0s5EwgEurq6XC4XIcTj8bhcLuVIycoFQfB4PL29vX6/XyYBQQpCajoUmtZZlUOUUlmLPekHJQVlcC6+G42kzNbfC1nkuPpDPpRKhFSq2mKpOTzn2tZFsRhtDVaPE7Isc5NMJBKCIBBCotGoy+Xied5ut9PVY2mhIAg8z3t2Lr9AdZfLhQvO+0Rvb69dkdFYH4Zhent7h4aGwHTYhQQEUbIn8+VhtxvIHvWDkoI2QYrvRiMps/9WX4TT+fdFKlGG7H98oz2myLuxkOo7TIepqaloNEoI4Xl+eHh4amqKbK9A8Dzv9/t5nodlCYZhEokEwzDRaBQMBfiry+Xy+/3RaBTnJyMkEgm/3y8IQjAY9Pv9iUQCupcQorqiIOtV6fVEe63I5/P19vYKgsAwDI4LgiC7w/bUU+34WIgQQmSmA5174AXDMC6XKxgMMgwzPj7u8/ngr729vT6fDxYYpqenYc7jeR7MjtbW1vHxcdzyMEI0Gg2Hw4SQ1tZWsCFmZma2tragbzs6OjyFx+lTArZdIpGg0rhMJvLwIb2gxWo119RYqqudVmvxH4cgyJHEVlV1/dA9iCP7g15cB7vd7vP5wDiYnp4Gy4BsT0UMw/h8vvn5eULI1NSUcv8CyQusGSQSiTpJJuJoNMrzPCEkr0eC6sqEkkQiQQiRjo6NYdzNzfA6mcnMZTKEkOTiIiGEy2RsDGOpqTFXV9sYhhACV9oYpgEfOBAEQZC8IaG6uroCgQDDMOC3r3Olz+fDlYbdEQgElF3HMAxsABUvf2pq6tq1a9ISS02Ne3s03WrDKq6vc4uLhJBkJhObnyeEJBcX04Jgrq621NQ46usJIe7mZlyoQJBKI5FgHz6MllqLknHlSp4jMxWCpukAW+Mej4dhmKmpqd7eXumf4P/p6WlYb+/t7fV4PLC1QQgBV8r9V/4oAHaD1ESA5YH5+fmpqaniTYfh4eFwOAwLD8ahtoXSsEgJQloQwKRILi6K6+vZjQ0bw4A9AXsfquYIgiBHgJWVFM/HS60FUmLkpkNzc3MgEIhGo/Q4X1dX19DQkHS5e2pqanx8HFz8oNxutweDQZfLBYcvYK3iIJtxSBkeHn799ddff/31uro6MMjItunA8/z09LR+dahL37a3t8Om0vT0NNgKsIuUSCT20JJrYJgGhpEZB7BKkRaEuUwmLQgT9++nBQHsiadrahxWq7O+3lJTs1c6ICVnTRTv3bmTW11tamnZReZMYIHjEpHIea9XdqCAi8Vk4YSVJKJRCI5LFLmejVQHimkFKC8tkcYjMqID/XTT00+f7+yUHdYw2AouFuNiMaX+WuUGJeSt/hv/7uWqU059BQyOAhVyqqGBhqYwODSqfQj3xleuXs17/kX/SlkTlv/vG79cThppTiUgT38F5yPALKCF0iUHeAuTk3SZHU5YBINBmds/okMgENja2gK/SEKIx+PZ2tqKRqPg7qC/5BCNRrd2AnYD1KVCwuHwAawAwSqF1+W63tEx3NU1duXKO1//+pDPB34Ssfn54PT0pbfe6pucHIlEJu7fj/G8uL6+31oh+8RSKvVmX19uddXpdoeDwbyhG7W4HQqtZbMQeEcqPBwMygqV3GXZtWxWVTcj1UnRreDi8YXk5xPJ+5OTuW19DOpAPz23ujp4+fKaKBbaikQ0yo6OOt3u2UhEmjdSq9yghCKrG9cf4GIxdnT0VEMDRGImhQyNVh8mZmYeGRtQrSsLakIForJhQWcs2LOIRCKwKyGFYRjV4A3oKYlQwJ6Qrk/AZkdsfj768OHE/ftcJuO0WnFl4tBxe3CQhj7s7O+Xpic2DjsyogyYf49l77KsweTyrR6PLPJdQdWLbMXFnh661DEeCHT298PDsXEdTtpssOLidLsXkslHHAfNMS6BHR2FYMlOt3vg0iW6fqNVblBCkdULGgVCCBeLOdvapMGYjQ+Nah+6PJ67LGvko7WuLLQJFYiem+T09PTU1FRXV5fUSsCIT8iuUW52yIwJus3RYrU6rFY801GGrInicjrtaGtjR0ZONTRIdwoWOI4dHSWEyOIiK1lKpRaSyVfGxmQPtTB/GMzGt5xOQ2ZFakAYr67Viluh0COOM1ksOVHMG8Qa4GKxXDZLJRjXoXd71ZaLxZbT6UJbscBx0uV0V3t7Mh4/1dCgVW5QQi6bLaY6bDoY7IFENBoOBuH1+5OT5zo7Lw8M6NxgSrT6kBByl2XZ0dHldPp8Z6d+/GbllQXdh5WJnung9/ul2xYAHqNA9hClMcFlMuAzwSYSsmUJ9L4sBx5xXE4Ubw8ONjkcH3Lc3e7ugdu34U80QR9kPnS0tWk9rd4eHCw0V5OSuyzrbGtby2bfm5jImyrCYCuedTohNzHkDeo18HPHjo7uui3wdPsomewsXEJOssEBQIpLrXKDEpSP+AVV19ZXBZfHMz47C+Yjnd11bjBVdPoQKr7R15c3HbnxKxEgz+FMBDlgnFar02r1nDkDb8EBE5YlRiMRQoiNYSAuBYaaKBXSjEq3QiH4tV3guFw2uzA3B96LJrM5MTOj+sh4j2UJITlR5GKxtWx2IZk07k9HgQyNMh2Kb8VZj+fenTuwmGEE2IbXz2eoAzzdronim319p2y2XTucHjFUh0brYq0+vLBd5YLXOxuJ6EgwfiVCQdMBKWuUDhMxnoc9Dgg1QTc4bAxTwiATPM/zPA8BWPNeDNleINPpAei2tzTuXMOvNZulz5p0EnW63Y26q/3gE7ecTj/iuNzqqv6SsoylVIqLx6npINPBCKqtgMWSiz093TdunGpoMLJezcXjSo8NIyylUsl4HGapWovl4tWrC3NzBZkOjU6n1IlvKZ1u7ejQKTcoocjqxvXXQv8Gk2KwD01ms8GPNn4lcqRMB9XYSkceQRACgQCkFIFTstIJSRCEoaEhWdarcDgMYUABZU6saDQKEULtdjuc190/gYUi27ZICQIExJRucDScOGFTnCDdNfS+0mkmHFc2mJU0kUhA0I7DeLvWWiwnbTYuFnO63WuiyMXjsFzf5HSazGZI00wIuRUK5bJZ1blQ6hDHjoxIzzQaJJfNvn/zJixpSHUoshWQ7xh0M3jgIre6Wuh6CfAombwreZiejURaCuyEWovF0db23sTExZ6eBY57lEzC9opWuXEJRVYvEq0bTIl+H95lWbiv3puYaNW1yYxfiVDkSbcPNceOHanmGMTlckE8LkJIOByGpNv0r+FwOBAI+Hw+6TEZiA3a1dVFCIGUZsPDw9SvBZKSQIAvQRBmZ2elM1xegdS2MChwb6EbHGlBSC4umqurnVZrA8MU4ypB7ytZvw0NDUHeMlJ4tvrDmN2eJt1e4LhwMHjSZgNDge5KrIliOBiELfBGp/PywIC+wDf6+mC2pu70iWj0Lss+4riTNpt01VoJOzKSmJkBHahLpvHqRKMVVGxOFHPZrMls1neWHA8ELni9UtNnd01wtbfTdRfjEmCVXunUqVVuUIJqYZiQNrN57LnnIpGRH/5wEuI6qF5pXH+4EpYuTtpsF7xesDW1bjDjfTgeCOSyWRhEabkS1StVmwBxHb7xjVktURXFkZprK8p0CAQCra2tkGcEcl6oAgnMILcWLQTzgs5bkKYELoDXsPa+a4E8zzc3N8NY5BW438R4PpnJpASBy2SyGxuO+vpCXSWkpoNWMyvHdPjy9ttljjtZ6qzZxetQpIRNUawq7ghfOhaz7dZVAtBqgvGmqV4pK/yHnaaDqfHcE79WC39K/Xyx4XT9btXXxLjYf/rHn//2vzm95wrIWF9MfJZbRtMBUNmwgI1YIgnSoNyaTSQSLpcLyuEyyL5Nn3chgqEgCNK0WFAL5jkQBRLogU/YMCbbR0Blb5XXU7HEQLKoIwME7IK9CUEQfv7zn0PHKq+E3vP5fIFAIBwOK8/LAC6X6+OPP4bX8CStNc0bFChFX+Ce3Fr695Xbbj9FCL+x4W5udrlcj9fXucXF792//+OHD5ezWeeXvvTlL36x4cSJ/7e0VHf8uPTWyntf8Tx/+vS+/2CVG/9AXzmdpQ+XU7wORUoo/ui/271cpAStJhhvmuqVutVzj+7T13WEfDL/E4MfZRzjYutryCfzH+y5AogOctMhEAhMT09DTu1IJDI8PAz76C6XSxqdOhAIwE/q9PS03W6HXXaoCA9S8FDFMAzswcOvfyAQgLDW8NgKD6MulwtCWMLKOUwGEE9C+hYmKun1MEnAa5/PNzQ0dLBdt0sg2ibMi7Q/4VlWGohTdSMc9gKmp6eHh4chBzrDMK+++mpra+uLL74Imbulk/T4+DiTjCy6AAAV/klEQVSsrvt8vkgkojXTDw0N0fxYDx480HkU1hEoCAKEs+R5fnx8fHx8PK/Avbq1CrqvwuFwem7ue2+95fF4iNmc+tGP3F/5yiv9/ZlHj37tC194/JOf+AcHm5599n//1V/9w49+BGKVwwftpYnmK4Qz2ez8T3/qqK9/4exZU1VVqdU5UP7XRx/9UBR/4/jx/155xiLF8uSThJDWVm9zc1HLJLvm+98fefz4Z42Nv/sHf/DfSqIAQtlhOiQSCYgwTacfiGcMj1/wGEdTaDY3N/v9/t7e3ubm5kgk4vF4Wltb6YRBCOnt7QUTxOVywao1ISQSicDTYSKRoL+8tKLH46HTDPzc07fK62GuBfVgE/3b3/72/vbWHgGdDHM/mAjHjh0bHh4WBGFmZgbiUrtcro6ODml0TnjIhpwUUvtgeHi4o6MjEomMj48PDQ1Fo1H6oExTpcMwSdOSSfNc+Hy+4HZglryaawnkeT4SiUBStOnp6bxxRffw1iKF3FdwsezWMm1ufvjTn0IDvxuNWp544v7f/d2lP//z5C9/efLZZ8m3v80mEja1hZMKyfTGJhIT9++32e3/89KlCjwQm9rc/KEoEkJ+8emntqqqhgozm2QwTAPD7MYztEh4Pvb48c8IIY8e/X19vbOmBkM9lpIdpsPU1JTP55NOS/AjC68hkeb09DQ8dcEvJvwP18gWpeEtwzA+n4/6pdN0GOBvL51ghoeHPR5PNBoFp7+uri7pW+X1MoUPS4xLeMiGR2SPxxMIBAKBwIsvvkgvgAdlolgqB7eGcDgMqwvSP4FJAa6OsCxBCIH/6YxbV1dHx45oJ0mXJuKSoS+QLpPQbSZ9gXt7axm/rxiGyXtrWevq/uuVK8Nf+xohhMtk/qqvL7WyEpuff5hO/7rV+tyZM476eveJE9lUyufz7W12sXIjxvOjkYjTan37ypUKNBqAicePbU899cutraV//dcZQeh55plSa1SJ3L8/wTC2J544/otfLMzNRV0ujL5QSnaYDidOnJCePVOiNansDuXsBc+LYBDQNE7w1uVyqc52e6vSwQBe+uPj47Ag39raqkySCa4eMutheHgYFvZh4V05Y0lzlkYiEZ/P1yE5aT0+Pp73VKTP5xsfH4cVERkGBQaDQZ/PR006HYFS9nAc895X4Bihc2vBjhK8hlgR1zs6CCGJcNjlcl31eH6VI3Rzs/qZZ7768st/8OKLH3z4obm6OsbzRyPkZYznY/PzcZ631NQMPP98CQNmlAPekycHmpr6PvjgfzQ3Rw7hD87R4MwZj8vlnZzs6+5+6+HDmVKrU/FIUy/Oz8/X1dXNz8/D20gkEolE6urqVlZWtra2ZmdnT58+DX9tb2+PRCJwGRUSiUTa29vhdXt7+9TU1NbW1srKyunTp+Fiaa35+fnTp0+DZHhLP3dlZUWqBn0rux5UoupFIhFZc8oT2gRCyJUrV1599VV4PTs7K23Cq6++Cn9SZWhoqK6ubmpqCmrR8ldfffXatWtb250m+1xCCB0ILeEwXiAEgCoFCbx27RqVoCVwD2+tgu6rre2bZ0v71tK6r2TNHB8fh+pz6XS339/e1fXN7373axMTF/70T//zX/zFN7/73be+//23v/Wt7/zgB6u5HDSTfrT++JaEuXT65r17r37rW3/85pvf/O53vzM7+8/bfYLMffLJ1372s1JrUemk03MTE18rtRbI1tbW1o5VB7vdDqu7drsdtsCHh4eHh4ddLheUDA8PG1+bnZqagjg5fr9fufMNBwRAsiAIkGRraGgIPigYDMKqPn2rvB786eCsgcvlOiwnLKanp2E2Onv27Fe/+lU45Xj27FnYyCDbS/Q8zyuXIiiBQAD2LARBAAnQLYQQEBIOh2UP+gzDXLt2bWpqSt8LAdwPoVdBJuwmFCQQ7hPYF9ASCDsse3JrST0Z895X0EV5by2t+0rqIwJDBmpb6+qsdXUDzz9PCPF4PKe+/GWP15sWhD8LBH7z9Onoo0dcJvNPt26devbZ//Lyy4SQzMcfVx0vfUC2lCDEeZ7LZOI8D0E5r547V+FrDKpwuZzDZCq1FpXOxobIMLtJ04rsOeqBEGAbWFqidfxPC9hFdrlcMKvpXCn7LNg5lh6Qk75V1Q1iBhwWXwey7ZwP/Sl9vWsKioJsHGVX74fAfbq19O8r1RJZlYLuK4NxGrhMRtzYSGYyt0ZHf/HJJ//+T/4EImASQmwMY66uJoRAKExCiKW6eq9mcchQSgiJzc8TQtKCkN3YSAuCubq6zW53NzcfjX2W/SO0sOBhGDdmYS4piQS7spLq6CggZjmyT+xXDCX4fd/biQdBSLneWkWGhKJTe1oQUisrUJhcXCSEiOvraUGgNoSj3mjsHahOTROoCDlIiSKeN6LPH/74xz/4nd8ptRaVTiQyYrW2nDlTXt/9ymS/lkxlUZsQZK8o21srkUgYDJMVDodlay0NxkJbxrTDhirpOHMGtx72BG5trQ0TI5UBGxvZmhociLJgv0yHwxVhFzlElMOtNZJKzQiCw2Rqqa11mExui6WjwJyBELej0M/FpYKSEM9mW2prS60FQgQhXV9f4tjnCFBBSR8QZG+JiWIyl0ttbsZF0VZVBZZEm9lsKQP/R2QPCczPX33mGSdaD6Xmrbcuff3r75RaC4SQI5Z0G0EOErfFQv3mUpubyVxubm3t5uPH2U8/bbNYnCaT02TC+eYIkMzlcBzLATxeUT48+Y1vfEP/inA4vOvzC8XURQwCoRtramrgbSKRmJubIzsjMKoWKusW+rlwTNHI+CoV0FLpkGI5ftxeU+O2WLynTv2n3/zNqiee4HK5e6urf5lO//iTTzK//GXVsWOnnnqq1GoiBcMuLVU98cSFurpSK1LpZDIcz8e//OX/WGpFEEIIeSLvFRCmd3fSi6mL5AWc8iDPJJT4/f7x8XEI+xgOh2WFLpeLFirrUgKBgN1uh/gQgUDg2E6kcSMgmLQs/oS0OiEEEn2BAvRKVT2PEm6L5XpDw3Bz8ztf+tLVZ54hhNx8/PgPf/zjvg8+GEmlYqJYagURo0w8foyRp8uBjQ2xvt5Rai2QbfIGjZKG6iuUYuoieZmdnZ2dnSXbIR1XVlaGhobgTxCrEV7QSI60UFmXsrKycvbsWdWBm52dffHFF+E1DbYIoRh1ql+7do1qRa9R6lkhzH3yyXf+5V+++eGHf/yTn3ztZz9765//+Uerq6VWCtEEBqvUWiBbW1tb3//+W7Oz3ym1FsivUPF1SCQSEJdQFlEHHh9lJbKTcqp1AcgdABGQiOSInTQyD43JA0fXpB8KabsPS8jIg0HWG5DWHF7TjI7SAfJ4PB9//DF0slZPBgKB3t7eqakp5Z/8fj8sG8BQglj4FHrUUFYdYmLSfNw0l5VSzwrBWVtLd83BvzIqCIMLC+hlWZ5MPH789nPPlVoLhBBCFheTra2Y8qpckP9IBQKB6elpyGgciUTgINzQ0BBkN6Y5jv1+P8RA9Pv9NBaval2AHnaHVNqEEEjgBCU0wk9HR8fW1hYUwlQ0PT0NhoXdbgfh5XA2b2+BnFJ+vz+RSCgTURZKIpGYmpqCzNeEEKm9xfN8XV2djvkFKcfC4bDSdIARoYaC9E/SGNiy6pAf3OfzwRaGLNW1VM8KpKGqquHUKXgNZkRMFEdTKfPx421ms9QHEykJ7NJSm8VS4Sm2ywdBSJck2Teiyg7TIZFIQIh+medaV1eX3+/v7e1tbm6GyyDxICGktbV1fHx8eHhYqy7ZthvC4TDYDQZnxObmZvqhsKcOn1VMa8sQiA4EU7IgCDMzM1tbW7Cc09HRsYuYiTCvqz7KBwKB4eFhHbdEuEBHT3i9sh3uMG/12dlZQRDAW1YQhBMnTkDaa309KxClGSFdjUAzoiTgkkP5wPMxdHQoK3aYDpCDWDm1wO87/ZUH50fZrKZVNxAIPHjwAHJ5Qz4kePqkU4gW0g+lC92FN7CsEQRBmZAant2JYj8iHA5DN4JRpSUT/gRrQpCFgZbD2oZWRZjgQQFBEMCggf6HtR96A5w4ccJg9ebmZrq1wTBMe3t7IpGA0ZTq+fDhjxmGqanB2ZEQNCPKA1xyKCsymaTV2lJqLZDP2WE6nDhxAianvCg3DrTqulyu3t5e2NeAySwajYKdQZ3wK5ahoaHe3l7VfmMYRpYWi+4XGDGh7Hb7xx9/LJ2nW1tbjcRIhiyUDx48gA+CKrDqI9UEkn8C0qUmWXW6lwFIX0v1/Nu//dZTT324vi4KQtpqdVZXmxnGVlPztNXqqK62WK2VGz8OzYhSgUsOZUUmM3fu3NVSa4F8zg7TwefzuVwuSENMtBMn9vb2SpcNwNNNqy44NIDrA+x5+3w+yOstE1uZlgQ9v9rV1QUl0G/z8/NTU1My00FflNSTFGRSHxQjdoPf76fXHDt2DNKvw1ue56l6oKHP54Nxpx8Kji/K6pDVEzJc00TVMj3/6I9elN4PPB8jhMzPx+bnY4uLSUJIJsNZrU6GsVVXm0+caGAYW329s9JWKdCMODBCCwvtDINLDuXD4mKykh8hypAdpoPdbodffPihhzleWcdut0PWY3im7OrqgqP8OnXD4bDdbhdF8a//+q/hgmAwSKWBC97R24/IC6zcgGskzKlk23SAswn61QOBAHgMgBNJMBiEPSNwKR0fH4ep/fXXXyeE9Pb2Qi1YQpDV1XdAUZ6DgLH2+XzT09P6dcPhMFwZjUZhU8Plcin1lFax2930/51qxNbXs5nMXCbD3b8/IQhphrGBPWG1ttTUmJVVjipoRuwfoYUFQsj1BvTIKxcyGQ7jSJYb6jkstNYbjFyWt670NCYtwVOXewWcaJW6JhSP9DQmha4i5LX56MKDVKW90pPnY4KQXllJCUJ6YyML9gR4VDU3uxnGVlFe2WBGcLkcTauBZkRBjKRS2c8+G2hqKrUiyOckEuzKSqqj43qpFUE+B9NfIUcNQUgJQjqTSa6vry4uJulmB8M0WK2OytnpSG1uzgjC3NpaMpdDM8II7NISu7w89tu/jaE1yop33w2dOeOpnDXFQwGaDsjRZ31dXFzkqDFBVybAE7MSjAnlagSGn5KBdkPZggkzyxA0HZBKBFYm5udjsMdBjQmrtcVqdRztPQ5qRnC5HCGkzWxGM4JdWpp4/PiW01nJnVCeJBJsJsM9//xAqRVBdoCmA4IQQkgmwwlCOpOZo3sc9fWOEyca6uudR9i1W/z003g2O7e2Fs9mCSG/ShReSbnCU5ubo6lU9rPPbjQ14ZGKMmRyss/j6T/C38FDCpoOCKIC7HHMz8cEIb24mKyuNlutziPvLRETxZgopjc3pe4RTpPpqD6LTzx+zC4t9TzzjHf7uApSVghCamoq2Nt7u9SKIHLQdEAQQ/B8LJNJCkIqk+E2NrJ2extYEkfVewv2NVKbm0dyQSImiqPptNNk6rfZjqphdASIREZqap4+d66n1IogctB0QJDdILUkCCF0TeKoWhLKBYmGqqrDaEmIn3568/HjeDbbb7PheZMy5623Lvn9t47qIt+hBk0HBNkDwJIAV4nqarPd3naE/SRSm5vpzU1qSZiPH3eaTA3bGxyl1k4d8dNPo4IQE8VkLtfOMBjxqfzh+djsLNvVddRSJR8N0HRAkD1G5idx5M9uiJ9+yuVyyVwutbnJ5XLZTz91mEy2qqqW2lrzk0+W1piQWQwdDHPolkkqlnffDdnt7jNnCs4ejBwAaDogyP4iCCm6ICEI6fp6B8PYmpvdR3VrgxDCra1xuRwsTmQ/+4zL5Zwmk62qyvzkkwdgT8DnJnM5CIeFFsNhZH1dDIcvYziHsgVNBwQ5UDIZjufj1EniaG9tUGBlIr25Se2J9OamraqKEOIwmeCahqoqKLE8+aTBmV5LLMhEi+HwEomMEEIw+HTZgqYDgpQMurUhjXHZ3Ow+wuc/ZcREEV7A9A+vk7kcvOByuexnn8Fr21NP2dTiLtDFDNtTT6GhcDQQhNTk5Eu45FDOoOmAIOWCIKR4Pp7JcPTUhtXqhFOgpVYNQQ6OqalAc7Pb5fKWWhFEEzQdEKQckS1IVIKHBIIQQng+dv/+xJUrY6VWBNEDTQcEOQRQDwmej8PhzyN8ZAOpZMbHuzs6+tFELnPQdECQQ8b6usjzcXpkA3Y07Pa2o+1oiVQCmOzqsICmA4IcbqTRqKijJT60IYcOOJB55crbuJZW/qDpgCBHB5mjJe5rIIcIPJB5iEDTAUGOJrivgRwiHj6M3r9/88qVsQo5lnzYQdMBQSoCno/R2Nh4XgMpKzIZbno6iFsVhwg0HRCk4shkuMVFDk5t0BQbdnsbPvAhB8/6ujg52YenKg4XaDogSEVDU2zwfJxgHCrkwJmaClitLefO9ZRaEaQA0HRAEORzYF9jcTGZyXDgZYnuEcj+EYmMCEIaM2sfOtB0QBBEHdjRkB37rJz8Gsh+g66Rhxc0HRAEyY/y2OeJEw24r4HsGnCN9PmGcE3rMIKmA4IghSE79km9LHEOQAySSLD370+g3XB4QdMBQZCioOEseT5utTpxXwPRJ5FgZ2fZrq4hXLI6vKDpgCDIngH7GisrKTivgfsaiIz79ycePoygf8NhB00HBEH2BWnecHpew2p14PH9iuXdd0OCkPb5htBuOOyg6YAgyEEgzRtO41Bhfo3K4d13Q4QQzIp5NEDTAUGQg0bV0RIXJI4qmQz37rshu70NU1sdGdB0QBCkxCgXJNBD4miwvi7ev39zbm7m+edvoF14lEDTAUGQMkK2IIGhqA4vPB97993Blpb2c+eu4tgdMdB0QBCkfIFMXSsrKdzaOETAYgPPx59/fgAjNxxJ0HRAEOTQADEkBCEFQS0hWRfD2NCSKB8ePoxGo6MtLe3o2XCEQdMBQZBDCT38KQjpxcVkdbXZanUyTAOuSZQKiBFpt7e5XF5cbDjaoOmAIMhRQGpJCEKaEIKWxIFBjYZz53rQubUSQNMBQZCjiXJ3o7ra3NzsZhgbTm97wvq6mEjcSSRYNBoqDTQdEASpCHg+Jghp6nFJT4Giq8QuyGS4hw8jc3MzeICiMkHTAUGQSkQQUoKQnp+PbWxkMxluYyNbX+9gGJvV2sIwNtyqV4VaDHBitqXFg0ZDZYKmA4IgCCE7D4Kur4uEkJoaC12ZqOTAEmgxIDLQdEAQBFEnk+E2NkRYmRCEdCbDQVZxQkhzs7u62nKEFydoLvXFxSRaDIgMNB0QBEGMAuc4Mpnk+vrq4mKSEAL2RHW1mWFsNTVPW62OQ+qGKQgpaitg9C1EHzQdEARBioXnY4SQ+fkYIQT2OzY2sgxjYxhbdbWZEGK1ttTUmAkhZTITyxSmfqOYzhQxApoOCIIg+wIsURBC4GQHIQQWKqhhQQiB7Q+guflzq6LI3RCwDABYIwE1wCeULpOAG8chXSZBSgiaDgiCIKUBTnkQQtbXs5nMHC0H1wr6Fg6A0Lcw2ctEra+Li4tJu72NlkiNEjARSNmseSCHHTQdEARBEAQpgP8PiX9Jpbz+eJIAAAAASUVORK5CYII=\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451eec9230>" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": "f0._sapcar.files0[0].blocks[0].canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Compressed Data" }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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36NChowScttkuX78ubsk9fqzSLscyy560WMhz0N7XF5+fb7Nai7VrlJDLZDR2L4sE3NLeHhqS/4R7ZzHOVCNTKOkqPK95EbTfBordASDGMEG/Hz9/dPv2+UuXxFqEZyQLgniHs/X0sPPzZ2y2GMOEJyaMJpMiIxFDcbKvOJ3apwAA66kUG40CgJg9aB8DQnzPaP9DKCYBhYQnJnB4FwYGVOhXVhDWU6munp7w+Hib1apFQiMADP+7f7etR1XoqBIuXrjwb771rWqPQodWtJjN5PmYFYTY7Oxb772n0i5HThAkLWvJpEq7Rgnyl6Ri3csiQQXhiQn38LD6MVWfQklXYZfXTxGK3AYZzQLA7nROLSyEx8cBQN1Mc9pme8SyqGPAc9n7+taSybsjI9fv3GmzWj+cno7Pz6tIUJys8hSKz+CTcNjW05PNZD6cnv7u5CQK0T4GhPie0f6HUEwCANwdGblw7Zrd6cwKwuiVK109PcWYxyOWzQnC3dHRM11dP2PZTy5fvnH3rqIE046ARgD4D2+//a2rV3cdlg4dZcdyPA4AOm+oR2QF4Z2hoQsDA5LnUbH2I4JlloVnXz2PJsS3QWGN3b1D6Wgxm9+anAz6/SctlvVU6nRXl/H48djs7KuXLuG9d2Fg4N7ExJ7lPzOFIjNwDw8Ty8idkZEH4fCrbnepY9j/PSORsMyyuUxmeWlpeWkJAIwmU2x2VkUBZjSbkfGQWZy22eQS6IGn9qandgp1WqdDR4WAbxU66g7LLEteR7S0i3HaZltP/dbNbi2VcrhcKu0aJWjvXhYJxcDOz9s0hHJUfQolXYWtXefzLLTcBmXBGZsN/RsAYOTy5QvXrsVEIS1Z2Yu7BCrLpWUKa8kkWkbwa4vJ9FRnI7Jr7DoGkN0ze7gPFe86QiNsvb2nlfwcyenEX3EW2CiT8HRUX5KIyArCnZGRkcuX3x4aujMyIlGPZAUhPD4uaXwQDofHx8m/XTUqbDR6Z2QE5X84Pa0uvFT5xYSo40E4LO+yBzlyFJtsXaDYpr6H5VLsoqNOEWOYuyMjl2/csDudyyyLxl2VdglazOaunh78c1hm2UfxOD6ai7VrlKC9e1kkFEPu8WMtipaqT2H/V6EYNN4GZcGN11/HB8uDcNhoNp+x2fB1H3frj27dUu9ebLIap5DLZMgpsoLAzs/jRlvSGEB2z+zhEkgknLHZjCbTeipl6+219fYuMIyKraTFbD5pseAcySzUJUj9It8ZGrL19Lw1OQkAD+7dy2Weser8kGEe3LuXzWTEjioLDHPSYnFgmG8qNXrlint4uJgXxp2RkUcse+HaNaPJhGoQdeES+blMJuj3v1bcybaYEHXgKSR/7cvxuD1TgllODpXJ1gU+un1bURGluFwf3b5t6+0t9sRU7KKjHrHMskG//3RXF/GZsvf12Xp7i7UrCnEPD78zNMRGozlB8AYCu7ZrlKC9+/4lxBjmk3D4EcuGJyaMO4ZtAFhLpTQqnKs+hf1fBfkiKN4GNK38uqy4htiIL9zL8fhrbrea7qqrC50ojWYzDhU9+0avXDGaTPjS/PbQELk6WhZBeQouhWt6xmZ79dKlG6+/joaSCwMDeOlLHYP8ninpNlCUgBacT8JhADhts6kzD/fwMDH3vOp2oyglCU+tNceePHly7NixqYUFsl5E7SMHKoLujoy8/fHHpPHtoaEzXV3i2F/JAQT401988IFiVLeicLn8tWTye9/5Dg5YuxB1vD00RC55uaA+2brAoMOhuM6KyzXocLz13nvF1rDYCofHx+nW1sm33y7XmHVUDkO3b89znPfixXIJFEfNaWnXeKT27holBO/fx3yRUUF449NPv76Tv0ERWUEo6U/+YKZQandJexTgH3fyRUYi4z/4we0Xfu8tzN+gBYU19pd//w7JF/nnsT9/Hk6ehN/ftaMWrCWTRrN51zVfh7/7FaxjvsjIePIHt3/+wltg2JnBrstVYOGX7/w2X2Qs5hCg61P4rsbu6ih2z2gXW+pdp4hdT2cC9iy8I80XiXqJYp1RHWR3OsMTE+j9oSj6dFdXrshr+oe3bn3r2jXF6WkUro5iQnBG+Cu+8i6zLPqsincybBS7lpy0WFrMZvIB29lo9LTNhu05QcDPJU2WnEv8Co6DFI+BjUZR8yYeT04Q1neoZbF5kSGtJZNI21Va5LNAF5td71cUpUgU5LPbtYuOekHw/v1yivvpT0tr13ik9u6lS/hHgH9UkVbq41vxD62kTWifEoodqSrhl3//jlb5SvgVrKehTDeSFR4DqAVKFsEvRTN4DmzpfQxhP6QBAIptE9rFluXtVPvpnuENbVbrt65eHb1y5ZW+vjPd3a9euiQezYNw2O5yAYC9r28pGi22tX84PX3+0iXFnx7F48XCk9SFZzMZtL6sp1KfhMOS4NpdhYQnJhxO54fT0+gZGx4fx4gdFIuajA+np890dcVmZ093dQ2OjQHA6JUr+AKNjjZ42FoyeXd0dOSDD+6MjKynUme6uu6OjmKCDu2TDY+PL8fjZ7q62Pl5YnMJT0xgzq/Y7OxJi8VoMrVZLDhOPDUqzYwm06N43GgyvTU5KZmXZEjrqVR4YgKdZRYY5sqNG2w0KmmRz+LOyEh8ft7e1/ehqlkOl2s5Hg9PTGDQjvrs1LvoqAt0tbdX69T/+OhRYWvrnMXSYjBUZQA4d3NDQ4/JVJUBPN7a+kk+bzh27Otf/nJVBgAA5oYGAGhttdK0guNnLvd4dfUnjY2G06e/rti9tfXpQ5I20XsbwNrmWubXmZOGk+bn97VNtloNdI/Cdcw93lr9Sb7RcOz015UXudVanduv1iD1b3C/+aatt5eNRh+Ewx/duvXW5CThICQg+1W3+3vf+c5aMkk2y9jsLAbUoSnoW9eulToOFeGwk1gjm8n8kGG8gUCxF1YVIUvRKNpfllk2Njt7/c4dCUFzOJ2YhOR73/mORKzd5Qr6/bh5PwiH7X19yyy7nkqhvepMd/eDcFh7QAobjbLz87hxSiJr26xWMgakLGe6u9G8hEBTH/b6IcNI5iUZEgDYenrEA8MUbKRF3sXucsXn53FxsoLww9nZYrMgRse3h4bEqh2V2RXroqNe8GbpwQVlQTgWw3pa9q9+tVpjQNhaWibPnq3KqX2JxE/y+cKTJ8MWi0218EelYbe77XaFP97bt4cAYGur4HQOd3SovblePbuXsH++wL/743cB4LmG5/YmgcDubrO72+Ttt4ceAsBW4Ylz2NJhq+Yi1zgU8kWi/6T7zTfvjIx8eOsWvnzHGAYAHuzsYZJ4UPJOrA50D5S3qwsHAOLfsJ5KiQNUtAt5bWeXikUi9r4+uVYH86UoOu6hZykbjdp6e5GaPAiH11MpxVRxu04WN2/8jH6zZJziMSA3Mj77coNfW8xmTG8imZdkSE89a+bnbT09aDGxu1ziFnkXACCLo674IgM709UlDpRQmV2xLjp0qEDI5yd2IuuObDXOZKEw+/nn+Dm8vn6jqrxBERwX5bin/vaLixF13rA3zK0+rQ+xklvhCzxloMorn08WuPmnT+zFCK/zBhWo5Zl2OJ3hnYQVS9GoxDv6QTisnkhVjleczk/CYbljp3bhFwYGgn7/K06nfFfTKMR4/PgeNq3X3O4FhgEAYrPflSoVm6wE8tRge4Z8SH/xwQfx+fmFSCTo9393cvKMzSZuOdPVJekSHh/P7i+ERIIyzk5H1bG2Fi4UDprwLa+v+/7VZmaz0PT8c6mNKPvTx6ampgMeQ9Xx42z2L74MqULhd5qauM/zyYYXqj0iKX7xix9fuPAKz6defPGl9fVI2d8Ltn6z1Zz56b/5sumXhV8aG1t+8rP/zdpS5uCs5I+zF/4C+FThhd9p+gWXTyZrbpFrB8/wBoljIDoAAkBWEH7IMOIIhdM220e3b+MruPaTuYeHR69cuTMyQoIk8RTahdt6e19xOsMTE5IwS+0jtPf1jV65QpLZaZzCK04nRtNiOOirbvc7b7xB3B4lVhWVyaIuJ+j3Y1+Mzd01JS0BKjAw9enl69fRNoSQDwkA2qxWu9PZ1dPzvddfJ+MkLZevX5d0QYWEe3i4xWxWj7qWjIS0q8yuWBcd9QKeZzKZ3TPmlhdNAE3PQdtzAAAd7ZB7/E+5PbjA1TnayP+/hhcAfv7z6g5HAc89BwDw4osA8POTJ+HnP9+lmtQeQAFAAb4CAL8GAPj5F2WW/1wbAMCLbQAA7S/U4iLXDqTxFHdHRnKjo+i3DwAkkcMrz743t5jN5y9dWmCYknhDi9l8/c6duyMjGPCaEwSj2YxUQLtw9/Dw915/XVKCTPsI26xW9/DwO2+8gbGqKiHmEmldPT0/ZBgkASQ8F2dhd7nkio1ik7X19iKlwAG4h4e1ZzVYiEQw3PnVS5dsvb3iRFLyIbWYTGSa6HESn58XtyjO4tVLl773+uunbTZSmFXLSEi7yuyKddFRX3gIb5VLFMd+Fo1E/8j9R1+xvogtXwjZv733cfbxFy3Hv/xHl775ZbOyrhg7ilt+t/d3v9b7u/j5v0V//KLlRSKz1DHMhj/+RfLnLce//D8MfFu9798z0c+WPsPPkuM1joHM96Xul37P2btru/YBaAF2v3jtYrF11g7JMmq8jioSQNuFqOIUtsGIH5ZZVpykEnZs/VlBeHDvXu7xY+Px45IgAwmKSSh1CuSMZ7q795apE0ciL7/JRqMnLRZSn+KZ/A0IDJYTRwBWAqXqKmpzABqFKB5WasgvpkA4bbPlBEGFakjOJQkiVWyRdNEYD50VBJWRKM5O3kXP31BHePhwKJOZj8FUuQSOXL582mYTvwOMXL5s6+lB12x2fv6tyUnlIOfpaTYaJdT2o9u3vYEAPijXksnRK1dUvKfVx3BnZCQnCK+53eiIrZ4+7u2hoZMWS4vJBADG48fJy4PGMawlk0G/H+f7zhtvXL9zB/9kirVrH4BGYM3Gy9ev7/9JKFlGjddRRYLGC1ELUyh2N+5fQknj137bqED+Jwmi+7mn16yQvwHRZrUeQF6/qr9xlmUAGoUoHrY3Wtay244uOZf8LPIWuUpm/yNRnN2ug9dxdBAeH5dk1MdCwOhwY+vtDY+PP7h3r5ifE2mf8vkuDQ/jQ/ZBOPxJOKyxcrF8DMssG5+fxwAlW2/vyOXLu5J7yRO2pDHcHR0lgcqXhodJKcti7RoHoB12p1McrrVnSJZR+3UsJkH7haiFKSjejfuXUOoUSrptFCH/k4Qi97OaX6SOmsKZri7tD0QdOioHTKBuNJtzgnD5xg1xPUDFdjnWksnlePy7k5PiGiinbTaJ44u4PpAi8OlMnrkYxqwe6KQyBkxWRg6w9fSIqxYpAkPEQUS+NY4hKwjrqVRXT094fBwthurt2gcAAMssS3IkXxgYUI95/iQcDk9MrKdSr166hPuc9usISstY6nXc/4WQT6GkRdj/FBDiu3H/EkqaQrHbZj+LgFC8n6V1rXTULNxvvllRy5EOHRrxVZvNGwh8d3LywrVr4vxgxdrluDs6KncHbjGbyeaH/rO7JvkIT0yUqpxXGcNpm+0Ry2K+VBzArs/6T8JhNhpdYBiNZIXgEcvmBOHu6CgA/IxlRy5fVm8vaQB3R0Zec7u/Ozl5/c6dD6end40gu3H37tsff7wcj2Mcu/brCErLWOp1LMuFkEwBSlmE/U8BIb4b9y+hpCkUu232swgq0PUNOnToKA2vOJ0P7t2TR9wUa5cAn+w5QUDT9XI8LklJnhWEd4aGSNBTMeC+sjctfbExYC0f9Oo93dVlPH5cRYh7eFisayk1m5nRbCa1jsTdi7VrHMAyy2IVPSykJ0+HIwHJAfOa270Qibzqdmu8jrDbpdRyHctyIeRT0L4I+58CotjduGcJpV5H+W1z2mYryyLI8ZQ3TGomGjp0lBGr//zPL3s81R6FjhKAaUAvDAxcvn69zWolr7nF2osBo4HWU6lHLJt7/FisXr47MnLh2rVdTbyYwWw/c5GP4YzNRgr7YZG8Yn3Xkkmx8rzFZCopMczpZ3WHpHux9lIHQLYfW2/vaW16SszMVup1hCKXUvt1VJSg/ULIp0CgfRH2PwXFu3H/EjROQfG2wcZ9LoIiGgEg8mwESP3i+9///p/8yZ8otn/7299uL2t2/b/5m7955ZVXyitTCx4+fPjFF1+0t7cf/KkrBJreY7J6HVXBeiplNJnwDRjfkNTb5XhVVLUkPD4uDjmLMcyHt26hTV2STkaO3OPHe/bgLjaGG6+/joVaHoTD6jFluUzmo1u38O0tKwjs/Lx2NS8AtJjNJy0WDGUSdy/Wrn0AmN8WCyIDBiZkMir71ifhMM79w+lph9Op/TpC8WXUfh33fyHkUyhpEfY/BYT8btynhJKuo+Jts/9FKIZjT548Ufm5vnDsmPJ0jh07FolEnHuKZy0Gp9Pp9/vLK3NXeL1eAKAoimGYwcFB/Irw+XyhUCgYDB7wkHQcHZA4TKwMh2k/cpmM0WTCh2Ox9mIC3x4awl0K/cCXWXb0ypXTO/6/mF5F5aVnyud7ze0WP+BiDIM5Qk5aLGK1rQokY5jy+TCTvdFs9gYC6uE/ZL5ipzPtY1hmWaKKf9XtFvuyKbZrHAAAZAUh6PdjDp7TNptKNOmUz5fLZPB6/baEXonXUbKMp222kq6jXEJJF0JxCiUtQlmmILkbS72Z5RJKnYLibbOfRZDfz2Zzw63Jpq985arOG7TC5/MBwNjYGH7dP2/gOK6kV22e54PBIA6DYRiPx8PzPPnJ6XRSFHXwVEbH0UHZ8zfsE1lBqERYr8b8JQSlJmLR2F3c/jysGWC9mASO/Yy2vbTnAdQsfp78RYu5Zf8JnQ4GXwjZfQ51/xKgwjeDEZatcE85f0MsFsMNiexA2ELTNNnnYrGY3W7HdjyMYRiKoux2OzmApmme5zmOE8ux2+0cx8GOdhol2O12iqIAgOM4/BVbJF/lxxOxeIyWmUskyCcikRmLxSiK4nke14RhGPHZ5R01guf5UCiEJEAjKIoix0s4h8/nGxwcnJmZKXUYOnTULyqUC6RU28c+o5yKdRe3t8GDU/BRMQlnD2mU1dmKZxEqK/Z/M5bjdj6Ym0HKG1Ddje+ykUhkbGzM5/Ph5irWjft8PtynQ6EQTdMURdE0jR3xjRx3OIqicN9FVuHz+fr7+wOBgN/v93q9Xq+X4zi73e71ehmG4TjO5/PhHjwzM9Pf3y/+GgwGJcfjxomfPR5PIBDYdbZyCeKJ2O32UCiEg+c4Doft8XgcDsfCwgLDMADAcdzY2Bh2CQQC2IV01A5cK/zs8XhcLpfX643FYoQZ2O12otsQIxaLzczMhEIhckZcumAwqPMGHToOMU6evNTQYNr9uGrgZz+LNTebvvKVChYZ/9Evf/S71O82fqmCMYCL/5U/96/LXGbz8MFs7n3mGsRisVAohDs9tjAMwzAMvnzja7rT6cQNu7Oz0+v1Dg4OdnZ2ohXA4XBMTf1Whzk4OIj8A7dVJByRSARVCLFYjOM43IxJR6fTSTZLJA3kq/z4sbGxWCyGw0OVwPvvv68yW0UJkonAjiaA6Dn6+/vtdjvZ48V7eX9/v7hjSUgkErggwWAQdTN46tnZ2SdPnuCiuVwuuSYDr4VE2SAeFZtOTzAM+WpqarJQFAAcb27u6ujAxl7dFVGHjnoDRTnN5hot7PK3fzt08aKfoiqoIvibzNS3Tw9WTj4XFR7/ZM36P5f8MD+CeIY3zMzMeDwesQlA7BZAUZTT6STaddy68H88RtyRfKUoyuPxJBIJbBwcHCTnEpswAGBsbMzpdDIMg64D/f394q/y4yUDlpxdDkUJkomUhD13ZBjG4XAAAM/zU1NTnmcDEVF/AEUsL8g2UGvCcVwoFMKJMwzD8zxSkMmrV/HgKMfhh0w+v5RORxMJAIivrEzPzQHAPMf17Ay+q70dAKytrRaKslCUdbfF1KFDhw4Cnk9VlDRwAtdh7KicfADgUwXKYqjoKQ4NnuENra2tZINXBHHEKwuIUYMA9QHIBnD7JF/xpV+uui9pSIoSJKAoChkSumtodJsoCWgAAoBAIDA4OKi45ujeUezsNE1//vnnqHtAOQDwwx/+EDuSOAuxXsF57lyx8SC9QG6R3NiIr6wI+XxmcxM5hKmpqbujw9TcbGtvNzc3723KOnToOKzguGh7u1r53P0jISQqzRvSbO6cU39f0oRneANuz36/H9+hGYZxuVwej8fv96OnQiwW23XfJcAdHdX+wWBQ8uvg4CAqEohHJADQNO3xeNAUgq5/5CtGGIqPp2kadRJjY2Poi6A+HvkZi6kKKIoKBoNoLFCc1666DXU4HA7Uo8COFgQA+vv78VdsTyQSMzMz4gGIPU/JcjmdTsISjh07hjqbUsdD6IWEWwj5PLuygnwCAKbn5lI8b2pqMjc3o35C5xM6dOhIp+MdHd0VPQVf4M9RRd98yoI0m7t440xFT3Fo8AxvoGkaNx7ctvHtHN0AsWVsbEy7Wn5mZmZqaioWi3m9XkXrgN/vR8k8z/f391MUFQgE8ER+v59hGPFX+fHo0uj1emmaFisGJB6ChE8oSpAPG2NA0GXhxIkTuAgURXV2dvp8PnnihD3A4/GIvTdwGOh5Cju8AW0Q4l6ocSEuqFNTU5XOmGRubkZKIeETSZ5P8XyK55fS6czmJvIJC0V1tbejF4XOJA43YgyDmWtBqXwzG42qJ6lFZAXhwb17ucePz3R3l1r9b5llY89mqxNnqil1AMbjx1+9dElSWV6LBDySjUYlU1Bs1N69JAm1gHR66fz5axU9BZfh+jv7Kyc/L2xVTvjhg3LCA/I2TIAhFdrl4pu93W7HAE6VIyXnQgu9ONBR/FVxbBj7QI4h0ZtkJLvOToyxsbGNjQ3c0THEw+Fw4NZe6iKoYGxszOv17kFvIY+JrRGgciKeTj/O5+MrK4RJAEBvZ6fuM3EIQPI3vD00dNJiaTGZQMYb1pLJ0StXvIGAer65tWQy6Pfbenpsvb3vvPHG9Tt3Sopm/HB6mo1Gz3Q91Y1/dPu2NxDALVbjAABg5PJlHAAbjcZmZ6/fuYPUQbsETAjoHh7+JBxus1gwq49io/buiu0WCJ+Cj15++b3a9It8993X//RPP6icfL7A31++f/Xs1cqdgosKiwyv6xs0QjmmRb6t7m2/FG/nxSA5l+RE8vMWc2wUf1XfU3fV5HMcJzZGkA9l9HXwer110FZmAAAgAElEQVSBQEC70YegEv4WZQEqJ8QeFYRJRBOJ+MoKm07bOjrQYUJnEvUOh9Mp31YfhMOfhMNaqr3fHR0lCekuDQ+LiyZrwYWBAUJWpny+S8PDSBq0DwAATlosr7rdbVarrbd3OR5/xLK23t6SJIQnJjAXsq2398brr6M0xUbt3RXbLTWcySCdZivt3MAJXLuxsmn1E1Ghw2as6CkOEyoVCytJzVRH8Pl8wWCQxDhgNGnZz0JRlMvlKrvYmoJGJtHV3o5xHHp0aB1hPZXCeoli9oBZ7nethJQVhPVUqqunJzw+3ma1itUVd0ZGHrGs0WzOCcKuuY0BgI1Gc5kMkaBxAIjBHdbORqPrqRRORLuEZZYV2zLsfX3x+flcJiNvVOQNit3brFbF9m/UMG9YWWEpqjTaVyo2ChsdLZV1ilyJ58656nLDqgoqxRv28CZdO8CcVJU+yxFMCF2MSUQTiWgiMRGJAICto8NKUbqTRI3jk3DY1tOTzWQ+nJ7WUgZCjEcsmxOEu6OjZ7q6fsayn1y+fOPuXfzpqzYbVhxGXf3gbo+R8MRESdWkJEDtwqN4/FLpQjDnvxhryaRccVKsmqVi9+LttUsc0mn23LnKPsq4DHf+1PmKnoJPFTps9ZHQuhZQwdxbOnTsCjmTiHJcPJ1mFhfR3bKrvd1CUd0dHV0dHbpdo0aApfbw852RkQfhMKmopBHigk9iCa84nQ/u3UNNxq7Aao27eiGoALULWUF4Z2iozWKpCyfEmkI+L3Dc/MWLu1RL2g/4Ar+5tdncWMFXCD6pZ24oDTpv0FFbkNAINp1mV1aW0ulwLEZ8La2trbb2dltHZVWXOhSxlkyy8/OEN7SYTMVeqYvh9LPWByIhKwijV65cGBi4fP16m9W6q7GAnZ+39fSUdGqCtWQyPj+PZKXFbL5w7dry0lJJvOG0zYYFG58KTKUcLpdio/buxdulSogawdIS093dV9FTLPKL9rbKOnVx80J7l+7cUAK+VO0BHBCw5ER5ZQaDwbLL3BWYWltyXsXGwwFbR4fbbn/T5Zq8evWDP/3T6xcvdnd0JDc2bs3Nvf7uu0O3b49HIuFYjE2nqz3So4JcJvPRrVv4OSsI7Px8qW/8LWbzSYsFlQpiCVjAF90DUZewy0gePy61BhXBo3j8k3CYfF2IREoV1WI2d/X0fDg9DQDLLPsoHrc7nYqN2rurtNcmFhbCDkdpqqZSscgvVjpzw0ay0NlbkRpphxWHqo62CspSSluC/ZfSLhXodYFpqTCSU9xYltwS9QXiHpHi+fjKiq6NqChIHGZ4fDw2O3vSYllPpS4MDBAjRYxhPgmHH7HsSYtFbImQY5llg34/SnjV7SaOjURyThBymYzRZFLxjpzy+V5zu8WsRfsAxOdaT6XsfX0kilK7BDRwSLw4FRu1d1dsr804zHSaZZiJq1dL824pCXyBn+FmBm0VLEsBAFOX2auTLzebde27Vui8oQRgFgfi8rlP3qCSsFIRmHIbM29ioU6s7h0MBnFgpHFv4zkE0GlERYG8YRW+hV9/yq78C9u+ouP2KeELIf9l837N3j+KfvaN3pf2I0FxFtqnVuxIcbsRls0QrzXeEImMt7Za7fYK6hvmVucAoNJOkVOX2cG7h7QYeWUgZVjiPEskPxLmO8KMQ5IcTbhLyRvFSRTk3cUpk/GMmKWRyMEuJJsyEUvCO0lyJ2yRfFWfs0SUyuxQIKm3iZNlGEZ8FnlHLcD024oJK4tBfFKn0/n555/jBSJCSiUihw8SL0txsIZOI8qFU/DR0w82APinfYnap4RyqJYv9ALAw/1IUJyF9qkVO3L/y1tpLC3Ner13KnqK2FrMa6usAnWR4fXMDaVCqm8Qv0MfO/b0V6fTiXt8KBTCotgA4PP5QqEQvuBSFEVSJuM2LNaZi7sjKcFMyZjCmdTSxO0ZKYXT6ezv7w8EAn6/H6MischTKBRCQz7maQYAkjGafJXXwgCRvkEiCks8KM4O01DiS7zD4VhYWMCfMBs3lhQHAOxCOmoEltvAbd7j8bhcLq/XG4vFCAmw2+0qsaykHiahPjMzM6FQCEeifRhHCro2Yp/IZtnt7bI56M19+uncTx7+T7//e6dfeKFcMovh8eO5X/zirwDgq1/99889d7LSp9sPctvbgUePhq3Wtueek/xkNNoaG2vFDL+4yHBctKKRFOlsem51rqLppQHg/sgy3WvWK1qVBK0Wnc7OTq/XOzg4iIUbYrFYKBTCnZ4cwzAMwzD4mo4v9GRrFHfH/dvhcExNTWFHzK2EXUKhELKNSCSCKoRYLIaFMQGA9CLFHQBAXOtBHXJR2EsyO1QGEAVGf38/1oZAIeIT9ff3iztqRyKRwGkGg0HUuOB5Z2dnnzx5gkvhcrmKqTF8Ph9WzSDzgh3FDJvNToj8sVOFgrC9bTM+Q6i7RF97d1Lj2YxGc+NhtvDp2oh9oqWlbLrccCwWXshOXv1fDiZFx/LyKH749a/XX3jh3x7AGfeMW8kk/cLpzhdqN2EDYnExcgAekZ3m0p6rpSIvbHHzgp5eulRo3SdwTyJqcKxtLbEIiB0IUGdAVPHi7kTBQDriZ4qiPB4PKSo9ODhIziU2YQAAailQS+/3+7EqJvmqYqeQi1KcnXbsrSPDMA6HAwB4np+ampLkoySREcU0B16vF/UT4hYQKSEmz54tdmpha4vN5cjXqCBEBQEAUoVCZnsbAITt7VShQHgGMgyrwWAxGEBEMg4BtNAILNOlJ7IsL8YjkXmOm7x69WBIw9paGACam1/e2vqc5yOnTg3s2qVaSBYKszx/p5RSHVVBPi+srMRpurLOFkv8UqWNFEsM392naxpKxh7fL1tbW8kGXwxldNBDi4a4BdUGSF9woxV/LUmUHFhWw+l0ohNG2TX/kUgExxAIBAYHBxVXEp025Kf2er0Oh0MxaIKm6c8//zwWi6k4W5gbG8V7vzoPQEqR2d5eymaThUKqUJheXU0VCgCANELMKiwGg9VQx7lTitGI8MLC6P37pqYmkshSpxF7RjgWm56b66HpAyMNAGA02r72tQ8ePhw6e3ZqdfXWwZx0bxhdXh62Wmtf7Tc3d6ui7pCwU5OioumeACARFc5fO1XRUxxKFL1Bd9197XY7lreGHQ9Kl8vl8XjwjT8Wi8ViMY3ZppFhoHVA7p0wODgoViQQZ0mPx0NKfou/qpxILqrY8RjriPYCxQHvp/qGw+EgPqeoAgGA/v6nZjxsTyQSMzMzkrNjcU4xaRB7mIrdSMsCwiqcSpMliopkoZAsFOK5nLC9ndnaQg5hamioaz5RjEYwi4sTkUhmc1NPZFkSCGN47+rVA14uNK9sbwuNjWartWh1yqqDQR/zmr+XeD55AB6Ri/ziudbKpm3IC1sr8ZyeXnoPkPIGmqZ9Pt+uUQnoG0i2bdyz0cnAbrdj49jYmMY9bGZmZmpqKhaLeb1eRSMCluTGsIv+/n6KogKBAJ7F7/czDCP+CgA+n+/mzZtEAvH9lItSjGjA4A50WThx4gSqKCiK6uzs9Pl8+0+T4PF4xD4ZOAZ0JoUd3sBxnMTRkmEYnBSx4EQiEXS8IK6mU1NTBxZSUUxXgaYQoqWQ84nulhZTQ0N9mTzk+bDFiSz1Al0qqCJjqCMIW1sTyeR7xS2MtYO5uenz5weamyv495vOpvkCX+l0T7F767qRYm9QyN+AAREa+5P35j1LQAWA3W7H6E3t5xKHjMq/7mHYBGNjYxsbG7ipY+yGw+HA3b2kqakAszaVq2SoPPa1BoF8AvUT6E7B5nI2o7F+yYQYRCGR2dzE5JUWirJQ1JGtGC7k87fm5maXlvq6u90OR3VXQBCiPM+cOVNB5/99wpdIdLe0DJyqdZ05x0UjkYnBwbsVPcvth7fPnzpPmyv7NJu6zPYHaMpaf9rQqkPBTlHSvqi4++5hZ0WXgpLOJTnL/octBsdxYmME+VAuXwev1xsIBMpVNbQuYi8lrhUEUUFA5URmexv9J4hmotdsNjc02FrqQJGoWKArxfPoZYmVNSwUZWpq6u3sNDc1HeKQDTFjuOP11kJR01wubjDUboTCeDJpbmysfdIAAHNz0y7X3guQagEncABQadLAJwsAoJOGvaH6DjhaMjUdMHw+XzAYJGEOGCZa3lNQFOUqUvDmqAHJhNisSzQTUUFAMwcAWJ5/Hl0lLAZDXaglJNYKVEgQJiHk8wBgbm4mURv1Xjc8ynGEJNUOY0AUCkmKqtEqD+G1tflM5m7Nx1AAwOIiAwCVDqOYW52rdIJIAFgIr51z1da+U0c4KnmmddQ72Gw29atfoVoiVSigjaPLaDze2NhlNNZp8gk2nRY2N6OJBADEV1bQTwLVEt0dHabm5lp2lRDy+XmOW0qn5zkus7nZQ9O2jo4emq5BowzLXrbZKqta3xvYbNbPcXdstrq4e9999/WrV9+jqApqbjiBm1udu3r2auVOgXj39f/mvWPTa1LsDfqq6agP2FpabC0tYrUE+klEBYHh+enV1XpkEmitkJADNHAspdMpnp+em0MyAQDIJwCgt7MTvx78Dp3k+Xg6jVwBAHpouruj49r587WjWpBja6tGi1AnCwU/xwVouvZvVACYm5um6Z6KkgY4KGVDms22dxl10rBn6Auno15hNRisz9os1JlEXVg3QEYjEGjmAIB4Oo3elymeBwDCKrra2wEATR5aBCoC9R8AkOL55MYGNsZXVnAAKZ7HsJHa5wpi5HKs0VhzVgBha8vPccNWa1247+TzQiwWrnTs5cF4NgBALLyuF87eDzTZKXw+3+Dg4J599VW6i8s06NgVwWAQC2qIG+WhpJg2W/F4RQmHGIRJoHUDnS67jMbulhbL88/XxSNbC5I8jzQi+mwOMdzy5UjxPHHVBAA2nbZQFPIA5B8AgDGlAFDvXpzJ5LjBYG1rq2yeopKAmgb3yZPutrZqj0UTbt8eOnfOWelcTwcTRsEnC7ffePinH3ytomc53NCkb7h586bL5drz7k7KZsoxMzNT49GDNQXMBCXZ9cX+lRzHDQ4OzszMFDteUcIhhlwnQfwkIhsb8VzO1NiIHpe9ZnOdpqgCAOuOzaKW/SGqBZ6ftdkq+6JcEtCnYeDUqXohDZHIOEVZKk0aFvlFOBBlw9z06vmBOghdqWUchJ1CnHoSX47LFX+oA56NKXU6nX/8x39c9uiPwwS5n0RUEEjsRp2aNnQUQzbLGo1dtVNGMioIo8vLAZquF13X4iLDcfNXr05W9Cz5rTyTZA7AHZJPFvRCVvuHlDfwPI/FFYuFR5JfsRgmqgrkeYcwPxJmPsafMPk0Kh4YhhHLx3ay/2FfcaM4m7KOYsDSmrjmOrRDQg4wCpQ4SRxW08YRAc9HzObKxg1qR3htLby+XkekgeeTDDNx9ep7Fc0OCQAhLnT+1HnKUHE/X13ZUBZIeQOmiwYAj8fDcZyEOvh8PmxkGMbj8TgcDsxLjTu9OAGzz+fr7+8PBAJ+v39wcBBLZc7MzKDuAbNQIw8IBAJYPhv/x774UygUQsKBSZS1lKSqL/A8HwwGp6amSMVw4qxgt9tLmizP85h2QnLJxDLhtzUzo4lEFABaW60UZSG/UpSl0v7StQ/MT6Vo2givrZG0VFaDoV6iNo4yasdIMZ5Mzmcyky+/XC83TD4vzMz4nc7hA4ihaGposrdV/LUwzWZ1ZUNZIL2DUZ0AO1uaeMvBulOkxHN/fz9yBYZhsBdWgSJ+jpFIBA8m9RTIRijeEfv7+71e7+DgINaDQKBnHzYi53A4HFNTU+Wefk3gs88+ww88z8/Ozj558gRX0uVyaXdEwNIeGi0UFGXp6OhOp5c2NpIbG8mds6c2NzPkGI6bb2/vIu8Z7e1d+KGjo7u52QQATU3mjo6ac1OvBCSmDVRIxHO5qCCIozasBoPNaKyXV8mjAEGI1oKRIlkojC4vmxoa6og0AADDTNB0z7lzlfWF4gt8bC1W6XrZCGYidfG6ThrKAIWbGA0EPM9v7ARiqQA3dfyMtadDoRCyDUIX1IEkQ+IaKW5E+bWWU7IsUDS+YFlwKCV7dCgUIuxNAoneAo+hKCtFWbU/ETguih/S6Xg6vYSfV1biO7/O03QPfkZ6QdQY7e22Sms4Dx5yhQSW71rKZtFJwtTYaDMaMU+2rpCoInieaW2tclbW6dXV8NrasNVa+4UuxYjFwjyfqrRbAwDMcDNOq7PS9bIBgIsKAEDr4ZflwDNPNJ7nPR4PTdOdnZ3yfRpLSDidTtztihWYruBgjxIoikKbwq5H8jzv9XpJTfNKgGSWVU8xm06zm5tCPp9BTQYqMPJ5gedTqJlAVtHcfLyjowsOEavAqA2JryVRSBAPiTpKkn04IAjzVaxlRdQM9ZIOkiAWCy8shA+ANESSEdpEV7ruJUL3bCgjnrmbQ6EQRVHBYBAAFOtE46+oRccWl8vl8Xj8fj+6PcZiMY1WeXHVKB1ioH4lkUhgzOSux3u9XrvdrlgQ/IBBzBaKmgyeT/J8CgASiWgiEUVWwfOppiZTc7OZoixNTSZUVBwCC4hcIYEhG1FBmEilYKfcRndLC/KJ6o300ILnGbO5p1pnH08mZ3m+7tQMICINleb06Wx6iV86GAuFrmwoL57hDU6n0+fz4b4lDnBA8DzPcRx6IZw4cQK9FJ1OJ3o40jSN3o7qb72dnZ0+n0/sQXmUwXEc5loYGxsjGz8uO8dx6CUqwc2bN2/evEm+LiwsvP/++wBw7Ngx0tjX1yeOfa0RoHEElJQW+bywssISRQVaQNJpVqyl6OzsVexbF1BMI8HmcuhrSeqJ676WZcTaWvjUqYGDPy9Swx6Tqe7UDHCApCG/lb/96e2rL189AAsF6MqGckMhXyQGR8gPHRsb29jYQHUCx3E+n8/hcJDdrlgvObQfqUMH2j7S6Xg+/xhVFMgnmppMFGVB/cQhCAMhvpaPt7biuRwxbehpJPYGQYiurk6fPVtxTbsYGGZpbmgYOHWqHi/ZAZMGx0nHAcRQAMAiwy9GNvrHOnc/VIc2KNBhlU2d4zixfUFsaNBOBXTSoEM7UOUgVzOgq2YiEUX9RD4vbG5mKMqCygkM+qgj5YSir6W41obuJFESlpdHaTpwYKcLr61Nr672mM0Bmq5Tq9NhJQ18ssBMJK++d/YAznV0UFod7WAwSHIYDw4O6nkJddQU0IWC51PEKxOVE+g8UXdkQgKSRiJVKGC5DZ1JKGJtLZzLsQfgESlsbSGr6zGbB06dqlPGAAD374+k0+wBkAYAuP3wNm2mD6Do5dPTDT20u9vOOevMy6TGURpv0KGj7oDOEypkgqIsdeqGWaxqVx1VEi87trYElr1y9ux7BkMFTVcMz0cFYV4Q+ijK3dZWv4whnxcYZoLnUx5P4ABIw/3l+wBw8czFSp8IERlPbma29URPZYfOG3QcRUjIBPIJNHNgmGid+kwQJgEApNyGxWAwNTR0t7RgSolqj7GySCbHAcBqfbMSwtlsNry+Pi8IPWZzr9lcd7ESEqTT7P37IzTd43JVZLkkOGDSkGazIT/nvWNrNh859lxpVJA3aK++rVfT1ghSBRtzd2oPSMHs4GJ/lFAoNDU1Zbfb/X6/dlEcx3EcV6yEqc/nQ7fZOq3WjWYO9MFcWYnzfAp1EsQBs05tHFi4CymF2MABAL1ms7mh4dDkuERlg812p7w5ItlsNsLzszyPPqr1UsdSHXNz07FY+OLF6wdzV0eSEb7A93f2H8C5ACAvbAWvsJ4A3WE7JPd2TaGCvIGEaGo50u/31902c/Ag1USxVFgsFtuVbAWDwYWFhVAoFAqFyAqHQqGZmZnBwUGs/aFd1NTUlNPpZBgGQ3AlBxw79vR2OkxVTzkuigGimG0CbRwkNLSu1RIkfAMASN0NopmoRzNHMjne2Hi8LOGXmGwDY1uQLjgpqu4WRBH5vHD//ggAXLx442ASr0WSES7DHVjUJQDM+BId3S167GWFUME/g7KnEDhMu9Ge0d/fDwAURXm93kAggEm6VICZPb///e+LGxcWFsT6AC2iMPKWVDXzer3qhTdxnIcA+DYmzmSFNo50Op5IRFEtgToJrPpRF66X8nwSsBMLmioUlrLZzPY2Kb0BAKicqHE+IQjRTGbeZru7x+5bW/OZzFI2i1yhx2y2GY0uijo0yhgEx0Xv3x+1293nzx9Ecov8Vv7+8v3mxuZBm6ayA2VBLLy2mdnWSUPlINU37FrDulihbXl9bXyFxWNIBW1FpQLqGzBzFB4g7otjaGho+Ku/+isAwHpaYrHkK2rR5WM7NEAbAX5GlYO4ZqmkOjnDMGS1jx07RiqJcBwXCAQAoL+/n+T42lVULBaLRCKECx47diyRSJAq6gBgt9uJvkE8ziMCopYgeTDF3hJ1nVGbzWaF7W20dKCZQ9jezmxtoXLCYjDATi1yTF1VlUEWCsmHD9+g6UBLiyYXV4nGRRyc0mM216+fowryeWFu7hbHzV+8eONgHIH5Aj/DzZyjzh1Y9AQA8MnC7Tce6m4NFYWUNzidTvUa1ljxEgCCwSDZaeT1tT0eD9mrfD4fSsDcD3KFAQokfIWUfiY5prDSI+ZPpGkazR/4yovVt7EWlDjZ5a4v4rWAWCzm9Xp5nvf7/V6vt9Q62hIFDEbJ4tbu8/nEiyDmDcFgEHkDTdOEB+wqCguTinkDCvR6vXjROY57//33dTdbAvSWwHrlKytxcRBHZ2fvIcilDQDEAfPx1hZ+AIBUoQAAyCdQUQE7xAIAKuFOsbUlfPrp0KlT1yjqmdcS1KAAQGZ7eymbxbGJPTww8KSKdOfAEIuF5+amu7v7DsYFEgDS2XSICzmtzoMpP0EwdZk9f+2UHnhZUSjwBixsjSmlcW9AHzqJ3cHr9WLSaHzjJy/6wWAQmQduLRRFeTweVEIUG4TT6STZIHAASF+Qi5AtTby34RaLQwqFQgsLCyiqvqwYxEvA4XA8efKEYRiXy0XqaKO7qEp3uZ4AL4fD4SC7PkLMG0DJ4rOrqFgs5nQ68YBYLOZwOCQXl+f51tZWnTeog+OiGMSBuaoAAA0cde13WQyoqAARscA9G38VtrfjuVyPyUSORwXGHk7UKUx+/pumHzz/PxLJqUIBLSxEI4IUAUQM5ohgcZHBitjnzw8cmC9ObC02tzrnoT0dLR0Hc0bE/ZHlJlOD6836czmqLyhocnatYV1Soe2ZmRmPx7Or1YAcYLfbE4mE1+tFBYPX60V1glwsMWogcA9G5TzW2dp1bFUH7t+xWOzEiROkUXsdbdy2A4EAYQBjY2Ner3dmZkaxtsV+RNntdo/H43Q6kT3ggMUXV3HBI8lIq6GVMuwkGDVQ5PPRhJwZpNMsz6fS6aV0mp2bmyZ+l3UdDoogegUtWzUhGYogCgMQMQDEscf/+Risnzz9v/+LqhpKahAcF52bmwaAq1ffO8i7CL0gvTbvgXlBIu6PLAOAThoOAKVZgBQLbavX125tbU0kEnsYWX9/v8/nQ8mKxnJiOiHgOI5hGNzMarCwUzGQ8EUxNNbR9vv9GEuJlwNrjwGAxhrcJYkKBoPo7IIhGE6nMxKJqFdOb25s3ihsbBQ2AGBze5MvPD2Yy3C06ek1bWpoImSi0/w0hzxtPiruER0dto4Om9jvkoSDEr9LEg5a11mq1LGr8UIxWUI2y3Kf/19lD7ysd/B8MhKZ2NzMnD8/cJBKLL7A31++Txmog/SCRCBp0FM8HQxK4w3FCm3L62sTeDwe3I1w7xc764mB2w+mJUD5SEQwXFB+8ODgoFivgDscTdP4TlxHTnlIGsTrVlIdbZwy0RN4vV5cGbvdzjBMSeugRRSOzefz4dVHi9LY2Bi6tshl7uoPxRd4QiYSQiIhJABgJbcytzoHAOlcusPYASJu0dzYjC2HWHWBhUMlj3s0cKTTS4uLEXmWqrr2u9wztrYEjvPTdEAnDQQcF11cZDhu3ukcVixnXzlEkpElfungHRpAJw0HjtJ4g2KhbcX62kQbgW6MuJdzHIf7OvH+AwCkBTMzM1NTU+gnSIhFf39/IBAgXyU1uPH9mKZpnuf7+/spigoEAngWv9+/34U5EIyNjWFd7BMnTpAXd/U62nJg6gs0K+DeDwDoiMowTCAQwHgHvHDq/h/qopACoosDcjv0TqVpWq5n0gjx9l9Mx5Dfyq/kVgAgv51PZ9OEW4CIWFAGqqmhCXY0FoePVcjfGonfZSIRJQYOopaoi3DQ/aBQSHKc/9SpAY0BFIcb+bwQi92LxcLt7V3nzrkuXqx4bQ4x0tn0/Uf3aRN98LYJAIiMJ9NsbvCufhscHPaS90lSCFu9vjaBWNMg4Q34Los+E+JXZNRhiA+W1+CWKDAkAZxHBF6v1+VylaXMmIooDHURB+Ui5MkoDxiEWKRz6fxWHoqwCqKrOMRGkGLhoABwaII4AM0TnP/UqYG2Nne1x1JlEAVDd3efw+E+eG8YVDNcPHOxKn9WsfDaQnj96uTLetTlQaIM+SLHxsYWFhaCwSCa5H0+n8vl0p4CWREYsenxeFDsPkd46CEubl47omoEhFWgooIv8Ohp0dTY1NzQfOgphSR5trjgeJ26XiJp0J6q4VAinxeWlpiFhTBFWc6dcx2wSQLBCdz95fvdVPf5U+cPXs0AOmmoHsqTZ7rs9bVRIEaE7nt0OnQoQ51StBvbAQDjQQ6Z4UNOJgCgudlc+2RCEKLLy6NHljTwfHJxcTadXlpZiVdLwQAAfIGfW51L59IXT1884EhLAp00VBF6PUwdOhSADpvoUQHPGj7QSfPwqSjSaXZzUyCJqsSaCQCoBZ+JtbXw6ur0ESQNHBdNJKJLS7OY5IOme6plb0LGwAnc+VPn7W17cWkqC3TSUF3ovEGHjtLACRw8q6IgjhSookDfzMPBJ8QFx8WVvdABE5UTB+M2sbYWXl8Pv/zy5BGJnuD5JMfNYywuEpQd+hEAABGxSURBVIXubmcVo2ZqhDEAwP2RZT5V8ARonTRUCzpv0KGjPEhn08gh8lv5Q88nOC4KAEQ5AQDIJwCAuGECQFkCRLe2hOXlke3tzOEOuUSKlkhEeT61shJHTc+5c66qu7LWDmPIC1u3hz6le0x6cqfqooK8wefzkViJUn/VUQyYrBMzL1V7LDo0QZ1PoL2jqaGpWnbi8gKNHeg5gfoJrBeKzhMAgCoK0Ewp0KGhrc1dlurYtQY0QCBRaGoy0XRPa6u1vd1Wda6AqB3GAABcVLg/unx+4JTd3VbdkeioIG/ATADFwvolFRN0aAF6iWL6BMyyUO0R6dg7xHyimP/EoeET6IkJOyoKpBQg01KIWcVzz8Hq6q1MZv706RuHwKGBhMgCACm/3tFhoyhrR0dXrSXbiK3FEkJiJbdSC4wBAOamVxcj/MUbpztsh6qseZ2ianYKnTeUCkyhjQmXMAuTeo5nHXUKsf+EmE9gyOghju8AEavI5x/+zu/89Be/eO5nPztOURYAQI8KPB6NIFAmO0i5gIYG/IwaF8yiIfYIwQJmNRuuks6mF/nFJX6JNtP2k/Za4Kx5YYuZSOWFrYs3zugODTUCKW/gOI4UVcLKh5iQUVJECnYU5niYuAV2Mh6KUzBhyiAiFnTeUDrwKhD9jb6ARw2cwInjO/Lb+c2tTSQQh4xPJJPjPD8rjpsQb8nILQAAd2XymedTNN0jloM6DDFw2y51POSMYqBXB54aAIqRm1pTJCgiv5WPrccW+cXmhuZz1Lluqrsq+RjkSLPZ+yOPdIeGWsMzvIFhGHEaaVLHGQkE1iDAXR8LbdvtdixWSdO0z+fDhMSYOGhsbEy8sdntdvwQDAYxvaC+7cHOIk9NTSFXw+Lg+JPdblfJCY2LLy57reNoAm0ch4ZPYKQlRfWdOnVtPy6QRIGx8zW1sZHc59jknKMuOIEK8lt5LsNxAscJXDfV7Whz1NRNssjwzETSOWw956yhUekASX0K3Mgl2xXmcULVAha2jsViWHkSABwOx9TUVH9/fygUQm6heBpUNsDOTinPQn2U8dlnn+EHnudnZ2efPHmCq+1yuYrxKqyGxT9sePeNGGmkLM9TlqJFhNu7jJKW5uONHc82ttuMuiawvtDc2IwBGpJiQmI+sVHYqH0+IQjR1dXphgbT2bPvGQz7fbnE2mBlGdjhA1/gF/nFdDa9kluhzTRtpi+euVjtQT0DPlmITCQ3M9tX3ztLWfXC6DWHZzYJLG+IFR9IqUnyv8fjwYrYMzMzcrOFx+NRf/dFKwbP8xsbG+WfR31CXusBABiGIaYixV5YQgKdIv98YReXpbywtcLm5O3peC7/eCsRFUjLSvyZw7j5jISIEOYhJhyUxaD/YdcgtPMJqHY+K2QMAHDq1IDZXN9v8LUM5ApL/BJloGgzff7U+VpwX5AjMp5cmuX1uIlaxjO8AVXf6H+HJRCLdcOil+Tr2NgYUgpF8Dzv8Xhomu7s7NT16hqBxT7k1MHr9TocDu2RFM3mRrpXQd+r2KgCbodh5DPb6aUsEo7NzDafKjw9YD7T3mVsNjcAQJOpgRCOzp0TlXpGHZVAMT4BIn/MhJAgiYTF+Sc6WjqaG5rLGOKhM4ZKI51NcxlOrFqoVi0JLcBIy+4+ynvHpis+axnPXBuO47B6Mpa9xkZ0deR5PhQKoTP/4OCgWCGB1bHtdrvf78dekhqVoVCIVF7WQwd3BS5dIpGYmZmR8AasNVqVNRTv+urmRrGGIx3PIcNYiefmplcBgJvP0D0m/JUoMJBbNJkb9CCrKgL5hFzNgCm3+QIvTrnNZTjaRMMOpUCrh3ZKIQhRnmdyOdZiGdYZQxnBF/h0Lo1EgS/w7cZ2ykDVrGqBAIMmMAuk/hCofTzDGxiGCQQCNE1zHOf3+7FxZmZmamoqFot5vV7c0mia9vv9drsdQy36+/vR3I5sA2mEmDc4nU6xu+UR94UUg+M4rAc2NjZGfD5wfTiOC4VC4oMZhrl58yYADA4OYkttOpaKNRwqOgaiwEhEBcItACAvbPOpQofNCDvEgphFdPeLqkDFBwJTUPAFfqOwQawehFIgk4CdRJmUgTI1NKyv31tbCxuNXa2trjNnbhzgPA4n0PCEuRb4Ak8ZqHZje0dLR605Oapgbno1Fl6zu9su3jhT7bHo0ASF/A3i+EnUK9jtdsxRKDlSoldQbBGLLWaw16FDjjSb3RS2AUBiE0mzOTGrgB11he5pUVNAFQXsJKL4TX7pS/kffnnrnx89aftNy/lff+m4hFXAYcnAXVGks2n+Vzyha2hIaje2txpakS5Ue4ClgYsKkYlUh83oHLborwR1hF3yPiFvqMGXWh1HHHyygDSCuHaiukLCKlqtBspi0C0g1cLWliBWMFDU0ycJYRWYLhN2zB8gUlegnyYAEFfNQ5M9c1eIU4jiZyzvTj1PUQYKrUKYpLzaI90juKiwEF7bzGyfHzilez7VHXbhDT6fr7+/X9cT6KgvoK6CTxU2kgUoQimI+UN/bJUdhUJSEOYFIZrLxSmqr63NvYfQSvTTJKkpQMQtkHMQPTz6WCCI9gJ2yn/sdRKVBeoMYEcfg/oD2IltgR3ahJypdsJl949Fho+F1wBAZwz1C70epo6jBVRUYFQIAPCpwmZmO83mKIuh2dygayn2g2yWzWTmeT4CACZTD0W5DqCuBNmAYWcPRhCS8fSwXBoAcEsmEBMORRBVhxzo1aHSl1CBYgPAs5NTHCZyoIhYeG1uepXuMdvdJ/W/rLqGzht06HgKCaVQ1FJ0dLc0mxp0XwoxeJ7JZpd4ftZo7Gpp6aaovv0nbqo0iCFAEbtyAjkwSFXSeHQMKyrIC1tzt1aXZnm6x3x+4JT+h3MIoPOGuoReTbtywBItkpRccsNHXtj+9J//qeF43tp+puvcy0fN6iEIUUGIFgqpXC5uNveYzb0mU89+MkPrOHzgk4WF8NrSLN/dR52/dkr3fDw0kF5IrEkh2Y18Pt/g4KDKFqXYS0fZEQwGaZp2Op2xWAyzO6jUsNAIzMxR3pwQwWBQnAdM/eYhXXBqOKRAIIBpx8SZQghCodDU1BROH5eCHFPSdBTvW4w6lrj1KKpVg8EHMzMzn+e+9m97/z1Gk/Kpwtz06qFUURQKyVwuns0uZTLzW1sZs7nHaLQdjCVCR91hkeG5qMDNC+cHTulJnA4fpJdzZmZG/hZ78+ZNl8ul8uhX7KWj7EgkEolEwul0er1elQSdBJhxy+l0YsmxYodhdo0yXj68H/r7+wEAU5L7/X71vRxpkNPpxAwfdrsdeUMgEMDc54Q6hEIhQhR4nkdSK05vqn06xe5bu92uhXmQq0D3muVqBrR68KlCeimLQaR5YXszs0VZDJTF0GRqQBVFzXpRbG0JuRyLXCGXixsMFjRD7LPilI5DDC4qLDI8Ny/QPWa616znYzis0GlgnQE3Yy3w+XziqujFeANFUV6vNxAIYELPcgH5Cn52Op2dnZ0adQCYeYwMBjNi+Xw+0rKwsEA0E5hwDFOQBYNBr9dboensAZRVWcFA8mmiikLFi+Ig81wRlrC19TiXixcKKSQKjY3H29rcekpHHSpIs9nFCL80y7d3Gc+5WnW6cOih/FTC/UYlg5OicV29l479o5jCH1N18TwvXn88GBuJyh2zb+EVJEdigvCxsTGxOYBhGLvdTlowoxfP81jaVPyTYqMYHMe99NJLkgHj8fJEYcFgUFIYZXBwsLOzE4fHcRwmPseOxExjt9vJHOXTUZyLeHgHed+SfJpyFQXxokAVxdz0Kp8qNJkam80NqKIoS6AHUoTt7Uw2uwQAEpZgNvfqGgUdWkDcF9q7jJ29Zt0ecXSgcJkDgQBuLT6fj9S/JsBGfOIPDg6Sl0j1XjoIMGM3z/Oousfl6u/vxwLlJNu0ou9CMd079qIoCkuZo1YfD5b49/l8PvwaCoWwMDqIVA7iM3IcR5wMfD4f8gwEAHg8HnQeBADFRjIXJC5iKuD1ekOhEHZ5//33xZ65DMN8/vnnknwhNE2/9NJLsVgMDRYoSmWnl09HcS74U03dt8XYAKooMNBjI1lAFQWp9FFMRZHNstvbQqGQKhSS29uZQiG1vS1sbWUMBovBYGloMBkMVoPBorMEHSUhzWa5+cxihAeAcy69BtVRhML1RmsxADidTtT9kp/wqY2PV57n0SCN+5NKLx1iMAyDKnSHwwEAsViM7Kk8z8/Ozj558gTX1uVyaX8JxvXHjuqOgWgywJd40ojv6GIPRDTeY/lNMkiys/I8HwwGkRkoNkqAJdPw4FAohPSCYZj3339fy+wIYSIuBeoOoZLpKM4FURf3LVFRYEWxQiFZKKQAAIBPfza39asnhcx25slPHn/+5J//3ydNL3BfpL/aaPjSlxrh2K9fbjR86UTruWNg6nzFrvMDHXsDnyxw80/Nau1dxo7ulv4AXdd+vjr2AwXeQHYOu90ucb4TF1IiDnfkZbdYLx1ikL32xIkTuFAej4fUCwUAhmGIU4J2sbj+FEV5PB719cc9WKK6wI4SlcPY2JjX652ZmRFX2EIbB8/zGxsbKo1ifQmqCtCYgiXaiaJCMjacMiEZBLOzs6TQmhbIp6M4FyjffSsI0VK7EEuB4k87zAAAAFUFRuNvIxcaGkwGgwUAvvzCczvOB/9K7IUg9qLIAPCpQvTWKp9aFls9jlrsqI6SgFwhzea4eYGyGNq7jA53m36r6IB9+kWipVnHHoAOfYlEAvfXY8eOSfZFtOUfZIZvLGAmVjmgiQEAcCQ8z3s8HpqmOzs7xcfIGyVA5wNCMVVAUVRfX9/U1NTY2BjDMFNTU6FQKBQKvfTSS6X6H0imI5lLSaK0YHV1WvwVDQEa+yp6HTY0mPcT4qjiRSFxzMTYUYkjhV6D9GgiL2xx85n0UnZplm8yNdI9pnNOSvdz1CGBwhMB2QDGwUuc0l0ul8fjwWdxLBaLxWLkfU6llw4JSBSAeB8lJAw3yEQigaGJGmXuf/1pmpa8o6M5g0RFotsECif6fAz1lDRKEAwGY7EYHtPf349hmTRNK/oTYEF2PCwQCHi9XoZh9pCmQjIdyVyIPkN93dBfBIdKXEbEnwnOnp0sdYTVQkmUYjOzLa9sju6ZihJ01BcwWhiv9Uo8h1yho7tFT9OkQwUKd8bMzAymvvF6vZKXPPRgx7A3juPGxsbI01Ollw4xxsbGbt68efPmzRMnTqACn+O4V155hWQswNXjOE6iUVdHWdYfa5+SjRa3XgDA9A9/+Zd/iakRAIC4FjqdTnkjdkFagO/3ZKvGiFD7DuRjsNvtmI7J6XRSFPX973//z/7sz1SST2iZjnwuJCGE+rqhGynGghLLi/jzIYMKpYCdcA/inkkUFQCAWgokE+ihWUyIjuoizWZX2Bx61/KpAhogWq2Gcy6qNvOI6KhBKOeZRlu1SuYcDKkotZcOOSRxktqBugrcvfANHk0J+1x/r9eLWqViByheesVGFaBfJEVRx44VzXRO4iYw0mFv89p1OiC7b8ULuytKOvhwg4sKAEBScaOuAkSpKZBbgEhdoRtBKg28KImogMXbNjNb7V1GymLo7DXXewJTHVWE8h8tPtNVuiluErv20iGHJE5yn6L2v/6SLA5yFFMSlHQWLSSAvP0nEgn0eCjpFIhdpwP6fVsmqGsXiAUEiQWqK5BYkIBS2LGDgEhpUbP5NGsKhB/As/XYUAnUfLyxU8/eqKN80Ml+XQI9BshuXSzh0h5wwDtoX1/frsfs521+b9NBb4xdYzIlV0GHCogFRAW4+QFAOp7DkqSYn/vpr0r0gvhvPm0/vAoM8eLkH29BEX7Q0WXULQ46Ko3D+Td2FIAJHvBz/erJJakhawFkVbVAfBV07BOEWKgzDHSzAAD0tEjsbKi4jyKwGgjaRxDtInoBIn2GBAegvUdXRHk7mQjRxBDfEWyXKGN0fqCjWtDraOvQoeOQg7ysg8gDQwKyW8vb+dSvAKDJ1CAmIrsiL2yvxHPFekl4DKJzhzAdYsWJjkMAnTfo0KFDhw4dOrTi/we2LYR0reqebQAAAABJRU5ErkJggg==\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451e422b40>" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": "f0._sapcar.files0[0].blocks[0].compressed.canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "## SAPCAR Archive version 2.01\n" }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": "f1 = SAPCARArchive(\"archive_file.car\", mode=\"wb\", version=SAPCAR_VERSION_201)\nf1.add_file(\"some_file\")" }, { "cell_type": "markdown", "metadata": {}, "source": "The file is comprised of the following main structures:" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Archive Header" }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451e3057d0>" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": "f1._sapcar.canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Entry Header" }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451e40b190>" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": "f1._sapcar.files1[0].canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Data Block" }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451e1ddf00>" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": "f1._sapcar.files1[0].blocks[0].canvas_dump()" }, { "cell_type": "markdown", "metadata": {}, "source": "### SAPCAR Compressed data" }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": "<pyx.canvas.canvas instance at 0x7f451e2f8fa0>" }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": "f1._sapcar.files1[0].blocks[0].compressed.canvas_dump()" }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": "from os import remove\nremove(\"some_file\")\nremove(\"archive_file.car\")" } ], "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": "2.7.17" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
UPEI-CFD/Modelovani_procesu
Cv_01_example_01_v2.ipynb
1
7114
{ "metadata": { "name": "", "signature": "sha256:dea8bf36f5ddd54bc2522f58830f44a49ab587bb0d1345f99b6fd6ac24258718" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Energetick\u00e1 bilance oh\u0159\u00edva\u010de\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Zad\u00e1n\u00ed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C\u00edlem je vypo\u010d\u00edtat pot\u0159ebn\u00e9 teplo pro p\u0159edeh\u0159\u00e1t\u00ed plynu o slo\u017een\u00ed 10 % CH<sub>4</sub> a 90 % vzduchu (objemov\u011b) z teploty 20 \u00b0C na 300 \u00b0C p\u0159i pr\u016ftoku plynu 2000 L<sub>N</sub>/min. <img src=\"images/Ikona_ohrivac.png\">" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Postup \u0159e\u0161en\u00ed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V\u00fdsledek obdr\u017e\u00edme, jestli\u017ee provedeme energetickou bilanci ode\u010dten\u00edm vstupu od v\u00fdstupu se zanedb\u00e1n\u00edm potenci\u00e1ln\u00ed energie a pr\u00e1ce ($\\dot Q = \\Delta \\dot H$):\n", "\n", "$\\Delta \\dot{H} = \\dot Q = \\sum_{out} \\dot{n_i}H_i - \\sum_{in} \\dot{n_i}H_i$\n", "\n", "Pro kontrolu si ud\u011bl\u011bjme rozm\u011brovu anal\u00fdzu v\u00fdpo\u010dtu:\n", "\n", "$\\begin{equation*}\n", "[kJ/s] = [kW] = [mol/s] \\cdot [kJ/mol]\n", "\\end{equation*}$\n", "\n", "Pro proveden\u00ed bilance pot\u0159ebujeme z\u00edskat molov\u00fd tok nam\u00edsto objemov\u00e9ho. Pro p\u0159epo\u010det vyu\u017eijeme m\u011brn\u00fd objem plynu $V_{sp} = 22.414~m^3/kmol$ => $\\dot n = V / V_{sp}$\n", "\n", "D\u00e1le pot\u0159ebujeme zn\u00e1t funkci pro v\u00fdpo\u010det m\u011brn\u00e9 tepeln\u00e9 kapacity vzduchu a metanu. Standardn\u00ed forma je poskytnout koeficienty A a\u017e E pro rovnici $c_p = A + B \\cdot T + C\\cdot T^2 + D\\cdot T^3 + E\\cdot T^4$.\n", "\n", "| | A | B | C | D |\n", "|----------------|:---------:|:---------:|:---------:|:---------:|\n", "| CH<sub>4</sub> | 3.43E-002 | 5.47E-005 | 3.66E-009 | 1.10E-011 |\n", "| Vzduch | 2.89E-002 | 4.15E-006 | 3.19E-009 | 1.97E-012 |\n", "\n", "M\u011brnou tepelnou kapacitu vyu\u017eijeme pro v\u00fdpo\u010det entalpie: $\\Delta H_i = \\int_{T1}^{T2} c_{pi} dT$\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Doporu\u010den\u00e9 nastaven\u00ed\n", "from __future__ import division\n", "\n", "#Definov\u00e1n\u00ed prom\u011bnn\u00fdch\n", "x_CH4=0.1 #CH4\n", "x_air=1-x_CH4\n", "T1=20.0 #\u00b0C\n", "T2=300.0 #\u00b0C\n", "V_dot=2000.0 #L/min\n", "V_dot=V_dot/1000/60 #m3/s\n", "\n", "#------V\u00fdpo\u010det------\n", "V_sp = 22.414/1000 #m3/mol m\u011brn\u00fd objem plynu\n", "n=V_dot/V_sp #mol/s P\u0159evod na mol\u00e1rn\u00ed mno\u017estv\u00ed pomoc\u00ed m\u011brn\u00e9ho objemu\n", "\n", "#CH4 [kJ/mol]\n", "delta_H1 = (3.431e-2 * T2 + T2**2/2 * 5.469e-5 + T2**3/3 * 0.3661e-8 + T2**4/4 * 11e-12) -\\\n", " (3.431e-2 * T1 + T1**2/2 * 5.469e-5 + T1**3/3 * 0.3661e-8 + T1**4/4 * 11e-12) \n", "\n", "#Vzduch [kJ/mol]\n", "delta_H2 = (2.89e-2 * T2 + T2**2/2 * 4.15e-6 + T2**3/3 * 3.19e-9 + T2**4/4 * 1.97e-12) - \\\n", " (2.89e-2 * T1 + T1**2/2 * 4.15e-6 + T1**3/3 * 3.19e-9 + T1**4/4 * 1.97e-12)\n", "\n", "print (\"CH4: H1 = \"),delta_H1, \"kJ/mol\"\n", "print (\"Vzduch: H2 = \"),delta_H2, \"kJ/mol\"\n", "\n", "Qdot = n*x_CH4*delta_H1 + n*x_air*delta_H2\n", "print (\"Dodan\u00e9 teplo (kW): \"), (\"%.3g*%g*%.4g + %.3g*%g*%.3g = \")%(n,x_CH4,delta_H1, n, x_air, delta_H2), Qdot\n", "\n", "#Alternativn\u00ed tisk (nevypad\u00e1 tak hezky).\n", "#print (\"Dodan\u00e9 teplo (kW): \"), n,\"*\",x_CH4,\"*\",delta_H1,\"+\", n,\"*\", x_air,\"*\", delta_H2, \"=\", Qdot" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CH4: H1 = 12.1121257973 kJ/mol\n", "Vzduch: H2 = 8.31061066453 kJ/mol\n", "Dodan\u00e9 teplo (kW): 1.49*0.1*12.11 + 1.49*0.9*8.31 = 12.9246039348\n" ] } ], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Dopl\u0148ky pro zjednodu\u0161en\u00ed v\u00fdpo\u010dtu." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pro v\u00fdpo\u010det m\u011brn\u00e9 tepeln\u00e9 kapacity je mo\u017en\u00e9 pou\u017e\u00edt funkci, kter\u00e9 se p\u0159edaj\u00ed koeficienty a teplota:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def cp(T,k):\n", " ''' \n", " V\u00fdpo\u010det cp [kJ/mol-\u00b0C]; \n", " vstupy: T - teplota [\u00b0C]\n", " k - list koeficient\u016f\n", " '''\n", " if len(k) == 2:\n", " return k[0]+k[1]*T\n", " if len(k) == 4:\n", " return k[0] + k[1]*T + k[2]*T**2 + k[3]*T**3" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "P\u0159\u00edklad pou\u017eit\u00ed:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "k=[3.43E-002, 5.47E-005, 3.66E-009, 1.10E-011]\n", "T=50 #\u00b0C\n", "cp_50 = cp(T, k)\n", "cp_50" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "0.037045524999999996" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "N\u00e1sledn\u011b lze vyu\u017e\u00edt pro numerickou integraci:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Nutno naimportovat dopl\u0148uj\u00edc\u00ed knihovny\n", "import scipy\n", "from scipy import integrate\n", "\n", "T1=20\n", "T2=80\n", "H1 ,err=scipy.integrate.quad(cp, T1, T2, k) #Provede integraci mezi limity T1->T2\n", "H1 #kJ" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "2.2228270799999996" ] } ], "prompt_number": 8 } ], "metadata": {} } ] }
mit
abhi252/GloVeGraphs
Glove-Graphs/K-means.ipynb
2
58873
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import networkx as nx\n", "import gensim\n", "from sklearn.cluster import KMeans\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import silhouette_score\n", "from scipy.spatial.distance import cdist, pdist\n", "import pickle\n", "from sklearn.metrics import normalized_mutual_info_score as NMI\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def getNMI(folder,num):\n", " path = 'data/'+folder+'/'\n", " emb_file = path+'embedding_run_'+str(num)+'.emb'\n", " community_file = path+'community_run_'+str(num)+'.dat'\n", " graph_file = path+'network_run_'+str(num)+'.dat'\n", " W = pickle.load(open(emb_file, \"rb\" ))\n", " G = nx.read_edgelist(graph_file)\n", " nodes = G.number_of_nodes()\n", " emb = (W[0:nodes,:] + W[nodes:,:])/2.0\n", " community_truth_values = []\n", " for line in open(community_file):\n", " cols = line.split()\n", " community_truth_values.append(cols[1])\n", " num_clusters = len(set(community_truth_values))\n", " kmeans = KMeans(n_clusters=num_clusters).fit(emb)\n", " return NMI(community_truth_values,kmeans.labels_)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def getAverageNMI(folder):\n", " avg = 0\n", " for i in range(1,2):\n", " avg += getNMI(folder,i)\n", " return avg/1.0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.98560664419103339" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getAverageNMI('mu_0.4_N_1000')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plotNMIGraph():\n", " x = []\n", " y = []\n", " for i in np.arange(0.1,1,0.1):\n", " x.append(i)\n", " name = 'mu_'+str(i)+'_N_1000'\n", " y.append(getAverageNMI(name))\n", " return x,y" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x,y = plotNMIGraph()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_n,y_n = plotNMIGraph()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x114427f10>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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g8FyhGEHLhSJW9zZiSTAQOHuwGZzPLD0ptjaSSUEobCFXOBym4lQtpeVnKD1WRWn5GQ4f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\nATDGmARlBcAYYxLU/w8Ssb/xVUJ0/wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1140fa150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x_n,y_n)\n", "plt.xlabel('mixing parameter')\n", "plt.ylabel('NMI score')\n", "plt.title('NMI score Vs mixing parameter for N=1000')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.96533344331619653" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getNMI('mu_0.5_N_1000',3)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1140fa110>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BxBQbUdVG1NzHFK3eph5wrpv7sGInotoJqzaiqt24t979eq1ghTRCikqQ/WZ06I/WKBUp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1143f0250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_n,y_n = plotNMIGraph()\n", "x_n2v, y_n2v = [], []\n", "x_l, y_l = [], []\n", "y_louvain = pickle.load(open(\"../louvain/Generated Graphs/louvain_scores.pickle\",'rb'))\n", "y_spectral = pickle.load(open(\"../spectral/Generated Graphs/spectral_scores.pickle\",'rb'))\n", "x_lou_spec = [0.05*i for i in range(20)]\n", "doc = open(\"../node2vec Evaluation/results.txt\", \"r\")\n", "for line in doc:\n", " x_n2v.append(float(line.split()[1].split(\"/\")[1].split(\"_\")[1]))\n", " y_n2v.append(float(line.split()[2]))\n", "doc.close()\n", "doc = open(\"../LINE Evaluation/results.txt\", \"r\")\n", "for line in doc:\n", " x_l.append(float(line.split()[3].split(\"/\")[1].split(\"_\")[1]))\n", " y_l.append(float(line.split()[4]))\n", "doc.close()\n", "n2v, = plt.plot(x_n2v, y_n2v)\n", "glove, = plt.plot(x_n,y_n)\n", "line, = plt.plot(x_l, y_l)\n", "louvain, = plt.plot(x_lou_spec,y_louvain)\n", "spectral, = plt.plot(x_lou_spec,y_spectral)\n", "plt.xlabel('mixing parameter')\n", "plt.ylabel('NMI score')\n", "plt.title('NMI score Vs mixing parameter for N=1000')\n", "plt.legend([n2v, glove, line, louvain, spectral,], [\"Node2Vec\", \"GloVe\", \"LINE\",\"Louvain\",\"Spectral\"])" ] }, { "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 }
gpl-3.0
caganze/wisps
notebooks/tables for poinitngs.ipynb
1
9823
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "Warning: Creating an empty Spectrum object\n", "\n", "Warning: spectrum object has a flux vector of zero length - maybe empty?\n", "\n", "Warning: normalize is attempting to divide by nan; ignoring\n", "Warning: Creating an empty Spectrum object\n", "\n", "Warning: spectrum object has a flux vector of zero length - maybe empty?\n", "\n", "Warning: normalize is attempting to divide by nan; ignoring\n" ] } ], "source": [ "import wisps\n", "import splat\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import astropy.units as u\n", "import wisps.simulations as wispsim\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#wispsim.make_pointings()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pnts=pd.read_pickle(wisps.OUTPUT_FILES+'/pointings_correctedf110.pkl')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "tabl=pd.DataFrame()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def reformat_name(x):\n", " name=x.name.replace('par', 'wisps-').upper()\n", " prefix= name.split('-')[0]\n", " suffix= str(int(name.split('-')[1])).zfill(3)\n", " return prefix+suffix\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def get_observation(x):\n", " try:\n", " return x.observation_date[0]\n", " except:\n", " return " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'AEGIS001'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reformat_name(pnts[0])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#hjk" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "tabl['Pointing']=[ reformat_name(x) for x in pnts]\n", "tabl['l']=[x.coord.galactic.l.to(u.deg) for x in pnts]\n", "tabl['b']=[x.coord.galactic.b.to(u.deg) for x in pnts]\n", "tabl['G141time']=[x.exposure_time for x in pnts]\n", "tabl['obsdate']=[get_observation(x) for x in pnts]\n", "tabl['imgtime']=[x.imag_exptime for x in pnts]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "tbs=tabl[tabl.obsdate.isna()]" ] }, { "cell_type": "code", "execution_count": 11, "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>Pointing</th>\n", " <th>l</th>\n", " <th>b</th>\n", " <th>G141time</th>\n", " <th>obsdate</th>\n", " <th>imgtime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Pointing, l, b, G141time, obsdate, imgtime]\n", "Index: []" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbs" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "dfn=wisps.get_big_file()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "exposure_time 2506.0\n", "observation_date 2011-04-19\n", "expt_f140w 203.0\n", "Name: 191180, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfn=dfn[dfn.pointing.str.lower().str.startswith('goodsn-111')]\n", "dfn[['exposure_time', 'observation_date', 'expt_f140w']].iloc[0]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dfnsml=dfn[['exposure_time', 'observation_date', 'expt_f140w']].iloc[0]\n", "tabl.at[81,'G141time']=dfnsml.exposure_time\n", "tabl.at[81,'obsdate']=dfnsml.observation_date\n", "tabl.at[81,'imgtime']=dfnsml.expt_f140w\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pointing GOODSN114\n", "l 125d56m48.38805222s\n", "b 54d46m34.9692589s\n", "G141time 2506.0\n", "obsdate 2011-04-19\n", "imgtime 203.0\n", "Name: 81, dtype: object" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabl.iloc[81]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def round_float(x):\n", " return np.round(x, 1)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "mags_df=pd.DataFrame([x.mag_limits for x in pnts]).applymap(round_float)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "df=tabl.join(mags_df)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "df=df.sort_values('Pointing')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df.to_latex(wisps.LIBRARIES+'/pointings.tex', index=False, escape=False,\n", " na_rep='\\\\nodata')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/volumes/LaCie/wispsdata/libraries/'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wisps.LIBRARIES" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "import popsims" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "module 'popsims' has no attribute 'POLYNOMIALS'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/p5/jqfspzln0z965dsfd1pj_5900000gp/T/ipykernel_28024/3529441952.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpopsims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPOLYNOMIALS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'absmags'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dwarfs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: module 'popsims' has no attribute 'POLYNOMIALS'" ] } ], "source": [ "popsims.POLYNOMIALS['absmags']['dwarfs'].keys()" ] }, { "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
nmmarquez/pymc
pymc3/examples/discrete_find_MAP.ipynb
4
99823
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using `find_MAP` on models with discrete variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maximum a posterior(MAP) estimation, can be difficult in models which have discrete stochastic variables. Here we demonstrate the problem with a simple model, and present a few possible work arounds." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as mc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a simple model of a survey with one data point. We use a $Beta$ distribution for the $p$ parameter in a binomial. We would like to know both the posterior distribution for p, as well as the predictive posterior distribution over the survey parameter." ] }, { "cell_type": "code", "collapsed": false, "input": [ "alpha = 4\n", "beta = 4\n", "n = 20\n", "yes = 15\n", "\n", "with mc.Model() as model:\n", " p = mc.Beta('p', alpha, beta)\n", " surv_sim = mc.Binomial('surv_sim', n=n, p=p)\n", " surv = mc.Binomial('surv', n=n, p=p, observed=yes)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's try and use `find_MAP`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " print(mc.find_MAP())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'p': array(0.6086956533498806)}\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`find_map` defaults to find the MAP for only the continuous variables we have to specify if we would like to use the discrete variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " print(mc.find_MAP(vars=model.vars, disp=True))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Warning: vars contains discrete variables. MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization fmin_powell.\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: 3.111511\n", " Iterations: 3\n", " Function evaluations: 95\n", "{'surv_sim': 14.0, 'p': array(0.695652178810167)}\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the `disp` variable to display a warning that we are using a non-gradient minimization technique, as discrete variables do not give much gradient information. To demonstrate this, if we use a gradient based minimization, `fmin_bfgs`, with various starting points we see that the map does not converge." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " for i in range(n+1):\n", " s = {'p':0.5, 'surv_sim':i}\n", " map_est = mc.find_MAP(start=s, vars=model.vars, fmin=mc.starting.optimize.fmin_bfgs)\n", " print('surv_sim: %i->%i, p: %f->%f, LogP:%f'%(s['surv_sim'],\n", " map_est['surv_sim'],\n", " s['p'],\n", " map_est['p'],\n", " model.logpc(map_est)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "surv_sim: 0->-1, p: 0.500000->0.391133, LogP:-inf\n", "surv_sim: 1->1, p: 0.500000->0.500000, LogP:-14.298540\n", "surv_sim: 2->2, p: 0.500000->0.500000, LogP:-12.047249\n", "surv_sim: 3->3, p: 0.500000->0.500000, LogP:-10.255489\n", "surv_sim: 4->4, p: 0.500000->0.500000, LogP:-8.808570\n", "surv_sim: 5->5, p: 0.500000->0.500000, LogP:-7.645419\n", "surv_sim: 6->6, p: 0.500000->0.500000, LogP:-6.729129\n", "surv_sim: 7->7, p: 0.500000->0.500000, LogP:-6.035981\n", "surv_sim: 8->8, p: 0.500000->0.500000, LogP:-5.550474\n", "surv_sim: 9->8, p: 0.500000->0.558888, LogP:-5.161793" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 10->9, p: 0.500000->0.587384, LogP:-4.563607\n", "surv_sim: 11->10, p: 0.500000->0.500000, LogP:-5.167477\n", "surv_sim: 12->11, p: 0.500000->0.500000, LogP:-5.262785\n", "surv_sim: 13->12, p: 0.500000->0.500000, LogP:-5.550465\n", "surv_sim: 14->13, p: 0.500000->0.500000, LogP:-6.035970" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 15->14, p: 0.500000->0.681157, LogP:-3.133947\n", "surv_sim: 16->16, p: 0.500000->0.500000, LogP:-8.808570\n", "surv_sim: 17->17, p: 0.500000->0.500000, LogP:-10.255489\n", "surv_sim: 18->18, p: 0.500000->0.500000, LogP:-12.047249\n", "surv_sim: 19->19, p: 0.500000->0.500000, LogP:-14.298540\n", "surv_sim: 20->20, p: 0.500000->0.500000, LogP:-17.294273\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again because the gradient of `surv_sim` provides no information to the `fmin` routine and it is only changed in a few cases, most of which are not correct. Manually, looking at the log proability we can see that the maximum is somewhere around `surv_sim`$=14$ and `p`$=0.7$. If we employ a non-gradient minimization, such as `fmin_powell` (the default when discrete variables are detected), we might be able to get a better estimate." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " for i in range(n+1):\n", " s = {'p':0.5, 'surv_sim':i}\n", " map_est = mc.find_MAP(start=s, vars=model.vars)\n", " print('surv_sim: %i->%i, p: %f->%f, LogP:%f'%(s['surv_sim'],\n", " map_est['surv_sim'],\n", " s['p'],\n", " map_est['p'],\n", " model.logpc(map_est)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "surv_sim: 0->2, p: 0.500000->0.434783, LogP:-11.654827\n", "surv_sim: 1->3, p: 0.500000->0.456522, LogP:-10.081356\n", "surv_sim: 2->6, p: 0.500000->0.521739, LogP:-6.685637" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 3->7, p: 0.500000->0.543478, LogP:-5.861849\n", "surv_sim: 4->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 5->14, p: 0.500000->0.674290, LogP:-3.159870\n", "surv_sim: 6->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 7->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 8->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 9->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 10->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 11->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 12->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 13->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 14->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 15->15, p: 0.500000->0.717392, LogP:-3.149062" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 16->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 17->15, p: 0.500000->0.717391, LogP:-3.149062" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 18->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 19->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 20->14, p: 0.500000->0.712421, LogP:-3.142725" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For most starting values this converges to the maximum log likelihood of $\\approx -3.15$, but for particularly low starting values of `surv_sim`, or values near `surv_sim`$=14$ there is still some noise. The scipy optimize package contains some more general 'global' minimization functions that we can utilize. The `basinhopping` algorithm restarts the optimization at places near found minimums. Because it has a slightly different interface to other minimization schemes we have to define a wrapper function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def bh(*args,**kwargs):\n", " result = mc.starting.optimize.basinhopping(*args, **kwargs)\n", " # A `Result` object is returned, the argmin value can be in `x`\n", " return result['x']\n", "\n", "with model:\n", " for i in range(n+1):\n", " s = {'p':0.5, 'surv_sim':i}\n", " map_est = mc.find_MAP(start=s, vars=model.vars, fmin=bh)\n", " print('surv_sim: %i->%i, p: %f->%f, LogP:%f'%(s['surv_sim'],\n", " floor(map_est['surv_sim']),\n", " s['p'],\n", " map_est['p'],\n", " model.logpc(map_est)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "surv_sim: 0->5, p: 0.500000->0.500000, LogP:-7.645419\n", "surv_sim: 1->7, p: 0.500000->0.543478, LogP:-5.861849" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 2->10, p: 0.500000->0.608696, LogP:-4.071797" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 3->8, p: 0.500000->0.565217, LogP:-5.158052" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 4->10, p: 0.500000->0.608696, LogP:-4.071797" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 5->7, p: 0.500000->0.543478, LogP:-5.861849" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 6->12, p: 0.500000->0.652174, LogP:-3.385867" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 7->8, p: 0.500000->0.565217, LogP:-5.158052" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 8->11, p: 0.500000->0.630435, LogP:-3.679320" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 9->10, p: 0.500000->0.608696, LogP:-4.071797" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 10->12, p: 0.500000->0.652174, LogP:-3.385867" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 11->13, p: 0.500000->0.673913, LogP:-3.194359" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 12->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 13->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 14->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 15->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 16->15, p: 0.500000->0.717391, LogP:-3.149062" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 17->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 18->15, p: 0.500000->0.717391, LogP:-3.149062" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 19->18, p: 0.500000->0.782609, LogP:-4.247450" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 20->18, p: 0.500000->0.782609, LogP:-4.247450" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default `basinhopping` uses a gradient minimization technique, `fmin_bfgs`, resulting in inaccurate predictions many times. If we force `basinhoping` to use a non-gradient technique we get much better results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " for i in range(n+1):\n", " s = {'p':0.5, 'surv_sim':i}\n", " map_est = mc.find_MAP(start=s, vars=model.vars, fmin=bh, minimizer_kwargs={\"method\": /\"Powell\"})\n", " print('surv_sim: %i->%i, p: %f->%f, LogP:%f'%(s['surv_sim'],\n", " map_est['surv_sim'],\n", " s['p'],\n", " map_est['p'],\n", " model.logpc(map_est)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "surv_sim: 0->14, p: 0.500000->0.695652, LogP:-3.111511\n", "surv_sim: 1->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 2->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 3->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 4->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 5->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 6->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 7->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 8->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 9->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 10->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 11->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 12->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 13->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 14->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 15->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 16->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 17->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 18->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 19->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "surv_sim: 20->14, p: 0.500000->0.695652, LogP:-3.111511" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Confident in our MAP estimate we can sample from the posterior, making sure we use the `Metropolis` method for our discrete variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " step1 = mc.step_methods.HamiltonianMC(vars=[p])\n", " step2 = mc.step_methods.Metropolis(vars=[surv_sim])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "with model:\n", " trace = mc.sample(25000,[step1,step2],start=map_est)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", " [ 1% ] 335 of 25000 complete in 0.5 sec" ] }, { "output_type": "stream", "stream": 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OhwtOnz4dgwcPxrJly3DLUagRJ/ATQQ4ZMgQAc0ju378/pk2bhpKSEgDA1atX\noVarTfuo1WoYjUab9aGhoSZjTWkoYb4a0ug+ctfHMEgtwAUMUgtoECijPopPcXGx6XP9+nVkZmbi\n9u3bUssiCMKDHD4stQKCMOO0J2vfvn04d+4cVqxYgYEDB0Kn02HKlCmIi4tz6QB37tzBhAkTsGTJ\nEvj7+2P69Ol45513AABvv/02Zs+ejeXLl7uXiwekpKRAo9EAAIKCgjBgwADTkA6+oSHl8okTJ2Sl\nx94yj1z00LJnlgHLmK3u1hdrg8h9fbzG+u1vPz2zRrml15iWDQYDVq5cCQCm+7UnGDhw4INhooCv\nry80Go1ozxqCIAiCcIbLPllVVVXYuHEjZsyYgdatW6OmpgYffPABxo8f73Cf+/fvY8yYMXjiiSfw\nxhtv2Pyfn5+PsWPH4tSpU0hLSwMAzJs3DwAwevRoLFy4EN26dUNMTAx+++03AGwoR1ZWFr744gvL\njJBPFkG4BPlkua9R7PQaM3K7d0+dOhVbt25Fx44dcerUKYv/PvroI7z55pu4fv062rZta7Mv+WQR\nBEEoD8l8sn755ResXLkSW7ZsQWxsLLZs2YKBAwfi6tWrGDJkiEMji+M4TJs2DVqt1sLAKigoQKdO\nnQAAGzZsMPl7JSQkIDk5GbNmzYLRaERubi50Oh1UKhUCAwORnZ0NnU6H1atXY8aMGWLknSAIgmhg\nrF+/3tSDZQ8+FLsjpkyZgtdffx2TJk2yWH/58mXs2LED3bp1E0UnQRAE0bBxamTNmDED06ZNw/vv\nv4+WLVua1nfu3Bn/8z//43C//fv349tvv0W/fv0QFRUFAPjggw+Qnp6OEydOQKVSoXv37vjXv/4F\nANBqtUhMTIRWq4Wvry+WLVtmelAuW7YMKSkpKC8vR3x8vGKDXhgUMF8NaXQfuetjGKQW4AIGyH2e\nLCWgjPooHv/5z3/cMrKGDRuG/Px8m/WzZs3Chx9+iKeeespdiQRBEEQjwKmRtXXrVrRo0QI+Pj4A\ngOrqalRUVKBVq1Y2b/qEDB06FDV2wrw88cQTDveZP38+5s+fb7N+0KBBNsM2CIIgCMIa3t9LTDZt\n2gS1Wo1+/fqJnjZBEATRMHHqkzVkyBDs3LkT/v7+AIDS0lKMGjUKBw4c8IpAV5HbuH6CkCvkk0U+\nWXLCk/fuLVu2ICcnBxUVFaZ1fOCl2hD6C5eVlSEmJgY7duxAYGAgunfvjiNHjtidjFilUuGZZxaY\nlrVaPbSBSL/jAAAgAElEQVRavSh5IQiCIMQhJ8eAnByDafmHHxZK45NVUVFhMrAAICAgAGVlZaIL\nIQiCIAixePXVV1FeXo5du3bh5Zdfxvfff4/o6Og6p5OXl4f8/Hz0798fAHDlyhUMGjQIhw4dQseO\nHW22nzAh1V3pBEEQhAexfgH2ww8LPXIcp/NktWrVCkePHjUtHzlyBC1atPCImIaObdhr+UEa3Ufu\n+hgGqQW4gEFqAQ0CZdRH8Tlw4AC++eYbtG3bFgsWLMDBgwdx9uzZOqcTGRmJoqIiXLhwARcuXIBa\nrcaxY8fsGlgEQRAEweO0J+uTTz5BYmKiKSJgQUEBMjIyPC6MIAiCIOoL/zKwZcuWMBqNaNeuHQoL\nC53ul5SUhKysLNy4cQNdunTBu+++iylTppj+ry2oBkEQBEHwuDRPVmVlJc6ePQuVSoWHHnoIfn5+\n3tBWJ8gniyBcg3yyyCdLTnjq3v3uu+/i9ddfx65du/DnP/8ZAPDyyy/jvffeE/1YPDRPFkEQhPLw\n1DxZLhlZBw4cwIULF1BVVWV6i1dbZEGAzSkyadIkXLt2DSqVCq+88gpmzJiB4uJiTJw4ERcvXoRG\no8HatWsRFBQEAFi0aBFWrFgBHx8fLF26FHFxcQCAo0ePIiUlBRUVFYiPj8eSJUtsM0JGFtFACQxs\ni9LSmwgIaIPbt4vdTo+MLDKy5IQ37t0VFRWoqKgwPWs8BRlZBEEQysNTRpZTn6wXXngBf/3rX7F/\n/34cOXIEhw8fxuHDh50m7Ofnh48//hinT5/GwYMH8fnnn+O3335DWloaYmNjce7cOYwYMQJpaWkA\ngJycHGRkZCAnJweZmZl47bXXTBmePn06li9fjtzcXOTm5iIzM9PNbEuDEnwjSKP7iK2vtPQmAO7B\nt1gYREzLUxikFtAgkPv14in69euHDz74AHl5eWjevLnHDSyCIAiCEOLUJ+vo0aPIycmp8zj0kJAQ\nhISEAAD8/f3Ru3dvGI1GbN68GVlZWQCAyZMnQ6/XIy0tDZs2bUJSUhL8/Pyg0WgQFhaG7OxsdOvW\nDaWlpdDpdABYD9rGjRsVOyExQRAE4Xk2b96MjIwMJCYmQqVS4bnnnkNiYiK6du1a635Tp07F1q1b\n0bFjR9P8jG+++Sa2bNmCpk2bomfPnvj666/RunVrb2SDIAiCUChOe7L69u2LgoICtw6Sn5+P48eP\nIzo6GkVFRQgODgYABAcHo6ioCABw9epVqNVq0z5qtRpGo9FmfWhoKIxGo1t6pEKv10stwSmk0X3k\nro+hl1qAC+ilFtAgUEZ9FB+NRoO5c+fi6NGjSE9Px8mTJ9G9e3en+02ZMsVmtERcXBxOnz6NX375\nBb169cKiRYs8JZsgCIJoIDjtyfrjjz+g1Wqh0+nQrFkzAGzc+ebNm106wJ07dzB+/HgsWbIEAQEB\nFv+pVCqK1EQQBEF4hPz8fGRkZGDt2rXw8fHBhx9+6HSfYcOGIT8/32JdbGys6Xd0dDTWr18vtlSC\nIAiigeHUyEpNTQVg6ZzsqmF0//59jB8/Hi+++CLGjRsHgPVeFRYWIiQkBAUFBaa5RkJDQ3H58mXT\nvleuXIFarUZoaCiuXLlisT40NNTu8VJSUqDRaAAAQUFBGDBggOktLu+XIOXyiRMn8MYbb8hGj71l\nfp1c9NhbttYqtR5v6LP2T3I/vU9ESU+Qgsj6eI3iXC/WGuWWHr+ckPAMSktvokULf2zb9h/Z1kd3\n9axcuRIATPdrTxAdHY3KykokJibi+++/R48ePURJd8WKFUhKShIlLYIgCKLh4lJ0wfz8fJw/fx4j\nR45EWVkZqqqqEBgYWOs+HMdh8uTJaNeuHT7++GPT+jlz5qBdu3aYO3cu0tLSUFJSgrS0NOTk5CA5\nORmHDh2C0WjEyJEjcf78eahUKkRHR2Pp0qXQ6XR48sknMWPGDBufLCVEFzQYDFaNNPlBGt1HbH2e\niYy3G0CMzKML7gYbMth4ogt6Il25Xy+eunefOXMGERER9do3Pz8fY8eONflk8bz//vs4duyYw54s\nlUqFZ55ZYFrWavXQavX10kAQBEF4hpwcA3JyDKblH35YKE0I9y+//BJfffUViouLkZeXh3PnzmH6\n9On46aefak143759+NOf/oR+/fqZer4WLVoEnU6HxMREXLp0ySaE+wcffIAVK1bA19cXS5YswahR\nowCYQ7iXl5cjPj4eS5cutc2IAowsgqgPcjYQKIS7/I0suSPHe7c9I2vlypX46quv8NNPP6F58+Z2\n96MQ7sqgTRvgppjBWgmCUDSSzZPVv39/HDp0CEOGDMHx48cBAJGRkTZv+KRGjg9qghADORsIZGSR\nkeUucrx3WxtZmZmZmD17NrKystC+fXuH+5GRpQzatgWK3Z9ykCCIBoJk82Q1a9bMFPACgMWExETd\nsPVjkR+k0X3kro9hkFqACxikFtAgUEZ9lA9JSUl49NFHcfbsWXTp0gUrVqzA66+/jjt37iA2NhZR\nUVF47bXXpJZJuAE1YQiC8AZOA18MHz4c77//PsrKyrBjxw4sW7YMY8eO9YY2giAIgqgXd+/exT/+\n8Q9cunQJX331FXJzc3H27FmMGTOm1v3S09Nt1k2dOtVTMgkJICOLIAhv4HS4YHV1NZYvX47t27cD\nAEaNGoWXXnpJdr1ZchxyQhBiIOehbjRckIYLuoun7t2JiYkYNGgQvvnmG5w+fRp3797Fo48+il9+\n+UX0Y/HQcEFlEBoKKHS6TYIgPIBkwwV9fHzwyiuvYN26dVi3bh1efvll2RlYBEEQBCEkLy8Pc+fO\nRdOmTQEArVq18urxhw71TLpPP+2ZdBsiQ4bYX6/VeldHQ+bBLDyNjshI7xynZUvvHKc2EhLYt5Og\n4pIQFlb/fR88GjyKUyOre/fuNh+x5htpbCjBN4I0uo/c9TEMUgtwAYPUAhoEyqiP4tOsWTOUl5eb\nlvPy8iz8ix0xdepUBAcHI1LQiiouLkZsbCx69eqFuLg4lJSUOE2nS5f66XaGg8CGisDbDUZHMUqa\nOG35EPawd/48Vc/FxsdH3PS8dR0GBLifhrvGEf9+qk0b97WITUhI/ff1Rn6c3moOHz5s+uzduxcz\nZ87E888/73llBEEQBFFPUlNTMXr0aFy5cgXJycl4/PHHsXjxYqf7TZkyBZmZmRbr0tLSEBsbi3Pn\nzmHEiBFIS0vzlOwGTU2Nd4/naPQPDcZpfCj1nDeSUeP1xp3z6o064dTIat++vemjVqvxxhtvYOvW\nrS4lbu+NYGpqKtRqNaKiohAVFYUff/zR9N+iRYsQHh6OiIgIkw8YwObJioyMRHh4OGbOnFmX/MkK\nOU8IykMa3Ufu+hh6qQW4gF5qAQ0CZdRH8YmLi8P69evx9ddfIzk5GUePHkVMTIzT/YYNG4Y2Vq84\nN2/ejMmTJwMAJk+ejI0bN3pEc0OHGozKRsnnT6lGlpxQ8vmXCqfRBY8ePWrywaqpqcGRI0dQXV3t\nUuJTpkzB66+/jkmTJpnWqVQqzJo1C7NmzbLYNicnBxkZGcjJyYHRaMTIkSORm5sLlUqF6dOnY/ny\n5dDpdIiPj0dmZiZGjx5dl3wSBEEQjQDhMwsAOnXqBAC4dOkSLl26hIEDB9Y5zaKiIgQHBwMAgoOD\nUVRUJI7YRoa3e7IcQQ1u8VBKWSpFpycQyzhqaEaWN+qEUyNr9uzZpgeWr68vNBoN1q5d61Liw4YN\nQ35+vs16exE8Nm3ahKSkJPj5+UGj0SAsLAzZ2dno1q0bSktLodPpAACTJk3Cxo0bFWlkGQwG2b9V\nJo3u07JlAMrL7yAgoA1u35brjJcGqQW4gAHUm+U+cr9exEb4zLLH7t273UpfpVLVmv66dakAgLNn\ngepqPbRavVvHE5MmTaQ1dCIigAfzO4uOSiWPRqCvL1BVJd3xfXwAV96D16e8evYEfv3Vcp2L79xd\nomlToLJSvPSEiN2gbttW3PTsoVIxn7dr1+q2X7t2wI0bntEkpG9f2/qgFHJyDDh40IArVzx7HKfD\nBQ0GA3bv3o3du3djx44d+Oqrr/DQQw+5ddBPP/0U/fv3x7Rp00wOxFevXoVarTZto1arYTQabdaH\nhobCSLFXCRlTXn4HAIfS0ptSSyGIRofwmWXvUx+Cg4NRWFgIACgoKEDHWkKqTZiQigkTUpGamiqK\ngTVqFPt2NYpWt26263jH92eecVuOQ4YNM/+uS1Q/YePXnYbwc89ZLiclOffJSkpyLW0fH+Dxxx3/\nL3S+lzo4QGKia9v16we4EAfGAnsR9fz86n/erOuqMJiEveAkSUmunzMACAqyv16MYBJiGlmdO1su\n89d6nz7MsHUFjcb8Oy7Oub6ICNt1SUlA166O9+Gvpz/9iX3bqw/8Onvv9cSI5mcdfKW+dU+r1WPJ\nklTT/dpTOO3J+uijj2ze2vE9UfzQv7owffp0vPPOOwCAt99+G7Nnz8by5cvrlIYjUlJSoHlQ04KC\ngjBgwADTG1w+wpbUyzxy0aPEZb1eLys99pate4rkl54l7tZnueqzTM+sUW7p2ZanZQ9UQ7peDAYD\nVq5cCQCm+7UnKC8vx7Jly7Bv3z6oVCoMGzYM06dPR/N6hAVLSEjAqlWrMHfuXKxatQrjxo3zgOKG\nQ10aPsIeFbn0RhGuI/UwPFfrjNQ6a0OMOi/WdeNKObnSG+6t8pb7/cLpZMTJyck4fPgwEhISwHEc\ntmzZgsGDB6NXr14AgAULFtR6gPz8fIwdOxan7IwREP7HR2uaN28eAGD06NFYuHAhunXrhpiYGPz2\n228AgPT0dGRlZeGLL76wzAhNRkzIBLlPfEuTEcurDL2Rrpzx1L372WefRWBgIF544QVwHIc1a9bg\n1q1b+P7772vdLykpCVlZWbh+/TqCg4Px7rvv4qmnnkJiYiIuXbpkGjIfZOc1uXAy4qQkID3d/XyM\nHg1kZgLh4UBurvN0u3UDLl60XNe6NXDrFvDss4CT7NebP/0J2LOH/X7kEeDnn223mTgRyMiwXNdE\nMIRR+LuuWJdLUhLL87ZtttvGx7P1rp4jHx9g+HBg1y77/4eEAA86OtGhA/DHH3XXLxau5qlfPzak\n9d4999KOjgYOHapfY9e6rgYGArdvs9/26gLfiyXUUJuRFRQE8LMttGwJlJWx3wEBQGlp3fVaaxHj\n+gaATp2AggLzclgYcP48G46n1QKueOgIyzIpCfjvf4HiB94K9vIbEQGcOWO5LikJOHgQuHDBdn16\nunkS78ceA/bvt18GkZFsSPDjj9teL82a1a2+2aNVK+DuXfPysGHA3r31S0uo31OTETvtybp8+TKO\nHTuGgAf9qwsXLkR8fDz+/e9/1+uABQUFJkfkDRs2mCIPJiQkIDk5GbNmzYLRaERubi50Oh1UKhUC\nAwORnZ0NnU6H1atXY8aMGfU6ttQI30zLFdLYWDBILcAFDCCfLPdprNfL6dOnkZOTY1p+/PHHoXVh\nFtp0By2nnTt3iqZNKrz1drm+x5Grvrq0vRrJuxETcu4hIhj26qSj89akFiciPp369mQ1tmsDcMHI\nunbtGvz8/EzLfn5+uOaiF57wjWCXLl2wcOFCGAwGnDhxAiqVCt27d8e//vUvAIBWq0ViYiK0Wi18\nfX2xbNky0zDFZcuWISUlBeXl5YiPj1dk0AuCIAjCewwcOBA///wzHnnkEQDAwYMHMWjQIIlVeR5f\np091M35+wP37ntPiCp4MxNEYG3V1QYzykbORJcyfnHV6Ak++6JBLlFAl4DTwxaRJk6DT6ZCamooF\nCxYgOjraNF+IM9LT03H16lVUVlbi8uXLmDp1Kr755hucPHkSv/zyCzZu3GgKiwsA8+fPx/nz53Hm\nzBmM4r19AQwaNAinTp3C+fPnsXTp0npkUx4o4W0yaWws6KUW4AJ6qQU0CBrr9XLkyBE89thj6Nat\nGzQaDR599FEcOXIEkZGR6Nevn9TyaqVvX/NvvtHTr585+MKAAWzIDv+YFDqg9+1r36mdZ+hQ828X\npg2zoX1759vwmnkn+trejgsR5luII+f/+HjX0rUmIMAyUIcQRxH+a4l1AsCyXB97zPybDxLgCEFT\nx25aQp0jRlj+B5gDDISGAj16sN/8d13Qai3Pratl26WL+XdTQWCDuDgWuGHQIDZ81B61NdaFBlKL\nFpaBR4YPt7+Pdd20t11sLDB4sOW6yEimU4i1u6iw3Otb7+zh6L1PTAy7joVGj726GREBPPkk+2/Y\nMFZHrHng3WOBVmu+/vv0Meepf3/L7fggNsOHm891t26Oz8FDD7GhzR062P+/rvB1p21bYMwYc73Q\n6VheO3dm0RRdoT7Xhbs4fef1t7/9DaNHj8a+ffsAACtXrkRUVJTHhREEQRBEfcnMzBQ9zUWLFuHb\nb79FkyZNEBkZia+//hrNrEK0Cf0f6uuDEBnJfDIqKszrmjYF+HeSvXtbbh8aag6N3rIlawydOWM/\nnHeXLmZ/F0eNkxYtgPJy+/9ZR/eqjago4NIl1kC6ft359o564QSDaSxo3Zp9O4oiV1t0QUHQYgt6\n9ACOHbO/D2BZpsHBAD9lmtDYCA21/9sefBQ4YbrCtNRq9rlyxb6h98D7Al26AN27s9/R0cDvv5v3\ndyVMdf/+wE8/mZf5srWmTRvgpiBwro+P2S9q6FCzH067dpb1y56PnvV5FRoULVowH63CQnYdCd7H\nW0TjE/pktWljWc9atbI9Zvv2Zr8vHt64P3rUvO6RRwDhDETCcyIsG3d98Hr1Yn5O1vARK4WGaIcO\n7IVDXp55Xdeu5uihjuq0ry/g7w/cuWNe17Sp+RiRkeayFxrKwkiOnTubfb58fGwjIvL4+QEPP2xe\nHj4cyMqyv609X7Hmzc33vRYtmLErNHj5cy188RIX55p/nFptvi68hUvvl8rKyhAQEICZM2dCrVbj\ngrVXHOES9iKIyQ3S2FgwSC3ABQxSC2gQNNbrRaPRoHXr1rh9+zaKi4tNH41GU6+ohvn5+fjqq69w\n7NgxnDp1CtXV1fjuu+9sthN74k9Xhu8420YuQ6Wc6VDC8D65lKUY1Ke8a+t9qmvZ1LZ9fcq5tvwI\n01NCPXMVV8upoeRZaflw2pOVmpqKo0eP4uzZs5g6dSoqKyvxwgsvYP/+/d7QRxAEQRB15u2338bK\nlSvRo0cPNBGMV6vvXFmBgYHw8/NDWVkZfHx8UFZWhlBn3RReQg4ND7Hmu2oIiJ1/d9ITW4urhowr\n1La9q0NMpcbb1159jieH+wMgr1D13sKpkbVhwwYcP37c5DAcGhqKUndjXzZSlOAbQRobC3qpBbiA\nXmoBDYLGer1kZGQgLy8PTYXjX9ygbdu2mD17Nrp27YoWLVpg1KhRGDlyZK37uNPAVVpjwh5SG1ti\n9yoC9vMkRj6lON9iN9il7slqKNSWd6XdF7xp5MsRp+8KmjVrZvEW8K4wQD1BEARByJA+ffrgptB5\nxE3y8vLwySefID8/H1evXsWdO3fqNZVJXedCdqWRYv3Wn3cTa9GCffO+Mc7SatPG/Jv383BVD38s\noG5+W0L/FkdpO/IP4o/F+zVZY+Uu5xL2NAh9oewNO+PLrWlTSy28878r59zHp246xYD3yakLjsoa\nqD2f9s6F9XkV1rlOnWo/Fg9fxq1aMb8jR7Rvb/5f+N7FkT9fbTp5+PMdFORaQJj6IKxvfn62Whxd\nM66UXV2xd13zfnWOrrXarkF7ZSb0o7P3f/v29c+b8B7lLZz2ZD377LN49dVXUVJSgi+//BIrVqzA\nSy+95FLiU6dOxdatW9GxY0fTZMTFxcWYOHEiLl68aDOp46JFi7BixQr4+Phg6dKliIuLAwAcPXoU\nKSkpqKioQHx8PJYsWVLf/EqKEuarIY2NBYPUAlzAAOrNcp/Ger3Mnz8fUVFR6Nu3ryk4hUqlwubN\nm+uV3pEjR/Doo4+i3QOL5ZlnnsGBAwfw/PPPW2y3Zk0qKivZRK8+PnqEhelN/z37LGtM865cXboA\nly/bP56rb2wnTrScnBNgDuUAM67i41lDTOgsb02vXixIBWCeLDg+num0Dp5hbxiXXs8MDV9foKqK\nHXfiRHPQj9qMuz59mPM+x7HJlu3RoweLaMank5FhbmwmJNhu/9xz7LtVK6YDYPk/csT+tqdPmwOH\nAGyf48eBc+fYb2v9wkluARb5EQCeecZyOz7S27hxLH982TZtClRWAomJ5m39/FiQAH46NmF4fXvl\n9+STtRsVAwfaD+AhnBB66FBzXbTextG6Rx5hAQTseYz4+9vfFwCeftr2WOHhbOJdfn1YGLseIiNZ\nvQCAnJza648wQmZpKXDihK1u6/1btDBP8i2cEei554ADB1iwFmGe+f2jo4HsbPP21uebv2atJ9y2\nJjGRBSM5cMBWa237BgSwoDcREfbPm5CoKMfXk/UxrcvH0Tns0MH2v/HjmZa4OFsjxnpblYqtu3SJ\nBUKJjmY6f/jBvE1wMHDjBovQKQw2wuMsWifPc8+x4D2bNpnXtW3LIrOeOAF07GhAaqoBt26Zz7cn\nqNXI4jgOEydOxJkzZxAQEIBz587hvffeQ2xsrEuJT5kyBa+//jomTZpkWpeWlobY2FjMmTMHixcv\nRlpaGtLS0pCTk4OMjAzk5OTAaDRi5MiRyM3NhUqlwvTp07F8+XLodDrEx8cjMzOT5soiCIIgHDJp\n0iTMmzcPffv2NY3GULkxdiUiIgLvvfceysvL0bx5c+zcuRM6nc5mu+TkVNy5wyJzbdhgGSHQOnqe\nGMOCnPmu+Pg4n9dGpbJNh9dmrdGeZn5fYRpNmrg+VEilcr6tdU+PI33W63hNjiIUqlS2aTdpYj9P\njrRaa7EOWmK9jzBSofVx7eXBHkKN9nBlstm6TEjLr3N2rhxpcrSPvfV8xEJXcKWcaltvfV6s63Bt\nablybdjDx6f2MnaGmEPw6qqjtjKwV5+t7z3C+0JtdclRHahLvagtHyNG6DFihB5GI7BnD/DDDwtd\nS7iOOO3Jio+Px6+//mrqVaoLw4YNQ74wBiaAzZs3I+tBPMfJkydDr9cjLS0NmzZtQlJSEvz8/KDR\naBAWFobs7Gx069YNpaWlpofZpEmTsHHjRkUaWUp4m0waGwt6qQW4gF5qAYonMLAtSktvIiCgDW7f\nLpZajlfx9/fHjBkzREuvf//+mDRpEh5++GE0adIEAwcOxCuvvCJa+o6Qm2+KJ30i6pJXT5WLK5Ea\nrY2pulLX/dyJtCe3+kN4B2EdU5ofkyfxdlnUamSpVCoMGjQIhw4dsvvGrj4UFRWZJiAODg5G0YNJ\nJq5evYoh/KxnANRqNYxGI/z8/KAWBP8PDQ2F0d6kAgRBEIQFpaU3AXAoLW18La1hw4bhrbfeQkJC\ngsVcVgMdzTbrAnPmzMGcOXPEkOcUMRsDnm5ou9pL4SxPcmoM1qXnpT7bOENOZWGNXLWJUe5KM0ob\nWwj3uuBK2Xi6XJz2ZB08eBDffvstunXrhlYPPNJUKhVOnjzp9sFVKpVbwzesSUlJMc1/EhQUhAED\nBph6Pfi5YqRcPnHiBN544w3Z6LG3zK+Tix57y9ZapdZjb9na50l+6X0iSnqCFETWx2sU53qx1ii3\n9GzL09KXyr30DKKlJ9b1u3LlSgCo13xVrnLs2DGoVCocPHjQYn19Q7i7SlWV+bcY0QU90eiTIsiC\n2EjVGPaWAeVu48+bjWqlGSZyQ8yIjXJD6frdRcVx9k/vpUuX0LVrV+Tn50OlUsF6M1cfjvn5+Rg7\ndqwp8EVERAQMBgNCQkJQUFCAmJgYnDlzBmlpaQCAefPmAQBGjx6NhQsXolu3boiJicFvv/0GAEhP\nT0dWVha++OILy4zY0Sg3hI0cudKYNHpqKBV7ccABEKdOeia93QBi3E7PrA0QS5853d1gQwbdT1fu\n58RT6Yp5rj2FEu7drqJSqbBhA4fycuaTdfkysG+f+f+kJPadns6+Y2OB8+eBCxfM22g0LLjAxYvM\nn6FrVxZkoDaH79JSYMsWoHNnFjwBYIEcOndmQSg4jjmnT5zI/BSMRpZ2ly4swEP37uYoa+fPs4hg\nXboAv//O/MiOHGER9po3Z0EorlxhwSJ4xo9nwRx+/50FBuDzee8ec2rv2BG4do05o2dlMcf2ykq2\nzaOPsqAWAAs0cfQoc+5/8MhHeDjw8MOW+T17lkWi69TJcn16OmvU8YEvhOTnM2f77t0BwaAZAOxY\nJ06w9IYPZ2ncusXywwcE4cumeXNmpN66xYJ9FBczvdYYDCwgiPW6zp1ZgIDffzefq/R0tu7JJ1n5\nDR3KnPGrqlh537gBXL3KAkIAzIfkscfMxvKpUyxohDDwwJ07LF/duwMFBcAffwBFRezc5OSweuXv\nb66LSUksOMihQ+bzd+YMqx/8/zyXLpkDXyQlmetq9+625SCEPxYADB7MNAvXx8QAu3cDY8eag3o4\nOtc7drD8Dh1qXsdfBzxCzdbcugVs22a7TXExq998MBMhfB2qLV3hdgALtvDQQ+ZlXldFBSvbRx4B\ndu0yn5uzZ1nUR2EUwfR0832B5+ef2XGefdbW1xNg1/yePez6794duHmTBYLgg784y0NdSE+3PGfW\nOvggHQMHsrIoLwd++YVdhzU1lsE+tFpWP//0J6Au0xCePMnq4Z07bDkpyXz/Adh9b+JEVkfOnjXf\nU8rLWT0fONAzzyGHPVlPPfUUjh8/Do1Gg/Hjx2P9+vWiHDAhIQGrVq3C3LlzsWrVKowbN860Pjk5\nGbNmzYLRaERubi50Oh1UKhUCAwORnZ0NnU6H1atXizrO3pvI3XgBGpfGxjyUShn+TnqpBTQQ9FIL\nkIwtW7YgJycHFYLoE++8845Hjyl8c9ulC2swFRbahiNu1oyta9+eGVkhIZaR0njDA3A9ohbfaAfM\nDXJ7CBsvQiMCMDd8AdbAB1iDXEjbtszIatqUGVj20gXsBwbQ61kjh498Z69d07mz2ciyNrAA1lBz\nhL/kNFwAACAASURBVLPw8dYGlhDho6V169rLhm/0P/B+qDUte+vsRU7z8zMbDcIyb9fOHIYfsK0P\n9s61vz8zZABWx377jTXkAdaQ5ena1RxdzTpsf0QEMwSc9XwK66orjBtXezhtYWPd0bl2Mf5anWnb\n1v3w5xoN+6SnsxcMGo2lkQUwY11oNPE4yq/1dRAczIwsewYWwK414f2AD41+5Ijlem/CXystWpiv\nwyZNgJ49a49+6gr9+rHPunXmqJxC+DocEGBZli1a2F7nYuJSLJPff/+9XoknJSXh0UcfxdmzZ9Gl\nSxd8/fXXmDdvHnbs2IFevXph165dpp4rrVaLxMREaLVaPPHEE1i2bJlpKOGyZcvw0ksvITw8HGFh\nYYoMekEQBEF4j1dffRVr167F0qVLwXEc1q5di4sXL7qVZklJCSZMmIDevXtDq9XaDEW0h6PhMg2h\n885TQ4EaQtkoBSrrxofczrmzyb6VjFOfLHdIF/YNC9jJTwRhxfz58zF//nyb9YMGDTINN1QyjWko\nnidRgkb5Y5BagAsY0Jh7YcTDILUASThw4ABOnTqFfv36YcGCBZg9e7bbL+hmzpyJ+Ph4rFu3DlVV\nVbhrPUFVI8OVSHyEa8it4UsQYtMY7wcOjayTJ08i4MGMhuXl5abfABt3fvv2bc+rIwiCIIh60OLB\neKSWLVvCaDSiXbt2KCwsrHd6t27dwt69e7Fq1SoAgK+vL1oLHSccIIeGhbc0yNlQkMN5aCjI9TzT\nOXYNuZ6/hohDI6taOMU7IQpK6H0hjY0FvdQCXEAvtYAGgl5qAZIwduxY3Lx5E2+++SYGDhwIlUqF\nl19+ud7pXbhwAR06dMCUKVPwyy+/YNCgQViyZAlaWjkBtWjBnKl5mje3/AaY7421T4o3Gj5iT2Jq\n7f/UxMFEpcK8A47zyvuXWKcjBo58VwDHExV7Cx+f2n2UPAnvq+OIZs2Y752Q2srSHdxN1xP1Rkh9\n6glfdu3bs0AmJSW22whmmPCoFh5PnD93yl5Yv9y9Flu2BMrK3EtDTDw6XJAgCIIgpODtt98GAIwf\nPx5jxoxBRUWFSz1PjqiqqsKxY8fw2WefYfDgwXjjjTeQlpaGd99912K7vXtTwXFAaip7ITRsmB5h\nYZbRwsaOVf5b93HjbBtWTZsCY8aYl319gdGjmbO50LncUYOse3dmkPEGR0KCeHpDQy21CenZkwUe\nkYqnnvJ8fXBk2EZGsgiOjnjySVtttZWlO7Rv7166zoKeuEvnzkB8vOvbJySY6/KwYezbXlCG6GgW\nea8uqNX1K6sxY9j1KCZjxjgue5WK/S+M+mhNv35Ar16sjrZsySJ91peRI117YWUwGOxOnSI2ZGR5\nESX4EpHGxoJBagEuYEBj7YURF4PUArzKoUOH0KVLF3R6EP5t1apVWL9+PTQaDVJTU9G2nqHD1Go1\n1Go1Bj8I2TZhwgTT1CNC3nsv1WadMDIcUL8313LDUR6sG3Bt2rBv4dtzR0aWSsUasnwYZme9LHXF\nUeNSpbIfftpbSFkffH1rz7ur51ksPJWuGKhUli9LnCGsv/Z6tHl8feveu6RS1a+sPFG+ztJ09r+P\nj3jXunWvqyP0er1FO3LhwoXiCLDCw52rBEEQBOE9Xn31VTR70DLcs2cP5s2bh8mTJyMwMBCvvPJK\nvdMNCQlBly5dcO7cOQAsgFOfPn1E0UwQnsYbEyATnkPpPd+NFerJ8iJK6H0hjY0FvdQCXEAvtYAG\ngl5qAV6lpqbG1FuVkZGBV199FePHj8f48ePRv39/t9L+9NNP8fzzz6OyshI9e/bE119/LYZkr8Fx\n1FgjCCVCBrAykawnS6PRoF+/foiKioJOpwMAFBcXIzY2Fr169UJcXBxKBB6CixYtQnh4OCIiIrB9\n+3apZBMEQRAyprq6GvcfOD7s3LkTMYIZfquqqtxKu3///jh8+DB++eUX/PDDD275eBEMe41HalCK\nD5UpQXgfyYwslUoFg8GA48eP49ChQwCAtLQ0xMbG4ty5cxgxYoRpvHtOTg4yMjKQk5ODzMxMvPba\na6ipqZFKer3xhpOdu5DGxoJBagEuYJBaQAPBILUAr5KUlIThw4cjISEBLVu2xLAHHue5ubkICgqS\nWJ19OnUCunb1/HGU0otlz2+FcI+OHZ37vXjLL83a4BMz6IhG49p2SqtjPj5SK3AfV8u8SRMgMFC8\n44aF1R7cxZNIOlyQs7rSNm/ejKysLADA5MmTodfrkZaWhk2bNiEpKQl+fn7QaDQICwvDoUOHMGTI\nEClkEwRBEDLlb3/7Gx5//HEUFhYiLi4OTR5EWeA4Dp9++qnE6uzj7ghoV3opkpLcO4Y38fNTll4l\n0LGj82iNLVtKU+6Czma3eeQRoLAQqKiofbtmzZRTx5SiszbqkoeJE8U99oNYRZIgmZGlUqkwcuRI\n+Pj44NVXX8XLL7+MoqIiBAcHAwCCg4NRVFQEALh69aqFQaVWq2E0GiXR7Q5K8CWSu8bAwLYoLb2J\ngIA2uH27WGo5CkYvtQAX0EstoIGgl1qA13nkkUds1vXq1UuUtKurq/Hwww9DrVbjP//5jyhpEgRB\nEA0PyYys/fv3o1OnTvjjjz8QGxuLiIgIi/9VKhVUtYxtsPdfSkoKNA/6ioOCgjBgwACT0cAPMaNl\nZS+Xlt4EwKG0VGURyr2+6fGIrdd6iFZDTU+Qgsj6+DTrt7/j9MTWJ056tuVpOVWBXNITY9lgMGDl\nypUAYLpfK4klS5ZAq9WitLRUaikEQRCEjFFx1mP2JGDhwoXw9/fHV199BYPBgJCQEBQUFCAmJgZn\nzpwx+WbNmzcPADB69GgsXLgQ0dHRpjRUKpXN8EO5IWzkyBW5a2TG9W4AMW6fb5YWB0DcuiN2up5J\nT+wyBMQsR7NGvSjpyv2ceCpdMc+1p1DCvZvnypUrSElJwd/+9jf84x//sOnJkiovt28DW7cqZ1jR\n+fPA4cPs95AhbBJiMUhPZ35FY8eKkx4hLunp7Puppzw7cfCGDWy4oFKuB8IzVFYC69e7Vg88de+W\nJPBFWVmZ6S3g3bt3sX37dkRGRiIhIQGrVq0CwCaQHDduHAAgISEB3333HSorK3HhwgXk5uaaIhIS\nBEEQhDf4f//v/+Hvf/+7yc+LkB8KsdcJgmgESDJcsKioCE8//TQAFlL3+eefR1xcHB5++GEkJiZi\n+fLl0Gg0WLt2LQBAq9UiMTERWq0Wvr6+WLZsWa1DCeWKnHuIeJSgsTH6mIiPXmoBLqCXWkADQS9K\nKrw/JIBG6RO5ZcsWdOzYEVFRUXaHY/Kkpqaafuv1eoXcU71L9+6sJ+NBnCvRGD2aBc0g5MnQoezb\nk71YADByJFBd7dljEPKnaVNznbPGYDDUeh8XC1kMFxQDJQ05IeqPmMOplDXkS57peXa4oHjpyrkM\nPZmuUs61Eu7d8+fPx+rVq+Hr64uKigrcvn0b48ePxzfffGPahoYL1o30dHGHCxIEQdSHBjVcsLHi\nDavZXZSgsbHN++MZDFILcAGD1AIaCAapBTQIPvjgA1y+fBkXLlzAd999h8cff9zCwCIIgiAIIWRk\nEQRBEEQdUeKQdTmigE5MgiCIekHDBQmPIva8VjRcUF7p0XDBxlN3GvtwQVeg4YJ1Iz0diI4GevSQ\nWglBEI0ZT927JZsni2gcCOe1IgiCIMRHycEeWrWSWgFBEIRnoOGCXkQJ/k5K0Eg+JmJgkFqACxik\nFtBAMEgtgPAwLVoAEydKraLuTJwIBAdLrYIgCMIzKMbIyszMREREBMLDw7F48WKp5dSLEydOSC3B\nKUrQCChBo9xRQhkqQaMSoHIUg8uXLyMmJgZ9+vRB3759sXTpUqklWaCUqbuEL/KUolkKlPHCU1qo\njFyDykk6FHGLq66uxl/+8hdkZmYiJycH6enp+O2336SWVWdKSkqkluCQwMC2UKlUmDv3LamluIB8\ny1E5KKEMlaBRCVA5ioGfnx8+/vhjnD59GgcPHsTnn3+uyOeQ1FCDzzWonJxDZeQaVE7SoQgj69Ch\nQwgLC4NGo4Gfnx+ee+45bNq0SWpZDQred6qyskJqKQRBNAL4FztKISQkBAMGDAAA+Pv7o3fv3rh6\n9arEqgiCIAi5oggjy2g0okuXLqZltVoNo9Ho0WOuX78eEydOxOzZs1FaWipKmvn5+aKkA5gbKIGB\nbUVLUznkSy2gAZAvtQAXyJdaQAMhX2oBduFf7CiR/Px8HD9+HNHR0VJLIQiCIOQKpwDWrVvHvfTS\nS6bl1atXc3/5y18stunZsycH9sSmD33oQx/6KOTTs2dPbz9S3KK0tJQbNGgQt2HDBpv/6DlEH/rQ\nhz7K+3jqOaSIEO6hoaG4fPmyafny5ctQq9UW25w/f97bsgiCIIhGxP379zF+/Hi88MILGDdunM3/\n9BwiCIIgeBQxXPDhhx9Gbm4u8vPzUVlZiYyMDCQkJEgtiyAIgmgkcByHadOmQavV4o033pBaDkEQ\nBCFzFGFk+fr64rPPPsOoUaOg1WoxceJE9O7dW2pZBEEQRCNh//79+Pbbb7F7925ERUUhKioKmZmZ\nUssiCIIgZIqK4zhOahEEQRAEQRAEQRANBUX0ZNWG3CeI5KmurkZUVBTGjh0rtRS7lJSUYMKECejd\nuze0Wi0OHjwotSQbFi1ahD59+iAyMhLJycm4d++e1JIwdepUBAcHIzIy0rSuuLgYsbGx6NWrF+Li\n4iSfH82exjfffBO9e/dG//798cwzz+DWrVuy0sfz0UcfoUmTJiguLpZAmRlHGj/99FP07t0bffv2\nxdy5cyVSx7Cn8dChQ9DpdIiKisLgwYNx+PBhCRU6vl/L7ZqpD5mZmYiIiEB4eDgWL14stRyvo9Fo\n0K9fP0RFRUGn0wGo/bwuWrQI4eHhiIiIwPbt203rjx49isjISISHh2PmzJlez4eY1PX5UNcyuXfv\nHiZOnIjw8HAMGTIEFy9e9E7GRMReGaWmpkKtVpt6jH/88UfTf42xjID63TsbW1k5KiNJ65NHwml4\nkYKCAu748eMcx7GoT7169eJycnIkVmXLRx99xCUnJ3Njx46VWopdJk2axC1fvpzjOI67f/8+V1JS\nIrEiSy5cuMB1796dq6io4DiO4xITE7mVK1dKrIrj9uzZwx07dozr27evad2bb77JLV68mOM4jktL\nS+Pmzp0rlTyO4+xr3L59O1ddXc1xHMfNnTtXUo329HEcx126dIkbNWoUp9FouBs3bkikjmFP465d\nu7iRI0dylZWVHMdx3LVr16SSx3GcfY3Dhw/nMjMzOY7juG3btnF6vV4qeRzHOb5fy+2aqStVVVVc\nz549uQsXLnCVlZVc//79Zfkc8iT2rlNH5/X06dNc//79ucrKSu7ChQtcz549uZqaGo7jOG7w4MFc\ndnY2x3Ec98QTT3A//vijF3MhLnV5PtSnTD7//HNu+vTpHMdx3HfffcdNnDjRa3kTC3tllJqayn30\n0Uc22zbWMuK4ut87G2NZOSojKeuT4nuylDBB5JUrV7Bt2za89NJL4GQ4OvPWrVvYu3cvpk6dCoD5\nwLVu3VpiVZYEBgbCz88PZWVlqKqqQllZGUJDQ/8/e+ceF0X1///XInhBDcS7oKJiKopcBNHUxEup\nlHhX7JO31Mi8ZPXpU3YTv6WZdlMps36V+UnxQn5EDai8rJUGiKCZmCJKAirmNRERWOb3x2l2Z3dn\nd2eX2Z1deD8fDx7s7M7lNWfOzJz3eb/P+ygtC4MGDUKzZs30vtu9ezdmzJgBAJgxYwZ27dqlhDQt\nYhofeeQRuLmx2z8yMhJFRUVKSAMgrg8AXnjhBaxatUoBRcaIaVy/fj2WLFkCDw8PAEDLli2VkKZF\nTGPbtm21Xspbt24pfs+IPa+Li4ud7p6xlszMTAQEBMDf3x8eHh6IjY1FcnKy0rIcjuH7zdR1TU5O\nxtSpU+Hh4QF/f38EBAQgIyMDly9fxp07d7SesOnTp7tcXRBizfvBljIR7mvChAnYv3+/o05NNkw9\n/8XaSnW1jADrn511saxMlRGgXH1yeSNLiLNOEPn8889j9erV2kats3HhwgW0bNkSs2bNQlhYGObO\nnYuysjKlZenh4+ODF198ER06dEC7du3g7e2N4cOHKy1LlJKSErRu3RoA0Lp1a5SUlCisyDxffvkl\noqOjlZahR3JyMvz8/NC7d2+lpZgkLy8PP/30E/r164eoqChkZWUpLcmIlStXau+bl156Ce+8847S\nkrQIn9euds8YUlxcjPbt22uX/fz8tC/3uoJKpcLw4cMRHh6Ozz//HIDpZ+GlS5f0pmHhy8vwe19f\n31pXjnKWibDe8Z2jSodWy8W6desQHByM2bNna0PgqIwYUp6ddb2s+DLq168fAOXqk3O2+m2gtLQU\nEydOxJo1a9CkSROl5WjZu3cvWrVqhdDQUKf0YgFAVVUVsrOz8eyzzyI7OxuNGzfGypUrlZalR35+\nPj766CMUFBTg0qVLKC0txebNm5WWZRGVSgWVSqW0DJMsX74c9evXxxNPPKG0FC1lZWVYsWIFli1b\npv3OGe+dqqoq3Lx5E+np6Vi9ejUmT56stCQjZs+ejbVr1+LixYv48MMPtd5qpSktLcWECROwZs0a\nNG3aVO83Z79nxHA1vfbg8OHDyMnJQWpqKj7++GP8/PPPer+74nW1N1Qm4sybNw8XLlzA8ePH0bZt\nW7z44otKS3Iaatuz0x4Y2gNK1qdaYWRZmiBSSY4cOYLdu3ejU6dOmDp1Kg4cOIDp06crLUsPPz8/\n+Pn5ISIiAgAwceJEZGdnK6xKn6ysLDz00ENo3rw53N3dMX78eBw5ckRpWaK0bt0aV65cAQBcvnwZ\nrVq1UliROBs3bkRKSorTGav5+fkoKChAcHAwOnXqhKKiIvTp0wdXr15VWpoefn5+GD9+PAAgIiIC\nbm5uuH79usKq9MnMzMS4ceMAsPs6MzNTYUW65/W0adO0z2tXuWdM4evri8LCQu1yYWGhXk9oXaBt\n27YAWNjsuHHjkJmZafK6GpZXUVER/Pz84Ovrqxe6XFRUpHiIq9zIUSZ83fL19cXFixcBsE6f27dv\nw8fHx1GnYjdatWqlNRjmzJmjfW7V9TKy5tlZV8tKzB5Qsj65vJHFOfkEkStWrEBhYSEuXLiArVu3\nYujQodi0aZPSsvRo06YN2rdvj7NnzwIA9u3bh549eyqsSp/u3bsjPT0d9+7dA8dx2LdvHwIDA5WW\nJUpMTAy+/vprAMDXX3/tdIY/wDKhrV69GsnJyWjYsKHScvQICgpCSUkJLly4gAsXLsDPzw/Z2dlO\n1/AeO3YsDhw4AAA4e/YsKioq0Lx5c4VV6RMQEIBDhw4BAA4cOIAHH3xQUT2mnteucM+YIzw8HHl5\neSgoKEBFRQW2bduGmJgYpWU5jLKyMty5cwcAcPfuXfzwww8ICgoyeV1jYmKwdetWVFRU4MKFC8jL\ny0Pfvn3Rpk0bPPDAA8jIyADHcfjvf//rcnXBEnKUyZgxY4z2lZSUhGHDhilzUjJz+fJl7ef//e9/\n2syDdbmMrH121sWyMlVGitYnm9N4OAk///wzp1KpuODgYC4kJIQLCQlx2mxEarXaabMLHj9+nAsP\nD+d69+7NjRs3zumyC3Icx7377rtcYGAg16tXL2769OnarG5KEhsby7Vt25bz8PDg/Pz8uC+//JK7\nfv06N2zYMK5r167cI488wt28edOpNH7xxRdcQEAA16FDB+09w2fLUVJf/fr1tWUopFOnTopnFxTT\nWFFRwT355JNcr169uLCwMO7gwYNOoVFYF48ePcr17duXCw4O5vr168dlZ2crqtHU89rZ7hlbSElJ\n4R588EGuS5cu3IoVK5SW41DOnz/PBQcHc8HBwVzPnj2152/uui5fvpzr0qUL161bN20GTI7juKys\nLK5Xr15cly5duIULFzr8XOTE2veDtWVSXl7OTZo0iQsICOAiIyO5CxcuOPL0ZEHs/TRt2jQuKCiI\n6927NzdmzBjuypUr2vXrYhlxnG3PzrpWVmJllJKSomh9osmICYIgCIIgCIIgZMTlwwUJgiAIgiAI\ngiCcCTKyCIIgCIIgCIIgZISMLIIgCIIgCIIgCBkhI4sgCIIgCIIgCEJGyMgiCIIgCIIgCIKQETKy\nCIIgCIIgCIIgZISMLIIgCIIgCIIgCBkhI4sgCIIgCIIgCEJGyMgiCIIgCIIgCIKQETKyCIIgCIIg\nCIIgZISMLIIgCIIgCIIgCBkhI4sgCIIgCIIgCEJGyMgiCIIgCIIgCIKQETKyCMJJ8ff3x8qVK9Gz\nZ0/4+Pjgqaeewv3795WWRRAEQdQR6D1EELZDRhZBODFbtmzBDz/8gPz8fJw9exZvv/220pIIgiCI\nOgS9hwjCNsjIIggnRaVSYcGCBfD19UWzZs3w2muvITExUWlZBEEQRB2B3kMEYTtkZBGEE9O+fXvt\n5w4dOuDSpUsKqiEIgiDqGvQeIgjbICOLIJyYixcv6n1u166dgmoIgiCIuga9hwjCNsjIIggnheM4\nfPLJJyguLsaNGzewfPlyxMbGKi2LIAiCqCPQe4ggbIeMLIJwUlQqFZ544gk8+uij6NKlC7p27YrX\nX39daVkEQRBEHYHeQwRhO05jZGk0GoSGhmL06NFGv6nVanh5eSE0NBShoaGU2YaoM0RERODUqVO4\nefMmvvrqKzRs2FBpSQRR63jqqafQunVrBAUFmVxn0aJF6Nq1K4KDg5GTk+NAdQShLPQeIgjbcBoj\na82aNQgMDIRKpRL9ffDgwcjJyUFOTg71ohAEQRCyMWvWLKSlpZn8PSUlBefOnUNeXh4+++wzzJs3\nz4HqCIIgCFfEKYysoqIipKSkYM6cOeA4TnQdU98TBEEQRE0YNGgQmjVrZvL33bt3Y8aMGQCAyMhI\n3Lp1CyUlJY6SRxAEQbgg7koLAIDnn38eq1evxt9//y36u0qlwpEjRxAcHAxfX1+89957CAwMdLBK\ngnAsFy5cUFoCQRAAiouL9dJY+/n5oaioCK1bt1ZQFUHYH3oPEYTtKG5k7d27F61atUJoaCjUarXo\nOmFhYSgsLISnpydSU1MxduxYnD17Vm+dFi1a4Pr16w5QTBAEQchFly5dcO7cOaVlWMQwmkIstD0g\nIAD5+fmOkkQQBEHIgL3eQ4qHCx45cgS7d+9Gp06dMHXqVBw4cADTp0/XW6dp06bw9PQEAIwaNQqV\nlZW4ceOG3jrXr18Hx3Eu9zdjxgzFNZBu5/9zJt25uRzGj+fg68th+XIOxcXG69y9y+HDDzk0ajQD\nCxdyqKxUXrerlndt1sxxnEsYJb6+vigsLNQuFxUVwdfX12i9/Px8xcvT2f+WLl2quAZX+KNyojKi\ncnLcn73eQ4obWStWrEBhYSEuXLiArVu3YujQodi0aZPeOiUlJeA41ouYmZkJjuPg4+OjhFzZ8ff3\nV1qCTZBux+IMuk+eBGbNAgYPBvr1A/LygFdfBcTmpfT0BBYvBhYt8seZM8BjjwG3bjles604Q3lb\niytqdhViYmK076X09HR4e3tTqCBBEARhFsXDBQ3hQzA2bNgAAIiLi0NSUhLWr18Pd3d3eHp6YuvW\nrUpKJIg6w717QGoqkJAA/PEHMG8ecPYs4O0tbfuGDYHvvgOefx7o35997tzZvpoJwlqmTp2KQ4cO\n4dq1a2jfvj2WLVuGyspKAOwdFB0djZSUFAQEBKBx48b46quvFFZMEARBODtOZWQNHjwYgwcPBsBe\nbDzz58/H/PnzlZJlV7yltladDNLtWBypu7QU+N//2N/+/UBoKPD008DEiUD9+tbty9vbG+7uwLp1\nzFAbPBj4/nvA2fPWuGI9cUXNzkJiYqLFdRISEhygpPYTFRWltASXgMrJMlRG0qByUg6nMrLqIiEh\nIUpLsAnS7VgcoTsrC/j8c2DHDmDAAGZUffYZ0KKF7fsU6l6wgHnAhg1jHq2wMBlE2wlXrCeuqJmo\ne1CDTxpUTpahMpIGlZNyqDh+sJOLo1KpUEtOhSAcyt9/Ay+8wDxM8+YBM2eKj7OSi//9D4iLA779\nFhg0yH7HIVyD2vTsrk3nQhByotEA27cDU6cqrcS1qaoC3Mk9Ijv2enYrnvgCADQaDUJDQzF69GjR\n3xctWoSuXbsiODgYOTk5DlZHELWXAweA3r0BNzfg1CnTiSzkZNw4YPNmYMIEICnJvsciCIIglKe6\nWmkFNYPjnOMcduxgIf2Ea+AURtaaNWsQGBgoOu9ISkoKzp07h7y8PHz22WeYN2+eAgrth6m5wZwd\n0u1Y7KF7wwbgySeB9etZWOADD8h+CJO6H3kE+OEHloHwo4/kP25NccV64oqanYW0tDR0794dXbt2\nxbvvvmv0+82bNzFu3DgEBwcjMjISp06dUkBl3aSqijVwCUJJcnKYJ84Z+CcnD+ECKG5kFRUVISUl\nBXPmzBF11e3evRszZswAAERGRuLWrVsoKSlxtEyCqFV8+CGwciXw88/AqFHKaAgJAQ4fZgbewoX0\n4iCUQaPRYMGCBUhLS0Nubi4SExNx+vRpvXVWrFiBsLAwnDhxAps2bcJzzz2nkNq6x44dbLoIwnm4\nfBkwmKrULJWVLFzQlbl1i4x9wnoUN7Kef/55rF69Gm5u4lKKi4vRvn177bKfnx+KioocJc/uuOqA\nRNLtWOTUvXw58MknwKFDQJcusu1WFEu6O3YEjhwBzp9nCTGuXLGvHqm4Yj1xRc3OQGZmJgICAuDv\n7w8PDw/ExsYiOTlZb53Tp09jyJAhAIBu3bqhoKAAf/31lxJy6yR37yqtwLX59VdAMJd2jVGrWQeZ\nGBwHFBfrf5eUBKSny3d8gvHjj8CxY0qrIMyh6PC5vXv3olWrVggNDTUb6mLo4RILKwSAmTNnaifk\n9Pb2RkhIiLbhwe+flmm5Li9nZUXhm2+AlSvVOH8e6NBBeX3e3sCLL6rx9ddAREQUduwAysuV4UDk\nQwAAIABJREFU00PL9l1Wq9XYuHEjAOeYQFmsIy8jI0NvneDgYOzcuRMDBw5EZmYm/vzzTxQVFaFl\ny5Ym93v/PrBzp+sP9C8tBZo0UVpF3YLjABPNHNy8yYxOPz/T21++zMK/GzdmywUFQEUFIKjmNnH9\numWD96+/gJ9+Mq73towjunMHaNpUt8yPi6pXz/p91UauXWPPGcKJ4RRkyZIlnJ+fH+fv78+1adOG\n8/T05KZNm6a3TlxcHJeYmKhd7tatG3flyhWjfSl8KjZz8OBBpSXYBOl2LHLo3rSJ4zp04LiLF2uu\nRyrW6k5O5riWLTnu0085rrraPpqk4Ir1xBU1c5zyz+6kpCRuzpw52uX//ve/3IIFC/TW+fvvv7lZ\ns2ZxISEh3LRp07iIiAjuxIkTRvsSnsutWxy3ZYv+7+XlytZrni1bOK60VPq6V6/aV4+l42dn27bt\nX39x3I4duuXjxzlu1y5p296/z3FlZdLWra7muIoK6/WZYssWjisuFv8tLc24Xoltf+iQ/rJaLb7u\nX39x3J070nSlprJ9bdnCcbt36/926RIrg5ISY31btrDrYEn333/r6mVFhfH66ekct22bNK0cx3Hn\nznHc9u3S1zfHvn2W9TuCLVs47sYN3ec9e0yvu38/x12/Lm2/e/fa59lUXS2tzXHunPzHtgZ7vYcU\n9WStWLECK1asAAAcOnQI7733HjZt2qS3TkxMDBISEhAbG4v09HR4e3ujdevWSsglCJclNRV46SXg\n4MGa92bak5gY4JdfWAbCzEzg44+Bhg2VVkXUZnx9fVEoiKUqLCyEn4GboGnTpvjyyy+1y506dULn\nzp1F9xcfHw8AKC8HPDyiAERpf9u5k41F7NHDvKbKStaL7+3NMn/WlPv3gdxclkmU9wKUl+s8HZZQ\nejyNKa+OJW7c0B/r+ddfQFmZ+LqGXpN9+9h3U6ZYPs5vv7HyFfNalpay/bRta/54YttJpbKSXU9z\n+zPFjz8CzZsDjz5q/bZC1GrmPevbV/x3PjPfkSPAQw+Jr7N3L3vejxsnPv7p9m3r6uK1ayxxSl2l\npIR5NX18LK/799/28RLeuMHe6eY8+hUV7H1vzfCFnTuBxx8H6te3TZdardZGWNgTxcdkCeHDADds\n2IANGzYAAKKjo9G5c2cEBAQgLi4On3zyiZISZYcPp3E1SLdjqYnurCxgxgw2P5Wlxp3c2KL7wQeB\njAzWCBk61LoB1nLhivXEFTU7A+Hh4cjLy0NBQQEqKiqwbds2xMTE6K1z+/ZtVFRUAAA+//xzDB48\nGE1MxNDFx8cjPj4eS5bEIzAwyuj3e/fY/+vXTWs6fpzNW7dtm2X9UtJKX74M/PEHmwScR61mWv45\nLbNwHGvcJiZaXtcSFRXy7KemZGToX4O9e5kxymczLC+XnrKbD6G7cAE4d07/t8xMVtZCrl9nxwNY\nWVy9Kr5fqcmAjh7V7c8arL0OlhI//P235W0KC4GTJ03vw9CIcmQiz8xMZpip1YCjhv5XVLAQULm4\ncoXVWyn3tRwkJgL5+aZ/P3LE8j74ei419JHj2Lo1GasZFRWlfVbzHWP2wGmMrMGDB2P37t0AgLi4\nOMTFxWl/S0hIwLlz53DixAmEhYUpJZEgXI7CQmDsWJbBr39/pdVIp0kT1sAcMAB4+GHjgdQEIRfu\n7u5ISEjAiBEjEBgYiClTpqBHjx56nX25ubkICgpC9+7d8f3332PNmjU2H4/3rvzwg/735eW6DgWp\njfurVy0bYkLPjbBRUlHBGpTffsv+m+PWrZpn/7x6lRl1pjwLv/5q/hhVVcy7JBfnz7NrIPQYcRzL\nZnj2rG3es/R0ZvCYOseSEpYp0dCQEMuhUloqPo+gWOPZnLfm7Fn9ZY3GeH2pWfNMNdyFHWF8IgaN\nhiVX2rpVf93qauD33y0fiy//335j10RITg7rOJDK7dvApUuWG/H5+ezv8mXgzz/ZdyUlzJAwvEeu\nXwcMkpAa8d13TL85cnKAtDTz60ihuprdPwcPMu3ffmu8jkbDnjNi29aEzEzTvwnvrxMnjDshADZm\nEGAdq/fuGXdKGGLK8M7MdL5EIE5jZNVVHOGutAek27HYovvOHeZOf/55ZmgpQU3KW6UCVq8Gpk8H\nBg50bBpnV6wnrqjZWRg1ahTOnDmDc+fOYcmSJQD0O/v69++PM2fO4I8//kBSUhK8vLxsPtZffxk3\nNK5dY57m779n9fz8ed1vt26Z3pfQgCoqYj3iJ0+yhiFvxCUnm26E37nD/ltqfAq3z81ljU6hYZCX\nx7wYhqF4hYWsYXX0KLB/P2tQmqKggDWGxaiuZvsXM0ZMbSPElKcIAPbsMf5OrIf8xAldeVlixw5x\nwzQnh0UWGCIWGmi4LT9zjbkwQjFPEt/o5K/x/v3GRosUI6u01HSo5fff6z7zXpmkJGbYSD0Gf75i\nhrZh/f3jD2Pj0RyHDzODb+dO0+fAI7z3AF1WRkNDJDeXeZx5iov1zxdg1+PUKVZvjx1jGgwx3G9i\nouUODbGy3LZN3CgXcuwYe84I9yPVG/Tjj0B2trR7gPd6C+vq7t2szI4e1V9XaMD++CMz2C9fNu/d\nE2rOztZ56PPzWb0w7Ay4e9c2T68ckJFFELWQqiogNhbo1w944QWl1dSM//wHeO01luLd8CVGEM7I\n3bu6hqfYGBK+kVRVZdy7fOaM/ro//aT7XFFhugH2888sBO7331njTxgKZ6qBe+IE+2/KKyAMJ+P3\n8ccfuuPxZGWxho7QYCktZQ3M3Fzx3msp8A26M2d05Xn4sE5XdTWQkqILwTTF/v3WHVesvHJzWUNN\n+AwyZ5ymprJGr7XTevLXV+hJu3EDOHDA9Pp8HROGgxrC1wf+v7Ce8efLH/vqVeN6ZliPLY0Zs8Y7\nUlJi2UBITbV8PMOGOV+GwrI0mJ3BIoade4blwt9DP/0kbkQBzAN79iyrO4blJuYxNTQqt29noag8\nBw5InxhZ2AlheJ+cO8eMH55fftF/Ngm5do3VmePHWUiiuVBT3rMpNPqFhlFVlf6ymOFm6N3jQ5aL\nivTvu6tXjTsXDDsybt7UHeOnnxwXCgo4gZFVXl6OyMhIhISEIDAwUNuLKEStVsPLywuhoaEIDQ3F\n22+/rYBS++Cq4yhIt2OxVvcbb7CGW0KC7YPG5UCu8p4zB5g3j3nmbEkFbC2uWE9cUbPclJWV4Yyh\nlaIAf/2la5Ru3266B33HDtYwEzbcDBscd+/qGqzffssao4aNSb5hZOvYjrw8/ePyDRox+OeJmIFR\nXa1rYO3ZY6znzz/Fe855j4CwYZ6dLd77fPGiTiMfKlldzRp9hgaNsMwrKnQ93KZ64/lz48dkAcYN\nUz5ELDGRXVf+vCzBpx/ntQj3JTTqeINXrPyFDdvff2fakpJsm18wO1t/WRieuH8/MyqFyD02Ni9P\nVx6GIWy5ucZG161b+vXj0iV9Q+D0adYw/+svVk6Jiaa9nDdvsjonNFwMjUJToZFJSfrbiYWy82F7\nYgg1378vfpyKCn3jTqPRn2dMaFgL6xWPsIOF9+Levq1bj69H/LH5d+qlS6xsTp/WeTtv3dLv6FGp\nTJ+bIaa88Gq1vnFniZ07Wdjp9u2sc4cvc8M6zMPfs3fuGBt6xcXG96s9x4gqml0QABo2bIiDBw/C\n09MTVVVVGDhwIH755RcMHDhQbz3hmC2CIEyzZw+weTMLDfDwUFqNfLzyCut5e+IJFvJAc6UQQnbv\n3o2XXnoJ9+/fR0FBAXJycrB06VJF3hu8IcBz/75+D7qwUX3njn7YkRjbtgENGuiW09L0s3UZjnsx\nl4HNVIM8IwMYPpx9zsrS9z5VVQG7drHPwgaxYUMXYB4nc5nE+HChoiLWWOzUSedV2b8f8PUFAgKM\nPXqGCM+ZDze6dg1o3Zpte+mS/rkKx6gYGm/mQtl27QIee0y3fPWqfkeP1BDC69d1jX6+cSs0Lisq\nmJeOf2bz41RMcfKk+NgmYYNRaiKB8nKdoc9PMmx4XQ3DvHhsnZM7Kwto1oz9GRpZppLCGBrtFRXM\n+GvaVHeuwnF7fL0w7GgUekk8Pdm9Zegpu3JF57U1JD2dZWQE2DUV1p8zZ1jIobkw38REYOJEnZEO\nsLoQEMA+88lBOnTQv5fFvNilpcbjMoUGGm9wpKToryPsgBCONxOGfnKccbkI64XQ2Lx3j3nYhg7V\nfcd7+Qzh6wxfVy3dQ6bq8dWr4hk1ecMqNZWVX5s2bJkPnb14kY33dgSKe7IAwNPTEwBQUVEBjUYD\nH5F8k5zUkZkuhquOoyDdjkWq7vPnmddn2zbAzDypDkPO8lapgE8/ZS+Hl16SbbeiuGI9cUXNchIf\nH4+MjAw0a9YMABAaGorzhgMsHADHmR8DBBgP3JYSWiXW0DAVisY35sUGuZvqhRY2lg3D+0yN6Tp0\nSNcoF2LOs8Nry8hgjdXERP3zLy423UNtCsMMZtnZtnl3+EavYbUxbNwLl6UaMj/+qPtsmEThzz9Z\ng/TKFePf1GrTXiRLzSJhIx4w3WNfXq47Z76DwHDMk6mICKnJSMTq+MWLrCFseL2lpmnnOGaYZ2eb\nj9gwt78DB0x7vMyNIRQagkJvVna2eQOLx7DenD6tu2/4urtzp37njKlwRFtITdVdc0OvJY/YfSgM\nLRZ613btYsbNrl2WO43MIfSaAeavASBuoJWXs7rOX3e+PIXXqaDAtmeEtTiFkVVdXY2QkBC0bt0a\nQ4YMQWBgoN7vKpUKR44cQXBwMKKjo5FrqkYQRB2mvByYNAl49VXXyiRoDR4eLIxh507lBrISzomH\nhwe8vb31vnOTOMlUWloaunfvjq5du+Ldd981+v3atWsYOXIkQkJC0KtXL2zcuNHkvs6dszxw3bDR\nZ2tIryUbkm+wiCVbEOP+fTYuwxBrx1SZS9HNY+6cpXqH5MIwZMvw+hgacULj1Zb+XzEjnC9jw/DE\ny5cdMxZVLJHEr7/qGtpi5ykcl2cLZ86IX2upmQN5J7VGYz6M3JIhXNPsetbUAb5uiRlMfCZCU95B\nW72GYlRW6srM1P1ma/ixpWyl1mDKm2iIteGsv/7KOnrsPY+a4uGCAHsRHj9+HLdv38aIESOgVqv1\nxheEhYWhsLAQnp6eSE1NxdixY3FW5Ikwc+ZM+Pv7AwC8vb0REhKi3Q/fy0vL8izz3zmLntq+zH9n\nbv3164FOnaKwaJHyeu253KwZ8MILakyfDpw6FYW2bal+88tC7c6gR2xZrVZrjRT+eS0HPXv2xObN\nm1FVVYW8vDysXbsWD5ma9VSARqPBggULsG/fPvj6+iIiIgIxMTHoIZhULiEhAaGhoXjnnXdw7do1\ndOvWDU8++STc3Y1foVINGh6VSro3RIiUcQTWNiCysnTZ1OyNLedsibNngZ49rd/O0ONjCeE1Fsvo\nZwlrjUilprAoKGBh2WFh4t4gRyYQMIel8rFU14QeGVuwxmHOG5CO7EgQe1ZIMQzlNOpsQUpnDY8w\nzFEqZWXGmTblRsU5WRzeW2+9hUaNGuHf//63yXU6deqEY8eO6YUVqlSqWhtSSBCWOHyYebF++w1o\n0UJpNY5h6VL2ckxNBdycwidP2IJcz+67d+9i+fLl+OGf3OUjRozAG2+8gYYNG5rd7tdff8WyZcuQ\n9s9AjZUrVwIAXnnlFe06GzZswG+//YaPP/4Y58+fx8iRI0U7+lQqFbZsofdQkyaOSVAjxtSpjp3s\nODjY9NgTJfH1lc84e+wx85kL6zoeHjWfR45QlieesI8NoXjT5Nq1a7j1TwDrvXv38OOPPyI0NFRv\nnZKSEu3JZ2ZmguM40XFbrohhD7SrQLodizndZWXArFnAxx87n4Flz/J+4w3WG1iDeWFN4or1xBU1\ny0njxo2xYsUKZGVlISsrC8uXL7doYAFAcXEx2rdvr1328/NDsUHrdO7cuTh16hTatWuH4ODgGk1G\nXBdQysACLE9kKjfOaGAB8nq/yMAyj5IZfAnnRvFwwcuXL2PGjBmorq5GdXU1pk2bhmHDhmHDhg0A\n2ISQSUlJWL9+Pdzd3eHp6YmthqmUCKIO89prQEQEMG6c0koci7s7y6LYty8wciQgiO4i6iBDhgwx\n+k6lUuGAqQmGBOtYYsWKFQgJCYFarUZ+fj4eeeQRnDhxAk1FUlslJcVrPwcGRiEwMMri/gn5kDqe\nhyDkwlS6d8J5yc1VIzdXbffjyBIuePLkSQQFBcmhx2YoXJCoi/zyCzBlCgsT5FPK1jU+/RT46isW\nMikyRIZwcuR6dmcJBsqUl5fj22+/hbu7O1avXm12u/T0dMTHx2vDBd955x24ubnh5Zdf1q4THR2N\n1157DQP+yfs7bNgwvPvuuwgPDzc6FwoXJAiCcC2cOlxw3rx5iIiIwCeffILbpnJhEgQhK/fvA3Pn\nAmvX1l0DCwDi4tgYkPffV1oJoSTh4eHav4EDB+LDDz+UFEIZHh6OvLw8FBQUoKKiAtu2bUNMTIze\nOt27d8e+f3JVl5SU4MyZM+jcubM9ToMgCIKoJchiZP3yyy/YvHkzLl68iLCwMEydOlU7+Jgwj6uO\noyDdjkVM97vvAt26AePHO16PVBxR3ioV8MUXwHvvmZ7vw1pcsZ64omY5uXHjhvbv2rVrSEtLw98S\nUr+5u7sjISEBI0aMQGBgIKZMmYIePXpgw4YN2rD1V199FVlZWQgODsbw4cOxatWqWjMumCAIgrAP\nsgXXPPjgg3j77bcRHh6ORYsW4fjx46iursaKFSswYcIEk9uVl5dj8ODBuH//PioqKjBmzBi88847\nRustWrQIqamp8PT0xMaNG42SYxBEXeKPP5gHKyeHBt0CgL8/8NZbLAEIhQ3WTcLCwrTjq9zd3eHv\n748vvvhC0rajRo3CqFGj9L6Li4vTfm7RogX27Nkjn1iCIAii1iPLmKwTJ05g48aN2Lt3Lx555BHM\nmTMHYWFhuHTpEvr164eL/BTiJigrK4OnpyeqqqowcOBAvPfeexg4cKD295SUFCQkJCAlJQUZGRl4\n7rnnkG4wsQGNySLqCtXVQFQUS9m+cKHSapwHjgOGDwdGjAD+8x+l1RBSqU3PbhqTRRAE4XrYa0yW\nLP29ixYtwuzZs7F8+XJ4enpqv2/Xrh3efvtti9vz21RUVECj0RiFYezevRszZswAAERGRuLWrVso\nKSlB69at5ZBPEC7F//t/bDzWs88qrcS54MMGIyKA0aMp22Bd4dtvvzWbIXC8M8fTEgRBELUWWcZk\nfffdd/jXv/6lNZY0Gg3u3r0LAJg+fbrF7aurqxESEoLWrVtjyJAhCAwM1PtdbB6TImeZaryGuOo4\nCtLtWHjdBQUsZfuXXwL16ikqSRKOLm9/f+D//o+FDWo0tu/HFeuJK2qWgz179pj9IwiCIAglkMWT\nNXz4cOzbtw9NmjQBwML/RowYgSNHjkja3s3NDcePH8ft27cxYsQIqNVqREVF6a1j6MaTMrcJQdQm\nqquBp54C/v1voGdPpdU4L3FxwI4dwIcfsrIiajcbN25UWgJBEARBGCGLkVVeXq41sACgadOmKCsr\ns3o/Xl5eeOyxx5CVlaVnZPn6+qKwsFC7XFRUBF9fX6PtZ86cCX9/fwCAt7c3QkJCtPvhe3lpWZ5l\n/jtn0VPblwHg+efVuHcvCv/+t/J6nHnZzQ2YO1eNZ54BYmKi8OCDdad+C7U7gx6xZbVarTWM+Oe1\nXOzduxe5ubkoLy/Xfvfmm29K2jYtLQ2LFy+GRqPBnDlz9ObJAoD33nsPmzdvBgBUVVXh9OnTuHbt\nGry9veU7AYIgCKLWIEviiwEDBmDt2rXo06cPADYp5MKFC/Hrr79a3PbatWtwd3eHt7c37t27hxEj\nRmDp0qUYNmyYdh1h4ov09HQsXryYEl8QdYpz54D+/VnmvAcfVFqNa7B2LfNoHToEuMkSGE3YA7me\n3XFxcbh37x4OHDiAuXPnYseOHYiMjJSUYVCj0aBbt27Yt28ffH19ERERgcTERPQwMbBv7969+Oij\nj7RzZwnPhRJfEARBuBZOPRnxRx99hMmTJ2PgwIEYOHAgpkyZgnXr1kna9vLlyxg6dChCQkIQGRmJ\n0aNHY9iwYXpzlERHR6Nz584ICAhAXFwcPvnkEzlkOwWGPdCuAul2HOXlQHS0Gm++6XoGlpLlPX8+\nG5f16afWb+uK9cQVNcvJkSNHsGnTJvj4+GDp0qVIT0/HmTNnJG2bmZmJgIAA+Pv7w8PDA7GxsUhO\nTja5/pYtWzB16lS5pBMEQRC1EFnCBSMiInD69GmcOXMGKpUK3bp1g4eHh6Rtg4KCkJ2dbfS9cI4S\nAEhISJBDKkG4HM8/D7RtCyxYoLQS16JePZZt8OGHgcceAzp2VFoRYU8aNWoEgGWrLS4uRvPmzXHl\nyhVJ24olV8rIyBBdt6ysDN9//32t6uwjCIIg5Ee2KTuzsrJw4cIFVFVVaY0mKZkF6zrCMSCuBOl2\nDN98Axw4ABw9GuWSkw4rXd49ejAjNS4OSE2VPnGz0rptwRU1y8njjz+Omzdv4qWXXtKGrs+dO1fS\nttYkUtqzZw8GDhxocixWUlK89nNgYBQCA6Mk75sgCIKwP7m5auTmqu1+HFnGZD355JM4f/48QkJC\nUE+QV1pqyKAc0JgsorZx6hSbdPjAASAoSGk1rktlJRAeDrzyCkARXs6HPZ7d5eXlKC8vl5yUIj09\nHfHx8UhLSwMAvPPOO3BzczNKfgEA48aNw5QpUxAbG2v0G43JIgiCcD2cekzWsWPHcPjwYXzyySdY\nt26d9o+wjKuOoyDd9uXGDWDsWOCDD5iB5Sq6DXEG3R4ewIYNwAsvADdvStvGGXRbiytqlpPevXtj\nxYoVyM/PR8OGDa3K+hceHo68vDwUFBSgoqIC27ZtQ0xMjNF6t2/fxk8//YQxY8bIKZ0gCIKohchi\nZPXq1QuXL1+2advCwkIMGTIEPXv2RK9evbB27VqjddRqNby8vBAaGorQ0FC8/fbbNZVMEE5LZSUw\neTIwZgwwbZrSamoH/foB48czbxZRO9m9ezfq1auHyZMnIzw8HO+99x4uXrwoaVt3d3ckJCRgxIgR\nCAwMxJQpU9CjRw+9BEwAsGvXLowYMUI7/osgCIIgTCFLuGBUVBSOHz+Ovn37okGDBmzHKhV2795t\ncdsrV67gypUrCAkJQWlpKfr06YNdu3bppc5Vq9X44IMPzO6PwgWJ2sLChUB+PrBnD0veQMjD7dtA\nYCCwfTswYIDSaggeezy78/Ly8NZbb2Hz5s3QaDSy7tscFC5IEAThetgrXFCWxBfx8fEA9F+WUgcS\nt2nTBm3atAEANGnSBD169MClS5eM5ichA4qoC2zYAOzbB6Snk4ElN15ewIcfsiQYOTksjJCoXRQU\nFGDbtm3Yvn076tWrh1WrViktiSAIgqijyBIuGBUVBX9/f1RWViIqKgp9+/ZFaGio1fspKChATk4O\nIiMj9b5XqVQ4cuQIgoODER0djdzcXDlkOwWuOo6CdMvPvn3A0qXMg+Xlpf+bM+s2h7PpnjQJaN8e\n+Ogj8+s5m24puKJmOYmMjMS4ceNQXV2NHTt2IDMzEy+++KLSsgiCIIg6iiyerM8++wyff/45bty4\ngfz8fBQVFWHevHnYv3+/5H2UlpZi4sSJWLNmDZo0aaL3W1hYGAoLC+Hp6YnU1FSMHTsWZ8+eNdrH\nzJkz4e/vDwDw9vZGSEiINq0x3wBxtmUeZ9Ejdfn48eNOpcfVy/vrr9VYvBhITo5CQACVtz2X160D\n+vRRo2NHYPJk8fVdsbyPHz/uVHpMLavVamzcuBEAtM9rOfj666/RvXt32fZHEARBEDVBljFZwcHB\nyMzMRL9+/ZCTkwOATTJ88uRJSdtXVlbi8ccfx6hRo7B48WKL63fq1AnHjh2Dj4+P9jsak0W4Kn/9\nxRIzLF0K0NRyjmHZMuC334Bvv1VaCeEMz+60tDQsXrwYGo0Gc+bMEU3drlar8fzzz6OyshItWrQw\n6kgAaEwWQRCEK+LUKdwbNGigTXgBAFVVVZLHZHEch9mzZyMwMNCkgVVSUqI9+czMTHAcp2dgEYSr\nUlbGsgjGxpKB5UhefpkZWSkpSishlEaj0WDBggVIS0tDbm4uEhMTcfr0ab11bt26hfnz52PPnj34\n/fffkZSUpJBagiAIwlWQxcgaPHgwli9fjrKyMvz444+YNGkSRo8eLWnbw4cP45tvvsHBgwe1KdpT\nU1P1UucmJSUhKCgIISEhWLx4MbZu3SqHbKdArDfUFSDdNaeqihlXAQGApVkJnEm3NTir7oYNgY8/\nZpkc790z/t1ZdZvDFTU7A5mZmQgICIC/vz88PDwQGxuL5ORkvXW2bNmCCRMmwM/PDwDQokULJaQS\nBEEQLoQsY7JWrlyJL774AkFBQdiwYQOio6MxZ84cSdsOHDgQ1dXVZteZP38+5s+fL4dUgnAKOA6Y\nNw+4fx/44gtAouOXkJFHHwX69AFWrmThg4Rrc/fuXXzwwQe4ePEiPv/8c+Tl5eHMmTN4/PHHzW5X\nXFyM9u3ba5f9/PyQkZGht05eXh4qKysxZMgQ3LlzB8899xym0SR2BEEQhBlkMbLq1auHp59+Gk8/\n/bQcu6tT8APDXQ3SXTPi41ka8YMHpaUSdxbd1uLsuj/4AAgJAZ58EujaVfe9s+sWwxU1y8msWbPQ\np08fHDlyBADQrl07TJw40aKRJSW0vbKyEtnZ2di/fz/KysrQv39/9OvXD12FleYfkpLitZ8DA6MQ\nGBhl1XkQBEEQ9iU3V43cXLXdjyOLkdWpUyej71QqFc6fPy/H7gmiVrFuHbBlC/DLL0DTpkqrqdv4\n+QFLlgALFgBpaeRRdGXy8/Oxfft2bTh548aNJW3n6+uLwsJC7XJhYaE2LJCnffv2aNGiBRo1aoRG\njRrh4YcfxokTJ0SNrIkT420/CYIgCMLuGHaA7dxpn3AWWcZkHT16VPv3888/47nnnsMmNx4fAAAg\nAElEQVS//vUvSdsWFhZiyJAh6NmzJ3r16oW1a9eKrrdo0SJ07doVwcHB2gyGtQFXHUdBum1jyxbg\n3XeBH34AWreWvp3Sum3FFXQvWgQUFwPCXAauoNsQV9QsJw0aNMA9wQC7/Px8vYRMpggPD0deXh4K\nCgpQUVGBbdu2ISYmRm+dMWPG4JdffoFGo0FZWRkyMjIQGBgo+zkQBEEQtQdZPFmGg4AXL16MsLAw\nvPXWWxa39fDwwIcffoiQkBCUlpaiT58+eOSRR9CjRw/tOikpKTh37hzy8vKQkZGBefPmIT09XQ7p\nBOEw0tKA558H9u8HRJy/hEJ4eADr1wNTpwIjR5J30VWJj4/HyJEjUVRUhCeeeAKHDx/WzsdlDnd3\ndyQkJGDEiBHQaDSYPXs2evTooU28FBcXh+7du2PkyJHo3bs33NzcMHfuXItGlkrFxl4SBEEQdRNZ\n5sk6duyYNq69uroaWVlZWL9+PU6cOGH1vsaOHYuFCxdi2LBh2u+eeeYZDBkyBFOmTAEAdO/eHYcO\nHUJrgSvAGeZaIQhTnDgBDB8OJCcDDz2ktBpCjOnTgTZtgFWrlFZSt5Dz2X3t2jVtB1y/fv0cngVQ\nOE9Wly5Afr5DD08QBEHYgL3myZLFk/Xiiy9qjSx3d3f4+/tj+/btVu+noKAAOTk5iIyM1PteLPtT\nUVGRnpFFEM7K5ctATAxLGU4GlvOyahXQqxcwaxYgcKQTTo6wkw8A2rZtCwC4ePEiLl68iLCwMEV0\nUZ8fQRBE3UYWI0uOsQClpaWYOHEi1qxZgyZNmhj9bmhhimWEmjlzJvz9/QEA3t7eCAkJ0Wbc4jU6\n2zL/nbPokbr80UcfuUT5Kl3ekZFRGDsWGDpUjVatAMC2/VF5O2b59dejsHAh8NhjHyE01LXK+/jx\n49oJ3Z1Bj6lltVqtDePjn9c1QdjJJ8bBgwdrfAyCIIjajKcnUFamtIrahyzhgu+//77RS47frUql\nwgsvvGB2+8rKSjz++OMYNWqUtpEg5JlnnkFUVBRiY2MB1K5wQbVarW2IuBKk2zIcB/D5XzZvrlnm\nOipvx1BVBYSFAePHqxEfH6W0HKtwtbLmcdVntxgULkg4mvr1gYoK3fKwYWzcL2FffHyAGzcce8zG\njYG7d+2zb29v4NYt++zbFbBXuKAs2QWPHTuG9evXo7i4GEVFRfj000+RnZ2N0tJS3Llzx+y2HMdh\n9uzZCAwMFDWwACAmJgabNm0CAKSnp8Pb27vWhAq6YqMIIN1S+OwzIDdXnsmGqbwdg7s7kJAAfPll\nFEpLlVZjHa5W1nJz7949vP/++xg3bhzGjx+PDz/8EOXl5UrLIgiHwiImlKdXL6UV2BclpvsIDnb8\nMQHXmdqkWzelFRgjS7hgYWEhsrOz0fSftFzLli1DdHQ0Nm/ebHHbw4cP45tvvkHv3r0RGhoKAFix\nYgUuXrwIgGV2io6ORkpKCgICAtC4cWN89dVXcsgmCLtx8iTw+utsLqxGjZRWQ1jDww+zvxUr2B/h\nGkyfPh0PPPAAFi1aBI7jsGXLFkybNg07duyQtH1aWhoWL14MjUaDOXPm4OWXX9b7Xa1WY8yYMejc\nuTMAYMKECXj99dfN7rNRI0CQVV4WnLXHuU0b4MoVpVXUPZy1AVy/vtIKpNO8OXD9unXbtGlj/TY1\nReLUf7ITGwskJipzbFdHFiPr6tWr8PDw0C57eHjg6tWrkrYdOHAgqqurLa6XkJBgsz5nxlVDfEi3\nae7eBSZPBt5/X76eFSpvxzJ2rBrPPBOFp54CAgKUViMNVy1ruTh16hRyc3O1y0OHDpU8l5VGo8GC\nBQuwb98++Pr6IiIiAjExMXpTiQDA4MGDsXv3bkn7tFfjd9Qo52zwtGtHRpYpGjYE7OVUrV8fuH/f\n/DrjxwM7d9rn+HWV3r2BU6eUVuFYHnwQOHtWt9y5M3D+vHJ6XAFZwgWnT5+Ovn37Ij4+HkuXLkVk\nZCRmzJghx64JwuVYtAjo25elBCdckxYtgP/8BzARwUw4IWFhYfj111+1y+np6ejTp4+kbTMzMxEQ\nEAB/f394eHggNjYWycnJRus5YuzYAw9YXsfLy+4yrMbT0/HHdLexm9jW7WzFnjMJCHPH1Ksnvo6E\nObmN+MdhazNydDL07FnzfTgCue/HmnbOSim3du3M/y7wm2jhr+nUqazz0VIdcZPFwpDOP4llnQpZ\niuC1117DV199hWbNmsHHxwcbN27Eq6++Kseuaz2u2vNMusXZsQP4+WeWrl1OqLwdS1RUFBYvBvLy\ngO++U1qNNFy1rOUiKysLAwYMQMeOHeHv74+HHnoIWVlZCAoKQu/evc1uKzZNSHFxsd46KpUKR44c\nQXBwMKKjo/W8Zo4mOtq6cCzhBNuCKShlxdENKkDfwDBF9+7G3z36qO6zLQ0zkQTIehganPYM6ROG\nkE2eLN9+BbeDlgYNzE/W7u0t3/GtRcxw9vERX9fwe1PGqVRGjqzZ9jzh4brP1j7OrR2LN3iw+TFe\n/foZfyesxxERQMuW5o8hV7lIxdp7OSbGPjqEyPZYLCsrQ9OmTfHcc8/Bz88PFy5ckLTdU089hdat\nWyMoKEj0d7VaDS8vL4SGhiI0NBRvv/22XJIJQlaKioAFC4BvvrH8Eiacn/r1gTVrmDfLUjgOoTxp\naWk4f/48Dh06BLVajfPnzyM1NRV79uyxGOJnLgU8T1hYGAoLC3HixAksXLgQY8eOFV0vKSkeSUnx\n2LQpHr//rrblVFySFi0sN7oGDLBt31K8e1Onin8v9iwWGoS8ETZxonQ9lqqL0IgDAGvzdBlMFaql\nSxfr9lMTxAyUhg3Nb2OqXOTs/wkKYiGzhjz0kHj5iIV7R0ToL/v51UyTLR0MI0aw/0LjSDh+W8xg\nMOVIr18fCAmxXoO5eix2LGuNUTEP39ixQLNm1u1HaLyb6lwaP17avnjvW26uGqtXx2uf1/ZCFiMr\nPj4eq1atwsqVKwEAFRUVePLJJyVtO2vWLKSlpZldZ/DgwcjJyUFOTo7FgcauhnA+IVeCdOtTXQ3M\nnAksXMhCBeWGytux8LpHjmShF6tXK6tHCq5a1nLh7+8PLy8v/P3337hx44b2z9/f3+J8XL6+vigs\nLNQuFxYWws+g5dW0aVN4/uOiGDVqFCorK3FDJIfzxInxmDgxHuvWxSMsLMrq87Cn1yMkhA3yrwmm\nGpQNGgDDhzMvG4+hd6FDB/P7btNG/Hu+QWoLlsqTb7SJhUcJMWX4WCIy0rRHxRSmwrDEGr72qi/u\n7uLX2rCh7eur+yy3N9OwMT56NMtaaI3HjNfk4QGMGcOMcR8f4PHHdetIKUOhM9xSPZaC1HOwR7bI\nf2ZDgkZj/Nujj4obbI8/bj7xxvjxwLhxumVTXqxGjYB/ctyJ0rGj6d+aN9c3RIX3lZSQ2NhYNq4M\nAAIDoxAfH699XtsLWW6J//3vf0hOTkbjf66Ar6+vxdTtPIMGDUIzC2ZtbZlDhai9fPQRG9i8ZInS\nSgi5WbOGXd9z55RWQpjjjTfeQO/evbFw4UK8+OKL2j8phIeHIy8vDwUFBaioqMC2bdsQYxBLUlJS\non0XZWZmguM4+FhoPYuF5llqzJtDGIYm5bX40EP6y926Se+NNtW7P3Cg8XfCc/LyYh6FgADrPVem\nIgAMjbXBg6Xtb+RInVFpyoATw9Aj17u3fgPT2iZJ8+bA0KH630kdFzZ4sM6QETYwIyL0PQXC8C4p\nY4TatDE2/oYM0V82PE+VSn+drl1ZJtbwcF3DXQxbDUHDkEVzddfUmED+2PXr66/TuLH5Br05xOo1\nf31tCZmsqSfNWvgyETNMmjcHDPL9ADAfJgqwayP0dIo16ydMsKzN0n3B1zfA+qRUKpX1XrSaIouR\n1aBBA7gJujDuyjhbmjPFwdsDVx1HQbp1HD0KrFwJ/Pe/NY/tNgWVt2MR6u7YEXj5ZWD+fOsbV47E\nVctaLrZt24b8/HwcOnQIBw8e1P5Jwd3dHQkJCRgxYgQCAwMxZcoU9OjRAxs2bMCGDRsAAElJSQgK\nCkJISAgWL16MrVu3WtyvWCPG1GWy1BAdMEB8nIQ5OnYE+vTR9UxLCed6+GHgkUeAQYPE1/X1Ne7J\nN7wv+vZlRoAUr1nXrrpB+J06mV5PGHJnadA+T7Nm7G/qVNsa1HyDr2dP/Wtp6Tkg9OjwZS4l5FGM\ndu1075VevXRhUW3a6HsNhUhpfKpU+qFXpsItDRE2pHmPRNeu8nvUhM5n3tgSlqthXRFrPKtUpsfp\nuLnpd0IY3lteXvq/8+dnKvSTv76jRlmfWEXu94rYeDoxhNesZUt9Q4rv8BDWi5pc4yZNrBtHGhho\nbDg3b872wz9XeL1i95azzBcnS46dSZMmIS4uDrdu3cJnn32GL7/8EnPmzJFj19o4eE9PT6SmpmLs\n2LE4K8whKWDmzJnasBBvb2+EhIRoGx58KA0t07Kcy717R2HyZGDBAjX+/BPo1Mm59NGyPMuhoWp8\n8gmwfXsUpkxRXo8rL6vVamzcuBEALIbxWUPPnj1x8+ZNmyeqHzVqFEYZDPaIi4vTfp4/fz7mz59f\nI42AacOjeXPg2jX9hkzv3qyRnZZmbNh07Mi850VF5o/34INAVRX7bKqRJGyICsO/TCE1k6CUxqOP\nD/N8Xbpkfr2hQ+VLXS/UJfz86KPADz/olv39dR5soYfioYeA334DSkqYgZGXx4zSn39moY32yl7o\n5mZsuItdU6FWU2N1fHyYx+LgQfH5nlQqy9fPsFNx4EDg11+Bq1fFOxhatgT++sv8PnnNQm9Ko0Zs\nLI9wnyEh7PqY60eJjNR59SwZCJ06AenpumXegD1yRH89Q4+kUOMjj7DP/fuzuiCV5s2B27elry+G\nOS+Sqbn12rdnncSAftINfh9SDW8p9O+v+yysV4Zp4PnfmjdnGg4fZs+wmzdZhxGgu5Zubuw7YX3v\n2ZOl1u/aldVDMRyZer7GjwKO4zBlyhT88ccfaNq0Kc6ePYu33noLj/C1rYY0FZjWo0aNwrPPPosb\nN26IhmnwL24x+Je9sy2r/5nbxln0SF02/E5pPUqUd3U1y04zbhzw5pv21W/4nbOUp6Xl2lK/hw+P\nwubNwKRJrBGltD6xZd6QcRY9ppYN68OyZcsgB6+++ipCQ0PRq1cvNPinNaZSqSTPa+UoVCrWOOCn\nhzTVAGrcmDUYbt4U3w8/eF+K4WGusSwlfLF1a2ZQ8I2u4GDgjz9qdlweYWibm5vl9Q3x8LCccEMq\nljxvAwYAhYUsyYevLyuT8HD9BqphY16ql8LdXWcMG2Ktp0NoBIuFfgHMKPDw0A9BtIauXcWPO3Qo\nsHUr6wQwNFAs0b27cb0aO5Z5QAwNuoYNmTevY0fxsUWA9NTq/DULDweys3X3ptg6hgjvH2tS9atU\n7D5q3pzdXz17Wu4wMawHnp5AWRnbV+PGzFDkc855eekMNy8v1gnw999sHk+eBg2YkXLsmHTdlhC7\nhoDlcEMx6v/j+TKX8IUfY2UJ4bPFkYnJZOlviY6Oxu+//45HDVPqyEBJSQlatWoFlUolOQ6eIBzB\nqlXAjRvAu+8qrYRwBA89xIzqRYuATZuUVkMYMn36dLzyyivo1auXNnxdStZAJQgMBH7/nTWSDCcX\nVqlYD7PU8LL69YGKCvPreHiYHsckbJAbzt0cEgIcP27cwJRqDFkyDnijjTckfXyYzvv39b0KQnhP\nQkQEa5yGhhrre+AB1qCUmw4ddB5FW0O8oqMBtZo1ioVeHXd31vjjDe4mTYDSUvF9BAeb9yY2acJC\nNjMzbdMIiF9jvpzHjzdtnKtU5j0grVqZ9jCI3a6WjEBhSF9gIJCfb359c3TtyibUFho7zZqx+lm/\nvrEXa8AA8XLg60bTpoBYegK+fCTOlS6Z8HD9BB1CmjQRNy78/MQ7eeyJ2L3j52dsZLZpI5410MuL\ndazYmsTH0MsqTIIiNzU2slQqFfr06YPMzEz0tSGt2tSpU3Ho0CFcu3YN7du3x7Jly1BZWQmAhWok\nJSVh/fr1cHd3h6enp6Q4eFdCrAfdFajrunfuBNauBTIyajaQXSp1vbwdjSnd770HhIUB27YBU6Y4\nVpMlXLWs5aJJkyZYtGiR0jIkERTEjCxTY4vEkkuYIjoauHcP+P571iC9d098PVPHEno6DJ9lPXro\nQspsgW9M+fszr48UeJ2mjCzea2Vu3FFYmHE5CEOK7PnMtpTl7IEHWJa7jAzj0DmpfQJijXPDhquP\nj7iWzp2B4mJdA9XUWDOVyrSX1ZbJjXmGDWPPT2u8RVJp0oQZGb/9pv99t262ey8efRT45RfW4DfM\nrmcpy+DjjwN79zJDS+i9thfu7sahqmFh5scneXraJyOyXIjVNTc3lsnUGuTydluLLJ6s9PR0fPPN\nN+jYsaM2w6BKpcJvhjVdhEQLsQ5yxcEThFwcOgQ88wwbJyF1gClRO2jcGNi8mTVs+/eXJ5UvIQ+D\nBg3CkiVLEBMTow0XBNi4XkukpaVh8eLF0Gg0mDNnDl5++WXR9Y4ePYr+/ftj+/btGG9iYhZzHggp\nGDY0vbx0YxHEaNRI1zh+7DE2rsuabP7mkk0INVy5In2fPPxlEI7HqAlSx4i0aWNcjj4+zPNw+DAr\ns0mTmAewrMx4ey8vZgB16mTaYxUQYByOxuszFb5mCj8/1tgVeniEniRzCZVMZaZr1kzfCyA0mITf\n9+nDEmoI4ZNAPPoosH27bntbPS+G1yIgABAbWm9NuJ01SHgEmMTNjSWDsYYWLYzbBo0a6YfqWdpe\nDL4e+PsDBQW6780Zp926STumXBh6QHv0AE6ftu/UFEJM3a+GmTMdRY2MrIsXL6JDhw74/vvvoVKp\nKNW6DfBjVlyNuqr7xAn2ck5MrNmD21rqankrhTnd4eHACy8A06cD+/fbL6OktbhqWctFdnY2VCoV\n0g1cIJYyDGo0GixYsAD79u2Dr68vIiIiEBMTgx4Gg1k0Gg1efvlljBw50qp3Xbt2ppM6SGl4uLlZ\nHnfAy/Hw0HlspDyfDCfgNaUnNJSFqJlCbHwOwHrVpRhG/NiLmtKmDTt/KeXK9/obGlljx7LxPqGh\n7HdTDV53d9OZ68SqB+/F4LUJNfKZHIVVdcgQ3RitPn1Mj62S+vzx92ehn4aJMOrVMw495A3vevXY\nn0bDnnvWegPGj2dhmy1aMG8Kn1SlTx+dkSUMa1Qqulfu4zZqZJ032hBTnXe891Gu+8Ue9Oypb/i3\nbMmMLFOaDb+X61oY7sfcfu1Z72pkZI0ZMwY5OTnw9/fHhAkT8O2338qliyCcjpMnmQcjIUF8/hui\n7vDSSyw86803geXLlVZDALZPxpyZmYmAgABtpsPY2FgkJycbGVnr1q3DxIkTcZRPxyURqeOXavKi\nF4YhNWok3eMjNWxOpTLdmG/Zsua95Y0bs86rmmKpt1qKbcyPAZI7Q6Chd6tXL6CyErh4Ufdd9+46\nj6HQ8KlfX76GtbWhft27s2xttiQladBAZ5gZvjN5o71LF5asQUrWQXtg77mTaup74Lc3NIQjI1n5\nXr+un1nQ0UyZwsI/edzd9cdK+fqy8FghLVvqEmT07s06kbKz2W9inRC2IHVevBYtbE/+IgXZHiPn\nbcyH+NRTT+G7775Dq1atcPLkSdF1Fi1ahNTUVHh6emLjxo0INTddtIvhqj3PdU33gQNsssW1a4HJ\nk+XVJIW6Vt5KY0l3vXosjKZfP9aLP3OmQ2SZxVXLWk727t2L3NxclJeXa7978803zW5TXFyM9oLY\nHj8/P2RkZBitk5ycjAMHDuDo0aM2JdQYMMB4rAif3KJlS9YYCQ627YXfuLFxyJejsHZsBCCemMJe\nac+dBUMj1dOThZQJjayaNCzt1dDmpxGwNcmAKYShh0FB7B2rBIaTKHt5Wc7yZwteXsxraovR9cAD\nbGJtgNWbVq104YhSplywJ1KMb0MD0d2dXf8//mD3RePGzNguKpLPq+ThYT5jJ49MidBNovhjbdas\nWVi4cCGmT58u+ntKSgrOnTuHvLw8ZGRkYN68eUbhIARhTzZvZuFh27cD1I4leFq2ZIOao6JYKA7V\nDWWJi4vDvXv3cODAAcydOxc7duxAZGSkxe2kGEyLFy/GypUrtWHxtoTGCzPTAawHmG+g2GKoCHFz\nYw1VazA87VatpPf+8pgKlzNHbCybo6Ymme/kxFEhao0bG3fQtWxp2yTJhkycaN9kHvYaK2WILd4y\nuQkKkrfD4oEH2Ni/QYNsT3zx2GO6z4ZeIWdg2LCad5JInWDcGpwhuWyNiuW3337TzmN17949vTmt\nVCoV/paQQ3XQoEEoEI7gM2D37t2YMWMGACAyMhK3bt1CSUmJzRNOOhuuOo6iLui+fx94+WVg1y42\n9kapnmKgbpS3MyFVd48ebHwem6DY9LgJR+CqZS0XR44cwcmTJ9G7d28sXboUL774Ikby3b9m8PX1\nRWFhoXa5sLAQfgbZBI4dO4bYf7q8r127htTUVHh4eCAmJsZof1u2xKOiAjhzhnkX3d2jRI+rZIPS\nw8PYs2JtCPTYsbZ53VQqaY2fmBiWjvvUKeuPYQ0+Po4L/zYs80aN9FOQ24ojMtw6Amdo1kmtn1IZ\nOJB5r9zcnMOItAfmshdaS03L3t9fF5o7ciRLBPTrr8br5eaqER+vrtnBJFAjI0tjbQodGxAL5Sgq\nKqo1RhbhnJw5w3pcO3dmscI0NRthiqFDgdWrWSauAwdMJwEg7Eujf1r8np6eKC4uRvPmzXFFQkq8\n8PBw5OXloaCgAO3atcO2bduMst4Kw+FnzZqF0aNHixpYAPDEE/EoLdWNi+LHGjgTwp5xW7HnOAaA\neX7kbpSKTYiqUsnbSCRsxxk8D3JTk8RIrVsrHw7oaGpaB7y8dEl/+LnBxCalDgyMwtSpUdrlZcuW\n1ezAJlA8XFAKhqEZpsI7Zs6cqR287O3tjZCQEG3PLj8ompblWea/cxY9ci0//HAUNmwAXnlFjdmz\ngfffj4JKpbw+/july6euLPPfSV2/Qwc1YmOBoUOjoFYDhYXK6BdqV+L4UpbVajU2btwIANrntRyM\nHj0aN2/exEsvvYSwsDCoVCrMnTvX4nbu7u5ISEjAiBEjoNFoMHv2bPTo0QMbNmwAwMIQa0JICMu4\n5UzY20ByVnx8pCcFUQJK0EwIMZz82BpcsS41bcoMyzNn5N2vPRObWELFOUHe9YKCAowePVo08cUz\nzzyDqKgobahG9+7dcejQISNPFqWQJ2rKn38Cs2eziQO//pplvyEIa9iwAVixgoUOSpl/iLDPs/v+\n/fsoLy+Hl1gXph1RqVTYvZvT82QRxpw/zybjtVRGv//Osrq6WllWVQE7drDPkZEsIkIKJSXMG26P\n8z19mqVwN7fvy5fZs8vR5S0878RE1jFha+j1qVMswUzHjvKEYjqSoiLg55/lKf/vvmPJZeS8llLv\nW6lcvSruRU5MBAYPts84LQA4dw44elT/POxlQzh9hGhMTAw2bdoEgE167O3tXatCBQ17oF2F2qSb\n44AvvmDzgAwfziardDYDqzaVtytgq+64ODaOb8gQ9kJyJK5a1jUlMzMTly9f1i5//fXXmDRpEt54\n4w3cuHFDQWUEYR2tWtU8CQrBcDUDC2DX39KceLUJU2G6zZop632SE8XDBadOnYpDhw7h2rVraN++\nPZYtW4bKykoALEwjOjoaKSkpCAgIQOPGjfHVV18prJioTVy5Asydy3qQDhywPkMXQRjy7LPs/5Ah\nbHJRqb3YhG3ExcVh//79AICffvoJr7zyChISEpCTk4Onn34aSUlJDtUzYABLmkOYRjDMulYi7BBv\n3Fj6diqV9RP+yomXlzKhpM2a2c9r4UrUr88mapaDbt2AW7fk2ZejkZCvyGVQ3MgyHGAsRkJCggOU\nKINwDIgrURt0f/stMH8+MGcO+1zfiWdRrw3l7UrUVPezz7JB+1FRzNDq0kUWWWZx1bKuKdXV1fD5\nJzPNtm3bEBcXhwkTJmDChAkI5mc8dSCUJMcyHh7SOh9cfQSAME2/K+DpybJGOpr69Vl4GCEfAQFK\nK3BeHPlcUdzIIghHc+sWsGgRS+u5axebUJYg5OaZZ1jP9NChwKFDLLUsIT8ajQaVlZXw8PDAvn37\n8Nlnn2l/q7I0EyXh1FjjBXJGnMnAcvWylIqrG+ZE7cKJHgF1E1cdR+GquletUiM4mKX1PH7cdQws\nVy3vuq47Lg546SVmaAmmYrILrlrWNWXq1KkYPHgwYmJi4OnpiUGDBgEA8vLy4O3tLXk/aWlp6N69\nO7p27Yp3333X6Pfk5GQEBwcjNDQUffr0wYEDB2Q7B0Kczp2BSZOUVlE76NCBypIgHI1TGFmWXm5q\ntRpeXl4IDQ1FaGgo3n77bQVUEq7MX38BTz4JfPAB8NlnwCef1J2ePUJZFixgYalDhwKXLimtpvbx\n2muv4f3338esWbPwyy+/wO0f9wHHcVi3bp2kfWg0GixYsABpaWnIzc1FYmIiTp8+rbfO8OHDceLE\nCeTk5GDjxo14+umnZT8Xwhh3ireRDSpLoiY485AKZ0XxW45/ue3btw++vr6IiIhATEwMehjk7xw8\neDB2796tkEr74arjKFxFd1UVyxz45pvAtGlAfn6USxpXrlLehpBuxosvAhUVzNA6eBBo21bW3QNw\n3bKWg/79+xt996AVaboyMzMREBCgnbcrNjYWycnJeu+hxoIHR2lpKVq0aGG7YKJWU5MJaOs6HTpQ\nEgxnxc8PGDNGaRWuheJGlpSXG2A8ITFBmIPj2DwR//kP0KYNkJYGhIYqrYqoyyxZAmg0OkOrTRul\nFRE8xcXFaC9Ieefn54eMjAyj9Xbt2oUlS5bg8uXL+OGHHxwpkXAh3Nxcb24vZ2HAAKUVEObw9FRa\ngWuheLig2MutuLhYbx2VSoUjR44gODgY0dHRyM3NdbRMu+Gq4yicVTfHMYNq4G42HT0AABKJSURB\nVEBmYK1eDezfrzOwnFW3JUi3Y7GX7tdfB554gqV3v3JF3n27alk7AyqVStJ6Y8eOxenTp7Fnzx5M\nmzZNdJ34+HjtH10TgiAI56JZMyA3V633rLYXinuypLzcwsLCUFhYCE9PT6SmpmLs2LE4e/asA9QR\nroJGAyQnA++8A9y7xxqzkyZR2AbhfLzxBusMGDQISE2lVLvOgK+vLwoFmUkKCwvh5+dncv1Bgwah\nqqoK169fR/PmzfV+s+cLmyAI8/j4sKyuBGGKFi2At96KAhCl/W7ZsmV2OZbiRpaUl1vTpk21n0eN\nGoVnn30WN27c0M6NwjNz5kxt2KG3tzdCQkK04xT4HkValmeZ/05pPf36RWHTJuD//k+Npk2B5cuj\nMHYs8NNPavz8s/OUV20p77qyzH9nr/0//LAat24BAwdGYedOoKJCnv0LtcupV85ltVqNjRs3AoD2\nea004eHhyMvLQ0FBAdq1a4dt27YZzeGYn5+Pzp07Q6VSITs7GwCMDCyCIJSlXTsgNlZpFQTBUHEK\nD3aqqqpCt27dsH//frRr1w59+/ZFYmKi3piskpIStGrVCiqVCpmZmZg8eTIKCgr09qNSqWjcVh3i\n+nWWIfDjj9kM6S+/zDwD1INFuBKpqcD06cDatXV3DIezPLtTU1OxePFiaDQazJ49G0uWLMGGDRsA\nAHFxcVi1ahU2bdoEDw8PNGnSBB988AEiIiL09uEs50IQBEFIx17PbsWNLMDyy+3jjz/G+vXr4e7u\nDk9PT3zwwQfo9//bu/ugqMo9DuDfHeFWvoShQrRQyrIosnB2S0RLu8P1pVAjDb3CTGQjmRJZNuXk\nP3ciK5Xp7ZI4l+pqZmk2k12VG6C30tErAoagE5gXkvcXLwIboDdY4Hf/OLMrC0scbDlnz/L7zJwR\n9oX5np/PnrPPeXmefhMcqXXn1vdouZoolfs//xG/kB44IM5M//LLQGio9PdzveXFuYdWXAz8+c9A\nRASQni5eL34r1FprtW67HXGndWGMsdFipLbdil8uCIiXAEZHR9s9tn79etvPycnJSE5OljsWcxG9\nvcC33wI7dwL5+cCzzwI//sjDvDL3YDSKHa0tW4DwcOBvfwOWLuWzsowxxpiaucSZLGfgI4jup6IC\n+PRT4JNPxBsVN2wQJxS+4w6lkzE2Mr77Tpy4+O67xUFcHEz/5HbcadvtTuvCGGOjhVtfLugMvHNT\nv95e4MIFIDMT+Mc/gNpaYPVqIDGR57hio0d3N7BvH5CSAoSFAS++CCxcKM69447cadvtTuvCGGOj\nxUhtu910t60e/UcFUwtn5P7vf4Hjx4HUVCAmRjxbFRcHtLQAf/0r0NAg3qPizA7WaK63Ejj38Hl4\nAGvXivcfLl8ObN4MhIQA778P1NcP/j611poxxhhzRy7RycrJycGMGTOg1+uRmprq8DUvvPAC9Ho9\nBEFAUVGRzAlHTnFxsdIRbonU3D09wJUr4gTBaWnAc88Bf/oT4OcHzJghXhLV2AgkJAClpcDly2IH\n649/FL9sKpXb1XBueblC7ttvB9atE+/X+vvfgaIicZCX+fPFz8iPP4pnf61cIbOaDbUf2r9/PwRB\nQHh4OB566CFcvHhRgZTqxwcDpOE6DY1rJA3XSTmKD3zR09OD559/Ht9++y20Wi0iIiIQExNjN4R7\nVlYWysvLUVZWhvz8fCQlJSEvL0/B1M5jNpuVjnBLrLktFvGMU10dUF0tLlVVwM8/i0t1tXh/SXAw\noNeLR+RXrBA7WP7+8t/cr/Z6qw3n/v00GrFjNX8+0NkJ/OtfwOHD4lles1l8/P77gdJSM6qrxc+V\nu15aOFKk7IcCAwNx6tQpeHl5IScnB88++6zb7IfkpNZRMOXGdRoa10garpNyFO9kFRQUICgoyDYp\nZVxcHI4cOWK3czt69CjWrFkDAIiMjITZbMbVq1fh6+urRGRVIRLPJvX0iEe9rYv10lPr7z094r0g\nv/4qfpG7cQP45RegrU38InftGtDUJC4NDcD580BGhnhpn48PoNUC990H3Huv2KF69FFApwOmTROP\nyDPGfr/bbgOWLRMXAKipAf79b/Fs17lzwJw54uc1KEhc7r0XCAgQO14+PuIluZMnA3feCYwdyyMY\nWknZD83tMwpJZGQkamtr5Y7JGGNMRRTvZNXV1SEgIMD2u7+/P/Lz84d8TW1trao6Wf/7H7Bokdi5\nIbrZ0fn550r885/2jwM3f3fE+pz19T094hml7m7x366um4vFIn6R8vAAxowRj3BrNPaL9XEPD7FD\ndPvt4gh+d94JeHmJy5Qp4pcznU681C8joxIffQT4+orvV4v+k1irBeeWl1pyBwSIkxjHxwNXr1Zi\n716gvR0oLxeX6mpxAJm8PPEAybVr4tLeLh5QGTtW/Lzfdhvwhz8Anp7idqDv9mLMGOAvf7nZsXNH\nUvZDfe3evRtLliyRIxpjjDGVUryTpZF4KLX/qB/936fT6ST/LVfT3PzpiP59IrGzZbE49+9qtSOb\ne6R8+innlhPnls9wM3d0iMtQHnvsFgNJoNPpRu6PSzScfceJEyewZ88enDlzZsBzat4Pyen1119X\nOoIqcJ2GxjWShuv020ZqP6R4J0ur1aKmpsb2e01NDfz9/X/zNbW1tdBqtXavKS8vH9mgjDHG3JKU\n/RAAXLx4EevWrUNOTg7uuuuuAc/zfogxxpiV4rdHz5o1C2VlZaisrERXVxe+/PJLxMTE2L0mJiYG\n+/btAwDk5eVh4sSJqrpUkDHGmOuSsh+qrq7GE088gc8//xxBQUEKJWWMMaYWip/J8vDwQHp6Oh55\n5BH09PQgMTERISEh+PDDDwEA69evx5IlS5CVlYWgoCCMGzcOn3zyicKpGWOMuQsp+6GtW7eitbUV\nSUlJAABPT08UFBQoGZsxxpgL0xBPT88YY4wxxhhjTqP45YLDpdYJI4fKfeTIEQiCAJPJhAceeADf\nf/+9AikHkjJRNACcO3cOHh4e+Prrr2VMN7ihcp88eRJeXl4wmUwwmUx48803FUhpT0qtT548CZPJ\nBIPB4DLzXgyV+5133rHVOSwsDB4eHi4xD9VQua9du4ZHH30URqMRBoMBe/fulT+kA0Plbm1txYoV\nKyAIAiIjI1FSUqJASntr166Fr68vwsLCBn2N2iecl7qtdFdTp05FeHg4TCYTZs+eDQBoaWnBokWL\nEBwcjMWLF9t97rdv3w69Xo8ZM2bg+PHjtscLCwsRFhYGvV6PF198Ufb1cCZH7d6ZNens7MTq1auh\n1+sxZ84cVFVVybNiTuSoRikpKfD397ftN7Kzs23PjcYaAeJ9olFRUQgNDYXBYMAHH3wAgNtTX4PV\nSNH2RCrS3d1NOp2OKioqqKuriwRBoNLSUrvX5ObmktlsJiKi7OxsioyMVCKqHSm5Ozo6bD9fvHiR\ndDqd3DEHkJLb+rqoqChaunQpffXVVwokHZhnqNwnTpygxx57TKGEA0nJ3NraSjNnzqSamhoiImpq\nalIiqh2pbcQqMzOTFixYIGNCx6Tkfu2112jLli1EJNba29ubLBaLEnFtpOR+5ZVXaOvWrURE9NNP\nP7lEvU+dOkXnz58ng8Hg8PlvvvmGoqOjiYgoLy/PJbbbwzHcz4E7mjp1KjU3N9s9tnnzZkpNTSUi\noh07dtCrr75KREQlJSUkCAJ1dXVRRUUF6XQ66u3tJSKiiIgIys/PJyKi6Ohoys7OlnEtnMtRu3dm\nTXbt2kVJSUlERHTw4EFavXq1bOvmLI5qlJKSQu++++6A147WGhERNTQ0UFFRERERtbe3U3BwMJWW\nlnJ76mOwGinZnlR1JqvvhJGenp62CSP7mjt3Lry8vAC4zoSRUnKPGzfO9nNHRwcmT54sd8wBpOQG\ngJ07d2LlypWYMmWKAikHkpqbXOhKWSmZDxw4gNjYWNuoZ2pqI1YHDhxAfHy8jAkdk5Lbz88PbW1t\nAIC2tjZMmjQJHh7K3sYqJfelS5cQFRUFAJg+fToqKyvR1NSkRFyb+fPnOxyNz2qwCefVYrifA3fV\nf5va9/91zZo1OHz4MADxyo34+Hh4enpi6tSpCAoKQn5+PhoaGtDe3m47E/bUU0/Z3qNGjtq9M2vS\n92/Fxsbiu+++k2vVnGawbYOj/fNorREA3H333TAajQCA8ePHIyQkBHV1ddye+hisRoBy7UlVnSxH\nE0ZaC+iIq0wYKTX34cOHERISgujoaNtpTiVJyV1XV4cjR47YbgZ3hTlipOTWaDTIzc2FIAhYsmQJ\nSktL5Y5pR0rmsrIytLS0ICoqCrNmzcJnn30md8wBhvOZvHHjBo4dO4bY2Fi54g1KSu5169ahpKQE\n99xzDwRBQFpamtwxB5CSWxAE22W7BQUFqKqqcomDTb9lsAnn1WK4+yZ3pNFosHDhQsyaNQsff/wx\nAODq1au2kYB9fX1tHef6+nq7IfKt9er/uFardbs6OrMmfdudh4cHvLy80NLSIteqjKidO3dCEAQk\nJibaLoHjGokqKytRVFSEyMhIbk+DsNZozpw5AJRrT6rqZN3KhJGucG281NzLly/HpUuXkJmZiYSE\nhBFONTQpuTdt2oQdO3ZAo9GAiFzi7JCU3Pfffz9qampw4cIFbNy4EcuXL5ch2eCkZLZYLDh//jyy\nsrJw7NgxvPHGGygrK5Mh3eCG85nMzMzEvHnzMHHixBFMJI2U3Nu2bYPRaER9fT2Ki4uRnJyM9vZ2\nGdINTkruLVu2wGw2w2QyIT09HSaTCWPGjJEh3e/Tf9vhCgdspFJT1pFy5swZFBUVITs7G7t27cLp\n06ftntdoNFynfrgmjiUlJaGiogLFxcXw8/PDyy+/rHQkl9HR0YHY2FikpaVhwoQJds9xexJ1dHRg\n5cqVSEtLw/jx4xVtT6rqZA13wsijR4/+5iUqcpGa22r+/Pno7u5Gc3OzHPEGJSV3YWEh4uLiMG3a\nNBw6dAjPPfccjh49KndUO1JyT5gwAWPHjgUAREdHw2KxKHrERkrmgIAALF68GHfccQcmTZqEhx9+\nGBcuXJA7qp3htO2DBw+6xKWCgLTcubm5WLVqFQBxNvhp06bh8uXLsubsT2rb3rNnD4qKirBv3z40\nNTUhMDBQ7qjDImXCeVc23G28O/Lz8wMATJkyBStWrEBBQQF8fX3R2NgIAGhoaICPjw8Ax//f/v7+\n0Gq1dmcw1dYOpHBGTaxtS6vVorq6GgDQ3d2NX375Bd7e3nKtyojx8fGxdRieeeYZ21QJo71GFosF\nsbGxSEhIsB0Y5vZkz1qjJ5980lYjRdvT77/VTD4Wi4UCAwOpoqKCOjs7Hd5cXFVVRTqdjs6ePatQ\nyoGk5C4vL7fdcFdYWEiBgYFKRLUjJXdfTz/9NB06dEjGhI5Jyd3Y2Gird35+Pt13330KJL1JSuZL\nly7RggULqLu7m65fv04Gg4FKSkoUSiyS2kbMZjN5e3vTjRs3FEg5kJTcL730EqWkpBCR2F60Wu2A\nG/vlJiW32Wymzs5OIiL66KOPaM2aNQokHaiiokLSwBdnz55V3cAXw91Wupvr169TW1sbEYmDOD34\n4IN07Ngx2rx5M+3YsYOIiLZv3z7gpvzOzk66cuUKBQYG2rbHs2fPpry8POrt7VX9wBdEA9u9M2uy\na9cu2rBhAxERffHFF6obqMCqf43q6+ttP7/33nsUHx9PRKO7Rr29vZSQkECbNm2ye5zb002D1UjJ\n9qSqThYRUVZWFgUHB5NOp6Nt27YREVFGRgZlZGQQEVFiYiJ5e3uT0Wgko9FIERERSsa1GSp3amoq\nhYaGktFopHnz5lFBQYGScW2Gyt2Xq3SyiIbOnZ6eTqGhoSQIAs2dO9clOuVSav3222/TzJkzyWAw\nUFpamlJR7UjJvXfvXtuGzVUMlbupqYmWLVtG4eHhZDAYaP/+/UrGtRkqd25uLgUHB9P06dMpNjbW\nNtqqkuLi4sjPz488PT3J39+fdu/ePaCNJCcnk06no/DwcCosLFQw7a1x9P8yWly5coUEQSBBECg0\nNNS2/s3NzbRgwQLS6/W0aNEiam1ttb3nrbfeIp1OR9OnT6ecnBzb4z/88AMZDAbS6XS0ceNG2dfF\nmfq3+z179ji1Jr/++iutWrWKgoKCKDIykioqKuRcPadwtG1ISEigsLAwCg8Pp8cff5waGxttrx+N\nNSIiOn36NGk0GhIEwfb9Njs7m9tTH45qlJWVpWh74smIGWOMMcYYY8yJVHVPFmOMMcYYY4y5Ou5k\nMcYYY4wxxpgTcSeLMcYYY4wxxpyIO1mMMcYYY4wx5kTcyWKMMcYYY4wxJ+JOFmOMMcYYY4w5EXey\nGGOMMcYYY8yJ/g+4vm3+G+iD0wAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0xe197c50>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
spulido99/NetworksAnalysis
camilo_torres_botero/proyecto/.ipynb_checkpoints/test_network_creation-checkpoint.ipynb
1
1495
{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx\n", "from networkx.algorithms import bipartite\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "from scipy.stats import powerlaw\n", "%matplotlib inline\n", "sns.set()\n", "\n", "all_edges = set()\n", "with open('test_authors.txt') as f:\n", " for line in f:\n", " users = line.replace('\\n','').split(',')\n", " for user1 in users:\n", " for user2 in users:\n", " if ((user1,user2) not in all_edges and (user2,user1) not in all_edges) and (user1 != user2):\n", " all_edges.add((user1,user2))\n", " \n", "edges_file = open('test_users_network.txt', 'w')\n", "\n", "print('source\\ttarget', file=edges_file)\n", "for k, v in sorted(all_edges):\n", " print(k+'\\t'+v, file=edges_file)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ingelectronicadj/FisicaConPython
jupyter/Semico/.ipynb_checkpoints/Untitled-checkpoint.ipynb
1
5804
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/asus/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:18: RuntimeWarning: overflow encountered in exp\n", "/home/asus/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:18: RuntimeWarning: divide by zero encountered in reciprocal\n", "/home/asus/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1288: UserWarning: findfont: Font family ['bold italic'] not found. Falling back to Bitstream Vera Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n", "/home/asus/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:74: RuntimeWarning: divide by zero encountered in true_divide\n", "/home/asus/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:74: RuntimeWarning: overflow encountered in exp\n", "/home/asus/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:74: RuntimeWarning: invalid value encountered in multiply\n" ] } ], "source": [ "#Diego Javier Mena Amado Cod.:20092005053\n", "#Danny Tales Tales Cod. \n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.widgets import Slider, Button, RadioButtons\n", "import scipy.constants\n", "from pylab import plot,xlabel,ylabel,show\n", "from sympy import *\n", "\n", "#Ajuste de workstation\n", "fig, ax = plt.subplots()\n", "plt.subplots_adjust(left=0.1, bottom=0.25)\n", "t = np.arange(0, 0.99, 0.001)\n", "a0 = 0.7\n", "#s = a0*np.sin(2*np.pi*t)\n", "#print (t,a0) #Depurando errores de divisiones por cero halladas\n", "PLANCK =((t**5)*(np.exp(1/(t-a0)-1))**(-1))\n", "l, = plt.plot(t, PLANCK, lw=2, color='red')\n", "\n", "#Definimos limites de barrido\n", "#plt.xlim((0.0008, 1))\n", "#plt.ylim((0, 30))\n", "plt.axis([0.01, 1, 0, 30])\n", "x = np.linspace(0.01, 1, 1000)\n", "y = np.linspace(0, 30, 1000)\n", "\n", "#Asignamos nombres a nuestro sistema de coordenadas\n", "xlabel(\"t\")\n", "ylabel(\"x(t)\")\n", "\n", "#Se añaden constantes debido a falta de comprension de libreria para constantes fisicas\n", "k=1.38*10**(-23)\n", "h=6.62*10**(-34) #constante de Planck\n", "c=3*10**8\n", "\n", "#Se cargan los estilos para las curvas\n", "style = {'family' : 'bold italic','color' : 'blue','weight' : 'normal','size' : 14}\n", "style1 = {'family' : 'bold italic','color' : 'green','weight' : 'normal','size' : 14} \n", "style2 = {'family' : 'bold italic','color' : 'red','weight' : 'normal','size' : 14}\n", "style3 = {'family' : 'bold italic','color' : 'black','weight' : 'normal','size' : 14}\n", "style4 = {'family' : 'bold italic','color' : 'purple','weight' : 'normal','size' : 14}\n", "\n", "#Se cargan los label's para identificar cada curva y sus desasrrolladores\n", "plt.title('Fisica de Semiconductores', fontdict=style2)\n", "plt.text(0.53, 28, r'$\\ Diego \\ Javier \\ Mena $', fontdict=style3)\n", "plt.text(0.53, 26, r'$\\ Danny \\ Tales \\ Tales $', fontdict=style3)\n", "plt.text(0.23, 20, r'$\\ Ley \\ de \\ Planck $', fontdict=style)\n", "plt.text(0.52, 18, r'$\\ Ley \\ de \\ Rayleigh-Jeans $', fontdict=style4)\n", "plt.text(0.185, 25, r'$\\ Limite \\ de \\ Wien $', fontdict=style3)\n", "\n", "#Ecuación Ley de Planck\n", "plt.plot(x, ((x**5)*(np.exp(1/x)-1))**(-1), \n", " x, ((x**5)*(np.exp(1/(0.9*x))-1))**(-1), \n", " x, ((x**5)*(np.exp(1/(0.8*x))-1))**(-1))\n", "\n", "\n", "#Ecuación Rayleigh-Jeans\n", "plt.plot(x, 1/(x**4), x,1/(0.9*x**4),x,1/(0.8*x**4) )\n", "\n", "#Ecuación Limite de Wien\n", "plt.plot(x,np.exp(((1)/(x)))*10**(-0.87))\n", "\n", "\n", "#implementamos Slider para variaciones de Ley de Planck\n", "axcolor = 'lightgoldenrodyellow'\n", "axamp = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)\n", "samp = Slider(axamp, 'Amp', 0.1, 1.3, valinit=a0)\n", "\n", "\n", "#Establecemos la funcion a variar con el slider\n", "def update(val):\n", " amp = samp.val\n", " l.set_ydata(((t**5)*(np.exp(1/(t*amp))-1))**(-1))\n", " fig.canvas.draw_idle()\n", "samp.on_changed(update)\n", "\n", "#Creamos un boton reset para limpiar las variables color y amp\n", "resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n", "button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n", "\n", "def reset(event):\n", " samp.reset()\n", "button.on_clicked(reset)\n", "\n", "#implementamos Cuadro Selector de Color\n", "rax = plt.axes([0.025, 0.05, 0.15, 0.15], axisbg=axcolor)\n", "radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\n", "\n", "def colorfunc(label):\n", " l.set_color(label)\n", " fig.canvas.draw_idle()\n", "radio.on_clicked(colorfunc)\n", "\n", "#Mostramos el Grafico\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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
random-forests/tensorflow-workshop
archive/zurich/solutions/02_quickdraw_solution.ipynb
1
27146
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "yDtkhWqDLPAq" }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import os\n", "import numpy as np\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3GLuRaTlLPAv" }, "source": [ "Boilerplate for graph visualization\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "0Vnl3jP_LPAx" }, "outputs": [], "source": [ "# This is for graph visualization.\n", "\n", "from IPython.display import clear_output, Image, display, HTML\n", "\n", "def strip_consts(graph_def, max_const_size=32):\n", " \"\"\"Strip large constant values from graph_def.\"\"\"\n", " strip_def = tf.GraphDef()\n", " for n0 in graph_def.node:\n", " n = strip_def.node.add() \n", " n.MergeFrom(n0)\n", " if n.op == 'Const':\n", " tensor = n.attr['value'].tensor\n", " size = len(tensor.tensor_content)\n", " if size > max_const_size:\n", " tensor.tensor_content = \"<stripped %d bytes>\"%size\n", " return strip_def\n", "\n", "def show_graph(graph_def, max_const_size=32):\n", " \"\"\"Visualize TensorFlow graph.\"\"\"\n", " if hasattr(graph_def, 'as_graph_def'):\n", " graph_def = graph_def.as_graph_def()\n", " strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n", " code = \"\"\"\n", " <script>\n", " function load() {{\n", " document.getElementById(\"{id}\").pbtxt = {data};\n", " }}\n", " </script>\n", " <link rel=\"import\" href=\"https://tensorboard.appspot.com/tf-graph-basic.build.html\" onload=load()>\n", " <div style=\"height:600px\">\n", " <tf-graph-basic id=\"{id}\"></tf-graph-basic>\n", " </div>\n", " \"\"\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n", "\n", " iframe = \"\"\"\n", " <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"{}\"></iframe>\n", " \"\"\".format(code.replace('\"', '&quot;'))\n", " display(HTML(iframe))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5r42SomULPA2" }, "source": [ "Load the data\n", "---\n", "Run 00_download_data.ipynb if you haven't already" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "id": "OzoRsG-5LPA3", "outputId": "ef95b4a0-cef9-4671-fa63-14c2791d855c" }, "outputs": [], "source": [ "DATA_DIR = '../data/'\n", "data_filename = os.path.join(DATA_DIR, \"zoo.npz\")\n", "data = np.load(open(data_filename))\n", "\n", "train_data = data['arr_0']\n", "train_labels = data['arr_1']\n", "test_data = data['arr_2']\n", "test_labels = data['arr_3']\n", "del data\n", "print(\"Data shapes: \", test_data.shape, test_labels.shape, train_data.shape, train_labels.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a simple classifier with low-level TF Ops" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 8292, "status": "error", "timestamp": 1497369977531, "user": { "displayName": "Christian Buck", "photoUrl": "//lh5.googleusercontent.com/-i04j1OZ3aUs/AAAAAAAAAAI/AAAAAAAAABc/Dx1Bhh2XWUE/s50-c-k-no/photo.jpg", "userId": "108154180342320802225" }, "user_tz": -120 }, "id": "odSuMrBULPA9", "outputId": "6182b273-3fb2-486e-dbf8-7147c7de99f0" }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "input_dimension = train_data.shape[1] # 784 = 28*28 pixels\n", "output_dimension = train_labels.shape[1] # 10 classes\n", "\n", "batch_size = 32\n", "hidden1_units = 128\n", "\n", "data_batch = tf.placeholder(\"float\", shape=[None, input_dimension], name=\"data\")\n", "label_batch = tf.placeholder(\"float\", shape=[None, output_dimension], name=\"labels\")\n", "\n", "weights_1 = tf.Variable(\n", " tf.truncated_normal(\n", " [input_dimension, hidden1_units], \n", " stddev=1.0 / np.sqrt(float(input_dimension))),\n", " name='weights_1')\n", "\n", "# Task: Add Bias to first layer\n", "# Task: Use Cross-Entropy instead of Squared Loss\n", "\n", "# SOLUTION: Create biases variable.\n", "biases_1 = tf.Variable(\n", " tf.truncated_normal(\n", " [hidden1_units], \n", " stddev=1.0 / np.sqrt(float(hidden1_units))),\n", " name='biases_1')\n", "\n", "weights_2 = tf.Variable(\n", " tf.truncated_normal(\n", " [hidden1_units, output_dimension], \n", " stddev=1.0 / np.sqrt(float(hidden1_units))),\n", " name='weights_2')\n", "\n", "# SOLUTION: Add the bias term to the first layer\n", "wx_b = tf.add(tf.matmul(data_batch, weights_1), biases_1)\n", "hidden_activations = tf.nn.relu(wx_b)\n", "output_activations = tf.nn.tanh(tf.matmul(hidden_activations, weights_2))\n", "\n", "# SOLUTION: Replace the l2 loss with softmax cross entropy.\n", "with tf.name_scope(\"loss\"):\n", " # loss = tf.nn.l2_loss(label_batch - output_activations)\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(\n", " labels=label_batch, \n", " logits=output_activations))\n", "\n", "show_graph(tf.get_default_graph().as_graph_def())\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BnAyjAXnLPBB" }, "source": [ "We can run this graph by feeding in batches of examples using a feed_dict. The keys of the feed_dict are placeholders we've defined previously.\n", "The first argument of session.run is the tensor that we're computing. Only parts of the graph required to produce this value will be executed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "id": "GDn0ixriLPBC", "outputId": "43d24a35-8774-47bc-ff45-9ee2ae36125e" }, "outputs": [], "source": [ "with tf.Session() as sess:\n", " init = tf.global_variables_initializer()\n", " sess.run(init)\n", " \n", " random_indices = np.random.permutation(train_data.shape[0])\n", " for i in range(1000):\n", " batch_start_idx = (i % (train_data.shape[0] // batch_size)) * batch_size\n", " batch_indices = random_indices[batch_start_idx:batch_start_idx + batch_size]\n", " batch_loss = sess.run(\n", " loss, \n", " feed_dict = {\n", " data_batch : train_data[batch_indices,:],\n", " label_batch : train_labels[batch_indices,:]\n", " })\n", " if (i + 1) % 100 == 0:\n", " print(\"Loss at iteration {}: {}\".format(i+1, batch_loss))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5Je87CgiLPBF" }, "source": [ "No learning yet but we get the losses per batch.\n", "We need to add an optimizer to the graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "id": "IYoyWqrgLPBG", "outputId": "16393ac8-48b4-4bd3-8627-458ccbba7be8" }, "outputs": [], "source": [ "# Task: Replace GradientDescentOptimizer with AdagradOptimizer and a 0.1 learning rate.\n", "# learning_rate = 0.005\n", "# updates = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", "\n", "# SOLUTION: Replace GradientDescentOptimizer\n", "learning_rate = 0.1\n", "updates = tf.train.AdagradOptimizer(learning_rate).minimize(loss)\n", "\n", "with tf.Session() as sess:\n", " init = tf.global_variables_initializer()\n", " sess.run(init)\n", " \n", " random_indices = np.random.permutation(train_data.shape[0])\n", " n_epochs = 10 # how often do to go through the training data\n", " max_steps = train_data.shape[0]*n_epochs // batch_size\n", " for i in range(max_steps):\n", " batch_start_idx = (i % (train_data.shape[0] // batch_size)) * batch_size\n", " batch_indices = random_indices[batch_start_idx:batch_start_idx+batch_size]\n", " batch_loss, _ = sess.run(\n", " [loss, updates], \n", " feed_dict = {\n", " data_batch : train_data[batch_indices,:],\n", " label_batch : train_labels[batch_indices,:]\n", " })\n", "\n", " if i % 200 == 0 or i == max_steps - 1:\n", " random_indices = np.random.permutation(train_data.shape[0])\n", " print(\"Batch-Loss at iteration {}: {}\".format(i, batch_loss))\n", "\n", " test_predictions = sess.run(\n", " output_activations, \n", " feed_dict = {\n", " data_batch : test_data,\n", " label_batch : test_labels\n", " })\n", " wins = np.argmax(test_predictions, axis=1) == np.argmax(test_labels, axis=1)\n", " print(\"Accuracy on test: {}%\".format(100*np.mean(wins)))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VaEx4NK_LPBJ" }, "source": [ "Loss going down, Accuracy going up! \\o/\n", "\n", "Notice how batch loss differs between batches." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AlR_4etuLPBK" }, "source": [ "# Model wrapped in a custom estimator\n", "In TensorFlow, we can make it easier to experiment with different models when we separately define a model_fn and an input_fn." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {}, {}, {}, {}, {} ] }, "colab_type": "code", "id": "2_sBrEKhLPBL", "outputId": "fc5468ca-7e24-4a65-b6c5-aaf028f8a627" }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "# Model parameters.\n", "batch_size = 32\n", "hidden1_units = 128\n", "learning_rate = 0.005\n", "input_dimension = train_data.shape[1] # 784 = 28*28 pixels\n", "output_dimension = train_labels.shape[1] # 6 classes\n", "n_epochs = 10 # how often do to go through the training data\n", "\n", "\n", "def input_fn(data, labels):\n", " input_images = tf.constant(data, shape=data.shape, verify_shape=True, dtype=tf.float32)\n", " input_labels = tf.constant(labels, shape=labels.shape, verify_shape=True, dtype=tf.float32)\n", " image, label = tf.train.slice_input_producer(\n", " [input_images, input_labels],\n", " num_epochs=n_epochs)\n", " dataset_dict = dict(images=image, labels=label)\n", " batch_dict = tf.train.batch(\n", " dataset_dict, batch_size, allow_smaller_final_batch=True)\n", " batch_labels = batch_dict.pop('labels')\n", " return batch_dict, batch_labels\n", "\n", "\n", "def model_fn(features, targets, mode, params):\n", " # 1. Configure the model via TensorFlow operations (same as above)\n", " weights_1 = tf.Variable(\n", " tf.truncated_normal(\n", " [input_dimension, hidden1_units],\n", " stddev=1.0 / np.sqrt(float(input_dimension))))\n", " weights_2 = tf.Variable(\n", " tf.truncated_normal(\n", " [hidden1_units, output_dimension],\n", " stddev=1.0 / np.sqrt(float(hidden1_units))))\n", " hidden_activations = tf.nn.relu(tf.matmul(features['images'], weights_1))\n", " output_activations = tf.matmul(hidden_activations, weights_2)\n", " \n", " # 2. Define the loss function for training/evaluation\n", " loss = tf.reduce_mean(tf.nn.l2_loss(targets - output_activations))\n", " \n", " # 3. Define the training operation/optimizer\n", " train_op = tf.contrib.layers.optimize_loss(\n", " loss=loss,\n", " global_step=tf.contrib.framework.get_global_step(),\n", " learning_rate=learning_rate,\n", " optimizer=\"SGD\")\n", " \n", " # 4. Generate predictions\n", " predictions_dict = {\n", " \"classes\": tf.argmax(input=output_activations, axis=1),\n", " \"probabilities\": tf.nn.softmax(output_activations, name=\"softmax_tensor\"), \n", " \"logits\": output_activations,\n", " }\n", " \n", " # Optional: Define eval metric ops; here we add an accuracy metric.\n", " is_correct = tf.equal(tf.argmax(input=targets, axis=1),\n", " tf.argmax(input=output_activations, axis=1))\n", " accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", " eval_metric_ops = { \"accuracy\": accuracy}\n", "\n", " # 5. Return predictions/loss/train_op/eval_metric_ops in ModelFnOps object\n", " return tf.contrib.learn.ModelFnOps(\n", " mode=mode,\n", " predictions=predictions_dict,\n", " loss=loss,\n", " train_op=train_op,\n", " eval_metric_ops=eval_metric_ops)\n", "\n", "\n", "custom_model = tf.contrib.learn.Estimator(model_fn=model_fn)\n", "\n", "# Train and evaluate the model.\n", "def evaluate_model(model, input_fn):\n", " for i in range(6):\n", " max_steps = train_data.shape[0]*n_epochs // batch_size\n", " model.fit(input_fn=lambda: input_fn(train_data, train_labels), steps=max_steps)\n", " print(model.evaluate(input_fn=lambda: input_fn(test_data, test_labels),\n", " steps=150))\n", "\n", "\n", "evaluate_model(custom_model, input_fn)" ] }, { "cell_type": "markdown", "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "5bn1rtOnLPBP" }, "source": [ "# Custom model, simplified with tf.layers\n", "Instead of doing the matrix multiplications and everything ourselves, we can use tf.layers to simplify the definition." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "# Model parameters.\n", "batch_size = 32\n", "hidden1_units = 128\n", "learning_rate = 0.005\n", "input_dimension = train_data.shape[1] # 784 = 28*28 pixels\n", "output_dimension = train_labels.shape[1] # 6 classes\n", "\n", "def layers_custom_model_fn(features, targets, mode, params):\n", " # 1. Configure the model via TensorFlow operations (using tf.layers). Note how\n", " # much simpler this is compared to defining the weight matrices and matrix\n", " # multiplications by hand.\n", " hidden_layer = tf.layers.dense(inputs=features['images'], units=hidden1_units, activation=tf.nn.relu)\n", " output_layer = tf.layers.dense(inputs=hidden_layer, units=output_dimension, activation=tf.nn.relu)\n", " \n", " # 2. Define the loss function for training/evaluation\n", " loss = tf.losses.mean_squared_error(labels=targets, predictions=output_layer)\n", " \n", " # 3. Define the training operation/optimizer\n", " train_op = tf.contrib.layers.optimize_loss(\n", " loss=loss,\n", " global_step=tf.contrib.framework.get_global_step(),\n", " learning_rate=learning_rate,\n", " optimizer=\"SGD\")\n", " \n", " # 4. Generate predictions\n", " predictions_dict = {\n", " \"classes\": tf.argmax(input=output_layer, axis=1),\n", " \"probabilities\": tf.nn.softmax(output_layer, name=\"softmax_tensor\"), \n", " \"logits\": output_layer,\n", " }\n", " \n", " # Define eval metric ops; we can also use a pre-defined function here.\n", " accuracy = tf.metrics.accuracy(\n", " labels=tf.argmax(input=targets, axis=1),\n", " predictions=tf.argmax(input=output_layer, axis=1))\n", " eval_metric_ops = {\"accuracy\": accuracy}\n", "\n", " # 5. Return predictions/loss/train_op/eval_metric_ops in ModelFnOps object\n", " return tf.contrib.learn.ModelFnOps(\n", " mode=mode,\n", " predictions=predictions_dict,\n", " loss=loss,\n", " train_op=train_op,\n", " eval_metric_ops=eval_metric_ops)\n", "\n", "\n", "layers_custom_model = tf.contrib.learn.Estimator(\n", " model_fn=layers_custom_model_fn)\n", "\n", "# Train and evaluate the model.\n", "evaluate_model(layers_custom_model, input_fn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model using canned estimators\n", "Instead of defining our own DNN classifier, TensorFlow supplies a number of *canned* estimators that can save a lot of work." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "# Model parameters.\n", "hidden1_units = 128\n", "learning_rate = 0.005\n", "input_dimension = train_data.shape[1] # 784 = 28*28 pixels\n", "output_dimension = train_labels.shape[1] # 6 classes\n", "\n", "# Our model can be defined using just three simple lines...\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "images_column = tf.contrib.layers.real_valued_column(\"images\")\n", "# Task: Use the DNNClassifier Estimator to create the model in 1 line.\n", "# SOLUTION: DNNClassifier can be used to efficiently (in lines of code) create the model.\n", "canned_model = tf.contrib.learn.DNNClassifier(\n", " feature_columns=[images_column],\n", " hidden_units=[hidden1_units],\n", " n_classes=output_dimension,\n", " activation_fn=tf.nn.relu,\n", " optimizer=optimizer)\n", "\n", "# Potential exercises: play with model parameters, e.g. add dropout" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We need to change the input_fn so that it returns integers representing the classes instead of one-hot vectors.\n", "def class_input_fn(data, labels):\n", " input_images = tf.constant(\n", " data, shape=data.shape, verify_shape=True, dtype=tf.float32)\n", " # The next two lines are different.\n", " class_labels = np.argmax(labels, axis=1)\n", " input_labels = tf.constant(\n", " class_labels, shape=class_labels.shape, verify_shape=True, dtype=tf.int32)\n", " image, label = tf.train.slice_input_producer(\n", " [input_images, input_labels], num_epochs=n_epochs)\n", " dataset_dict = dict(images=image, labels=label)\n", " batch_dict = tf.train.batch(\n", " dataset_dict, batch_size, allow_smaller_final_batch=True)\n", " batch_labels = batch_dict.pop('labels')\n", " return batch_dict, batch_labels" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Train and evaluate the model.\n", "evaluate_model(canned_model, class_input_fn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Convolutions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "tf.reset_default_graph()\n", "\n", "input_dimension = train_data.shape[1] # 784 = 28*28 pixels\n", "output_dimension = train_labels.shape[1] # 6 classes\n", "batch_size = 32\n", "\n", "data_batch = tf.placeholder(\"float\", shape=[None, input_dimension])\n", "label_batch = tf.placeholder(\"float\", shape=[None, output_dimension])\n", "\n", "def weight_variable(shape):\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial)\n", "\n", "def bias_variable(shape):\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial)\n", "\n", "def conv2d(x, W):\n", " return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n", "\n", "def max_pool_2x2(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1], padding='SAME')\n", "\n", "# Task: convert the batch_size x num_pixels (784) input to batch_size, height (28), width(28), channels\n", "# SOLUTION: reshape the input. We only have a single color channel.\n", "image_batch = tf.reshape(data_batch, [-1, 28, 28, 1])\n", "\n", "W_conv1 = weight_variable([5, 5, 1, 32])\n", "b_conv1 = bias_variable([32])\n", "\n", "h_conv1 = tf.nn.relu(conv2d(image_batch, W_conv1) + b_conv1)\n", "h_pool1 = max_pool_2x2(h_conv1)\n", "\n", "W_conv2 = weight_variable([5, 5, 32, 48])\n", "b_conv2 = bias_variable([48])\n", "\n", "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n", "h_pool2 = max_pool_2x2(h_conv2)\n", "\n", "W_fc1 = weight_variable([7 * 7 * 48, 256])\n", "b_fc1 = bias_variable([256])\n", "\n", "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*48])\n", "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n", "\n", "# Task: add dropout to fully connected layer. Add a variable to turn dropout off in eval.\n", "# SOLUTION: add placeholder variable to deactivate dropout (keep_prob=1.0) in eval.\n", "keep_prob = tf.placeholder(tf.float32)\n", "# SOLUTION: add dropout to fully connected layer.\n", "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", "\n", "W_fc2 = weight_variable([256, output_dimension])\n", "b_fc2 = bias_variable([output_dimension])\n", "\n", "output_activations = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n", "\n", "loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(labels=label_batch, \n", " logits=output_activations))\n", "\n", "# Solution: Switch from GradientDescentOptimizer to AdamOptimizer\n", "# learning_rate = 0.001\n", "# updates = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", "learning_rate = 0.001\n", "updates = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n", "\n", "with tf.Session() as sess:\n", " init = tf.global_variables_initializer()\n", " sess.run(init)\n", " \n", " random_indices = np.random.permutation(train_data.shape[0])\n", " n_epochs = 5 # how often to go through the training data\n", " max_steps = train_data.shape[0]*n_epochs // batch_size\n", " for i in range(max_steps):\n", " batch_start_idx = (i % (train_data.shape[0] // batch_size)) * batch_size\n", " batch_indices = random_indices[batch_start_idx:batch_start_idx+batch_size]\n", " batch_loss, _ = sess.run(\n", " [loss, updates], \n", " feed_dict = {\n", " data_batch : train_data[batch_indices,:],\n", " label_batch : train_labels[batch_indices,:],\n", " # SOLUTION: Dropout active during training\n", " keep_prob : 0.5})\n", " if i % 100 == 0 or i == max_steps - 1:\n", " random_indices = np.random.permutation(train_data.shape[0])\n", " print(\"Batch-Loss at iteration {}/{}: {}\".format(i, max_steps-1, batch_loss))\n", " \n", " test_predictions = sess.run(\n", " output_activations,\n", " feed_dict = {\n", " data_batch : test_data,\n", " label_batch : test_labels,\n", " # SOLUTION: No dropout during eval\n", " keep_prob : 1.0\n", " })\n", " wins = np.argmax(test_predictions, axis=1) == np.argmax(test_labels, axis=1)\n", " print(\"Accuracy on test: {}%\".format(100*np.mean(wins)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "colab": { "default_view": {}, "name": "02_Quickdraw_FFNN.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
1x0r/pspis
marks/.ipynb_checkpoints/current_marks-checkpoint.ipynb
1
7443
{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'slice'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-0962a2d3344a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\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'2016.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Итог\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Итог 2\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Итог\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Итог\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;36m22\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32mE:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2057\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2058\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2059\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32mE:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2064\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2066\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2067\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2068\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32mE:\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1382\u001b[0m \u001b[1;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1383\u001b[0m \u001b[0mcache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1385\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1386\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'slice'" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "df = pd.read_csv('2016.csv')\n", "df[\"Итог\"] = df[list(df.columns)[3:]].sum(axis=1)\n", "df[\"Итог 2\"] = df[\"Итог\"] + (100 - np.max(df[\"Итог\", :22]))\n", "df.head(len(df))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 28.0\n", "1 30.0\n", "2 55.5\n", "3 39.0\n", "4 78.5\n", "5 60.5\n", "6 28.0\n", "7 61.0\n", "8 61.0\n", "9 81.0\n", "10 47.0\n", "11 70.0\n", "12 76.0\n", "13 62.0\n", "14 60.0\n", "15 57.0\n", "16 81.0\n", "17 78.0\n", "18 69.5\n", "19 75.5\n", "20 63.0\n", "21 73.0\n", "22 100.0\n", "Name: Итог, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Итог\"]" ] } ], "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
sampathweb/movie-sentiment-analysis
02-logisitc-regression-intro.ipynb
1
453229
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Objective\n", "\n", "* Overview of ML Model Build Process\n", "* Logistic Regression Introduction\n", "* Model Evaluations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/sampathweb/miniconda3/envs/py35/lib/python3.5/site-packages/matplotlib/__init__.py:913: 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": [ "from __future__ import print_function # Python 2/3 compatibility\n", "\n", "from IPython.display import Image\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Model Building Process" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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PjR1rdXDoz2QsRTs+ut12m1xt5lLIT+cy32uxaNMGAQQQQAABBBBAAIHUFChy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\n1RP4L9jA/8Y335KWbdu5bs89/e+Sm9q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r+qjQjoVwgcB/IbwpnBIC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\n8O+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AAQQQCLYAwX+wx5/emwUI/gn+zTOCLQQQQCDgAgT//p4ABP/+Hl96hwACCCCAAAII\nIBBsAYL/YI8/vTcLEPwT/JtnBFsIIIBAwAUI/v09AQj+/T2+9A4BBBBAAAEEEEAg2AIE/8Eef3pv\nFiD4J/g3zwi2EEAAgYALEPz7ewIQ/Pt7fOkdAggggAACCCCAQLAFCP6DPf703ixA8E/wb54RbCGA\nAAIBFyD49/cEIPj39/jSOwQQQAABBBBAAIFgCxD8B3v86b1ZgOCf4N88I9hCAAEEAi5A8O/vCUDw\n7+/xpXcIIIAAAggggAACwRYg+A/2+NN7swDBP8G/eUawhQACCARcgODf3xOA4N/f40vvEEAAAQQQ\nQAABBIItQPAf7PGn92YBgn+Cf/OMYAsBBBAIuADBv78nAMG/v8eX3iGAAAIIIIAAAggEW4DgP9jj\nT+/NAgT/BP/mGcEWAgggEHABgn9/TwCCf3+PL71DAAEEEEAAAQQQCLYAwX+wx5/emwUI/gn+zTOC\nLQQQQCDgAgT//p4Az+zU5DNXHLbt5KqvjJKzPlRh+xk7EUAAAQQQQAABBBBAwP0CX7vjDvnVL39p\nauj3/vM/5UOnn27axwYCQRD4rz5Nbr3riG1Xf/bwaJk1Pb///q3Q1Mu2dBfs3LNnj0wk+HfBSNAE\nBBBAwD0CBP/uGYtitCTdHf/FqI8yEUAAAQQQQAABBBBAAAEEEHCbwFYV/H+M4N9tw0J7EEAAAQSK\nKUDwX0zd8pdN8F/+MaAFCCCAAAIIIIAAAggggAAC5RUg+C+vP7UjgAACCJRBgOC/DOglrJLgv4TY\nVIUAAggggAACCCCAAAIIIOBKAYJ/Vw4LjUIAAQQQKKYAwX8xdctf9ksva3LuRfZr/Je/dbQAAQQQ\nQAABBBBAAAEEEEAAgeIL7Nw8WqZ8kDX+iy9NDQgggAACrhEg+HfNUBSlIX8Ji5w845BEDhSleApF\nAAEEEEAAAQQQQAABBBBAwNUCxx4j8rvnx8jYo/NrJg/3zc+PsxFAAAEESixA8F9i8DJU98gTmtx0\nx2EZ+lMZKqdKBBBAAAEEEEAAAQQQQAABBMokUPW3It+5e7Rc8pn87vbXm0/wX6ZBpFoEEEAAgdwE\nCP5zc/PiWb9Ry/4MEv57cehoMwIIIIAAAggggAACtgJfu+MO+dUvf2n67Hv/+Z/yodNPN+1jA4Eg\nCpz4HpEPfSD/wD9mR/Afk+AvAggggIAnBAj+PTFMNBIBBBBAAAEEEEAAAQQQSBKYO3euPProo6b9\nO3fulGnTppn2sYEAAvkLEPznb0gJCCCAAAIlFCD4LyE2VSGAAAIIIIAAAggggAACBRQg+C8gJkUh\nkEGA4D8DEB8jgAACCLhLgODfXeNBaxBAAAEEEEAAAQQQQAABpwIE/06lOA6B/AUI/vM3pAQEEEAA\ngRIKEPyXEJuqEEAAAQQQQAABBBBAAIECChD8FxCTohDIIEDwnwGIjxFAAAEE3CVA8O+u8aA1CCCA\nAAIIIIAAAggggIBTAYJ/p1Ich0D+AgT/qQwPRWTvq33SvaNbQk9vk54dr8tA/NgJMnlGjZz/97Nk\n1sxZUnNaVfwTJ2+GXu2R0B8iUqkODstYmTq9RqrGODmTY/wjEJG+nT0ydEjvkZoFx54hNWeOz6N7\nqrxnVXmxEsZUSc30KWp2ufB1aEh6dvZJJNo01feqqVLz4ey+Q6Xs1dDL6vs6FPu+Vsks3dXl39dt\nd86RCzpel5oXe2TOtgFpOi+fuZVae+/jy2TCxZulprpHJn++W9q/PDP1wQX8hOC/gJgUhQACCCCA\nAAIIIIAAAgiUUIDgv4TYVBV4AYJ/6xRQoeS2h++XW65eJj3Wz1JtV9dL27dWyLzzpqQ6wrA/Iutq\nKmXBi4ldLT1huaHalRFtopG8K6xApEdmV06VzbFSq1sl3NOQe1BvLU/qJBTulBoXTqvIb9ZJ5VkL\nYj0XybfviZKK8M76fXWva6zzw2H8XSObDRJ6u1Vqjo19WuC/Q9tl9vGz4vO4efugNJ5b/Is4BP8F\nHkeKQwABBBBAAAEEEEAAAQRKJEDwXyJoqkFACRD8G6ZB5NVtMv+DF0iHYV92bxuka1eLnH9aurQ1\nIhsvr5T5jyRKbn0pLA1npjsncSzvfCJwqE8WHHWGrIt1Z26bhH88L/fg31qe1EtvuF2muHBaRV7e\nKJVT5sd6LpJv3xMlFeGd9fvqXtdo5y1BfOOmAWm+qDh3+8ew+zYskDOujs3kBjXvWos+7wj+Y/r8\nRQABBBBAAAEEEEAAAQS8JUDw763xorXeFiD4Hxm/oWfXyfEzDXchW8e1uk4aZuh39Kslf3Z0yGbD\nHfvWQ1t7BqWhOtVdr9YgUYTg3yoYgG1rUF+rgv/NAQ3+8+17UaeL9fvq7uB/880VMvu+GMgKGdCa\npLixv6pLzeVb1EWse0eqrVmulkf7SnGX/CH4j40xfxFAAAEEEEAAAQQQQAABbwkQ/HtrvGittwUI\n/tX4De28X46f8SWbkayR5o77Zf6nZ8p4y1IZkaG90v34uhRLAtVI5+6Q1L3fpki1sjl3/Nu5BGwf\nwX9iwAn+Exb5vFO/pKgw/JJixbZBtbZ/qguQ+VSUfO7ex29Ra/3Hon+RzgFN6op4xYHgP3kM2IMA\nAggggAACCCCAAAIIeEGA4N8Lo0Qb/SJA8L9frbV+nGGt9ZGRrVnaJpuXz5PxmR7ieWivdNzTIHO+\nGl+tfaQEdbftQXW3bdL5BP9++fLk1Q+C/wQfwX/CIud3ln+vVDfLYE+jlCb2V40+9Lq66//U+F3/\nsqhTtG/V5dybTCcS/GcS4nMEEEAAAQQQQAABBBBAwJ0CBP/uHBda5U+BwAf/226fKhfcY36Mb1NH\nr6y41MmDehOTYtuq2XLBEnP4X78mJO0LaxIHRd9ZAjq1j6V+LERB2CT4T4wywX/CItd3ZbzbP9bk\n1x+7RU6tL81d/wT/MXX+IoAAAggggAACCCCAAALeEiD499Z40VpvCwQ7+P/DNpl6wgVijP3rVoek\n88vWsN7JIA/J/TXHy5dMa//XSfdgp8w03XabIfg/FJGhoSFVoXoq65ixMvZY9U/SrwactEcdc0gk\nsn9IIupv9DV2rFSp8vJ6RdsXSRSh2hgtM9c2xkqKqH7vV+UWqrxYuU7/KiPdXe9ZTGhsVVXu9nq9\nRn/lM/ZYQ3kR9XDfSsPDffMNv60XEor9cF9j3/S+qrk1Vp+vMTx9X4pX0sN9LX2PqHEYUuXrRY3V\ny817zuYzttbvq8M1/m188v7upfDUd5vX9m+U/oPNMjnf72Sa+mw/ivTInMqp8Yej1yzvUmv9n297\naL47Cf7zFeR8BBBAAAEEEEAAAQQQQKA8AgT/5XGn1oAKaC5+7d69W1PDoi1btqworQytrouWr9cx\n/E+D1hvOo6pd7ZbyRGvY2GspMKy1zY3VN/y3bZc65OCA1ra8Iel8vV11C1doXT0DlnJSbYa13m1t\nWtNca99iddZoDYuatc4d/akKsN0/+FKXtmJh6jIbV7Zr/W/bnprYeXBQa1vaqDUuatIaFzZoLdv0\nPoW1rpXWftdojSt/qH132fCxTeqchoVNWve+RFFp34V7tWZVfuPSJq1pkSpjebs2mOqEfb0p3aP2\ni1q07l0pz7YtNbw7pLUutfZp2L9+aYsWig5lr9YQn3fqs9o2JZHH66ClPKnPby7bNWVfv9a5vllr\nqK2xnafR71B1nda0uk3r3Ze6N+G+NvP5c9uitQ1sb9MaqmPz1PBXlblifZc2kGl+WdtckLG1fl/T\nuRbnu2ftVtL2vi5NXaqMm9asDCUdou/oXtOkvvuNWpP6Xujfv7Yep/N6UGtfbvguLmrVBg7aVqF1\nLTXOjTotlO2Y2RebtPdzn/ucdvTRRyftZwcCCCCAAAIIIIAAAggggIC7BS677LL4f78OZ3Gi7dy5\n092NpnUIeFRA3Nzu4gb/A9oKQ1gWDXlX2wdm2Rh1LkoEcNF/gdW2WgJda5AoWtOaVnMIbGlX7F+E\nsrAtZeAWbeNgSGtMda7d/tpmrT9FgBfvs35BYpExzLP0z1Ju8ybrhY54SSrj79XqDcc3rO9WoX+q\niwmiXXC+ua769WnKNlQzsLXJ/H8iC9stYzB8cGh9o/k4Q9vi5iP76ld22ZZhqFa9HdQ6l9c7KrN5\nY6vJwt3Bf1iFxs6tYnYrNtlfXEoK/mubtNaVztxadzi7AFa4sbV+X1ME/8X47pknV8qt0GqznVo6\nzPbYpAud6nvh6LXbckGzujnlhbTB7c2m+Z984dNRjRkPIvjPSMQBCCCAAAIIIIAAAggggIArBQj+\nXTksNMqnAsEN/i13yephZfTO+zwHenCHOfgSqdO6THeqG4NEQ6Bud6ezXRBd22IfuqmA3nohIxbA\npv2rQryUUaoKM013pdu1x2ZfnQrJbV9Jd6Wbg31zO+u0Z59pNYWIIqkDx0R9Rt/h8lt2WO9sVr88\nWJiu7hSfqQslqe501uvvXJriPBujWF/jd2q7+I5/813c2fWxpSc5hDYF/07nvcGwZbt1PBOjr198\nKezYWueTTfBfjO+esUtp3w9oTQYbEfWrpVQX8wY6Ld+nOq07HeVIvdYLBo0dKf+NoWlvhzT1SN9E\nPdUp/n2Vtk+ZPyT4z2zEEQgggAACCCCAAAIIIICAGwUI/t04KrTJrwKBDf7DO1oS4VQ0qEoTmGUz\n+tbgS5Xdago/rUGimJbpaNKXNFHLpITfHtT6d3TaLn1SZ7OUR2iN+a5fPVhuXN2u9e4e1MLh4fJ6\nVXlNtYZQbiSgW7HNLv1LcSFhbrPW9VK/Nvj2SJn68iwj5cTCbP1vQ4fN3d4Zgv+aasOFkIWd0eWP\nzKGmfnEmOUg2DU/SBZ3GpF812AfZaumTbSFtcFD1S3kN9HVrzZYlmaL9S3GXtPVO52GLOq1160iZ\ngwNaaFOLaayNXm694z/5QpaaP9XKSvVrYJ+aW/o8UH3r3d6uNdrMLbHxMgX/+twxjru6+19f1kof\ng8EBtbTQarslk1IH1oUfW+v3NTn4L/x3zzSj029Y78a38U4UENZaLWOUNsTXT1QXNczfwUzL91jr\nqLFc+Ey0Jp93BP/56HEuAggggAACCCCAAAIIIFA+AYL/8tlTc/AEAvtw3761c+SM6ztU9jrysjxg\nNLY7+79Dsq7ueFmwJXGmWnpDGs6MPfXU+rDQ2HF10rWrXc4/LXZcbP+QbLz5eJl/X2xb/2t9aHBy\nmW2qznnxOo3nimy7c7Zc8NXNiZ2LOkX7lrpP1/B6/bFb5NT6ew17RFZs6pWmi6aY9g1v6G08X7XR\n+Jhkm4egJj2AdqSoha3Sv7JBJusPQVYPD+77RYcMTKyX8z88VqzjpC56SOfi1A9f7tuwQM64el28\njUkPa361Qyo+OCf+uf6mbnmntH+lLv5QX+OHPQ8vk6nz7jLukiTbQ3tl2VETxHRUbbP0dzTKZOtw\n7u+TZX9/htxlegi0Kj7f+Zdka+Nv6oWTjeR5JYvaJfytelsrvcSetQtk6vUJf3UHuvSGW2WKwSHp\n4b4jTalTD4NtVw+DNRwa/WToxY1yfM38kaOG/6h17CVknQfFGFv1uOeNl1fK/Edi1VtdrZ9L8vyI\nnar+OvnuGQ7P+Hbv48tkwsWJmVe/plfaF9p9R4eL2vszdfxnEsdLbauENzckmccqjry4TiprFsQ2\nRWz+XZH4cPhdz3/Mlqk3Jv790rx9UBrPNT3h3HpK1tv6w30fffRRafp/7d1PaBxXnsDxn8EhFonB\nhlmwIYF1yMI4zDCWmTkkMId4mIN7mEM6+BCHNQRFp3gPRmZBKzMHp7MLXnkPiXLxKBcP7SUTZIMH\nOcQg7c0+ZNIOOLQGPFgDNrTAgRbY0A021L7qVnW9P1VdVa2udrf6Kwiq7q5679XnvdaMf+/V782p\naQl+EEAAAQQQQAABBBBAAAEERkbgq6++kmq1arT3u+++k6NHjxrv8QIBBPogMMxzHXnm+K9etlYS\nbzfVSgfSXiEsnpmb3v1cdaNXXuu2kr3uzVspUYwyn943c/s7+wp0Gtc+UKvi/TrD/6xV8c4qX7WC\nPzG/ft1ZTezk945c8T8Xn2rIb20toa3GrblOSw+MEzxnDwa1b0I3ef9qZ0W3dY272j9hk2j1VIi+\n10GrH7Y7/hxbd2W6KZHi1eNb1hMKavV21MMhRlH2CvGiV7GAnRX//lhUK9Wt04xS66t2Ci33CZ08\n+tbffNrcjNty7fd3z7jr5Bcr57WnZJSjm9bKKqPhjr3lLpl7Vqz9PRLLV9XVrpt7bKjJN6sR23/p\nr/jftWuX9jdM/3vGcfi3HQssGAOMAcYAY4AxwBhgDDAGGAOMgeEfA2zuu/1/J1MCAlECY5vqp3rF\nCvyfSA4ARwG679mBQhX4v6RvSut+LmdicuJrhbvB5ZIWMK85EwOLTl57rTAVzKzeXvEqKmVPK12L\nlRPcrUsF561z9NKC48ZdKye/vQmoE5xWGxvf7BJ1bBWsvKz0JFF541un3iubgUB7AqR+y8w/rgKl\ny9bEQHAvxm8nWGqmmrGDr8akjFFQ+KLvE0+OrRWgDqtOf/So4i2cL3kz08X2BEDXNDJhsV0D5eq0\nqMB/t+Bzu2R3Uqe0qo2dnPo2MfCvvoX2pNx2vnuhYpojO62OePZEV1Qpdjok8++TdoUz7q0JQu1U\n/dDp3+1OaumFbx0T+B/+/+POP67oI8YAY4AxwBhgDDAGGAOMAcYAYyDtGCDwH/EPX95CoA8CYxv4\nV2lYrCDx8wv8q9QxyV2pArszxip9fSW1Co5HbFZbnF3wVr6teiptfaafqrVfQJpAdrsC68kD0duo\nznCC05OpAu+1myWzr2ajJ0oqFwrGeTPXtcCwqr5x15oYUJM9aX+WLN/Ovg3Oxq4pc5rbexFsNzjq\n2PYh8J8WRz+vXvXmjYkatx1OYDjlpNv9K+Z3Vg9Y59K3rfuyJ+rs++nvd0+nTDxuVK0nR9ynIKLK\ncCboRJ9EDK+o3exx5b49YZCyf8Oak48I/PMPiLT/gOA8xgpjgDHAGGAMMAYYA4wBxgBjYLjHwAsv\nvOA9fvw4+R+CnIEAApkFxjfH/59Ujv9TWo7/E2Vp/PlkbK5r9T8UKX/cnN9qVbuUfntg63r7c5UD\n/anKgb47qXj7OjPP/8Zfzqpc32ZOfr3EyRMzMvXOMTn267fk8Cvd823befVFilK+ovLiP9NLjDpe\nl/dPndM+sPKhO3noU957c03enXhDwt4qSqWxJJN6Mvhn63L2hdckFFDnPFbnvBw2p/mDylf+cy1f\nufpo/nJZDoanRB/tbsjqyQ9Fz1zfyfOfpm1Rpdr7Agxljv+ohm+912zK5uam1H6sSW19Te58X5HV\nqxflhr13gRo7VdVX3XL8q4klWfrX+Lz0nVb87YrsOhzm+i9cuKX2e3iz9XEufdsuOSHHv0g/v3ud\ne01z4HyfIr4XkeVsyCe7Dor+TVWpxuSk2lMj/LH/3oiopwmk+Ep4RuyR0y53n4fYa1N+4Of4//LL\nL/2J65RXcBoCCCCAAAIIIIAAAggggMAwCpw+fVo+++yzYWwabUJg5AXGNvC//r9q89qTYZg4aZPL\n1D3tBL3UZp9GUM0OqKUN1tnXiahV5zJ1JAzWrf7HUfnNf+kb7Ma1elJmLpyV998pyOTr9iSAW09c\nKWneN+7dsUl77/6mqOre/hDeW2m1LnNvh21v/vVzmfjV6bBJZ1bUhsXHwtfqaM2e7DE+zfZC7Xkg\ni36w2r4ntVlqXW2WGrYsrlzLedgD/81Nuf31kix/fUNu/PGqhD0Rd3/B+ykC/wkb0gYlOZsCaxvT\n5tK3rYqtfoqYyPBP6893L7jTlL/tsSdzUntakgOJk4jud2FSTaJUtiZRWrX/uCpH/+k3YT8fWZD6\nnY9SjGt1tdOu9N/zlHcufuD/6tWr8s0336S9hPMQQAABBBBAAAEEEEAAAQSGSEDt29ba0Hfv3r1D\n1CqagsAOE8j8jMAAL8hzc18n3YhKS1NNkcc+8fbVZrSTRkoe8RaNVD5JqUPialA5zruW276ucqXk\n1K+GrJECx3g9vWjl77fb1+XabuVufWbcu52ORu0BYCbjibv3iLzwVvoQe2PXTioerUgnr36K9htW\n2vmFYN8G+55k3kvc/7bVJst5iFP9VK/bG+tmGRN2ahy3LzuWWl9FHdZvWymftDGQS99G9ZP/dyIm\nddb2v3tRd93lPXvsaR5drmp/ZKeaEjN/v+05c/V+YpGdE+x2dTHrXJPxwE/18+KLL2a8itMRQAAB\nBBBAAAEEEEAAAQQQQACB8REY2xX/4qRo8VfQ19UK+uS12t3mftavqScJitqTBDKjUvnMa6l80q0g\nduuwr1PtvatW/P8sXPHfueZZU9Z/uCU3/nJDvvjDxXDVbucE60ClOaqrNEftO3frESnI3OxhUdld\nMv1sPtyU4n8uSOH1rTbaK4EzpVfalIu79svZTgu0VEdP7si7e49qqYCiVz5HrQqfmp2TfVlv7Mmm\nHPz9nMz8/pC7ujn1yn3LOfV1HQDzwLaNWZluXpT8ak09GfOG/mRM1CVHCjL19lvy5q/flGNvHpIb\nx1+T098HJ+a54l+l57rRTs+VS9+2bsHqpyTXbX33ArOUv+0+z/R9su9LZOHbunz0S/+vgPquTarv\nWqcPCypt1rKRNqtrC+12KTMnNVfXApI/9Ff8X7t2Tf1NyvhHKblozkAAAQQQQAABBBBAAAEEEEAA\nAQR2hsAwz3HkueLfv297w1aV7seLWcybkqnmlbRV4WqEeHLG3ojWWukt6TbkVOukvfIJfaW1tXFu\nlxbWa1Xv1vWyVzpTjF35P387XKe+fGbSOK+81qXwLB/ZK4EzrnKvXpky2hWsQrY3IS1eqkS2qnbd\n3Kw0/abFkcW133Q2WI1fEW6WYvVnRguzLPXKtu3DKuvGvSXDuzWe1Zj2N41evl3xarW614h4SsYe\np/YKeftpm9T9sGZuzqxv7ptL37aQrX7K6Jr1u+f0a7c3nD5Xf0sy/AGrr1pPUJxZbtd2z3SW6aVu\nrXA/22a73ALdd1jx75rwDgIIIIAAAggggAACCCCAAAIIIKAL+JsjDu1P3oH/+m03hYla9dqzR+2m\nFUhTQVK3PDuQKN7ygxRVPr3vzRmTCjGB/0ZC5K9R9ypX3fvWg6h2mg/9sxQtjT/FDghmDXbb6UlU\nahP/xww0T3orcfmDrMCxZEmN4lcUEeT2A+4zRr8UvEqazejVdUX9uqwWfnv0H9s2Y4BaLyo4rl6y\nJ4qK3sqDhPGlEh0tHNEnqNyJLTvw706OBS0wf9vBfSNFUB5926re/r52mdjpw3fPvOOEVz1POm2V\nGzVm1BivflowJnzcv2EJ7bLLzfo9Syje/5jAfwokTkEAAQQQQAABBBBAAAEEEEAAgbEWGOvAv6cy\nzDsr9NUK/FSBW3vYqNz+BT2Q2zqOyvduBxLFm7kaF6kOK6l/u2AE40RKnfz4jbVlb2666BW2Aq4z\n15PLs4Oo/qreIKR731pZL2lz8T+ueFOt+570CiemvKlZa/8AOyCYOdit7KbNoPKtuyvpA+j2SmZR\nkwSPQuP4o7DPJo8XvanpORUAD89emTWfkJi6Ug0/jDlq3LH6M7OFVbBtu+3Af3jPwUr/4AkLq2bz\nZf2Ws8dE+V4wstqnOoF/K7+8WWDwyp5QEK90UxvnOfVt1JM2+qr6fn/3grtN99vuIzUZmGbSSSv8\n1nlz7C6qJzkWjuvfMTP3v3Zp/KG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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(\"images/model-pipeline.png\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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RYeDxR3r5vFZVJgTlGXZeQwixodjvUwoLJk7zVhnX/axrtAD0QojlQBTwgZTy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\nBQzCHZ+maVo0KKJ607EfsFsptQdARN7HMyLxXaU2NwDvKM/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"text/plain": [ "<matplotlib.figure.Figure at 0x10db03be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "centers = np.array([[0, 0]] * 100 + [[1, 1]] * 100)\n", "np.random.seed(42)\n", "X = np.random.normal(0, 0.2, (200, 2)) + centers\n", "y = np.array([0] * 100 + [1] * 100)\n", "\n", "plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.RdYlBu)\n", "plt.colorbar();" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.09934283, -0.02765286],\n", " [ 0.12953771, 0.30460597],\n", " [-0.04683067, -0.04682739],\n", " [ 0.31584256, 0.15348695],\n", " [-0.09389488, 0.10851201]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X[:5]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(array([0, 0, 0, 0, 0]), array([1, 1, 1, 1, 1]))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[:5], y[-5:]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Logistic Regression - Model\n", "\n", "Take a weighted sum of the features and add a bias term to get the logit.\n", "Sqash this weighted sum to arange between 0-1 via a Sigmoid function.\n", "\n", "* Sigmoid Function\n", "\n", "<img src=\"images/sigmoid.png\",width=500>\n", "\n", "\n", "$$f(x) = \\frac{e^x}{1+e^x}$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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oQAEKUIACFKAABShAAQpQgAIUoAAFfBBggN0HNJ5CAQpQgAIUoAAFKEABClCAAhSgAAUo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H59dJtTzJvU6KzyUoXycrwfnVoqsy1hPwQQQAABBBBAIMcFTM31lBrS5uTmP8xzs+ZsjneIEyKQ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\nwFy4FEbPftAnjoHWvXOQ9IrdoED4CBhDX4L5/kyxMKWYvV6pQ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IKoKth+8DCQJw+0BuLX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\n4rYbUp7HAQQiA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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(\"images/logistic-regression.png\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "## Build the Model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "## Step 1 - Instantiate the Model with Hyper Parameters (We don't have any here)\n", "model = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Step 2 - Fit the Model\n", "model.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Step 3 - Evaluate the Model\n", "model.score(X, y)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def plot_decision_boundaries(model, X, y):\n", " pred_labels = model.predict(X)\n", " plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.RdYlBu,\n", " vmin=0.0, vmax=1)\n", " xx = np.linspace(-1, 2, 100)\n", "\n", " w0, w1 = model.coef_[0]\n", " bias = model.intercept_\n", " yy = -w0 / w1 * xx - bias / w1\n", " plt.plot(xx, yy, 'k')\n", " plt.axis((-1,2,-1,2))\n", " plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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qrG9iSzNxbd8Rhy/ZZndJWWt9vLjj7edo9OTAPOe1eOXW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\nu9pcsr4sMDCQF198keeee47Vq1czcuRITpw4YXV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"text/plain": [ "<matplotlib.figure.Figure at 0x1175bbe80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_decision_boundaries(model, X, y)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Dataset - Take 2" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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eUEHKul3beF5+sQdVqnitz8hII/fc1Zqff92a06hYqxUIIfB4pN8mqV6v4esv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\nmHlF2OWKuAvwBhAJ/EcIseuamLhLRGn6JgghIoBfgETgJDBCSpnuYtx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"text/plain": [ "<matplotlib.figure.Figure at 0x11777bbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "centers = np.array([[0, 0]] * 100 + [[1, 1]] * 100)\n", "np.random.seed(42)\n", "X = np.random.normal(0, 0.5, (200, 2)) + centers\n", "y = np.array([0] * 100 + [1] * 100)\n", "\n", "plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.RdYlBu)\n", "plt.colorbar();" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.92\n" ] } ], "source": [ "# Instantiate, Fit, Evalaute\n", "model = LogisticRegression()\n", "model.fit(X, y)\n", "print(model.score(X, y))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "y_pred = model.predict(X)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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hwyCaNAqmTp0AOravmhlWPnH8Q0wc/1ABfon7CYHSBXli8oNHYd9jjBkzhnLl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UF/0yBwHugO988GuobZNRC8HT5B0JWSpebbgR3AMWA7MHOkZoKRDvvDcjgLzaP5DGgCmsK6CCZb\nPwEcJGKUTs40rwr9uR/YBSzOWO9HBNNi/hNexy8XwMdxmvPm4yUE74MOAD1haci7n8tZPDXdcRyn\nIBStS8RxHOeGxQO24zhOQfCA7TiOUxA8YDuO4xQED9iO4zgFwQO24zhOQfCA7TiOUxD+Ba7RaMNd\naOqsAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x117776630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_decision_boundaries(model, X, y)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Other Evaluation Methods\n", "\n", "* Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[90, 10],\n", " [ 6, 94]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm = confusion_matrix(y, y_pred)\n", "cm" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Predicted</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>All</th>\n", " </tr>\n", " <tr>\n", " <th>Actual</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>90</td>\n", " <td>10</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>94</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>All</th>\n", " <td>96</td>\n", " <td>104</td>\n", " <td>200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Predicted 0 1 All\n", "Actual \n", "0 90 10 100\n", "1 6 94 100\n", "All 96 104 200" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(y, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)" ] }, { "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
jsmanrique/grimoirelab-personal-utils
Light Git index generator.ipynb
1
5707
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Git Index Generator\n", "\n", "This notebook generates a ElasticSearch (ES) index with information about git (commits, files, lines added, lines removed, commit authors) for a given list of git repositories defined in a `settings.yml` file.\n", "\n", "Let's start by importing the utils python script, setting up the connection to the ES server and defining some variables\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import utils\n", "utils.logging.basicConfig(level=utils.logging.INFO)\n", "\"\"\" You can comment previous line if you don't want logging information\n", "\"\"\"\n", "\n", "settings = utils.read_config_file('settings.yml')\n", "es = utils.establish_connection(settings['es_host'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let's define an ES index mapping for the data that will be uploaded to the ES server" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "MAPPING_GIT = {\n", " \"mappings\": {\n", " \"item\": {\n", " \"properties\": {\n", " \"date\": {\n", " \"type\": \"date\",\n", " \"format\" : \"E MMM d HH:mm:ss yyyy Z\",\n", " \"locale\" : \"US\"\n", " },\n", " \"commit\": {\"type\": \"keyword\"},\n", " \"author\": {\"type\": \"keyword\"},\n", " \"domain\": {\"type\": \"keyword\"},\n", " \"file\": {\"type\": \"keyword\"},\n", " \"added\": {\"type\": \"integer\"},\n", " \"removed\": {\"type\": \"integer\"},\n", " \"repository\": {\"type\": \"keyword\"}\n", " }\n", " }\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's give a name to the index to be created, and create it.\n", "\n", "**Note**: `utils.create_ES_index()` removes any existing index with the given name before creating it" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "index_name = 'git'\n", "utils.create_ES_index(es, index_name, MAPPING_GIT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import the git backend from [Perceval](http://github.com/grimoirelab/perceval)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from perceval.backends.core.git import Git" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each repository in the settings file, let's get its data, create a `summary` object with the desired information and upload data to the ES server using ES `bulk` API." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for repo_url in settings['git']:\n", " \n", " repo_name = repo_url.split('/')[-1]\n", " repo = Git(uri=repo_url, gitpath='/tmp/'+repo_name)\n", " \n", " utils.logging.info('Go for {}'.format(repo_name))\n", " \n", " items = []\n", " bulk_size = 10000\n", " \n", " for commit in repo.fetch():\n", " \n", " author_name = commit['data']['Author'].split('<')[0][:-1]\n", " author_domain = commit['data']['Author'].split('@')[-1][:-1]\n", " \n", " for file in commit['data']['files']:\n", " if 'added' not in file.keys() or file['added'] == '-':\n", " file['added'] = 0\n", " if 'removed' not in file.keys() or file['removed'] == '-':\n", " file['removed'] = 0\n", "\n", " summary = {\n", " 'date': commit['data']['AuthorDate'],\n", " 'commit': commit['data']['commit'],\n", " 'author': author_name,\n", " 'domain': author_domain,\n", " 'file': file['file'],\n", " 'added': file['added'],\n", " 'removed': file['removed'],\n", " 'repository': repo_name\n", " }\n", " \n", " items.append({'_index': index_name, '_type': 'item', '_source': summary})\n", " \n", " if len(items) > bulk_size:\n", " utils.helpers.bulk(es, items)\n", " items = []\n", " utils.logging.info('{} items uploaded'.format(bulk_size))\n", " \n", " if len(items) != 0:\n", " utils.helpers.bulk(es, items)\n", " utils.logging.info('Remaining {} items uploaded'.format(len(items)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
adukic/nd101
face_generation/dlnd_face_generation.ipynb
1
3591033
null
mit
childresslab/MicrocavityExp1
tools/.ipynb_checkpoints/Find taget mode-checkpoint.ipynb
1
31685
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Find target modes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load file" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filepath = r'C:\\Users\\ChildressLab\\Desktop\\Rasmus notes\\Measurements'\n", "filename = '2017-08-14_141938_full_sweep_data'\n", "delimiter = '\\t'\n", "\n", "with open(os.path.join(filepath, filename), 'rb') as file:\n", " data = np.loadtxt(file, delimiter=delimiter)\n", "\n", "times = data[0:4,2000:]\n", "volts = data[4:8,2000:]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.04, -0.04, -0.04, ..., -0.04, -0.04, -0.04])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.argmin(volts[1])\n", "volts[1]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "514554 46572 982536\n" ] }, { "data": { "image/png": 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r51l+NOx7ztQuizcbzv2D4K44Mij+NgAA54H+sO85K2TZ85T57x6tXuaqjvDY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\nJdSRrRvh2hM748qB4Ud8TMaWmIL85hg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vfoY5sRGRBjuSEqWj4Q/oly94RZ1wANrnqSQ4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\ndAAAFaIDAKgQHQBAxQNZcscy+IZnVgAAAABJRU5ErkJggg==\n" }, "metadata": { "image/png": { "height": 361, "width": 541 } }, "output_type": "display_data" } ], "source": [ "total_trace = 2 # sec\n", "ramp_period = 1.5 #sec\n", "period_index = len(times[0])* ramp_period / total_trace\n", "ramp_mid = np.argmin(volts[1])\n", "\n", "low_index = ramp_mid - int(period_index/2)\n", "high_index = ramp_mid + int(period_index/2)\n", "print(ramp_mid, low_index, high_index)\n", "volts_trim = volts[:,low_index:high_index]\n", "times_trim = times[:,low_index:high_index]\n", "\n", "plt.plot(times_trim[3],volts_trim[3]/np.mean(volts_trim[3]))\n", "plt.plot(times_trim[1],volts_trim[1])\n", "plt.plot(times_trim[0],volts_trim[0]/np.mean(volts_trim[0]))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit scan" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initial guess\n", "freq0 = 2/3\n", "start0 = 0.0\n", "stop0 = -3.0\n", "phase0 = 0.0\n", "\n", "#Fitting setup\n", "parameter_guess = [start0, stop0, freq0, phase0]\n", "func = mynicard.sweep_function\n", "xdata = times_trim[1]\n", "ydata = volts_trim[1]\n", "\n", "#Actual fitting\n", "popt, pcov = curve_fit(func, xdata, ydata, parameter_guess)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Guess plot\n", "ramp = mynicard.sweep_function(times[1], start0, stop0, freq0, phase0)\n", "plt.plot(times_trim[1],volts_trim[1])\n", "plt.plot(times[1],ramp)\n", "plt.show()\n", "\n", "# fit plot\n", "plt.plot(times_trim[1],volts_trim[1])\n", "plt.plot(xdata, func(xdata, *popt), 'r-', linewidth = 3, label='fit')\n", "plt.show()\n", "print(popt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Qudi", "language": "python", "name": "qudi" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": "3.6.0" }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
oseledets/talks-online
siamcse-2015/talk.ipynb
1
23365
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Low-rank approximation of matrices and tensors with application to dynamical and optimization problems\n", "## Ivan Oseledets\n", "## Skolkovo Institute of Science and Technology (Skoltech), Russia" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Low-rank approximation\n", "Low-rank approximation plays an increasingly important role in **fast methods** for **large problems**\n", "\n", "- Integral equations & fast direct solvers for sparse matrices (H-matrices)\n", "- High-dimensional PDEs (Fokker-Planck) \n", "- Stochastic PDEs and multiparametric PDEs, uncertainty quantification\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Low-rank approximation of matrices\n", "In two dimensions, we have the SVD:\n", "$$A \\approx UV^{\\top}, \\quad U \\in \\mathbb{R}^{n \\times r}, \\quad V \\in \\mathbb{R}^{m \\times r}$$\n", "The approximation is sought on the **manifold** of low-rank matrices." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Low-rank approximation of tensors\n", "There are different ways to generalize SVD to tensors\n", "- CP-format: $A(i_1, \\ldots,i_d) \\approx \\sum_{\\alpha=1}^r U_1(i_1, \\alpha) \\ldots U_d(i_d, \\alpha)$\n", "- Subspace approaches (Tucker format, HT-format, TT-format),\n", " that represent a \"good\" manifold as an intersection of \"low-rank\" manifolds in $\\mathbb{R}^{n^d}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example: Tensor Train decomposition\n", "Given a tensor $A(i_1, \\ldots, i_d)$ we define unfoldings $A_1, \\ldots, A_k$ as **reshapes** of the tensor $\\mathbf{A}$,\n", "\n", "and the TT-manifold is specified by the ranks of these unfoldings" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## TT-decomposition: algebraic structure\n", "$$A(i_1, \\ldots, i_d) = G_1(i_1) \\ldots G_d(i_d),$$\n", "where $G_k(i_k)$ is $r_{k-1} \\times r_k$, $r_0 = r_d = 1$.\n", "\n", "**Hypothesis:** if these is a good algorithm for the low-rank matrix approximation, then there is a good algorithm for the subspace tensor representation." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Algorithms in the TT-format\n", "- We can do basic linear algebra (add, multiply, compute norms)\n", "- We can do full to TT compression \n", "- We can do \"rounding\" (reapproximate a given tensor with another with guaranteed complexity)\n", "- We can do cross approximation of the tensor\n", "- It is all in the open-source software (TT-Toolbox in MATLAB and Python)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## General concept for solving problems\n", "We have selected our manifold $\\mathcal{M}$ of structured solutions, and we want to approximate the solution of our original problem. \n", "\n", "For example, a linear system: $A(X) = f$, \n", "\n", "where the solution $X$ can be indexed by $d$ indices (high-dim PDE, QTT-format)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Options for finding a manifold solution\n", "**Option 1**: Do ordinary method (preconditioned Krylov) with tensor arithmetics, \n", " $X_{k+1} = P(F(X_k))$\n", " \n", "**Option 2**: Reformulate as minimization problem, $(Ax, x) - 2(f, x) \\rightarrow \\min$ and replace the minimization by the minimization over the manifold.\n", "\n", "\n", "**Minimization over the low-rank manifold is crucially important!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Dynamical low-rank approximation\n", "Dynamical low-rank approximation is yet another crucial concept. \n", "\n", "We have time-varying matrix $A(t)$, and we want to approximate by low-rank matrix $A(t)$ \n", "\n", "(the time $t$ later can be discrete and generalize to any iterative method)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Dirac-Frenkel principle\n", "\n", "Dirac-Frenkel principle gives the variational formulation for dynamical low-rank approximation\n", "$$\\Vert \\frac{dA}{dt} - \\frac{dX}{dt} \\Vert \\rightarrow \\min$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Equations of motion in 2D case\n", "(Lubich, Koch, H.-D. Meyer,...) \n", "\n", "$$\\begin{split}\n", "&\\frac{dU}{dt} S = \\frac{dA}{dt} V,\\\\\n", "&\\frac{dV}{dt} S^{\\top} = \\frac{dA^{\\top}}{dt} U, \\\\\n", "&\\frac{dS}{dt} = U^{\\top} \\frac{dA}{dt} V.\n", "\\end{split}$$\n", "Suppose, you start from rank-$1$ and evolve into rank-$10$ case: the **matrix $S$ will be singular!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Equations of motion, 2D case (2)\n", "The equations of motion can be rewritten for the full matrix \n", "$$\n", " \\frac{dX}{dt} = P_X(\\frac{dA}{dt}),\n", "$$\n", "where $P_X$ is the **projector onto the tangent space**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Projector-splitting\n", "**Theorem:** $P_X$ can be represented as\n", "$$P_X(Z) = Z - (I - UU^{\\top}) Z (I - VV^{\\top}) = UU^{\\top} Z + Z VV^{\\top} - UU^{\\top} Z VV^{\\top},$$\n", "\n", "$$P_X(Z) = P_1 + P_2 - P_3$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Integration of the substeps\n", "**Theorem** (O., Lubich, 2014): The equations with $P_1$, $P_2$, $P_3$ can be efficiently integrated\n", "\n", "- **Step \"L\":** $$\\frac{dX}{dt} = UU^{\\top}\\frac{dA}{dt} \\rightarrow \\frac{d(VS^{\\top})}{dt} = \\frac{dA^{\\top}}{dt} U, \\frac{dU}{dt}=0,$$\n", "\n", "\n", " Thus $L_1 = L_0 + (A_1 - A_0)^{\\top} U_0, U_1 = U_0$.\n", "- **Step \"K\":** $$\\frac{dX}{dt} = \\frac{dA}{dt} VV^{\\top} \\rightarrow \\frac{d(US)}{dt} = \\frac{dA}{dt} V, \\frac{dV}{dt}=0,$$\n", " Thus $K_1 = K_0 + (A_1 - A_0) V_0, V_1 = V_0$\n", "\n", "- **Step \"S\":** $$\\frac{dX}{dt} = UU^{\\top}\\frac{dA}{dt} VV^{\\top} \\rightarrow \\frac{d(S)}{dt} = U\\frac{dA}{dt} V, \\frac{dV}{dt}=0, \\frac{dU}{dt} = 0,$$ \n", "\n", "\n", " Thus $S_1 = S_0 \\mathbf{-} U^{\\top}_0(A_1 - A_0) V_0, V_1 = V_0, U_1 = U_0$ \n", " Here $A_0 = A(t), A_1 = A(t + \\tau)$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## K-S-L order vs. K-L-S order\n", "We can do a short demo of the different orders of splitting by taking two values $A_0, A_1$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error: 1.64942923088e-15\n" ] } ], "source": [ "import numpy as np\n", "\n", "n = 5\n", "r = 2\n", "m = 5\n", "def random_usv(n, m, r):\n", " u = np.random.randn(n, r)\n", " u = np.linalg.qr(u)[0]\n", " v = np.random.randn(m, r)\n", " v = np.linalg.qr(v)[0]\n", " s = np.random.randn(r, r)\n", " return u, s, v\n", "u0, s0, v0 = random_usv(n, m, r)\n", "u1, s1, v1 = random_usv(n, m, r)\n", "\n", "#Generate A0, A1 from the manifold\n", "A0 = u0.dot(s0).dot(v0.T)\n", "A1 = u1.dot(s1).dot(v1.T)\n", "\n", "u = u0.copy(); v = v0.copy(); s = s0.copy()\n", "# K-step\n", "K = u.dot(s); K = K + (A1 - A0).dot(v)\n", "u, s = np.linalg.qr(K)\n", "# S-step\n", "s = s - u.T.dot(A1 - A0).dot(v)\n", "\n", "# L-step\n", "L = v.dot(s.T); L = L + (A1 - A0).T.dot(u)\n", "v, s = np.linalg.qr(L)\n", "s = s.T\n", "\n", "\n", "Appr = u.dot(s).dot(v.T)\n", "print 'Error:', np.linalg.norm(A1 - Appr)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Exactness result\n", "The order of the splitting steps is crucial, moreover, we have the following **exactness result**\n", "\n", "If $A_0 = U_0 S_0 V^{\\top}_0$ and $A_1$ is of rank $r$, the projector-splitting scheme in the **KSL** order is **exact**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Projector-splitting in the dD-case\n", "$$\n", " \\frac{Y(t)}{dt} = P_{Y(t)}(\\frac{dA(t)}{dt}), \\qquad Y(t_0)=Y_0\\in \\mathcal{M},\n", "$$\n", "$$\n", " P_X =\\sum_{k=1}^{d-1} (P_{\\le k-1} P_{\\ge k+1} - P_{\\le k} P_{\\ge k+1}) + P_{\\le d-1} P_{\\ge d+1},\n", "$$\n", "$$\n", "\\begin{split}\n", "&P_{\\le k}\\colon \\R^{n_1 \\times \\cdots \\times n_d} \\to T_X \\mathcal{M}, \\ Z \\mapsto \\mathrm{Ten}_k ( \\matP_{\\le k} \\matZ^{\\langle k \\rangle})\\\\\n", " &P_{\\ge i}\\colon \\R^{n_1 \\times \\cdots \\times n_d} \\to T_X \\mathcal{M}, \\ Z \\mapsto \\mathrm{Ten}_{k-1} ( \\matZ^{\\langle k-1 \\rangle} \\matP_{\\ge k} ) .\n", " \\end{split}\n", "$$\n", "\n", "$\n", "\\matP_{\\le k} = \\matQ_{\\le k}\\matQ_{\\le k}^\\top, \\qquad\\hbox{and}\\qquad\n", "\\matP_{\\ge k} = \\matQ_{\\ge k}\\matQ_{\\ge k}^\\top,\n", "$\n", "\n", "Lubich, Vandreycken, O., SINUM, 2015 - accepted." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## KSL as retraction\n", "Thus, KSL can be used as **rectraction** from the tangent space to the manifold, \n", "i.e. $A_1 = A_0 + \\delta A$, then KSL gives simple formulas for such projection (and it is a second-order retraction as shown in O., Absil, 2014)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Riemannian optimization\n", "The simplest \"geometrical method\" for the optimization over the low-rank manifold is the projected gradient iteration:\n", "\n", "$$\n", " X_{k+1} = R(X_k + P (\\mathrm{grad}(F))),\n", "$$\n", "where $P$ is the projection onto the tangent space, $R$ brings the iterate backs to the manifold.\n", "Suggestion: use KSL for the retraction. The gradient method often has slow convergence.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Riemannian optimization \"for the poor\"\n", "\n", "There are several second-order methods; a simpler way is a Gauss-Newton type method.\n", "Suppose our initial point is $x_0$, and the vector on the tangent space can be parametrized as $Qp$.\n", "\n", "Then, consider the next step as a minimization of the target functional over the full tangent space.\n", "\n", "For quadratic functionals (i.e., tensor completion, linear systems, eigenvalue problems)\n", "\n", "we have \n", "$$Q^{\\top} A Q p = Q^{\\top} f$$ \n", "\n", "as the system for $p$. For the TT-case, vector $p$ consists of $\\delta G_k$, so the system is meant to be solved iteratively (and preconditioned by the ALS method). The matrix-by-vector product has a simple meaning.\n", " \n", "Preliminary implementation for the tensor completion shows that it works well (and ALS may not always work well!).\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Numerical experiments\n", "Now we can go to numerical experiments" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Vibrational spectra\n", "We compare the TT-KSL scheme with the [MCTDH package](http://www.pci.uni-heidelberg.de/cms/mctdh.html) for the benchmark problem with [Henon-Heiles potential](http://dx.doi.org/10.1063/1.1521129) \n", "\\begin{equation}\n", " \\frac{d\\psi}{dt} = i H \\psi, \\quad \\psi(0) = \\psi_0,\n", "\\end{equation}\n", "where $H$ has the form\n", "\\begin{equation}\n", " \\def\\Hlap{-\\frac12 \\Delta}\n", " \\def\\Hhar{\\frac12 \\sum_{k=1}^f q^2_k}\n", " \\def\\Hanh{\\sum_{k=1}^{f-1}\\left(q^2_k q_{k+1} - \\frac13 q^3_{k+1} \\right)}\n", " H = \\Hlap + \\underbrace{\\Hhar + \\overbrace{\\lambda \\Hanh}^{\\textrm{anharmonic part}}}_{\\textrm{Henon-Heiles potential}~V(q_1,\\ldots,q_f)}.\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Spectra comparison\n", "<div style=\"float: left; width: 40%; margin-right: 5%; margin-bottom: 0.5em;\">\n", "<p style=\"text-align:left\"> Spectra for a 10D problem </p>\n", "<img src=\"ksl_mctdh_plot.png\" >\n", "</div>\n", "<div style=\"float: left; width: 40%; margin-right: 5%; margin-bottom: 0.5em;\">\n", "<p style=\"text-align:left\"> Zoomed spectra for a 10D problem</p>\n", "<img src=\"ksl_mctdh_plot_zoom.png\" >\n", "</div>\n", "<p style=\"clear: both;\">\n", "Computational time: **54354** (MCTDH) vs **4425** (TT-KSL)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Low-rank in space & time for dynamical problems\n", "We want to utilized the low-rank in space and time:\n", "\n", "Given an ODE \n", "\n", "$\\frac{dy}{dt} = Ay, \\quad y(0) = y_0$ \n", "\n", "we want to find an optimal small-dimensional subspace $U$ for the solution:\n", "\n", "$$y(t) \\approx U c(t), \\quad U^* U = I_r.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lyapunov equation\n", "The optimal $U$ can be recovered from the leading eigenvectors of the solution of the Lyapunov equation:\n", "$$\n", " A X + XA^* = -y_0 y^*_0,\n", "$$\n", "\n", "$$\n", "X \\approx U Z U^{\\top}.\n", "$$\n", "In http://arxiv.org/abs/1410.3335 (D. Kolesnikov, O.) we have proposed to use\n", "$$\n", " F(U) = \\int^{\\infty}_0 \\Vert y - U e^{Bt} U^{*} y_0 \\Vert^2 dt,\n", "$$\n", "\n", "where $B = U^* A U$ as the functional for low-rank solution of the Lyapunov equation.\n", "\n", "This functional is computable, given an unitary $U$!\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ALR method\n", "The final **alternating low-rank method** is very simple to implement:\n", "\n", "$$U_{k+1} = \\mathrm{orth}([U_k, v_k, w_k]),$$\n", "where $w$ is the next Arnoldi vector, and $v_k$ is the rational Krylov vector\n", "\n", "$$v_k = (A + \\lambda_k I)^{-1} y_0,$$\n", "and $\\lambda_k$ is computed as \n", "\n", "$$\n", " \\lambda_k = q^* B_k q, \n", "$$\n", "\n", "where $q$ is the last column of the solution $Z$ of the \"small\" Lyapunov equation\n", "$$ B_k Z + ZB_k^* = c_0 c^*_0$$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Papers and software\n", "My webpage: http://oseledets.github.io \n", "\n", "**Software**\n", "- TT-Toolbox: https://github.com/oseledets/ttpy (Python)\n", "- https://github.com/oseledets/TT-Toolbox (MATLAB)\n", "\n", "**Papers**\n", "- I. V. Oseledets. Tensor-train decomposition. SIAM J. Sci. Comput., 33(5):2295–2317, 2011\n", "- [D. A. Kolesnikov and I. V. Oseledets. From low-rank approximation to an efficient rational Krylov subspace method for the Lyapunov equation. arXiv preprint 1410.3335, 2014](http://arxiv.org/pdf/1410.3335v2.pdf)\n", "- \n", "- Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator for dynamical low-rank approximation. BIT, 54(1):171–188, 2014\n", "- [Christian Lubich, Ivan Oseledets, and Bart Vandereycken. Time integration of tensor trains. arXiv preprint 1407.2042, 2014.](http://arxiv.org/abs/1407.2042)\n", "- [Jutho Haegeman, Christian Lubich, Ivan Oseledets, Bart Vandereycken, and Frank Verstraete. Unifying time evolution and optimization with matrix product states. arXiv preprint 1408.5056, 2014.](http://arxiv.org/abs/1408.5056)\n", "- Grasedyck, L. and Kressner, D. and Tobler, C. A literature survey of low-rank tensor approximation techniques. \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Conclusions\n", "- Manifold optimization is the key to fast algorithms\n", "- Projector-splitting (KSL) scheme is extremely simple and efficient " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Publications, software\n", "- http://oseledets.github.io - group website, papers, etc.\n", "- http://github.com/oseledets/TT-Toolbox\n", "- http://github.com/oseledets/ttpy" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##### Questions?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " /*width:80%;*/\n", " /*margin-left:auto !important;\n", " margin-right:auto;*/\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " }\n", " h3{\n", "\t\tfont-family: 'Fenix', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", "\th4{\n", "\t\tfont-family: 'Fenix', serif;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\t \n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " font-size: 160%;\n", " line-height: 121%;\n", " /*width:70%;*/\n", " /*margin-left:auto;*/\n", " margin-right:auto;\n", " }\n", " li {\n", " line-height: 121%;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", "\t\tline-height: 110%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\t\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #CD2305;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", ".floated_img\n", "{\n", " float: left;\n", " }\n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML at 0x11b6c8b10>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../common/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
hanhanwu/Hanhan_Data_Science_Practice
sequencial_analysis/after_2020_practice/basic_lstm_gru.ipynb
1
127254
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Forecast with Basic LSTM and GRU " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "from sklearn.preprocessing import MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(43824, 13)\n" ] }, { "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>No</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>hour</th>\n", " <th>pm2.5</th>\n", " <th>DEWP</th>\n", " <th>TEMP</th>\n", " <th>PRES</th>\n", " <th>cbwd</th>\n", " <th>Iws</th>\n", " <th>Is</th>\n", " <th>Ir</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1021.0</td>\n", " <td>NW</td>\n", " <td>1.79</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-12.0</td>\n", " <td>1020.0</td>\n", " <td>NW</td>\n", " <td>4.92</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>6.71</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-14.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>9.84</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>NaN</td>\n", " <td>-20</td>\n", " <td>-12.0</td>\n", " <td>1018.0</td>\n", " <td>NW</td>\n", " <td>12.97</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " No year month day hour pm2.5 DEWP TEMP PRES cbwd Iws Is Ir\n", "0 1 2010 1 1 0 NaN -21 -11.0 1021.0 NW 1.79 0 0\n", "1 2 2010 1 1 1 NaN -21 -12.0 1020.0 NW 4.92 0 0\n", "2 3 2010 1 1 2 NaN -21 -11.0 1019.0 NW 6.71 0 0\n", "3 4 2010 1 1 3 NaN -21 -14.0 1019.0 NW 9.84 0 0\n", "4 5 2010 1 1 4 NaN -20 -12.0 1018.0 NW 12.97 0 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/pm25.csv')\n", "\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train: (33096, 15)\n", "Shape of test: (8661, 15)\n" ] } ], "source": [ "df.dropna(subset=['pm2.5'], axis=0, inplace=True)\n", "df.reset_index(drop=True, inplace=True)\n", "\n", "df['datetime'] = df[['year', 'month', 'day', 'hour']].apply(\n", " lambda row: datetime.datetime(year=row['year'], \n", " month=row['month'], day=row['day'],hour=row['hour']), axis=1)\n", "df.sort_values('datetime', ascending=True, inplace=True)\n", "\n", "scaler = MinMaxScaler(feature_range=(0, 1))\n", "df['scaled_pm2.5'] = scaler.fit_transform(np.array(df['pm2.5']).reshape(-1, 1))\n", "\n", "split_date = datetime.datetime(year=2014, month=1, day=1, hour=0) \n", "df_train = df.loc[df['datetime']<split_date]\n", "df_val = df.loc[df['datetime']>=split_date]\n", "df_val.reset_index(drop=True, inplace=True)\n", "print('Shape of train:', df_train.shape)\n", "print('Shape of test:', df_val.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def makeXy(ts, nb_timesteps):\n", " \"\"\"\n", " Input: \n", " ts: original time series\n", " nb_timesteps: number of time steps in the regressors\n", " Output: \n", " X: 2-D array of regressors\n", " y: 1-D array of target \n", " \"\"\"\n", " X = []\n", " y = []\n", " for i in range(nb_timesteps, ts.shape[0]):\n", " X.append(list(ts.loc[i-nb_timesteps:i-1]))\n", " y.append(ts.loc[i])\n", " \n", " X, y = np.array(X), np.array(y)\n", " return X, y" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train arrays: (33089, 7) (33089,)\n", "Shape of validation arrays: (8654, 7) (8654,)\n" ] } ], "source": [ "X_train, y_train = makeXy(df_train['scaled_pm2.5'], 7)\n", "X_val, y_val = makeXy(df_val['scaled_pm2.5'], 7)\n", "\n", "print('Shape of train arrays:', X_train.shape, y_train.shape)\n", "print('Shape of validation arrays:', X_val.shape, y_val.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of arrays after reshaping: (33089, 7, 1) (8654, 7, 1)\n" ] } ], "source": [ "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n", "X_val = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n", "print('Shape of arrays after reshaping:', X_train.shape, X_val.shape)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, GRU\n", "from tensorflow.keras.layers import Dense, Dropout, Input\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "\n", "from sklearn.metrics import mean_absolute_error\n", "\n", "tf.random.set_seed(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_6\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_6 (LSTM) (None, 32) 4352 \n", "_________________________________________________________________\n", "dropout_6 (Dropout) (None, 32) 0 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 1) 33 \n", "=================================================================\n", "Total params: 4,385\n", "Trainable params: 4,385\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(LSTM(32, input_shape=(X_train.shape[1:])))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(1, activation='linear'))\n", "\n", "model.compile(optimizer='rmsprop', loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n", "Epoch 1/20\n", "2069/2069 [==============================] - 25s 10ms/step - loss: 0.0262 - mae: 0.0262 - val_loss: 0.0133 - val_mae: 0.0133ss: 0.0276 - ETA: 2 - ETA: 1s - - ETA: 0s - loss: 0.0264 -\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_lstm_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_lstm_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0153 - mae: 0.0153 - val_loss: 0.0138 - val_mae: 0.0138 3s - loss: 0.0153 - - ETA: 2s - loss: 0.0153 - ETA: 1s - loss: 0.0153 - mae: - ETA: 1s - loss: 0.0 - ETA: 0s - loss: 0.0153 - \n", "Epoch 3/20\n", "2069/2069 [==============================] - 26s 12ms/step - loss: 0.0152 - mae: 0.0152 - val_loss: 0.0137 - val_mae: 0.0137\n", "Epoch 4/20\n", "2069/2069 [==============================] - 25s 12ms/step - loss: 0.0149 - mae: 0.0149 - val_loss: 0.0149 - val_mae: 0.0149.0148 - mae:\n", "Epoch 5/20\n", "2069/2069 [==============================] - 23s 11ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0118 - val_mae: 0.0118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_6_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_lstm_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_lstm_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 6/20\n", "2069/2069 [==============================] - 17s 8ms/step - loss: 0.0150 - mae: 0.0150 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 7/20\n", "2069/2069 [==============================] - 17s 8ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0138 - val_mae: 0.0138\n", "Epoch 8/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0123 - val_mae: 0.0123\n", "Epoch 9/20\n", "2069/2069 [==============================] - 25s 12ms/step - loss: 0.0149 - mae: 0.0149 - val_loss: 0.0141 - val_mae: 0.0141\n", "Epoch 10/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0135 - val_mae: 0.0135\n", "Epoch 11/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0137 - val_mae: 0.0137\n", "Epoch 12/20\n", "2069/2069 [==============================] - 22s 11ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0121 - val_mae: 0.0121\n", "Epoch 13/20\n", "2069/2069 [==============================] - 22s 11ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0133 - val_mae: 0.0133\n", "Epoch 14/20\n", "2069/2069 [==============================] - 21s 10ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0147 - val_mae: 0.0147\n", "Epoch 15/20\n", "2069/2069 [==============================] - 26s 12ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0144 - val_mae: 0.0144\n", "Epoch 16/20\n", "2069/2069 [==============================] - 22s 11ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0170 - val_mae: 0.0170\n", "Epoch 17/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0146 - val_mae: 0.0146\n", "Epoch 18/20\n", "2069/2069 [==============================] - 21s 10ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0120 - val_mae: 0.0120\n", "Epoch 19/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0138 - val_mae: 0.0138\n", "Epoch 20/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0142 - val_mae: 0.0142\n" ] } ], "source": [ "save_weights_at = 'basic_lstm_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=16, epochs=20,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 11.7606\n", "MAE for the scaled validation set: 0.0118\n" ] } ], "source": [ "# load the best model\n", "best_model = load_model('basic_lstm_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "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": [ "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GRU" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_7\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "gru (GRU) (None, 32) 3360 \n", "_________________________________________________________________\n", "dropout_7 (Dropout) (None, 32) 0 \n", "_________________________________________________________________\n", "dense_7 (Dense) (None, 1) 33 \n", "=================================================================\n", "Total params: 3,393\n", "Trainable params: 3,393\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(GRU(32, input_shape=(X_train.shape[1:])))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(1, activation='linear'))\n", "\n", "model.compile(optimizer='rmsprop', loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "2069/2069 [==============================] - 26s 10ms/step - loss: 0.0197 - mae: 0.0197 - val_loss: 0.0188 - val_mae: 0.0188\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/20\n", "2069/2069 [==============================] - 20s 9ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0141 - val_mae: 0.0141\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/20\n", "2069/2069 [==============================] - 19s 9ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0173 - val_mae: 0.0173\n", "Epoch 4/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0152 - mae: 0.0152 - val_loss: 0.0133 - val_mae: 0.0133\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/20\n", "2069/2069 [==============================] - 19s 9ms/step - loss: 0.0150 - mae: 0.0150 - val_loss: 0.0138 - val_mae: 0.0138\n", "Epoch 6/20\n", "2069/2069 [==============================] - 20s 9ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0139 - val_mae: 0.0139\n", "Epoch 7/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0149 - mae: 0.0149 - val_loss: 0.0155 - val_mae: 0.0155\n", "Epoch 8/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0150 - mae: 0.0150 - val_loss: 0.0147 - val_mae: 0.0147\n", "Epoch 9/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0139 - val_mae: 0.0139\n", "Epoch 10/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0175 - val_mae: 0.0175\n", "Epoch 11/20\n", "2069/2069 [==============================] - 21s 10ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0121 - val_mae: 0.0121\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n", "WARNING:absl:Found untraced functions such as gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_fn, gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses, gru_cell_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: basic_gru_model\\assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 12/20\n", "2069/2069 [==============================] - 20s 10ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0133 - val_mae: 0.0133\n", "Epoch 13/20\n", "2069/2069 [==============================] - 19s 9ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 14/20\n", "2069/2069 [==============================] - 22s 10ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0121 - val_mae: 0.0121\n", "Epoch 15/20\n", "2069/2069 [==============================] - 26s 12ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0159 - val_mae: 0.0159\n", "Epoch 16/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0133 - val_mae: 0.0133\n", "Epoch 17/20\n", "2069/2069 [==============================] - 28s 13ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 18/20\n", "2069/2069 [==============================] - 26s 13ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 19/20\n", "2069/2069 [==============================] - 20s 9ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0146 - val_mae: 0.0146\n", "Epoch 20/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0171 - val_mae: 0.0171\n" ] } ], "source": [ "save_weights_at = 'basic_gru_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=16, epochs=20,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 12.0311\n", "MAE for the scaled validation set: 0.0121\n" ] } ], "source": [ "# load the best model\n", "best_model = load_model('basic_gru_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "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": [ "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
bfollinprm/Nquintessence
class/source/.ipynb_checkpoints/test-checkpoint.ipynb
1
32612
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import background" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "x = background.background(.7, .02, 1.0e-5, 0, 10.25)\n", "y = background.background(.7, .02, 1.0e-5, 2, 10.25)\n", "y.z0" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "55.51603069472758" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "y.hubble(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "array(0.0012474669695379948)" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "z = linspace(0,10,10000)\n", "\n", "plot(z, [x.hubble(i) for i in z])\n", "plot(z, [y.hubble(i) for i in z])\n", "figure()\n", "plot(z, array([x.hubble(i) for i in z])/array([y.hubble(i) for i in z]))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "[<matplotlib.lines.Line2D at 0x106f0f390>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BAAQHByM8PBwet2xLFBERgZCQEACAn58fCgoKkJeXB4PBcMe27u7uNZF1RKSi05dOY1nS\nMnyT+A2cGjnh1Y6v4rv+36GRYyO1S1PdrZeTZs0Chg2z0DOG/2G2xJycHLRufXMqu0ajQU5OToWO\nyc3NLbetKQaDAT4+PvD398eePXsq/EKIqPaVSil+PvEzBqwfAM8Fnjh14RS+//v3iB8djxE+I2w+\nHIzGm5eTHByUy0mvvGId4QCUM4qporsuSTV1GLdq1QpZWVlo1qwZEhMT0adPH6SkpKBJk7q/bC+R\nNfnj6h9YcXAFFiUsQkP7hhjbcSyW9V6G++65T+3SLMavvwITJgANGgA//qgssGdtzAaEs7MzsrKy\nyu5nZWVBo9GYPSY7OxsajQbFxcXltv1fjo6OcHR0BAD4+vpCq9UiPT0dviamEU6dOrXse39/f/j7\n+5t9bCK6O/LnENWFCQvxQ9oP6O3eG6v6rEJnTec6u4VnVeTkAFOmADExwCef3N3aSXcrJiYGMTEx\nVX8Acx0UxcXF4urqKgaDQQoLC8vtpN67d29ZJ3VF2vr7+8uBAwfK7ufn50tJSYmIiGRkZIizs7Oc\nP3/+rjtaiKjqCq4VyFf7vhKv+V7S9qu28kXsF3L26lm1y7I4166JTJ8u0ry5yDvviFy6pHZFf1XZ\nz85yj46KipK2bduKVquVGTNmiIjIwoULZeHChWXHjB8/XrRarXTo0OG2UUmm2oqIbNq0STQajTRo\n0ECcnJykZ8+eIiKyceNG8fLyEm9vb/H19ZUtW7ZUy4skosopLS2VX0/9KsM3D5ems5rKwA0DZceJ\nHVJaWqp2aRantFQkPFzE1VWkd2+R4xY8YKuyn52cKEdEZfKv5GN18mosSVwCoxgxymcUhumHwamx\nk9qlWaSjR5Wd3bKygDlzgOefV7si8yr72cmAILJxxlIjfj7xM5YkLcFPGT+hj3sfjPIdhSdbP8m+\nhTu4cAGYNg1YvVoZvjp+vDJKydJxPwgiqpCTBSex/OByLD+4HA81eggjfUZiyUtLcH+D+9UuzWLd\n2KPhvfeAF18EUlKAhx5Su6qaw4AgsiFFxiJEpEVgSeIS7M/dj8HtBiM8OBzeLa1wDGYti41Vhq3e\ncw+wZQvw2GNqV1TzeImJyAak5qdiaeJSrE5eDa+HvDDKZxT6efRDQ4eGapdm8XJzlWGrO3cCoaHA\n4MHqDVu9W7zEREQAgAvXL2B9ynosP7gcmQWZeMX7FcSOjIVbcze1S7MK164Bs2cDX3wB/POfwG+/\nAY1tZytsAAwIojrFWGrEDsMOrDi0ApHHIvGc63N4+6m3EagLtMmNeKpCBFi/XjlreOwxYN8+QKtV\nuyp18BITUR1w7OwxrDy4EquSV8GpkRNe8X4Fg9oNQot7W6hdmlWJjwfefPPm2cMzz6hdUfXiJSYi\nG3HjEtKKQyuQcS4DQzsMRdTgKLR3aq92aVYnOxt4+21gxw7g44+V1Vbr11e7KvUxIIisyJ0uIfXQ\n9oBDfSsYiG9hrlxR1kuaNw8YOxZIS7O9fgZzGBBEViDtjzSsPLQSqw6tQsvGLfGK9yv4sueXvIRU\nRaWlQFiYMsmta1cgKQl45BG1q7I8DAgiC3X+2nlsSN2AFQdX4MT5ExjaYSi2DtnKS0h36ddfleUx\n6tdXOqO7dFG7IsvFTmoiC3K95Doij0VizeE12G7YjgDXAIToQ9DTrScvId2lzExlZNLevcqubsHB\n1rNxT3XhWkxEVqZUSvFL5i9Yc3gNNh3dBO+W3hjSfgj6e/ZH0wZN1S7P6l28CMycCXzzjXLmMGkS\ncO+9alelDo5iIrISyWeSEZYchm+PfIsWDVtgSPshSB6bDM195jfWooopKQGWLQOmTlVWWU1OBpyd\n1a7KujAgiGrRqQun8O3hbxF2OAwXrl/AkPZDsHXIVrR7qJ3apdUZIkBkJPB//we0bAn88INtrJtU\nE3iJiaiGnb92HhtTN2LN4TU4/PthDPAYgCEdhuCpR55CPTsbuwhewxISgH/9CzhzBvj0UyAoyHrX\nTaoJ7IMgsgCmOpuHdhiKQLdA3GN/j9rl1TknTypDVnfsUC4pjRgB2PP6yF8wIIhUUiql2HVyF8KS\nw9jZXEsKCoAZM4ClS4HXXgMmT+ZEN3PYSU1Uy5LPJGNN8hqsPbIWzRs2x9D2Q9nZXMOKioCvv1bC\noVcv4PBhoFUrtauqexgQRFWQdSELaw+vxZrDa1BwvYCdzbVEBPjvf4F//xto2xbYvh1ox7e8xvAS\nE1EFFVwvwMbUjQhLDsPh3w+jv0d/DO0wlJ3NtSQ2VumAvnZN6YB+7jm1K7I+7IMgqkbXS64jKj0K\nYclh7GxWSXq6stJqfLyy0urQobY3A7q6MCCI7tKNzuY1yWvw36P/ZWezSvLygA8/VNZLmjRJmQXd\nkDuk3hV2UhNV0eEzh8tmNjdr2IydzSq5cEG5hPT118ArryhLcLfgorWqYECQTTPV2Rw1JIqdzSq4\nfh1YsAAIDVUmuHEJbvUxIMjmmOpsnhc0j53NKjEagdWrgQ8+ALy9lcluXl5qV0UAA4JsRGFJISLT\nlZnNP5/4GQGuAXjD7w0E6YLY2awSEWWdpHfeAZo1A9auBZ58Uu2q6FbspKY6q1RKsfvkbqw5rHQ2\n65307Gy2EHv2KHMZCgqUpbhffJFrJtUGdlKTzUv5PQVhyWFYc3gNmjZoiqEdhuLgmINofX9rtUuz\neUeOKENWk5OVEUpDhyo7u5FlYkBQnZB7KRffHfkOq5NXI/9KPoa0H4Itg7egg1MHtUsjKIvpffAB\nsHWrcuawYQPQoIHaVVF5GBBktS4VXsL3v32PsOQwHMg9gL7uffHF81/g6b89jfr1+GepJcjLU9ZL\nCgsDxo8Hjh0D7r9f7aqoohgQZFWKjcX46cRPCEsOQ1R6FJ7+29MY7Tsa4cHhaOjAWVSW4tw54JNP\nlG0+hw0Djh4FnJzUrooqiwFBFk9EsD93P8KSw7AuZR20zbQY2mEovgz8Eg/c+4Da5dEtLl4EZs8G\nvvoK6N8fOHQIaM2uH6vFgCCLdeL8CYQlhyEsOQwAMLTDUMSOiIW2uVblyuh/Xb0KzJ+vzIDu0QPY\ntw/Q8j+T1WNAkEW5XHQZG1M3YsXBFUjJT0GwVzDC+oWhU6tOsOM4SItTVAQsXqz0M3TpAuzcyUlu\ndQkDglRXKqX4JfMXrDi0AuG/heMZl2fwht8beKHtC3Cs76h2eWRCSYky+3naNMDTE4iIAB57TO2q\nqLqVu65AdHQ03N3dodPpEBoaavKYCRMmQKfTQa/XIykpqdy2GzZsgJeXF+rXr4/ExMTbHmvmzJnQ\n6XRwd3fHtm3bqvq6yApknMvA+zvfh+tcV0z8cSJ8Wvrg2OvHEB4cjr4efRkOFqi0FPjuO+UsYeVK\nZXRSVBTDoc4SM0pKSkSr1YrBYJCioiLR6/WSmpp62zGRkZESGBgoIiJxcXHi5+dXbtujR49KWlqa\n+Pv7S0JCQtljpaSkiF6vl6KiIjEYDKLVasVoNP6lrnLKJgt28fpFWZq4VLou6yoPfvKgvLH1DUk6\nnaR2WVSO0lKR8HCRDh1EHn9cZNs25WdkXSr72Wn2ElN8fDzc3Nzg4uICAAgODkZ4eDg8PDzKjomI\niEBISAgAwM/PDwUFBcjLy4PBYLhjW3d3d5PPFx4ejkGDBsHBwQEuLi5wc3NDfHw8OnfufNdBSOoR\nEew6uQtLk5YiIi0C3dp0w6QukxCoC+RZgoUTAbZsAaZOVRbV+/BDZQ9odgfZBrMBkZOTg9a3jFHT\naDTYt29fucfk5OQgNze33Lb/Kzc397YwuPFYZJ1+v/I7Vh5ciSVJS2Bfzx6jfEbh8+c/x4ONHlS7\nNCqHCBAZqQRDcbHytXdv7uRma8wGREVHjUgNLpzHkSvWpVRK8VPGT1icuBg/n/gZfT36Ynnv5eii\n6cL/llZAROlTmDoVKCxUlsfo25fBYKvMBoSzszOysrLK7mdlZUGj0Zg9Jjs7GxqNBsXFxeW2Le/5\nsrOz4ezsbPLYqVOnln3v7+8Pf39/s49NNSv7YjaWJy3H0qSlaN6wOUb7jsbSXktxfwOuq2ANRJR1\nkqZOBa5dU4KhXz8Gg7WLiYlBTExM1R/AXAdFcXGxuLq6isFgkMLCwnI7qffu3VvWSV2Rtv7+/nLg\nwIGy+zc6qQsLC+XEiRPi6uoqpSZ6wsopm2pJibFEfkj7QV5Y84I0m9VMXv3hVUnITSi/IVmM0lKR\nqCil49nLS2T9ehET40KojqjsZ2e5R0dFRUnbtm1Fq9XKjBkzRERk4cKFsnDhwrJjxo8fL1qtVjp0\n6HDbqCRTbUVENm3aJBqNRho0aCBOTk7Ss2fPst9Nnz5dtFqtPProoxIdHV0tL5KqV/6VfJm1e5a4\nzHGRjt90lKWJS+Vy4WW1y6JKKC0V2bpVxM9PxNNTZN06BoMtqOxnJzcMogqLz4nH/P3zEf5bOPq4\n98H4TuPRybmT2mVRJYgAP/2kXEK6cEH5+vLLvJRkKyr72cmAILOuFV/DupR1mL9/Pv64+gfGdhyL\nET4juEielbkxKunjj28PBm7WY1sYEFQtci7mYF78PCxJWoKOrTpifKfxCHQL5D4LVqa0FPj+eyUY\nRID33lNGJTEYbBO3HKW7kng6EbPjZiPyWCSGdhiKvSP3wq25m9plUSWVlADr1gHTpwNNmigT3Ljv\nM1UWzyAIpVKKyGOR+CLuCxw/dxwTHp+AUb6j0KxhM7VLo0oqKlIW0Zs5E3B2Vs4YnnuOwUAKnkFQ\nhV0tvoqVB1didtxs3HfPfZjUZRIGeA6AQ30HtUujSrp+HVi6VNnFzd0dWLYMePpptasia8eAsEEF\n1wuwYP8CzN03F501nbGk1xJ0faQrZzpboStXgEWLgM8+Azp2BNavB/z81K6K6goGhA35/crvmBM3\nB4sSFuEF3QvYGbITng96ql0WVcGFC8oObnPnAs88oyyP4e2tdlVU1zAgbMCpC6fwWexnCEsOQ3C7\nYBwYfQBtmrVRuyyqgtOngTlzgCVLgKAgICYGuGVxZaJqxekxdVhmQSZGRYyCzyIfNLBvgJRxKVjw\nwgKGgxVKTwfGjFE26rl2DUhIUDqjGQ5Uk3gGUQdlX8zG9F3TsT51PcZ1HIf019PRvGFztcuiKkhI\nAEJDlb2ex40D0tKAB7laOtUSBkQdknc5DzN3z8Tq5NUY7Tsaaa+lccazFRIBduwAZs0CfvsNmDRJ\nGZXUuLHalZGtYUDUAWevnsWsPbOwNGkphumHIXV8Klo2bql2WVRJRqMy6zk0FLh8GZgyBRg8GHDk\npnukEgaEFbtech1f7fsKn8R+gv4e/ZE8Nhma+8zvuUGWp7BQ6U/45BOgRQtlcttLL3EBPVIfA8IK\nlUopvj38Ld7d8S68W3pj9/DdcH/A9D7fZLnOnVPmMMybB+j1ysikrl0565ksBwPCyuw07MTknyaj\nnl09rOq7Ck//jdNlrU1GhjJUdc0aZZ/nrVuBDh3UrororxgQViKzIBNv/fgWkvKSMLP7TAz0Goh6\ndrwGYU1iY4HPPwd27QJGjwaOHAFatVK7KqI7Y0BYuOsl1/HJr59g7r65eLPzm1jbfy0a2DdQuyyq\noJISpeP5iy+A338H3nwTWLUKaNRI7cqIyseAsFAigi3HtmDijxPh09IHif9MxN+a/k3tsqiCLl9W\nhqbOmQM8/DAwebJyOYn7MJA1YUBYoJMFJzE+ajyOnzuOhS8sRIA2QO2SqIJycoCvvlI6nLt1A9au\nBTp3VrsqoqrhRWwLYiw1Ym7cXDz2zWN4svWTSB6bzHCwEvv2AUOGAO3bA1evAvHxwIYNDAeybjyD\nsBCHzxzGqB9GoaF9Q8SOjEXbFm3VLonKUVQEbNyorKianw+89pqywmrTpmpXRlQ9uKOcygpLCvHx\nro+xMGEhZjw7AyN9R3J0koU7c0aZv7BwobJY3oQJynae7F8gS8cd5azI4TOHMfT7oXBp6oJDrx5C\nqyYc82jJEhKAL78EIiKAgQOBbduAdu3Uroqo5vBPVRUYS434LPYzPLvqWUz0m4jNf9/McLBQxcXK\nLm1PPgn066cst52RoZxBMByoruMZRC07WXASIZtDYBQj4kfFc28GC3XmjDIS6euvAa1WWVG1Vy/A\nnv9iyIYZLY+rAAAQqUlEQVTwDKIWrTuyDh0Xd0SQLggxITEMBwsjosxyHjQIcHcHDAZgyxbgl1+U\nsweGA9ka/i9fC66XXMdbP76FbRnbED0kGo+1ekztkugWFy8qq6l+/bUy83nsWOV7jkYiW8eAqGHp\nZ9MxcONA6JrrkPDPBNzf4H61S6I/HTqkBMG6dcBzzykd0N26cTVVoht4iakGrU9ZjyeWPYHRvqOx\nbsA6hoMFKCxUVlF98knghReUxfJSUpRJbc8+y3AguhXPIGqAsdSId7a/gw2pG/Dj0B/h+7Cv2iXZ\nvIwMYPFiZX0kb2/gX/9SNuVhvwLRnfGfRzU7f+08Bm8ajCJjEeJHx3NPaBVdv66spLp4sbK09j/+\nAezZA7TlJHWiCmFAVKOj+UfR+7veCNIF4bPnP4N9Pb69ajhyRBmiumYN4OMDvPqqspLqPfeoXRmR\ndeEnWDXZmr4VIZtDEPpcKIb7DFe7HJtz+bLS2bxkCXDqFDBihLJgXhuOJCaqMq7FVA2+SfgG7+98\nH5v+vglPtH5C7XJshghw4IByCWnDBuCZZ4BRo4CePdm3QGQK12KqRSKC93a8h/Wp67F7+G7oWujU\nLskmnDmj7LOwYoVy5jBqlDISidt3ElUvBkQVFZYUYmTESGScz0DsiFg82OhBtUuq0woLlVnNK1YA\nu3crfQqzZwP+/kA9DtYmqhHl/tOKjo6Gu7s7dDodQkNDTR4zYcIE6HQ66PV6JCUlldv23LlzCAgI\nQNu2bfH888+joKAAAJCZmYmGDRvCx8cHPj4+GDdu3N2+vhpxqfASgtYG4WrxVWwftp3hUENElH6E\n8eMBZ2dlr4UBA4DsbGDlSmXeAsOBqAaJGSUlJaLVasVgMEhRUZHo9XpJTU297ZjIyEgJDAwUEZG4\nuDjx8/Mrt+3kyZMlNDRURERmzZolU6ZMERERg8Eg7dq1M1eS/NlnUu4xNeXs1bPy+OLHZXTEaCkx\nlqhWR12WnS0ya5aIh4eIVivy4YciBoPaVRFZv8p+dpr9+ys+Ph5ubm5wcXGBg4MDgoODER4eftsx\nERERCAkJAQD4+fmhoKAAeXl5Ztve2iYkJASbN2+u/uSrAXmX8+C/wh9dH+mKRS8uQv163CGmuly6\nBISFAT16KNt2Hj+udD6npwP/+Q/g4qJ2hUS2x2xA5OTkoHXr1mX3NRoNcnJyKnRMbm7uHdueOXMG\nTk5OAAAnJyecOXOm7DiDwQAfHx/4+/tjz549d/HSqtfJgpPourwrXvZ8GZ8GfAo7rslw1woLgfBw\n4O9/BzQaZZjqK68AOTlKODz5JJe+IFKT2U7qin4ISgWGTYmIycezs7Mr+3mrVq2QlZWFZs2aITEx\nEX369EFKSgqaNGnyl3ZTp04t+97f3x/+/v4VqrUq0s+m47nVz+HNzm9iYueJNfY8tsBoVJbUXrsW\n2LRJ2XRn8GBgwQKgRQu1qyOqW2JiYhATE1Pl9mYDwtnZGVlZWWX3s7KyoNFozB6TnZ0NjUaD4uLi\nv/zc2dkZgHLWkJeXh5YtW+L06dN46KGHAACOjo5wdHQEAPj6+kKr1SI9PR2+vn9dy+jWgKhJGecy\n8OyqZ/H+0+9j9GOja+U56xoRZbvOtWuVswQnJyUUDh4EbjnJJKJq9r9/PE+bNq1S7c1eYurYsSPS\n09ORmZmJoqIirFu3Dr169brtmF69emHVqlUAgLi4ODRt2hROTk5m2/bq1QsrV64EAKxcuRJ9+vQB\nAPzxxx8wGo0AgBMnTiA9PR2urq6VekHVKbMgE91Xdce7Xd9lOFSSiBIA770HPPooEBwMNG4M/Pwz\nkJioLJbHcCCycOX1YkdFRUnbtm1Fq9XKjBkzRERk4cKFsnDhwrJjxo8fL1qtVjp06CAJCQlm24qI\nnD17Vrp37y46nU4CAgLk/PnzIiLy3//+V7y8vMTb21t8fX1ly5Yt1dITXxWnCk5Jmzlt5Mu4L2v8\nueqK0lKRAwdE/v1vETc3ERcXkcmTRfbtU35HROqq7Gcnl9owIediDvxX+mNsx7F4q8tbNfY8dYEI\nsH8/sHGjcqtXD3j5ZWW+gq8vO5mJLAmX2rhLeZfz0H1Vd4zyGcVwuAOjEYiLUzqZN24EGjRQQmHT\nJkCvZygQ1RUMiFv8fuV3dF/VHUPaD8GUp6aoXY5FuXoV+OknZVjqli1Ay5ZAnz7K9+3aMRSI6iJe\nYvrT2atn0W1lN/R+tDc+evajan1sa3XmDPDDD0BEBBATA3TqBPTqpdy4jDaR9ansZycDAsoucN1X\ndcfz2ucxs/tMm50EJ6KsirplixIKqanKzObevYHAQKBZM7UrJKK7wYCopILrBQhYHYCuj3TF589/\nbnPhcOGCMvQ0Olq52dsDQUFKKDzzDHdhI6pLGBCVcLHwIp5f/Twed34cc3vOtYlwEAEOHQK2blVu\nSUnKkhY9eypnCW3bsj+BqK5iQFTQpcJL6LmmJ/ROeswPml+nwyEnB9ixA9i+HfjxR6BJk5uB8Mwz\nwL33ql0hEdUGBkQFXCm6gsA1gXB/wB0LX1yIenZ1a1OBs2eVTuXt25VgyM8HunVT9k/o0QPQatWu\nkIjUwIAoR8H1Ary49kW0bdEWS3otqRPhUFAA/PqrEgY7dgAnTgBPPaUEwrPPKnMTuLEOETEgzMi7\nnIeeYT3h7+KPL3p8YbXhcOqUEgh79ii3EyeUIag3AqFTJ8DBQe0qicjSMCDuIDU/Fb2+7YUQfQje\ne/o9q+lzKClRhp7eGgjXrytnCDduPj4MBCIqHwPChO+Pfo8xW8bg04BPEeIdUoOV3R0RICND2Yd5\n/37ldvCgspnOk08qt6eeAnQ6jjQiospjQNzi/LXzeGvbW4jJjMH6AevRyblTLVRXMaWlyqWhQ4eU\n5a/37wcOHFCWxO7USbk9/jjw2GPA/ferXS0R1QVcrA9A/pV8LElcgtlxs/Gy58tIfjUZTe756650\nteXiReDwYSUMDh0CkpOBI0eA5s2BDh2US0Svv66EQsuWqpVJRHQbqw2I/Cv5OHftHM5fP4+zV8/i\n3LVzOHb2GOJy4rA/Zz96u/fGruG74P6Ae63UIwLk5QG//Qakpd3+NT8f8PJSwkCvB4YMUb5v2rRW\nSiMiqhKrvcTUIrQFmjVshhYNW6B5w+Zo3rA52jRtg8edH0e3Nt3Q2LFxtT+v0QhkZwOZmYDBoNxO\nnFCCIC1NWZbC3V3ZQe3Wr23aAPXrV3s5RESVwj6IKjIalb/0c3OV2+nTytdTp24GQk4O8OCDygd+\nmzaAi4vy9UYYNG9erSUREVUrBsSfCguBc+eUWcV3uv3xhxIEp08Dv/+uXPJp1Uq5Pfyw8lWjuRkI\njzzCxeuIyHrZTEAMGya4dAm4fBm4dAl/+V5E+Yu+RYs73x544GYQODkBjo5qvzIioppjM6OY/P2V\nReeaNFGGht74/sb9e+7hXAEiorthtWcQVlg2EZGqKvvZaZ2LERERUY1jQBARkUkMCCIiMokBQURE\nJjEgiIjIJAYEERGZxIAgIiKTGBBERGQSA4KIiExiQBARkUkMCCIiMokBQUREJjEgiIjIJAYEERGZ\nVG5AREdHw93dHTqdDqGhoSa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"text": [ "<matplotlib.figure.Figure at 0x106b7a8d0>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "x.hubble(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "0.00073100846194857564" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
materialsvirtuallab/ceng114
grades/statsw2016.ipynb
1
500492
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview\n", "\n", "This is a generalized notebook for computing grade statistics from the Ted Grade Center." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#The usual imports\n", "from __future__ import division\n", "import math\n", "from collections import OrderedDict\n", "\n", "from pandas import read_csv\n", "import numpy as np\n", "\n", "from pymatgen.util.plotting_utils import get_publication_quality_plot\n", "from monty.string import remove_non_ascii\n", "\n", "import prettyplotlib as ppl\n", "from prettyplotlib import brewer2mpl\n", "import matplotlib.pyplot as plt\n", "\n", "colors = brewer2mpl.get_map('Set1', 'qualitative', 8).mpl_colors\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define lower grade cutoffs in terms of number of standard deviations from mean.\n", "grade_cutoffs = OrderedDict()\n", "#grade_cutoffs[\"A+\"] = 1.5\n", "#grade_cutoffs[\"A\"] = 1\n", "grade_cutoffs[\"A\"] = 0.75\n", "grade_cutoffs[\"B+\"] = 0.5\n", "grade_cutoffs[\"B\"] = -0.25\n", "grade_cutoffs[\"B-\"] = -0.5\n", "grade_cutoffs[\"C+\"] = -0.75\n", "grade_cutoffs[\"C\"] = -1\n", "grade_cutoffs[\"C-\"] = -2\n", "grade_cutoffs[\"F\"] = float(\"-inf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load data from exported CSV from Ted Full Grade Center. Some sanitization is performed to remove non-ascii characters and cruft" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def load_data(filename):\n", " d = read_csv(filename)\n", " d.columns = [remove_non_ascii(c) for c in d.columns]\n", " d.columns = [c.split(\"[\")[0].strip().strip(\"\\\"\") for c in d.columns]\n", " d[\"Weighted Total\"] = [float(i.strip(\"%\")) for i in d[\"Weighted Total\"]]\n", " print(d.columns)\n", " return d" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([u'Last Name', u'First Name', u'Username', u'Student ID', u'Last Access',\n", " u'Availability', u'Weighted Total', u'Total', u'SectionId', u'PS1',\n", " u'PS2', u'PS3', u'PS4', u'PS5', u'Midterm1', u'Midterm2', u'Final',\n", " u'WT'],\n", " dtype='object')\n" ] } ], "source": [ "d = load_data(\"gc_CENG114_WI16_Ong_fullgc_2016-03-15-19-58-36.csv\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def bar_plot(dframe, data_key, offset=0):\n", " \"\"\"\n", " Creates a historgram of the results.\n", " \n", " Args:\n", " dframe: DataFrame which is imported from CSV.\n", " data_key: Specific column to plot\n", " offset: Allows an offset for each grade. Defaults to 0.\n", " \n", " Returns:\n", " dict of cutoffs, {grade: (lower, upper)}\n", " \"\"\"\n", " data = dframe[data_key]\n", " d = filter(lambda x: (not np.isnan(x)), list(data))\n", " N = len(d)\n", " print N\n", " heights, bins = np.histogram(d, bins=20, range=(0, 100))\n", " bins = list(bins)\n", " bins.pop(-1)\n", " import matplotlib.pyplot as plt\n", " fig, ax = plt.subplots(1)\n", " ppl.bar(ax, bins, heights, width=5, color=colors[0], grid='y')\n", " plt = get_publication_quality_plot(12, 8, plt)\n", " plt.xlabel(\"Score\")\n", " plt.ylabel(\"Number of students\")\n", " #print len([d for d in data if d > 90])\n", " mean = data.mean(0)\n", " sigma = data.std()\n", " maxy = np.max(heights)\n", " prev_cutoff = 100\n", " cutoffs = {}\n", " grade = [\"A\", \"B+\", \"B\", \"B-\", \"C+\", \"C\", \"C-\", \"F\"]\n", " for grade, cutoff in grade_cutoffs.items():\n", " if cutoff == float(\"-inf\"):\n", " cutoff = 0\n", " else:\n", " cutoff = max(0, mean + cutoff * sigma) + offset\n", " plt.plot([cutoff] * 2, [0, maxy], 'k--')\n", " plt.annotate(\"%.1f\" % cutoff, [cutoff, maxy - 1], fontsize=18, horizontalalignment='left', rotation=45)\n", " n = len([d for d in data if cutoff <= d < prev_cutoff])\n", " print \"Grade %s (%.1f-%.1f): %d (%.2f%%)\" % (grade, cutoff, prev_cutoff, n, n*1.0/N*100)\n", " plt.annotate(grade, [(cutoff + prev_cutoff) / 2, maxy], fontsize=18, horizontalalignment='center')\n", " cutoffs[grade] = (cutoff, prev_cutoff)\n", " prev_cutoff = cutoff\n", " \n", " plt.ylim([0, maxy * 1.1])\n", " plt.annotate(\"$\\mu = %.1f$\\n$\\sigma = %.1f$\\n$max=%.1f$\" % (mean, sigma, data.max()), xy=(10, 7), fontsize=30)\n", " title = data_key.split(\"[\")[0].strip()\n", " plt.title(title, fontsize=30)\n", " plt.tight_layout()\n", " plt.savefig(\"%s.png\" % title)\n", " return cutoffs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PS1\n", "191\n", "Grade A (100.0-100.0): 0 (0.00%)\n", "Grade B+ (100.0-100.0): 0 (0.00%)\n", "Grade B (100.0-100.0): 0 (0.00%)\n", "Grade B- (100.0-100.0): 0 (0.00%)\n", "Grade C+ (100.0-100.0): 0 (0.00%)\n", "Grade C (100.0-100.0): 0 (0.00%)\n", "Grade C- (100.0-100.0): 0 (0.00%)\n", "Grade F (0.0-100.0): 0 (0.00%)\n", "PS2\n", "191\n", "Grade A (108.3-100.0): 0 (0.00%)\n", "Grade B+ (103.5-108.3): 0 (0.00%)\n", "Grade B (89.0-103.5): 171 (89.53%)\n", "Grade B- (84.2-89.0): 0 (0.00%)\n", "Grade C+ (79.3-84.2): 2 (1.05%)\n", "Grade C (74.5-79.3): 0 (0.00%)\n", "Grade C- (55.1-74.5): 7 (3.66%)\n", "Grade F (0.0-55.1): 11 (5.76%)\n", "PS3\n", "191\n", "Grade A (109.5-100.0): 0 (0.00%)\n", "Grade B+ (104.3-109.5): 0 (0.00%)\n", "Grade B (88.6-104.3): 174 (91.10%)\n", "Grade B- (83.3-88.6): 1 (0.52%)\n", "Grade C+ (78.1-83.3): 3 (1.57%)\n", "Grade C (72.9-78.1): 0 (0.00%)\n", "Grade C- (51.9-72.9): 3 (1.57%)\n", "Grade F (0.0-51.9): 10 (5.24%)\n", "PS4\n", "191\n", "Grade A (107.4-100.0): 0 (0.00%)\n", "Grade B+ (101.6-107.4): 0 (0.00%)\n", "Grade B (84.0-101.6): 160 (83.77%)\n", "Grade B- (78.2-84.0): 4 (2.09%)\n", "Grade C+ (72.4-78.2): 6 (3.14%)\n", "Grade C (66.5-72.4): 3 (1.57%)\n", "Grade C- (43.1-66.5): 7 (3.66%)\n", "Grade F (0.0-43.1): 11 (5.76%)\n", "PS5\n", "191\n", "Grade A (106.7-100.0): 0 (0.00%)\n", "Grade B+ (100.3-106.7): 0 (0.00%)\n", "Grade B (81.2-100.3): 151 (79.06%)\n", "Grade B- (74.8-81.2): 10 (5.24%)\n", "Grade C+ (68.4-74.8): 6 (3.14%)\n", "Grade C (62.0-68.4): 2 (1.05%)\n", "Grade C- (36.5-62.0): 9 (4.71%)\n", "Grade F (0.0-36.5): 13 (6.81%)\n", "Midterm1\n", "190\n", "Grade A (86.5-100.0): 46 (24.21%)\n", "Grade B+ (82.7-86.5): 24 (12.63%)\n", "Grade B (71.4-82.7): 49 (25.79%)\n", "Grade B- (67.6-71.4): 12 (6.32%)\n", "Grade C+ (63.8-67.6): 14 (7.37%)\n", "Grade C (60.0-63.8): 6 (3.16%)\n", "Grade C- (44.9-60.0): 32 (16.84%)\n", "Grade F (0.0-44.9): 3 (1.58%)\n", "Midterm2\n", "191\n", "Grade A (84.0-100.0): 47 (24.61%)\n", "Grade B+ (78.8-84.0): 19 (9.95%)\n", "Grade B (63.1-78.8): 50 (26.18%)\n", "Grade B- (57.9-63.1): 17 (8.90%)\n", "Grade C+ (52.7-57.9): 17 (8.90%)\n", "Grade C (47.4-52.7): 10 (5.24%)\n", "Grade C- (26.6-47.4): 20 (10.47%)\n", "Grade F (0.0-26.6): 8 (4.19%)\n", "Final\n", "191\n", "Grade A (69.5-100.0): 47 (24.61%)\n", "Grade B+ (64.7-69.5): 16 (8.38%)\n", "Grade B (50.4-64.7): 48 (25.13%)\n", "Grade B- (45.6-50.4): 19 (9.95%)\n", "Grade C+ (40.8-45.6): 15 (7.85%)\n", "Grade C (36.1-40.8): 9 (4.71%)\n", "Grade C- (17.0-36.1): 35 (18.32%)\n", "Grade F (0.0-17.0): 2 (1.05%)\n" ] }, { "data": { "image/png": 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T26xVkiRJUp/qygIj/SQzf9MiqDWuX0+xmXVjlcvDIuKFS6s+SZIkSePDkDNr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\nfW1V7eXn53t+fn5KYxTJNsuWLWPRokVs2rSJQw89lKFDhyY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"text/plain": [ "<matplotlib.figure.Figure at 0x10d7b7ed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zzTe9fv36PmLECF+4cKG7u48bN87NzOfMmVMglq5du5bbMUk0jujv7LZt2wps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"text/plain": [ "<matplotlib.figure.Figure at 0x10cbdd790>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SSwwdOpR+/frx7LPP8uabb/LQQw/x3XffsW7duqLtYv8dFhQUFJ1Bfvvtt3nl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"text/plain": [ "<matplotlib.figure.Figure at 0x10d6e7990>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6/T5Llixhzpw5TJw4kfr16wOB4WvFihXjggsuAGDlypVccsklR/RRmHECrF69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Dldk+U8UGM9x03nOS3797GIuLQnDThuOs7mue35Vco+mtverHinY8fyGhkPkjana5mjl0mjNsrJ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\nAtE1Y6KZm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"text/plain": [ "<matplotlib.figure.Figure at 0x10d752110>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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S+OKLL5g2bRqvvfZa7viZnCpuHTp0AILxNaeddtqfjk3kOb148WK2bNmyx1iq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\nN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"text/plain": [ "<matplotlib.figure.Figure at 0x10b6ac7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GdOnShVdffZUDBw5QvXr1wu5WsXdVTQU0xhhggNNLMc77RWQvsBnrHqzatoWGM6snEPi3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\nSVKPTKwkS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"text/plain": [ "<matplotlib.figure.Figure at 0x10d7e03d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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h27ZtgQxZcihXy8/dd9/NiRMn2LPHMQVjwYIF2bRpE/ny5SM+Pp6NGzdy8OBB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\n4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"text/plain": [ "<matplotlib.figure.Figure at 0x10e283290>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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h5+cHb29vZGRkYOXKlahbt26BYyDXKmxbuXHjBgIDC1+SfPXq1Rg7diz279+P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\n8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"text/plain": [ "<matplotlib.figure.Figure at 0x10c4a4d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for c in d.columns:\n", " if \"PS\" in c or \"Midterm\" in c or \"Final\" in c:\n", " if not all(np.isnan(d[c])):\n", " print c\n", " bar_plot(d, c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Overall grade\n", "\n", "Overall points and assign overall grade." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "191\n", "Grade A (77.8-100.0): 55 (28.80%)\n", "Grade B+ (74.3-77.8): 17 (8.90%)\n", "Grade B (63.9-74.3): 59 (30.89%)\n", "Grade B- (60.4-63.9): 6 (3.14%)\n", "Grade C+ (56.9-60.4): 14 (7.33%)\n", "Grade C (53.4-56.9): 17 (8.90%)\n", "Grade C- (39.4-53.4): 20 (10.47%)\n", "Grade F (0.0-39.4): 3 (1.57%)\n" ] }, { "data": { "image/png": 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wt5+8fgAuUtWt4VTaGGOquvr16yMiDB48mM8//5y77roLgCuvvNJ6rqKkvM/J\n2rVruf322xk2bBi33347RxxxBPn5+QwePJhBgwYxY8YMfvvtNz744ANiY2PDGkpXndpSHSzKyuKe\n3bu4JLkmV9WsyRFx8fxeUMCnuTn8WVh4SPqyDtf79ttvGTduHEOGDOHuu++mefPmAFx00UVs2LDh\nkN7Dw/28mMqhQq5AcVwmIlNF5D0RmSki14hIjQgVcRVwKfAssAqnJykH52XAO4A1wBTgfKBTKUEV\nqpqnqoOBc3GmZv8FZ2r1bcAnwC3Asar6TYTqb4wx5aK8J89RVVq1asUpp5xCQUEB48aNo0uXLtx1\n111FehYOt4mSSlOe58V9ThYvXlwu5+T777/nzz//pF+/fkyffvDNJD179uSqq64iNjaWpUuXcvHF\nF6OqxMTElKm8im6L+7mqSLelopXHNbYpP49n9+2lZ2INhtVMYd4Bp4cqWYRGsbEIcO/unTy2ZzeL\nspxBQWWdJv/HH39k06ZN9OzZ0xdUAezcuZMDBw5w7rnnMmDAAMaPH+87L4V+AjtjKlKZAysRSRKR\nWSIyW0RelgBfR4hIGrASmInTE3QWTk/QC8BaEQnUwxQ0Vd2tqm+q6k2qerqqNlXVJFVNVNX6qnq8\nqv5dVRdqCP+6VfUDVb1MVVuqarKqNlTV01T13/6GERpjTGVz//33l2v+IkJMTAwNGzZk2bJlHHvs\nsTz++ON07dqVu+66i1deeYVffvmFW265hczMzHKtS1VSnufFfU6WL19eLufkhx9+oKCggGOOOYb7\n77+fvLw84uLiEBHi4uLo1KkTHTt2ZN68eTzzzDO+elX2tgDl1paKVh7X2M7CQurHxnB+UjIt4+J4\ncp/zBps3DxxgdW4ucw4cYHF2NpP27+Mfu/5iwt49QNmO148//khubi7169f3ffbCCy8wffp0du7c\niYiQmZnJfffdR//+/X3BlTHRFM4V2Ae4BBgAZJUQsEwCunJwKkRxLUcCizwTSxhjjKlivN8Q9+7d\nmy1btpCdnc0JJ5zA+PHjOfXUU7njjjs4//zz+fe//81ff/0V5doeHtznBCiXc5KRkQHAI488AjjP\nQXmfeVm7di3dunXj9ddfp27duixcuLDKtOXAgQPl1pbqoFN8AqNSanFuUpLvszkH9jN2zy6GJNfk\npfS6LKvfkPn16pMeE8OUffv4OCe7TGV17dqVjIwMhg8fzpAhQxg0aBDXX389o0eP5p133mHhwoV8\n9tlnXHjBg02UAAAgAElEQVThhSxYsIDx48dHqpnGlFk4gdUZrnW/042LyAnAxTjPJAH8BSwAluI8\nwwTQgkOfYTLGGFMFeL8hPvbYY/npp59YsWIFMTExnHzyydxzzz2kpKTwzTffMHToUDp06BDl2h4e\n3OcEKJdz0r9/f4466iheeOEFAH7//Xe+/vprRo8ezeLFi7n44otp164dl156KYsWLeKHH36oEm25\n5557yq0tVZ2qEi9Cx4QEAAo936f/JzeH21NrcXutNI5JSCApJoYO8Qk8UbsO+7SQdXl5ZSrv2GOP\n5dFHH6VLly7s2bOHXbt2ccIJJ3DzzTfTsmVLABo2bMgzzzxDgwYNWL16dUTaaUw4wgmsjvX8zMMZ\n6ueP+51RG4AOqtpPVXsB/XGCKwGGiYj13xpjTBWkqmRkZNC8eXM2b94MwP79+5kwYQJZWVkcddRR\nzJs3j5kzZ7J79+7oVvYw4T0nQMTPSUFBAampqcydO5dWrVoB0KJFC0444QQmTZrE888/z8knnwxA\n586dAcjPz68SbXn66afLtS1VWfHhfDGe3++vVZsRKanU9gTBBZ6AKyM2jlhgexlm71NV4uPjGTx4\nMHPmzGH+/PmICM2aNaNZs2aoqq83MyMjg9q1a7Nr1y6bKdBEXTjBTAs8s+OpaqC/Mhe41u9V1T+9\nv6jq28B8z6/1ORioGWOMqUJEhIYNG9K2bVveeecddu/ezaBBg1i5ciWTJk1i4sSJtG3blqeeespu\nfCqI95wAET8nsbGxqCpt27b19RKMGjWK2267jbVr1/omfADYsGEDGRkZNGnSpEq05ZFHHinXtlRH\nNWNiSPQEWYWqvpcEr8rJpmZMDCclJgKhTTDi75msvLw8Nm7cyL59+3zP3gEsX76c3bt307VrV3vG\nykRdONOt1/X83O5vo4i0Brx/ffYC7/hJ9i5OzxXA0cCXYdTHGGNMMWPHji33MrzTHB9zzDEsWLCA\niy66iC+//JJnn32Wiy++mLi4OCZOnEi9evVIT08v9/pUBeV9Xrzn5NRTT+XHH3+M+DkREQoKCkhJ\nSWHs2LGMGzfukGm1v/rqK1atWsVJJ51Egmf4WGVvyx133AEcOkV4pNpSkSri3/4tKam+9UJVXy/W\nN3m5zM06QIvYOI6Ld45XOBN+qCrt27dn8eLFjBw5kqeffpq0tDRWrlzJww8/jIgwYsSIKjGpiKne\npKxThopIPs4wvsWqeraf7VcA03F6td5W1X5+0nTHed5KgTGqOqFMlanExo0bp+U93bExxpS3YN5F\n89lnn3H66adTs2ZNnnvuOS688EKSXA+5m/AUPwfleU727t1Lampqqenc9XC/R+jdd99l4sSJfPrp\np6xatYp27doFlVdJytKWUI9ZRbUlmrZmNCs1TVnfPQWwJDuLl/bvY21uLnPrNaBtfLzfdB/nZJMy\nfRp9+vQJKt99+/bRo0cPMjMzOeKII6hVqxZ//vkn+fn5fPDBB3Tq1KlM9TXVQqWJqMPpsToA1AQC\nvZ2vm2s90DNY7icaI/VOK2OMMWEqKCigoKCAwsJCatSo4bvJKumG65hjjmHWrFnExsbSs2dPC6oi\nLDs7m4KCAnbs2EGLFi187wcq6Qa4LOfkvffeY+rUqQwdOpS+ffuWmt5bvjcQmTRpEvfffz+pqaks\nW7bMbyCiqhQUFBAbGxv0DXxZ2hLqMStLW6qTpdlZNIuN48j4+JCDq92FhYzZtZPN+flkq/JmCUHV\n/KwDjNz5F8PnzQsqsPL2KC5fvpzbb7+dzMxMcnJyuPDCC7nlllto3bp10PU0pjyF02O1EWgF7AHS\nVbWw2PafgWY4vVGnquqnfvLogzNEUIFbVfWpMlWmErMeK2NMVbNo0SLmzZvHV199RWpqKueccw59\n+/b13VS6v80vLj8/HxHxPZdiIuPDDz9k+vTprF69mqysLO68805uuOGGoPYN5Zy88sor3HTTTZx2\n2mlccsklDBkyJOS6rl+/ntWrV9OrVy9atGhxyPb333+fd999l/Xr19OsWTN69+7NwIEDSU5OBkoO\n3kNpSzjHLNi2VEWBeqyWZWcz9K/tNIiJ4fW69WkdYnCVVVjI8J07aBEbx/UpqTSP8//d/esH9nPb\nrp0IkFCjBh9++CGnnXZaqfnn5+cTFxdHYWEheZ6ZBuPi4uxvjYFK1GMVzlN+azw/U4Hz3RtE5Eyc\noApgH/B5gDz+5lr/PYy6GGOMiYBXXnmFiy66iJUrV5KamsqPP/7I7bffzpAhQ3xTa8fExPhm5Cou\nLsDNlCm7GTNmMGjQINauXUvbtm0pKChg5MiRrFixIqj9gz0nCxcu5IYbbuDKK6/k8ccfDxhUlfaF\nbMeOHRk2bJjfQGTGjBn079+fZcuWkZuby6JFi7jqqqsYPHgwS5YsAfD1KoXTlnCPWTBtqU4KVVmX\nlwtAPnDZju1szMsr8VwUlxQTw/T0etyXVjtgUDXbE1SNTEnlztQ0cnJyfO8GK23iEe+5LywsJDEx\nkcTERAuqTKUTTmA137U+UUR6ikiCiJwEvOj5XIH5qhroX0tn1/r3YdTFGGNMmD7++GNGjhzJ8OHD\nefPNN1m8eDHvvfced955J+vXr+eBBx7A2wMfExNT5IZr06ZNTJo0ifz8fLvZiaA5c+ZwzTXXMHjw\nYF599VUWLFjAzJkzERG2bdt2SPqynBNVJTc3l9dff51TTjmF6667ztc7uXDhQmbOnMns2bPZtGkT\ngO8ZJH/lePnr5VizZg233XYb1157LW+99RaffPIJS5cu5eGHH+bDDz9k1KhRvP766779y3p9ReqY\nldSW6iZGhKPj44kH+iclk63KFX8dDK4KAwRXP+fnM2P/PvI92+NESApwvGYf2M+tu3YyvGYKw2qm\ncE1KCl26dGHGjBns2LGjxPPqPi/25Y2p1FS1TAuQCGwCCgIshThffBwbYP8Y4H+edFlAfFnrUpmX\nsWPHqjHGREswf4MKCwtVVfXBBx/U+vXra2ZmZpHt33//vXbu3FlFROvVq6dPPfVUke05OTl66aWX\nqogEVZ4J7rxkZmZqp06ddOjQofrTTz/5Pt+4caMec8wx+uabb+rUqVN17ty5um3bNt/2wsJC3zkB\ngipr165dmpGRobfeeqvvs4EDB2qNGjVURFREtEOHDjphwgTf9oKCAs3JydEOHToEde4XLFigiYmJ\numjRoiKf5+bm6vz58zUtLU3btGmjCxYsKLI9lLZE4phVl+s4UBu2NGl6yPJr4wxd0aChHhEbp3Pr\n1teH0mprsohmxMbq0voND0n/c+MM3dQ4Q9vExamA3pKS6jdf7/JQWm2NBR1RM0UzGzb2lTl69GgV\nEX3ooYe0oKDAb32r23kx5SLq9/veJbyd4RScZ6wKXUuBa318Cfv2dqX/KNoHorwW+yNgjIkm5/uz\n4Fx++eXasGFD3+95eXm+9YEDB2rnzp21du3a2q5dO/3qq69U9WBQtnTpUu3YsaN+++23Eap59Vba\neSksLNTJkydr586ddcmSJUW2Pf744yoiWqdOHV/Q07NnT33//feLpFu6dKkCQZ2TX375RZs2baqT\nJk1SVdXzzz9f69Wrp08++aTOmTNHn3rqKU1PT9fY2Fh97LHHDmlLMOf+mWeeURHRH3/8UVWdgMrt\nvffe09TUVO3WrZtu3LjRdxyCbUukjll1uY4DXWPeoMZfAHREbJze7AmS/lkrTVM9wdVHDZzg6vbU\nWkXSA9ouLl6X+wm+vAHYj40ztEVsrF6eXFO/9ARV3uWPP/7QZs2a6emnn675+fmqevCcu1Wn82LK\nRdTv971L+BlAW2Cep9fJG1B9D1xTyn4LXenvjvaBKK/FAitjTDQFE1gVFhZqQUGBDhs2TEVE33jj\nDc3OzlZVJ7jKzs7WU045RR988EGdNGmSioiOGzfukHyysrIiXv/qKpjzsmfPHn377beLfPb8889r\nQkKCXn311fr+++/rmjVr9LHHHtO0tDTt06eP7t+/X1UP3pyWVo43eM7Pz9cTTjhBTz31VP3222/1\nmGOO0VdeeaVI8LN+/XqtX7++HnHEEbpmzRpVdXqtgKDO/dKlS1VEdOTIkb7eieI30ZMnT9bY2Fi9\n9957D9m/pLZ489m9e/chPV6hHrPqch2XFFh95eo12tKkqf7SOEN/bZyh/ZKStHdiDd3SpKluapyh\n93mCq8YxsXpqQqIK6Bt16/v2BfSHAEHaliZNfb1T/23URNcUC6p+aZyhOTk5euONN6qI6NSpU0ts\nT3U5L6ZcRP1+37tELiOIBRoBaUGmPx04w7OkR/tAlNdigZUxJppC6bH67LPPNC0tTbt166bz5s3T\nnJwc/eOPP/TWW2/VpKQk/e6773Tnzp16zDHH6FFHHaX79+/3fctsQhPovHz44Yd+P8/Pz9edO3dq\nixYtdMyYMbpjxw7ftv379+uYMWNURA4ZZhdsOYWFhXrLLbdofHy8XnrppZqQkKAff/xxkfJVVWfM\nmKEi4uvZKqmML774QpcsWeKr6969e7VTp05ar149nTdvni+QcQdXv/32m55zzjmanp6uv/76a1Bt\n8Zbz559/Fvm8rMesugh0vP5dO10FdGG9BocEQhNq19G6MTH6nwaNfJ/dmVpLE0DjQP9ZK+2QHqtA\nQZW3nHf8lONdVFVXrFihIqL9+vXT7Oxsvz1WxpQi6vf73iWcySuKUNUCVf1dVXcHmf4jVV3hWf6K\nVD2MMcaUbt26dcyYMYMbbriBuXPnsmXLFrp06cLYsWNZu3Ytl1xyCccccwwnnngizz77LM888wxt\n27aldu3anHHGGWzfvh1VtYkqImjatGmcddZZTJ48+ZBtsbGx1K5dm9WrVzN27FjS09MBZya15ORk\nevToAcCOHTtCLkfVmVL7zjvvpFGjRsyePZtatWqRkJAAONNce6fX79ixIzExMfz8888lljFr1iwu\nuuginnvuObZs2YKqkpKSwsSJE1FVHn30UT7++GNf2d7JMBo3bky/fv3YuXMnu3eXfjvhLud///tf\nuR2z6mLatGncvMu55fooJxuAAj04MUXj2FiyVNnreYNOvipf5OYSI0K8CNP37+OHvLxDMy7mjQP7\nfeWs8lOO2+mnn86QIUN49913Wbdu3WExWYipviIWWBljjKkaXn/9dQYOHMiNN97I888/z+WXX87z\nzz8PwPDhw1mwYAG9evWicePGHHfccaxatYphw4b59t+6dSsZGRnUrFkzWk2odl5++WWuvvpqRo8e\nzbnnnuv7XF03o6pKgwYNfMe9sLDQF9h+/vnn1KlTh/bt24dcjoiQl5dHgwYNePnll2nWrBk7duxg\n1KhRZGdnExcX57vZ3bRpE7Vr1/bNGqh+bpZnzZrF0KFD6dOnDzfccAOdOnXy7X/88cczfvx41q9f\nz5133smSJUsoKCgo8l60/fv3k56eTmJiYolt8VeOm6pSWFgY9jGrLrznfkBSMq3i4piXdYAcVWJd\nMzCelJBIvZgYVuTkOO+l+msHn+fm8P/SanNrai1+KSjgup1/kRsgSAJn9r/Ru3b6LSeQHj16kJ+f\nz9NPP82BAwci3nZjKkxZu7pwZgTcBEwJI48XPHn8GO2uu/JabCigMSaaiv8NmjlzpiYkJOiwYcN0\nwYIFunHjRu3Tp4+2aNHC91yVqvqegSk+1O/TTz/VTp066U033aQFBQU2bKeM3OdlypQpKiI6atQo\n/eWXX4La3z2DWmZmpp588sl69tln686dO8MuZ/78+dqyZUsVEe3Ro4du3LhRt23bpsuXL9devXpp\nq1atigzTc5fx3//+Vzt06KDXXnutbtq0yff5n3/+qTt27PA9z/Tkk09qWlqatm3bVidMmOC79tau\nXas9e/bULl266K5duwK2JVA527Zt0x07dhyybyjHrLpwH6+pU6eqiOitt96q/2nQSP9eM0UF9N5i\nQ/u2NGmqJyYk6Hk1krRvjSStJaL/rp1eZHa/FQ0OTlRRfDbACbXrqIBeWzOl1HK8QwG9Tj/9dG3e\nvPkhQ0CNCULU7/e9SziBlXdGv4Vh5DHPm0+0D0R5LRZYGWMqiw8//FAzMjL0+uuvLzIV9bhx47RL\nly6am5ure/bs8d2UFhYWFpm8YNmyZdq3b19t1KiRb9Y2E55p06b5bnj/+OMP3+czZ87UBx98UIcP\nH64zZ87Ujd98o6rOOXEHu0uXLtXevXtrenq6btiwoczlDBs2TGfMmOELUr766is97rjjVES0du3a\n2rBhQ23cuLE2btxY165dq4UBJhJYvny5pqWl6bvvvuv7bNSoUdq5c2dt1qyZdu7cWd9++23duXOn\nzpkzR5s2baoiou3atdPu3btr27ZttW7durp+/foSj1tJ5TRv3lw7d+6sc+bM8TvTXLDHrLqYPHmy\nioiOHj1at27dqluaNNX/NGikaSJ6pmeiii1NmupmzyQU99RKUwGtExOjz9ZJ140lTE7hXh5Pc4Kq\nETVT9AvPRBX+yikeWHkDXm8977777mgeLlM1Rf1+37vYW9aMMeYwsGvXLqZOnUrLli25/vrradmy\npW/btm3b2LlzJ506dWLnzp20adOGBx98kDPOOIP4+HgA/vWvf/Hyyy+zb98+Fi1aROvWraPUkupj\nz549jBgxAoBmzZrRoEEDAAYMGMDChQt9w+emTJnCcccdx7hffuX4BGd4XLYqY3fv4ru8PP4oLGBW\nel1q9TqLrX7K2VtYyIjffwMg9cXJ5L32OluBv/+1g6XZWb5ypk6ZwtHx8YxPq80JCYm8WljI/Fpp\nrM/NY0d2NkfHJ3BRUjJ1zz0P2fqr3zZlZmYC0KdPHwDOOussPvnkE0488UTq16/Phx9+yAUXXMDD\nDz/MnXfeSdeuXXniiSdYs2YNBw4coHv37owePZo2bdqUeOxKK2fx4sUMHDiQ8ePHM2bMGOLj48nK\nymLUqFGsX7+e3377jWXLlnHUUUcFc6qqrJUrV3LdddcxcuRIxowZQ6NGjdiiSsPYWM6pkcTsrAO8\nl5XFuUlJxHmugx6JNdhRs4BjExLomVgj4At/3T7LyeGu3Tu5qmYK16Wk0iA2Fg1QTnHeYaCnnXYa\nbdq04bLLLovsQTCmAkU7sPL+aw08WNcYY0zYUlJS6NGjB/Hx8XTs2NH3+fPPP8/EiRM57bTTOPbY\nY9m3bx+vvfYa55xzDosXL+bUU08lJyeHmjVrcuGFFzJs2DBatWoVxZZUH7Vq1eKzzz7jzDPP5Ikn\nnqBx48a8+uqrrFq1ivHjx9OrVy9SU1N59tlnefLJJxkTG8sLderSOj6eb/Py+DQ3h7Zx8TxZpw5H\nxMUHLCc1Joa36zfg0h3bmbR/Hw1jY3kr6wCrc3O5vVYa3RITqSkxTNu/jxf37+P2XTt5pk5djoqP\n54qaKSG1qUGDBmRlZbFy5UrWrFnDF198wbx58zj11FNJTk5m4cKFPPHEE9x99900btyYK6+8ksce\ne6zIBBbu563CKWfChAnce++9tGjRgsGDB7N27VpWrFjB0UcfzbRp0zjyyCNDaltV1KpVK6ZPn06P\nHj1o1KgR4DxTlwCck+QEPPOzDnBGYiKJIsSK0C4+nptTa5EoQkKQE0m0iIvjqdrpdE1MpIHnGbZA\n5dQQIaZYvoWFhbRt25avvvqKJD/BlzFVhaiWLaYRkUKcgGiRqvYpYx4fAacBe1S1dpkqUsmNGzdO\nx40bF+1qGGMMeXl5vh4ogBUrVtCjRw9Gjx7NTTfdRPPmzQGYPHkyI0aM4IILLmDWrFnUqFGDwsJC\n8vPzfTPFmchZt24d3bp1Y+/evRx55JE8+eST9OzZkxo1avjS3HvvvTz80EPcnlqLf6TWQlX5s7CQ\nZBFSgwhEADbk5TJg+zb2qfK32DjGpqVxamINarhuch/bs5v/27fXV06+qq8nQ1V9vVsZAXqsNmzY\nQOfOnRk5ciQJCQn85z//4d133yUhIcEXMC1ZsoQrrriC5ORkVq9eTXp6esgzwYVazhdffEGdOnX4\n3//+R82aNalVq1ZI5VVlhYWFRYLVrRnNfOsj/trBJzk5LKhfnyPi4ilUPSToCbqcEvYtXo73Wgp0\nHRkTokozlWTUZgUUkdrA8TjB2f9KSW6MMSZM7qAKIDk5mdmzZ3Pffff5giqAa665hnPPPZd169b5\nxo3HxMRYUFVOOnXqxKpVq6hduzYnn3wy3bt39wVV+fn5ANxzzz00ionl09wc334NY2ODDqoA2scn\nMLdeA2qJcHxCAl0TEn1BVb7nS9Z/pNYqUk6c60Y5mOCnXbt2DB06lH/961888cQTJCYmUqNGDWJi\nYnxtOfPMM7nooovYunUr+/fvL9P02qGWs3fvXsCZzv1wCqqg5B7A0xIT2a2FPLt3LzlhBFVAifsW\nL8emVDfVVVB/kUXk9OKLa3O6v+0BljNE5BwR+QewDEj25JEZ6YYZY4yBknrMTzzxRAYMGOC70fQO\nxQI4cOAAderUISkpyW6CykHx89KxY0c+++wz7rjjDpKTk32fx7qGVQEkeL6YDfacPLGn6LugjoqP\n5+36DbghJZUk1w23921k3lwTQvgC2N2WmJgY7rzzTo4//njy8/NZs2YNH3zwAQBxcXHeiasQEerV\nqxfSsK+KKqe6CGa0zODkmrSPi+fz3Fz2eP79F4Ywkqn49VVe5RhTVQT7VddynEDIvYDzN/hEP9sC\nLUuBd4GnAPcLJ2aG0QZjjDEB3H///SVu996gu4cLffDBB/z666/06tXLPYuriSB/5+XII4+kffv2\nvuPtHnr33nvvkY1ygqfXMNhz8uS+vYd8dkRcPG3i4/2Wsyw7O+RyirelZcuWTJs2jdatW/P7778z\nceJEPvroI8C53tasWcOnn35Kp06dQnoXWkWVU12U9m+/QJ13Sw1MTuangnxmHdgPlNzzVJy/66s8\nyjGmqojG5BXF/yW9pKrvRaEexhhjKBpUffnllzz++OMUFBRw3XXXWW9VFHgncvCek8zMTJ577jlq\nidA/KdmXJiLluIZ/rcvNZfr+fREpp0OHDrz11ltce+21vPPOO6xdu5azzz6bxMREVq5cyU8//cT0\n6dOL9M5V5nKqI+8Le7sl1qB2zF7eOHCA/knJNI2L7K1hRZVjTGUQyjNWUmwJ9HlJC8A+YCMwC+ir\nqiPCqL8xxpgweW/gJ0+ezG233caXX37J/Pnz+dvf/hblmh2+vOdk0qRJjBkzhq+++orJ6fXIiPDN\nqDeomrF/H+P37Obr/LyIldO2bVtmzZrFo48+Sk5ODlOmTGH27Nmkp6ezatUq2rdvH3YZFVlOddUu\nPp4LaiSxS53JUKp6OcZEU1B/OVX1kAAsErMCGmOMib7c3Fxuuukm3n33XTIyMli5cqXdjEZZXl4e\nDz30EFOmTKFBgwZ89NFHpPU+O/LlqPLvvXt4PesA9WJimFO3Pm3iA0/dHqpGjRpx6623cuWVV7J9\n+3aSk5NJT08nJSW0adwrSznVVb+kZK5NSSU9Nrb0xFWgHGOiJdyvpOwrB2OMqeISEhIYPXo0Xbt2\npXfv3jRu3DjaVTrsxcfHM2zYMNq0aUP37t1p0qSJ35f/hl2OCJcl1+SIuHhOSUykUTnd8NarV496\n9eqVS97RKKe6OTExsVqVY0y0hBNYXe35WR5/640xxkTA2LFjg0rXpk0bjjzySHumqoIEc16aN2/O\n5ZdfHtY5uSUltdQ0GXFx9IuNLXM5wV5j4aqocqqLijhewVxfxhxOyvyCYBMce0GwMcaYSHC/2DXa\n7MWuVZddR6YaqjTfCEbtBcHGGGOMMcYYU11YYGWMMcYYY4wxYYrYvK0i0gw4FegA1AaSCb5rTlV1\neKTqYowxxhhjjDEVKezASkTaA08CZxLeGEcLrIwxxhhjjDFVUlhDAUXkXCAT6OXJK5SXBft72bAx\nxpgIsslzKqeKOi9P7Nld7mVUVFvsWg5NRRyviri+jKlKyjwroIg0BDYCKTgvChYgG1gDbAH2h5Kf\nql5deqqqx2YFNMZEk4hgs79WPmU5L2WZza3pb1vY0qRpyPuVxj2bW0VdY3YthybQ8YrkrIDhXl82\nK6CJkErTSRPOUMBRHAyqCoFxwL9VdW8E6mWMMcYYY4wxVUY4gdXZrvVRqvpsuJUxxhhjjDHGmKoo\nnGesWnp+7gSeC78qxhhjjDHGGFM1hRNY1cAZBviN2qBnY4wxxhhjzGEsnMBqq+dnpXlgzBhjTFFj\nx46NdhWMHxV1Xm5JSS33MiqqLXYth6YijldFXF/GVCXhzAo4C7gE+J+qZkS0VtWIzQpojDEmEiI5\nm1u4bDa3qsuuI1MNVZpOnnB6rF72/GwkIqdFoC7GGGOMMcYYUyWVObBS1feBd3CixKdFJDlitTLG\nGGOMMcaYKiScHiuAq4CvgOOAJSLSOuwaGWOMMcaESLOzo12FIipbfYwx5a/M77ESkaGe1cnA/cBJ\nwLcisgT4GPgdyAk2P1WdHkZdauG8V6sHcDzQGqgF7AN+8dRnqqp+UUo+U4ErQ6hzuIGpMcYYYyJA\natSodM8PVab6gD3TZEx5C/cZq6nAM0BdnKnXY4HewDjgec/2YJcyEZExwB/A68B1wIlAHU9d0oCO\nwPXAahGZLiJJQWSrQSyFZa2zMcZUFJs8p3KqqPPyxJ7d5V5GRbXFruXQVMS5r4gyjKlKwpkVsBAn\nwBDPzyKbQ8xOVTW2jPV4ERjuqcMvwIdAJrAdJ8A6ExiAE2gJsEhVzw2Ql7fHSoFrgT9LqfSC0upn\nswIaY6JJRLBXDVY+ZTkvZen9aPrbFrY0aRryfqVx93xU1DUWTDmVqYco2j1W/s59pOsU7vVlPWgm\nQirNrIBlHgqIE8RUhv+tFVgIPK6qK/xsnywipwLvATWBs0TkSlWdVkq+H6jqLxGuqzHGGGOMMaYa\nKnNgpaotI1iPcNyuqrtKSqCqH4vIXcD/4QRiVwGlBVbGGGOMMcYYE5QqP/lCaUGVyxuen4Lz3JUx\nxq9XxFYAACAASURBVBhjjDHGRESVD6xCsNe1HswEFsYYY4wxxhgTlMMpsDra81OBn4NIP1lEfhaR\nbBHZKSLfiMgkEelWjnU0xpiIGjt2bLSrYPyoqPNyS0pquZdRUW2xazk0FXHuK6IMY6qSMs8KWNWI\nyGRgGE5g9YSq3u4njXtWQL/ZeH6+CwxV1Z2llWuzAhpjjImEyjbjXWVU2Y5RZaoPVL46VdbryFQ5\n1WJWwCJEJBG4HOgFdAbq47xHClU9pBwROY2DPWYrtRwjPBHpijNhBUA28FQJyffgTNm+GvgVKACa\nAmd5FoDzgOUicqqq7iuPOhtjjDHGGGOqjogEViIyDHgEqOf+2PMzUMB0G3C+Z/0cnGAm4kSkEc7L\ng2M8dblXVX8LkPzfwA2qmuVn25OeadvfBBriDC2cAIyIfK2NMcYYY4wxVUnYz1iJyPPAizhBlbiW\n0jztSjco3HoEqFsyMB/IwAmq3lHVJwOlV9WvAgRV3u0f47xs2Pti5KtEpHFka22MMcYYY4ypasIK\nrDzvhvL22AiwERgL9AM+L2X35cAfnv3OKjlpmeqWCLwNnIgTCK0CLgs3X1X9BPjA82sscHa4eRpj\njDHGGGOqtjIPBRSRDOBe10ePAP9U1ULP9utL2l9VVUQ+AIYAjUSklar+WNb6FKtbPDAP6IETVH0G\nnFdSb1SIlnMwoGpXUsJjjz02QkUaY0zoli9fTvfu3aNdDVNMWc5L6uhbQi7nPzk5nJKYGPJ+oaio\nayyYcspyjMpTNOsT6NxHsk4VcX0ZU5WUeVZAEbkPGIcTuExV1WuKbX8PJ/hQVY0NkMfNwJOePC5U\n1XfKVJmiecYBc3Ce31LgS6CXqu4ON29XGdcAkzz5v6iq1wVKa7MCGmOiSUQ4XGZ/rUrKcl7KMptb\n09+2sKVJ05D3K417NreKusaCKaeyzXgXzfr4O/eRrlO415fNCmgipNLMChjOUEDv8D2laM9VKDa5\n1puHURcARCQWmMXBoGodcFYkgyqPuq71XRHO2xhjjDHGGFPFhBNYtcIJXjao6u9lzMMdlIT1ljkR\niQFmAhd56vUN0DuYd02VwRmu9f+WQ/7GGGOMMcaYKiScwCrd8/OPMPJwDxEsLGsmIiLAVOASnKDq\nO+BMVd0eRt0ClXUqB5+vKgAWRboMY4wxxhhjTNUSTmC1x/MzJYw8GrnWd4SRzyScSTAUZ2bCM1V1\nWygZiMgQEelVSprTcJ7fEk9Z00p4J5YxxhhjjDHmMBHOC4J/x3nW6CgRES3bk6unuNY3l6USIvIw\nMBwn0MnDecnvSU4nVokWqWq26/fjgZtF5FecXqj1wDacXqmmOM+UncXBoGo9MLosdTbGmIoyduzY\naFfB+FFR5+WWlLBG2Qelotpi13JoKuLcV0QZxlQl4cwK+DzOO6wUZyrz94ttL3FWQBGpAfyC82Lh\nHKBOsUAn2Hoso+gzT8Fqqaq/uPJ5ErjJ+2uAfbwHay4wIpjnt2xWQGOMMZFQ2Wa8q4wq2zGqTPWB\nylenynodmSqn0swKGE6P1QIOvhz4URFZqqq5Iez/EE5QpRzaexSqUKNDf+kfw3mp8Sk4vVcNPfWr\nAewGfgI+Aaar6pqyV9UYY4wxxvx/9u48TIrq7P//+3aAAQQUUCQMuyKKCIhLEBXEBTWSGFEUUSPu\niisuMTyaZxiMSySAggviAsjPhQBJ1K+CK5IIeQRFZZGwKMuwi0SGgWE/vz+qZ+hhumfp6q5e+Lyu\nq68uu06dc1efDuGmqu4jkmliTqycc++b2VzgJOBE4F0zu8o5t7m840Il0R8Fwleoe9xHHD1iPfaA\nftYBb4ReIiIiIiIilebnihXA7cBneFd1zgOWmNlY4GPCilqYWQe8QhWnA9cBLUK7HPCcc262zzhE\nRERERESSxldi5ZybY2b9gDeBbLwS7PdRuqiDAV8f8N/Ft+K9T+krVyIiIiIiImnHT7l1AJxzbwNd\n8daOAi9xKn6IzIVeFrYPYA/wJPAb59xevzGIiEhkKp6TmoKal2EFWxI+RlDnot9y1QQx90GMIZJO\nYq4KWKYjr775JXi3+p3F/gWEwy0BpgJPO+dWxmXgFKeqgCKSTGZGvP6cl/iJZV5iqebWdO1qVjdp\nWuXjKhJezS2o31hlxkm1infJjCfS3Mc7Jr+/L1UFlDjJiKqApYTWsfpH6IWZNcVb5+pQ4GdgfUWF\nLURERERERNJR3BKrAznnVgOrE9W/iIiIiIhIqvD9jJWIiIiIiMjBTomViIiIiIiIT0qsREQyWG5u\nbrJDkAiCmpeBdeomfIygzkW/5aoJYu6DGEMknZRbFdDM/jeoQJxzQ4IaK0iqCigiIvGQahXvUlGq\nfUepFA+kXkyp+juStJM2VQEHs38x30TLyMRKREREREQyX2WqAlY1CyxOxA48Ltrn4ftERERERETS\nTkWJ1T+pXNLTHm9BYAu9HLAc+AnYCdQDWgLFN+MW9/kVsK1KEYuIiIiIiKSYchMr59zZ5e03MwMe\nBbrhJVSfA6OAqc65wgjt2wHXAHcCdfASrRuccwtiCV5ERERERCQV+K0KOBgYhHcF6l7nXDfn3KRI\nSRWAc+4759z/AO2A+UBb4CMza+wzDhERiUDFc1JTUPMyrGBLwscI6lz0W66aIOY+iDFE0km5VQHL\nPdCsI96tfAY84Zx7pIrHNwYWAPWBd51zv40pkBSnqoAikkxmRqx/zkvixDIvsVRza7p2NaubNK3y\ncRUJr+YW1G+sMuOkWsW7ZMYTae7jHZPf35eqAkqcpExVQD9XrG4KHb8T+HNVD3bOrQfG4H0ZvzKz\no3zEIiIiIiIikjR+EqseeLcAznfObY2xj89D71nAWT5iERERERERSRo/iVVO6N1PVb/wY3OithIR\nEREREUlhfhKrrNB7ax99hB+bFbWViIiIiIhICvOTWK3Gez6qmZmdGWMf1xzQn4iIxFFubm6yQ5AI\ngpqXgXXqVtzIp6DORb/lqgli7oMYQySd+KkK+DRwN95zVv8BznLOba7C8XcCI0P/uQdo4pzbFFMw\nKUxVAUVEJB5SreJdKkq17yiV4oHUiylVf0eSdjKiKuBLeAkRwPHAF2Z2fkUHmdnhZvYM8EzoIwdM\nycSkSkREREREDg7VYj3QObfQzJ4A/oiXHB0NTDOzpcA0vAWAfwJ2AXWBVsAvgQuAbPZnl5uAe2ON\nQ0REREREJNliTqwAnHO5ZnYY+28JNOBYoE05h1moLcAG4Dzn3AY/cYiIiIiIiCSTn1sBAXDO3Qv0\nAdaFfRztXsfwz98COjjnFvqNQUREREREJJl8J1YAzrkpQEvgCryEaTleEhX+KsJbEPgx4FjnXD/n\n3I/xGF9ERCJT8ZzUFNS8DCvYkvAxgjoX/ZarJoi5D2IMkXQSc1XACjs2ywLqAzWAAudcYUIGSnGq\nCigiyWRmJOrPeYldLPMSSzW3pmtXs7pJ0yofV5Hwam5B/cYqM06qVbxLZjyR5j7eMfn9fakqoMRJ\nylQF9PWMVXmcc3vxClOIiIiIiIhktLjcCigiIiIiInIwU2IlIiIiIiLikxIrERERERERn2J+xsrM\nXo1jHM45d2Mc+xMRESA3NzfZIUgEQc3LwDp1Ez5GUOei33LVBDH3QYwhkk5irgpoZvvYv9Cvb865\nrHj1lUpUFVBEROIh1SrepaJU+45SKR5IvZhS9XckaSdjqgLGciIuwnGqBSwiIiIiImnLT2I1vgpt\ni9e0OhFoHvrMAR8B63zEICIiIiIiknQxJ1bOuetjOc7MTgaeAM4DTgAGOee+jjUOERERERGRZAu8\nKqBz7ivnXE/gJSAHeN/Mjgo6DhERERERkXhJZrn1O4DvgUbAc0mMQ0QkY6l4TmoKal6GFWxJ+BhB\nnYt+y1UTxNwHMYZIOklaYuWc2wO8ilfI4je6aiUiEn95eXnJDkEiCGpeRhRuTfgYQZ2LfstVE8Tc\nBzGGSDpJ9gLBc0PvWcBZyQxEREREREQkVslOrLaHbTdNWhQiIiIiIiI+JDuxah22nZELBIuIiIiI\nSOZLdmJ1Y9j2mqRFISIiIiIi4kNSEiszq21mY4AzQx85YHoyYhERyWS5ubnJDkEiCGpeBtapm/Ax\ngjoX/ZarJoi5D2IMkXRizrnYDjT7XRUPqQ40ADoAvwIOx6sI6IC/OueuiikQL5Z6wAVAD6AzcAxQ\nDygEVgEzgbHOuS+r0OeFQH+gC3AUUAAsBSYDY5xz26Mfvd/gwYOdSsSKiIhfa3KaJTuEEjlr8pMd\nQkSp9h2lUjyQejGl6u9I0o4lO4Bi1XwcOw4vKYpFcUIFsAy4J9YgzOxBYAiQHfooPKbDgBPxkrnb\nzez/A251zhWV018NYDxw5QH9HQEcCXQF7jCz3s65+bHGLSIiIiIimcNPYlUs1izRgCnAnc65jT7G\nPxYvqXJ4V6c+Ar4CNgH1gXOBy/CKY1yDlxxdVE5/rwFXhPr7CRgDzMdLrK4BTgOOBqaa2S+dc3o2\nTERERETkIOcnsVpF1a5Y7cK7nW4l8CUwxTm31Mf4xRzwPjDUOTcjwv6XzewMYCpwKNDTzK5zzo0/\nsKGZXcL+pGoVcOYBidNzZvYKcD3wC2A4+69siYiIiIjIQSrmxMo51zKOcfjxe+fcz+U1cM7NNLNB\nwCi8pKk/3u1+Bwp/Mva2KFej7sC7CtYcuNzM2jnnvospchERERERyQjJLrfuW0VJVZhJoXfDe+6q\nFDM7BuiEl3gtdc59EGW8HcBLYR9dUfloRUSCpeI5qSmoeRlWsCXhYwR1LvotV00Qcx/EGCLpJO0T\nqyrYGrZdK8L+C8K2IyZVYaaFbV8Yc0QiIgmWl5eX7BAkgqDmZUTh1oob+RTUuei3XDVBzH0QY4ik\nk5hvBTSzbqHNzc65BTH20Q6vKATOuX/GGksltQ+9O7znvKLtB6/4RXm+AfbiFcRo5z80ERERERFJ\nZ36KV3yGl6R8gLcuVSweA34T6iceFQrLc2vY9v+LsP/YsO0V5XXknNtrZmvwnrM61MyaOOfW+g9R\nRERERETSUSrcCmgkeGEvM+uKV7ACYAfwdIRmh4dtb6pEtz9FOVZERERERA4yqZBYJZSZNQYm4p2r\nAx6JcnWpTtj2jkp0Hb7IcN3YIxQRERERkXSX7MSqeuh9dyI6N7PawNtADl5S9f+ccyMSMZaISCrK\nzc2tuJEELqh5GVgn8f/uF9S56LdcNUHMfRBjiKSTZCdWzUPvBfHu2MyygXeBU/GSqs+BvuUcUhi2\nXbMSQ4RXFlRZHBFJSSpRnZqCmpf76x2W8DFUbj01BTH3QYwhkk4SXTAiKjM7F68SnwOWxbnv6sDf\ngR6h/r8ALnbOFZVzWPh6WEdUYpiGUY4tpVOnTpXoSkREpHx17xuY7BBSXqp9R6kWD6RmTCKZwpxz\nFTcyezXCx/3xkpa1wEeVHQ/vSk8boCP7n3t6yjk3qJJ9lD+AWTVgCvDrUN9zgfOcc+WuYmdmL+BV\nDnTA9c6518ppm4X3HFYWUOicqxet7eDBg53+lU1ERPxak9Ms2SGUyFmTn+wQIkq17yiV4oHUiylV\nf0eSdhJaBK8qKnvFqj9ewnEgA5oA18UwdvGXsB14MYbjy3boJTxvsT+pmgf0rCipCglfi+tkIGpi\nBXTCS6oc8F1s0YqIiIiISKaoyq2A0bJBP1niOqC/c26Fjz68IMwOAV4HeuMlPAuB851z/61kFx+E\nbV9QQdsLw7anVTpIERERERHJSJUtXjE+wgu8BGZNlP2RXmOBZ4GH8ZKTFs65yt5GGJWZWajvK0Ix\n/Qc41zlXmfWovBNxbhnwNV6i2MbMIiZXoaIYN4d99NdY4xYRSTTdipyagpqXYQWVuWHDHxWvSE1B\nzH0QY4ikk0o9YxXxQLN9eEnMB865X8U1qqrH8hJwYyiepcDZzrn1MfTzG+AfoX5WAt2dc/lh+w14\nGbg+1GaSc668SoN6xkpEksrMiPXPeUmcWOYllmdjmq5dzeomTat8XEXCn40J6jdWmXFS7fmhZMYT\nae7jHZPf35eesZI4SbtnrKJJ+omY2ePsT6p2AyOBX3o5ULk+cM6VWgjYOfeOmU0ErgRaAnPN7EVg\nPl4VwN8Bp4WarwXuj9NpiIiIiIhIGos5sXLOJXsNrGKnh94NqIF3q2FltARWRfj8d8A+vDWvGgD/\nc8D+4vLwvZ1za6oarIiIiIiIZJ5USY78clV87YvakXO7nXNXAxcBk/CSrx3Aj8AsYCDQyTm3MFEn\nIyIiIiIi6SXQBYLNrAXQGNjsnFsajz6dcz3i0U+Efj8EPkxE3yIiIiIikll8XbEys+PMrF3oFfWh\nJjO7yMz+A/yAd9XnP2a2ysxu8jO+iIiULzc3N9khSARBzcvAOnUTPkZQ56LfctUEMfdBjCGSTvxU\nBTwOb60ogG+dc52jtPst3i11h1C22IUD/uycO/A5poyhqoAiIhIPqVbxLhWl2neUSvFA6sWUqr8j\nSTtJL6ZXzM8Vq9+w/0TGRGpgZrWB0UBWlD4MeMjMuvuIQ0REREREJKn8JFa/DNv+f1Ha/A5oxP6C\nEY8BnYFuwIxQGwN0fV9ERERERNKWn+IVbULvPzrnVkdpE7547jPOuT8W/4eZ/QpYBDQHuplZI+fc\nRh/xiIiIiIiIJIWfK1Y5eFeilkfaGboN8PSwj0qtL+WcKwLGFzcHTvERi4iIiIiISNL4SazqhN63\nRtn/S6A6XvK10Dm3IkKbr8K2W/qIRUREIlDxnNQU1LwMK9iS8DGCOhf9lqsmiLkPYgyRdBKPBYKr\nR/k8/GrV9ChtNoVt14tDLCIiEiYvLy/ZIUgEQc3LiMJo//YZP0Gdi37LVRPE3Acxhkg68ZNYFf8z\nRdMo+8MX7p0ZpU3NsO19PmIRERERERFJGj+J1VK8Z6Nam1lO+A4za4hX+a/YP6P0cWTY9s8+YhER\nEREREUkaP4nV52HbQw7Y9wj7n6+a55xbH6WPE8O2V/iIRUREREREJGn8lFt/DXggtN3fzNrgJVsn\nAT3D2r1aTh9nhW0v8BGLiIiIiIhI0sScWDnnFprZaOB2vCtTZ4Re4b4HXox0vJk1DrV3wBrn3NpY\nYxERkchyc7X+eioKal4G1qmb8DGCOhf9lqsmiLkPYgyRdGLOudgPNsvCS5xuiLD7B+Bi59ziKMc+\nDDyKl1i95py7PuZAUtjgwYOdSsSKiIhfa3KaJTuEEjlr8pMdQkSp9h2lUjyQejGl6u9I0o4lO4Bi\nfm4FxDm3F7jJzEYBvYBmQBEwB5jsnNtVzuEnAjNC22/6iUNERERERCSZfCVWxZxz3wLfVvGYvvEY\nW0REREREJNnisUCwiIiIiIjIQU2JlYiIiIiIiE9KrEREMpiK56SmoOZlWMGWhI8R1Lnot1w1Qcx9\nEGOIpBMlViIiGSwvLy/ZIUgEQc3LiMKtCR8jqHPRb7lqgpj7IMYQSSdKrERERERERHxSYiUiIiIi\nIuKTEisRERERERGflFiJiIiIiIj4pMRKRCSD5ebmJjsEiSCoeRlYp27CxwjqXPRbrpog5j6IMUTS\niTnnkh1DRhs8eLBTiVgREfFrTU6zZIdQImdNfrJDiCjVvqNUigdSL6ZU/R1J2rFkB1BMV6xERERE\nRER8qlbeTjO7O7S5wjn3TgDxiIiIiIiIpJ1yEyvgacABHwClEisz+9/Q5jLn3BsJiE1ERERERCQt\nVJRYlWcw+5MuJVYiIiIiInLQqugZK1W2EBFJYyqek5qCmpdhBVsSPkZQ56LfctUEMfdBjCGSTipK\nrLaF3g9LdCAiIhJ/eXl5yQ5BIghqXkYUbk34GEGdi37LVRPE3Acxhkg6qSixWoNXwrCDmdUJIB4R\nEREREZG0U9EzVv8HtAVqAzPMbCSQD+wJa9PAzLr5DcQ590+/fYiIiIiIiCRDRYnVK8B1oe1OwKsH\n7DfgVGC6zzhcJWIRERERERFJSeXeCuic+xx4Ci+BCn/FUyL6FBERERERCUxFz1jhnPsD8BvgXWAD\n3m2Axv6KgQcmXVV9iYhIguTm5iY7BIkgqHkZWKduwscI6lz0W66aIOY+iDFE0ok5F1tFdTPbR2gd\nK+fcr+IaVQYZPHiwU4lYERHxa01Os2SHUCJnTX6yQ4go1b6jVIoHUi+mVP0dSdpJmQs1FV6xEhER\nERERkfL5TaxSJkMUERERERFJFj+V+FqF3oviEYiIiIiIiEi6ijmxcs6tjGcgIiIiIiIi6Srhz1iZ\nmW4XFBFJEhXPSU1Bzcuwgi0JHyOoc9FvuWqCmPsgxhBJJzFXBYzYmdnpwKXA6cAxQH2gOrAV2Ah8\nCcwA3nDObY3bwClMVQFFJJnMjHj+OS/xEcu8xFLNrena1axu0rTKx1UkvJpbUL+xyoyTahXvkhlP\npLmPd0x+f1+qCihxkjIXcfw8Y1XCzDoCY4BTwj8O264Xeh0NXAk8ZWYjgEedc3vjEYOIiIiIiEiy\n+L4V0Mz6A1/gJVXFyVS0zLH487rAH4HPzewwvzGIiIiIiIgkk68rVmb2K+AlIAtvsWCAbcDHwDzg\nR2An+69WdQU6Fh8OnAa8Y2Y9nHP7fMZyCHA8XoJ3cui9I1Ar1GSwc25IJfoZC1xX2XGdc1oLTERE\nRETkIBdzYmVm2cAL7E+qCoHBwIvOue3lHNcRGAacg5dcnQncGurLj0l4z3eFc+xP+KqqMsfpwQUR\nEREREfF1xeoaoBlecvETcJ5zbl5FBznnvgXOM7MX8BIqA/6A/8TqEEonOptDcR1L7AnQrXhFN0RE\n0lJubm6yQ5AIgpqXgXXqJnyMoM5Fv+WqCWLugxhDJJ3EXBXQzP4G/BYvabnaOfdWFY/PAr4G2of6\nOKkyiVk5/f0B79mtr4CvnHMrzew6YGyo/7wq3grogFbOuVWxxgSqCigiIvGRahXvUlGqfUepFA+k\nXkyp+juStJMRVQE7hd5/Av5a1YOdc3vN7GXg6bD+Yk6snHNPxnqsiIiIiIiIH34KLzTCu6qz2Efh\niYVh20f6iEVERERERCRp/CRWxfcQ+rn8ljKX7kRERERERGLlJ7HaiJcYHR96XioWJx7QX6p52cxW\nmtkOM/uvmS00szFmdlayAxMRERERkdThJ7H6OvR+OHBVVQ82s2rATWEffeMjlkQ5F2gKVMdbi+s4\nvJhnmNm7ZlY/mcGJiFRExXNSU1DzMqxgS8LHCOpc9FuumiDmPogxRNKJn8TqvdC7AU+H1qeqiufw\nFvR1wCrn3HwfscRbAfA3vDLwVwN9gQeAD9m/NtbFwGdmVidZQYqIVCQvLy/ZIUgEQc3LiMKtCR8j\nqHPRb7lqgpj7IMYQSSd+EqvXgZV4SUYD4F9mdr+ZHVreQWZ2kpl9QumrValU0W8k8AvnXB/n3FDn\n3FvOuUnOuRHOuYuAbuy/bbE9MDxpkYqIiIiISEqIudy6c26Xmd0OvIuXoNUBngIGm9kM4FvgR2AX\n3vpSRwNd8a5Swf7CFf8EXoo1jnhzzn1dwf6ZZnYZ8C+8c+hvZrnOuXWBBCgiIiIiIinHzzpWOOem\nmdmNwBi855AADgUuCr0iMfZXFPw/4Dc+yrUnhXNulpl9CFwAZIXexyU1KBERERERSRpfiRWAc+41\nM/sWeAHoEvq4+GpUtJLsW4FhwOPOub1+Y0iSz/ASKvCKWkTUqVOnaLtERBIuNzc32SFIBLHMS937\nBlZ9nK0F1K1br8rHVcTt2YNV8/4KEdRvrDLjxPIdJVIy44k29/GMye/vK/x3lCpSMSZJH3H55Tjn\nvgW6mtnJQG/gdOAYoD6QDfyM91zSV8AMYKJzbls8xk6in8K2D4/W6JtvvuG3v/1tAOGIiEgm2zp8\nRJWPKSjYwtZ6h8U9lnr338eanGYlY6x56ZW4j3GgisbJWZMf03eUKPXuvy+p8USa+3jH5Pf3Ff47\nShU5a/KTHYKksbim5M65r/CSp4NBw7Dtn5MWhYhIOVSiOjUFNS/3JyCpSsYYQY6TKTJp7kXShZ+q\ngAe77mHbi5MWhYiIiIiIJJ0SqxiY2Rnsf75qL/BBEsMREREREZEkU2IVxsyuNbPzKmhzJjCF/dUN\nxzvn1gYRn4iIiIiIpKaMKXtiZi2BGw/4uEPY9jlmVv2A/ZNDhTeKdQbuMbN8vKtQ8/HW4toLNAV6\nhl7FSdV84L44nYKIiIiIiKSpTLpi1QJ4+IDXr0P7DOgWYX/HCP04vCTqJuAZ4A1gIl55+AvC2vwN\n6OGc25qAcxERiQsVr0hNQc3LsIItGTFGkONkikyae5F0kUmJFXgJT2VfkRYlfgq4Fnge+DfwA1AA\n7MK7cjUbL9k62TnXxzn330SejIiIX3l5eckOQSIIal5GFCb+3/6CGCPIcTJFJs29SLrImFsBnXMz\ngCyffazDu0L1RlyCEhERERGRg0KmXbESEREREREJnBIrERERERERn5RYiYiIiIiI+KTESkQkg+Xm\n5iY7BIkgqHkZWKduRowR5DiZIpPmXiRdKLESEclgKreemoKal/vrHZYRYwQ5TqbIpLkXSRcxVwU0\ns9+F/ec059zGOMQjIiIiIiKSdvyUWx+Htx7UNuCouEQjIiIiIiKShvzcCrgTMGCxc64oTvGIiIiI\niIikHT+J1Qa8K1YFcYpFREREREQkLflJrL7Hu2LVNE6xiIhInKl4RWoKal6GFWzJiDGCHCdTZNLc\ni6QLP4nV30Lvx5hZ63gEIyIi8ZWXl5fsECSCoOZlROHWjBgjyHEyRSbNvUi68JNYvQ6sD20/FYdY\nRERERERE0lLMiZVz7mfgOmA3cKmZvWxmteMWmUgG2rNnD2PGjKFbt240a9aM1q1b07VrV6ZMmVLu\ncStWrOCee+6hffv2tGrVimbNmtGvXz+WLl0aUOSedevWceSRRzJ79uxAxxURERFJdTEnVmbWHFiM\nl1wVAtcDP5jZcDP7rZl1MLOWZta8Mq84nY9Iytq4cSNnnHEGQ4YM4YEHHiA/P58ffviB8ePHC/Su\nzAAAIABJREFUM3LkSF5//fWIx02aNIl27drx008/8dlnn7F8+XIWLVpEtWrVOPXUU5k3b15g53D3\n3XezefNmduzYEdiYIiIiIunAzzpWK/CqAhYzoBFwT+hVFc5nLCIpraioiIsvvpgffviBr776ipYt\nW5bsmzp1KrNnz2bTpk1cffXVpY6bPn06ffv25YQTTmDChAmYGQB16tRhzJgxTJs2jV69erFkyRJq\n1qyZ0HN47733mDJlSkkMIiIiIrKfn2esihX/LctRNtGqykskY910003MnTuXp556qlRSBfDSSy+x\nc+dOVq5cWerzvXv3csMNNwDQv3//MglNzZo16dmzJ2vWrGHUqFEJjb+oqIhBgwYldAxJjNzc3GSH\nIBEENS8D69TNiDGCHCdTZNLci6QLv4mVhb0rURKJYO7cubz55pu0adOGG2+8scz+W2+9lbp16zJg\nwIBSn0+bNq0k2ercuXPEvrt164ZzjrFjx8Y/8DCDBw+OGLukPpVbT01Bzcv99Q7LiDGCHCdTZNLc\ni6QLP7fftYpbFCIZLC8vDzPj8ssvj7j/zjvv5M477yzz+bRp00q2GzZsGPHYZs2aAbB48WKWLVvG\nMcccE4eIS5s3bx4zZszg3//+NwMHDox7/yIiIqnC7diBJfjW+qpItXikfDEnVs65lRW3Ejm4bd26\nlalTpwJw2mmnVenYVatWlWwfeuihEduEJ1xz5sxJSGI1YMAAnn/+eT1bJSIiGc9q1mRNTrNkh1Ei\nZ01+skOQKojHM1YiEsUnn3zCnj17AMjJyYm5n2hJTY0aNUq2Fy5cGHP/0bzwwgt07tw56q2IIiIi\nIuJRYiUpb+zYsdSvX5+333474v558+bRpEkTHn744YAjq9i///3vku0jjjiClStXcvnll3PMMcfQ\nuHFjLr74Yj766KOIxxbf5gewc+fOiG1+/vnnku3Vq1fHKWrP+vXree6553j88cfj2q+IiIhIJlJi\nJSnvscceo6CgIOpVmzFjxrBhwwYKCwsDjqxia9euLdnOz8/nnnvu4cknn2TZsmWsWLGCtm3bcsEF\nF/Dggw+WOfb8888v2d60aVPE/hctWlSyvXnz5jhGDgMHDmTIkCHUqVMnrv1KsFS8IjUFNS/DCrZk\nxBhBjpMpMmnuRdJFXBMrM2tlZjeZ2Wgzm2JmH5vZJ/EcQw4uxYvoZmVl0aNHj4htpk+fDsA555xT\nYX9Lly6lbdu2tG7d2terVatWtG7dmhdffLHc8TZu3FiyPWTIEF544YWS56Bq1qzJ8OHDOfPMMxk+\nfDjPPvtsqWMvvPBCmjRpAsC3334bsf8PPvigZDuei/ZOmzaNwsJCevfuHbc+JTny8vKSHYJEENS8\njCjcmhFjBDlOpsikuRdJF3FZlNfMTgCeBC6idKl1o/TaVuHHfAmcFNp/knNufjxikczy6aefAnDy\nySdTt27Z9TI2bNjAokWLOOSQQ+jevXuF/bVp04bFixfHPc5owq+ytWnThl/84hdl2lx99dV8/vnn\nPPzww1x33XUl55mdnc3IkSO5/PLLGTduHHfccUep4+bPn1/qKt1hh8Wn7O2OHTv4/e9/z3vvvReX\n/kREREQOBr6vWJnZNcBs4Feh/iq7ltWIsHbX+o1DMtOnn36KmXHeeedF3Q/QsWNHDj/88CBDq5Tw\n2+h69uwZsc0JJ5wAQGFhIZMmTSq1r3fv3owcOZKvv/6am2++mY0bN+KcY+bMmTzwwAOlFhlt1KhR\nXGIeMmQI1157balnvERERESkfL4SKzP7FTAWqImXIO0BpgNPA99XcPjfgO2h7Yv9xCGZqzhxOvfc\ncyPunz59OmYW9TbBZGvcuHHJdps2bSK2OeKII0q2P//88zL777zzTr755ht2795Njx49OO6443jl\nlVcYP358qcStbdu2vuNdsGABH374Iffdd5/vvkREREQOJjHfCmhmtYAxQBbe7XyfATc451aE9h8P\nHB3teOdcUej5q18Dx5lZI+fcxmjt5eCzZMkS1qxZQ+3atTnjjDMitilOvFI1sWrdunXJdq1atSK2\nyc7OLtlet25dxDbt27dn3LhxZT5fsGBByXY8voMBAwbw7LPPkpWV5bsvERERkYOJn2es+gNN8JKq\nfwM9nXN7qtjHbLzECqA98KmPeCTDFCdNXbt2pXr16mX2r1q1ih9++IFq1apV6vmqZDjllFNKtovX\nszqQc/sfQ6xfv36V+l+yZAkARx11FCeeeGIMEe63evVqvvvuO/r16xdx/969e0u2+/btS3Z2NmbG\n559/XlJkQ1JP+O2ikjqCmpeBdco+m5qOYwQ5TqbIpLkXSRd+Eqvw2/fujCGpAlgUtt0aJVYSpvj5\nqi5dukTdD9C5c+eSW+Ly8vK49NJL6dChQ8Rjli5dSq9evdi9e7ev2JxzmBkPPfQQt956a9R2v/zl\nL6lbty6FhYVs3Lgx4u2A27dvL9k+/vjjS+374x//yMiRI3nssce48847yxw7c+ZMzKxMYYtYNG3a\nNGpZd4CVK1fSqlUrACZOnMhZZ53le0xJPJVbT01Bzcv99eJT1CbZYwQ5TqbIpLkXSRd+Eqvifx5f\n6Zz7JsY+/hu2nXqVBySpPvvsMwCaN28ecf8//vEPzIxu3bqVfDZlyhT+8Ic/RO0z6KqA2dnZXHrp\npUyYMIGFCxdGvKVx1apVJdt9+/YttW/EiBEUFRUxZsyYMonV1q1befvtt6lduzYDBgxIzAlEEX6V\nTURERET8Fa84Eu82wBU++gi/bBCX0u+SGebNm1dy9eTHH38ss3/8+PG8++67wP6qenPnzuW4444r\n9cxSKhg0aBBZWVm8/fbbEfd/+OGHAPTr16/MFa06depQo0aNiFfFHn30UYqKihg1alTEWwjz8/Pp\n2LEjOTk5JWt9iYiIiEhi+EmsdoXeyz78UnkNw7b/G7WVHHSKb/MDePnll1m/fj0Au3bt4i9/+QsT\nJ07kmWeeKfkMYOjQoYFfuamMtm3bkpuby7Rp05g2bVqpfYsXL+aVV16hXbt2jBw5ssyxl112GWec\ncUaZxOq1115j+PDh3HbbbfTv3z/iuJMnT2b+/PmsX7++zOLDlbVv3z7y8/P58ssvGTp0aMnno0eP\nZs6cOeTn57Nv376Y+hYRERHJJH6uEv0I1ANa+eijc9h25HJoclD65JNPMDPuv/9+Nm/ezPnnn8+h\nhx5KVlYWV155Je+99x5mRkFBAUOHDmX06NFcfPHFnH322ckOPaKHH36YPXv2cNlll3HDDTdwwgkn\nsGLFCl599VV69erF6NGjIy7w+9RTT9G/f39OPfVUrrrqKmrVqsXHH3/MJ598Ql5eHg8//HDUMS+/\n/HLGjx/Phg0buP3222OKOz8/n1atWpUsdFz8PnHiRCZOnAjA8uXLo96uKSIiInKwsFiflTCzvwKX\n490O2Mk5N/+A/VOBCwDnnItYu9nMFgLHA/uAo5xzP8UUTAobPHiw08PjVbNv3z4aNGjA1q1bmTdv\nXsmtfplg9erVfPDBB2zatImcnBy6du1aqiR7NAsWLGD27Nls2rSJ5s2b07NnTxo0aBBAxJLuBg8e\nrAIWKSiWeVmTU/VFu4cVbElIgYGcNfkl8SRqjANVNE54TKkg2fFE+r7iHZPfuU/2dxRJqsWUsyY/\n2SGkA0t2AMX8JFbXA6/gJVZvO+d6H7C/3MTKzPoDr4aO/9I598uYAklxSqyq7osvvuD000/nqKOO\nirquk4hUjpmp2EgKimVeYvnLXtO1q1ndpGmVj6tI+F8+EzXGgSoaJxX/QpzMeCJ9X/GOye/cJ/s7\niiTVYlJiVSkpk1j5ecbqTWBtaPsSM8ur7IFmdiEQ/tDHX3zEIRmm+PmqVL2tT0RERETkQDEnVs65\nHcBD7M8SHzGz6WZ2sZnVOrC9mWWbWXczmwC8C9TGu1r1L+fcpFjjkMxTvH7VOeeck+xQREREREQq\nxVeJc+fc62Z2AvAHvCSpW+jlgJIFg83sv3iFLko+Cr2vBPr4iUEyy65du5g1axaAEisRERERSRu+\n145yzv2Pma0ChgM1Qx8bXhn24hvIIz3ZOBO4zDlXdpEiOWgVFRVxxBFH0LlzZ44++uhkhyMiIiIi\nUilxWZTXOTfazN4H7gOuBeoT/UGyb4GhwJtOT1TLAQ477DBWrlyZ7DBEMkZubm6yQ5AIgpqXgXXq\nZsQYQY6TKTJp7kXSRVwSKwDn3CrgXjMbCJwIdMBbAPhQ4GdgPfBv55zKvImIBERVSVNTUPMSRBn0\nIMYIcpxMkUlzL5Iu4pZYFQtdhZoXeomIiIiIiGQ8P+XWRUREREREBCVWIiIiIiIivsX9VkAzqwl0\nBo7FK2KRDWwBNgBfOedUmUBERFKW27EDq1mz4oYiIiJh4pZYmdn5wB3AReX1a2b5wMvAaOfcpjiO\nfwhwPHAKcHLovSNQvFjxYOfckCr2eSHQH+gCHAUUAEuBycAY59z2uAQvIpIggwcPVgGLKrKaNVmT\n0yyhYwwr2FKlB/9z1uQHMk6qjhHkOJkik+ZeJF34vhXQzBqa2WRgGvBrvPWrwkutH7jdHMgDFpnZ\nlX7HDzMJmA+MBe7ES4Zq4q2lVaWy7mZWw8zeBN4HrgCaATWAI4CueGt2fWtmJ8YtehGRBMjLy0t2\nCBLBiMKtGTNOJp1LJsmkuRdJF74SKzNrDMwALiXyulXbgJ+APRH2NwTeCJVnj4dD2J9EudC4S6PE\nVZHXgCtD/WwCngD6AXcDX4Q+PxqYamY5viMXEREREZG05veK1ZtAu7D/Xg3k4t2GV9s5V885d6Rz\nLhvvqs/lwD9CbR1e0vMXMzvHZxzgJTxPAn2A1s65I/ESoioxs0vwrlI5YBVwknPuEefcROfcc865\n04Fxoea/wLt6JVJpTz/9NCeccIKvPtatW8eRRx7J7Nmz4xRVaXPnzuWKK66gWbNmtGjRghYtWnDd\nddfx/fffJ2Q8ERERkXQXc2JlZn2A7uy/ze4FoK1z7lHn3Fzn3M7w9s65Nc65vznnegNn4i0YXJxc\njYw1jrD+n3TOPRwaw0+BjNyw7ducc2sitLkDL+ky4HIzaxehjQgA+/btY9WqVYwfP57TTjuN++67\nj6KiIl993n333WzevJkdO3bEKcr9Jk+eTJcuXdi9ezfz5s1j5cqVzJo1i6VLl3LyySfz5Zdfxn1M\nERERkXTn54rV1WHbrznn7nDOVepvec65fwPnAcXJ1/Fm1slHLHFhZscAnfASvqXOuQ8itQud50th\nH10RQHiShiZMmMCxxx5Lnz59+PLLL2natKnvPt977z2mTJkSh+jKWrduHTfffDMNGzbk9ddfp379\n+gDk5OQwadIkduzYwcUXX0xBQUFCxhcRERFJV34Sq5NC73uBh6p6sHNuEfBq2EedfcQSLxeEbUdM\nqsJMC9u+MAGxSAa49tprWbZsGV988QWjRo2iY8eOvvorKipi0KBBcYqurAcffJCCggL69+9P7dq1\nS+3LycnhN7/5DZs2beLPf/5zwmKQ+MrNza24kQRuYJ26GTNOJp1LJsmkuRdJF34Sq0Z4V3YWOOc2\nxtjHR2HbR/qIJV7ah21/VUHbb/CSSqP0c2YiCTN48GBuvPHGhPS9fft2/v73vwPw29/+NmKb3r17\n45zjtddeS0gMEn8qtZ6agipRHcQ4mXQumSST5l4kXfhJrDaH3v8bhz4O3E6WY8O2V5TX0Dm3Fyh+\n/upQM2uSqKBEAObNm8eMGTO4++67E9L/tGnTKCoq4pBDDqFDhw4R2xRfcVu7di1ff/11QuIQERER\nSUd+EqvleFdr/JQbD3/gZLmPfuLl8LDtyixe/FOUY0XibsCAATz//POYxbKCQMWKE6VGjRpRq1at\niG1at25dsj137tyExCEiIiKSjvwkVpND723M7PgY+7g09L4Z+MxHLPFSJ2y7MoU4wku76UZjSZgX\nXniBzp0707lz4h5FXLZsGUBJwYpIsrOzqVmzJgBLlixJWCwiIiIi6cZPYjUer2Q6wAtmVr0qB5vZ\nr/DWtXLACOfcHh+xSAbbsmULTzzxBJ06deLQQw/lkEMOifpq2bIlzrmKO00j69ev57nnnuPxxx9P\n6DirV68GoG7d8v+NoHj/2rVrExqPiIiISDqJObFyzv0X6ItXMv0s4EMzO7qi48xzO/uveE11ziX2\nb4yVVxi2XbMS7cPvl9oa51gE+OSTT2jXrh2PPPII33//PY0bN6ZGjRqYGWbGUUcdxXHHHVfyOvfc\ncxN2q1yyDBw4kCFDhlCnTp2KG/uwdetWzIzq1cv/N5Li/Vu2bEloPBIfKl6RmoYVBPO/nyDGyaRz\nySSZNPci6aJaeTvNrHkFx68ArgReBroBC83sA2AqMB/vGaRdeLfJtQJ+ibfmU8vQ8W8AfzSz5s65\nVbGdQlz9HLZ9RCXaN4xybIlOnZK+PFfaevfdd+nTpw/16tXj9ddfp0+fPmRlZVFUVMSAAQMYP348\nXbp0KalkV5GlS5fSq1cvdu/e7Ssu5xxmxkMPPcStt97qq6+KTJs2jcLCQnr37p3QcQC2bdsGQLVq\n5f6xULI/EYsTi6SKuvcNTGj/9bYWULduvSodE0tMsYxTWcXxJHKMcJUZJ9HzVlXJjCfa9xXPmOIx\n96k2Z5BaMbk9e7AK/n85SKkWT6qx8m6bMrN9eLfqVaqv0HtF7SO1c865uM+SmV0HjA2NleecG1JB\n+xeAW0Ptr3fORa0pbWZZeM9hZQGFzrmIf7IMHjzY6V+Mq27ZsmV07twZ5xyzZs3ixBNPLLV/586d\nNGnShC1btrB582bq1Uv8/6nHQ15eHnl5ebRs2ZIffvih3LY7duzgtNNO47333qNZs2al9h1yyCGY\nGdOnT6dbt25xia1t27YsW7aM7t278+mnn0Zt16RJEzZs2MAFF1zA+++/H5exJXHMLONujw3Cmpxm\nFTfyoena1axuUvkFw3PW5McUU1XHiSWeRI1xoIrGifU7SpRkxxPp+4p3TH7nPtnfUSSpFlMqxpOC\nUuZWpcreCmgVvMBLRlw5x1BBu1SwIGz75AradsJLqhzwXcIiOkjddtttbNu2jeHDh5dJqsArotCm\nTRuccxUmKOlqyJAhXHvttWWSqkQ59NBDK9Wu+Ipfom9NFBEREUknlblKVJmkJ15tku2DsO0LKmh7\nYdj2tATEctCaNWsWn376KU2bNuX666+P2u7nn727Lw85xE8NltS0YMECPvzwQ7744ovAxmzUqFGl\nrmwUP1t1+OFaYUBERESkWEWJVatAokgRzrllZvY1cBJeGfkLnHMfHNjOzLKBm8M++mtQMR4M3nrr\nLcyMPn36RH3eZ/v27axYsYJq1aqVWlspUwwYMIBnn32WrKyswMZs1cr7n3thYWHUNtu2bWPPnj2Y\nGUcfXWGtGhEREZGDRrmJlXNuZVCBpJA84B+h7RfMrLtzruSGUvNKzj0PNMe7DXCSc063AsbRnDlz\nADj77LOjtvnkk0/YtWsX5513XsbdkrZ69Wq+++47+vXrF3H/3r17S7b79u1LdnY2Zsbnn39OkyZN\nYh63uNDKunXrorZZvnz/Ot5t27aNeSwJTm5ubrJDkAgG1glm6cMgxsmkc8kkmTT3IukiY8p6mFlL\n4MYDPu4Qtn1OhLW2Jjvnvg3/wDn3jplNxKt22BKYa2Yv4lU5bAj8Djgt1HwtcH884pf9im/xa9Gi\nRdQ2o0ePxswYOLDylXvSpSpg06ZN2bRpU9T9K1euLLm6NHHiRM4666y4jHvmmWcC3vpUBQUFEQuC\nLFy4EICsrKy4Fc2QxFLxnNR0f73DMmacTDqXTJJJcy+SLjImsQJaAA9H2Wd45eAP/JvgUuDbss35\nHbAPb52uBsD/HLDfAcuA3s65NbEGLJG1bNmSJUuWRF1PadasWUydOpVevXpx0UUXVbrfNm3asHjx\n4niFmRLiWe3thBNO4Nhjj2Xp0qV89NFHXHbZZWXafPzxx4B3NbFBgwZxG1tEREQk3WXaU/+uCq99\nUTtxbrdz7mrgImASsAqvtPqPwCxgINDJObcwYWdyELv66qsBmD17dpl9GzZsoG/fvpxwwgmMGzcu\n4MjSR35+Ph07diQnJ4fp06dX+rhbbrkF51zE73bnzp1MmTKl5GqdiIiIiOyXMYmVc26Gcy6rCq9q\n5a1TFerzQ+dcX+dcS+dcbefcUc65M51zI51zRUGd28Hmmmuu4ZJLLiEvL4/vv/++5PMvvviCs88+\nm/bt2zNjxoy0uGKye/duVq1axfz585kwYQITJkwAYNWqVeTl5TFz5kyWL19eUmkvmn379pGfn8+X\nX37J0KFDSz4fPXo0c+bMIT8/n3379v9bweTJk5k/fz7r16/n2WefrXS8d911F+3ateP9998vs0bV\nn/70J7Zs2cJ1113HueeeW+k+RURERA4GcbsV0MyaAWcA7YD6QG0qX2LdOecOfD5KDmJTpkzhmWee\noU+fPmRnZ1OtWjUaNGjA0KFD6dWrV7LDq7RZs2bRo0cPvJonnuIFW4cMGcKQId6a1ddddx2vvvpq\n1H7y8/Np1apVST/F7xMnTmTixImAV1iiefPmAFx++eWMHz+eDRs2cPvtt1c63urVq/PZZ59x1VVX\n0adPH+644w6OPvpo/vnPfzJx4kSuu+46Xnzxxap9CSIiIiIHAd+JlZl1AIYBPfC3VpUSKylhZtx7\n773ce++9yQ7Fl+7du5e6khSrFi1aVKmfZs2a8c0338Q01hFHHMFHH33E/PnzmTNnDhs3bqRHjx4M\nGTJEJdbT0ODBg1XAIgUNK9gSyIP/QYyTSeeSSTJp7kXSha9bAc3scmAOcE6oL4vxJSIp5sQTT+SG\nG27gD3/4AzfddJOSqjSVl5eX7BAkghGFWzNmnEw6l0ySSXMvki5ivmJlZkcDE4DqeMUgAAqBb4B1\nwHbf0YmIiIiIiKQBP7cCPgBk4yVVRXiV8l5zzu2MR2AiIiIiIiLpwk9idX7Ydj/n3Dt+gxERERER\nEUlHfp6xaoJ3tWqVkioRERERETmY+Ums9obevy+3lYiIJE1ubm6yQ5AIBtapmzHjZNK5ZJJMmnuR\ndOEnsfoBr6Kf/lclIpKiVGo9NQVVojqIcTLpXDJJJs29SLrwk1h9FHpvb2Y14xGMiIiIiIhIOvKT\nWD0H7AJqArfHJxwREREREZH0E3Ni5ZxbDvwP3u2Aj5lZz7hFJSIiIiIikkb8XLHCOTcc+F+89aze\nN7MxZnaqmfnqV0REREREJJ34ToCcc38CeuOVXr8R+D9gm5mtNrMfKvlSZUGRFLFixQruuece2rdv\nT6tWrWjWrBn9+vVj6dKlUY8ZN24ctWrVokWLFrRp04a2bdty3HHHcfzxx0d8Pf744wmLf926dRx5\n5JHMnj07YWOkExWvSE3DCrZkzDiZdC6ZJJPmXiRd+E6szOxe4JVQXxZ6ZeOtc9WiEq+WoZeIJNmk\nSZNo164dP/30E5999hnLly9n0aJFVKtWjVNPPZV58+ZFPO67775j586d5Ofn8/3337N06VKWLFnC\n4sWLy7yWLFnCaaedlrBzuPvuu9m8eTM7duxI2BjpJC8vL9khSAQjCrdmzDiZdC6ZJJPmXiRd+Eqs\nzOzPwDCgQbQmlXiJSAqYPn06ffv25ZhjjmHChAkcccQRANSpU4cxY8ZQo0YNevXqFTFhWbRoEWZW\nqdctt9zCeeedl5BzeO+995gyZUpC+hYREREpT7VYDzSzC4AH8W4BBG/B4Ol4twKuB7b7jk5EArF3\n715uuOEGAPr3749Z6X/zqFmzJj179uTNN99k1KhRPPjgg6X2L1q0iJkzZ9KhQwdq1KhBVlZWmT6W\nLVvGJZdcwtChQxNyDkVFRQwaNCghfYuIiIhUJObEitIl1v8DXOqcW+wzHhFJgmnTprFy5UrMjM6d\nO0ds061bN9544w3Gjh1bKrHasWMHWVlZdOnSJWr/+/bto3///owaNYo6derEPX7wniW68cYbGThw\nYEL6FxERESmPn1sBw/8WdZmSKpH0NW3atJLthg0bRmzTrFkzABYvXsyyZctKPv/Pf/5Dp06dyu3/\nqaeeokOHDpxzzjlxiLasefPmMWPGDO6+++6E9C8iIiJSET9XrOrj3Qa40Dm3KE7xiEgSrFq1qmT7\n0EMPjdgmPOGaM2cOxxxzDAAdOnTgtddei9r3/PnzGT9+PF9++WWcoi1rwIABPP/882VuPxTIzc1N\ndggSwcA6dTNmnEw6l0ySSXMvki78XLH6MfS+MR6BiEhqiJac1KhRo2R74cKFJduHHHII2dnZUfu7\n7bbbeOaZZ6ImbH698MILdO7cOeotjAc7lVtPTffXOyxjxsmkc8kkmTT3IunCT2L1PV5VvyPjFItI\niZdeeokzzjiDE044gYcffhjnvBopy5Yt47bbbqN79+507dqVDh06MHz4cPbt2wfAtm3bePzxx+na\ntWvJ8XfffTcFBQUVjrlw4UKuv/56jj/+eE4//XR69uzJd999x8aNG5k8eTJ79uyJeuxrr73G2Wef\nzZlnnkmHDh0YNWoU4D1/dNddd3H66afTvXt3rr32WjZt2hSHbyi+im/zA9i5c2fENj///HPJ9urV\nqyvV74QJE6hVqxY9e/b0F2AU69ev57nnnkvoulgiIiIileHnVsBJwFlAOzM7yjm3IU4xyUFu5syZ\nTJ06lZkzZzJp0iSuvPJK6tWrR4sWLZgwYQJPPvkkJ554IgAjR47k3nvvZcOGDdx8881cf/313HLL\nLcyaNQvwnr056aSTWLt2LZMnT4465ksvvcRdd93FVVddxVdffUXt2rVZsmQJffr0oWbNmsyZM4cP\nP/wwYpnwm2++mcMPP5ypU6dSq1YtZs6cyVlnnUVhYSEzZ87kmmuuYdSoUbz00kvcf//9VK9enVdf\nfTUxX16Mzj//fJ5//nmAqInfokX77/jdvHlzhX1u27aNQYMG8eKLL8YnyAgGDhzIkCF/v2sQAAAg\nAElEQVRDElYQQ0RERKSy/CRWr+GVW28KPArcEpeI5KA3fPhwHnjgAcC7zQy8BKpr16688847ZGVl\nlbS94IILAHj99df517/+xdixY2nbtm3J/g4dOtCoUSPeeecddu3aVep2tmLPP/88d911F7/+9a8Z\nO3ZsyefHHnss/fr1Y9CgQWRlZXHqqaeWOfa5556jXr16pUqIn3HGGTRs2JBHHnmEW265hb59+7Jl\nyxZuv/12nHMlV9eiWbp0Kb169WL37t2V+bqics5hZjz00EPceuut5ba98MILadKkCevWrePbb7/l\nzDPPLNPmgw8+KNmuzOK7o0ePprCwkAsvvLDqwVfCtGnTKCwspHfv3gnpX0RERKQqYk6snHMFZtYX\n+Ai40cw2Av/rnCv/b40i5di5cyfz5s2ja9eugFf4ALzne8aNG1cqqQJKbvFbt24dL7/8cqmkqlhh\nYSF79+6lsLCQBg1Kr2U9d+5c7rnnHmrWrBnxysqxxx4LwEknncRhh5W+l3zHjh288MILZYoy7Nix\no+S2uTvuuAOAunXrctVVV7Ft2zb+9Kc/lfsdtGnThsWLgy2ymZ2dzciRI7n88ssZN25cSdzF5s+f\nT2FhYcl/H/hdHMg5x/PPP89ZZ51VZs7iYceOHfz+97/nvffei3vfIiIiIrGI+RkrM2sOrAGuBDYD\ng4CFZvagmZ1pZseYWfPKvuJ0PpLm1q5dy0033VTy39OnT8fMePjhhyMWP/jqq68AOOeccyJeGVm1\nahXbtm2jbt26ZZIqgJtuuol9+/Zx5ZVXctRRR5XZP2PGDMyMc889t8y+JUuWcMcdd1CzZs1Sn8+d\nO5e9e/fyi1/8gvbt2wPelbcJEybwt7/9jaZNm1bwLSRH7969GTlyJF9//TU333wzGzduxDnHzJkz\neeCBB0pVl2vUqFG5fb377rssX76cDh06JCTWIUOGcO2115Z6NkwiU/GK1DSsYEvGjJNJ55JJMmnu\nRdKFn+IVK4DlwDtAA7xCFm2BJ4EZwOLQ/sq8fvARh2SQVq1a8dBDDwGwfft2/u///g/wngGKpDjx\nipT4AHz66aeAt7jtgT7//HO++eYbAPr06RPx+E8++QQgYv8dOnTg9ttvL/P5xx9/HPWYVHfnnXfy\nzTffsHv3bnr06MFxxx3HK6+8wvjx40s9xxTpymC4V155BTOjRYsWcY9xwYIFfPjhh9x3331x7zsT\n5eXlJTsEiWBE4daMGSeTziWTZNLci6QLP89YFTO89azcAZ+J+PKvf/2L3bt307p166h/Qf/ss8+A\n6EnMlClTMDN+/etfl9n3xhtvAFCrVq2Ix//4448sXLiQ7OzsiM8cRfPRRx9hZhELXaSD9u3bM27c\nuDKfL1iwoGS7R48eUY/fuXNnSULauHHjuMc3YMAAnn322YTcYigiIiISK7+JlR3wLhI35V0tAq/i\n348//sjhhx/OKaecUmb/li1b+Oijj8jKyuLSSy8ts794LaZTTjklYlGL4qtdp59+epnb/aLZunUr\nX3zxRblxp6slS5YAcNRRR5VUZYxk9uzZbN++HTOLePulH6tXr+a7776jX79+Effv3bu3ZLtv375k\nZ2djZnz++ec0adIkrrGIiIiIhPOTWLWKWxQiEXzyySeYGeecc07U/QDdu3ePuKjtW2+9xa5du+jV\nqxdHHHEEAH/9619p3rw5Xbp0Yf369ZgZJ598crnjhydI9957L08//XTUmKdPn86ePXto27Ztmb/I\n79mzh0GDBpWqIBhJMqoCAvzxj39k5MiRPPbYY9x5551l9s+cORMzK1PY4kBz5swp2Y53GfSmTZuW\nuw7YypUradXK+6Np4sSJnHXWWXEdX0RERCQaP1UBV8YzEJFwmzdvLnn+qbzEqrznq9566y3MjGuv\nvbbks2eeeYZ33nkHgCZNmrBs2TJ+8YtflDl29+7dfPjhh8D+296+//57li5dWtLm7bff5vnnn+eW\nW27hsssuA2Dq1KkAdOnSpUyf//jHP0pdUYkmGVUBAUaMGEFRURFjxowpk1ht3bqVt99+m9q1azNg\nwIBy+1m2bFnJdu3atRMSa2UULyotIiIiEgQ/xStEEmb69Ok452jfvj1HHnlkmf179uzhn//8JxA9\n8Zo7dy7Vq1enV69egPc81tFHH03Dhg0BuOSSS3DOsX79+lLHOee4+eabyc/PB6BTp06AdwWkuMhF\nUVERV111FR9//DFvvvkmAD///DOTJ0/GzMrEvHnzZp544gnuv//+mL6PINSpU4caNWpEvLr16KOP\nUlRUxKhRo6hfv365/axbt65ku3r16hWOm5+fT8eOHcnJyWH69OlVD1zKFV7NMVW5SqyLlmkG1qmb\nMeNk0rlkkkyae5F0EY/iFSJxV9HzVbNnz6awsJDGjRvTrl27iG1OPPFE/vOf/1CrVi02bNjAI488\nwqRJk0r233777YwdO5a33nqL3//+9zRq1Ii1a9dy1113cfrpp3PFFVfw17/+le3bt7Njxw4mTZpU\nUqWw+Da79u3b89hjj1FUVET//v0ZNmwYQ4cO5eOPP2bnzp1kZ2ezfPlyrr/+ev7yl7+Qk5MT528q\nfi677DIWL15cJrF67bXXGD58OLfddhv9+/evsJ9t27aVbFemwMTkyZOZP38+Zsazzz5bbmGMSPbt\n28eaNWvYsGFDqaIbo0ePplatWjRu3JicnJySxaYPNulQbt1q1mRNTuqUzs9Zk5/wMe6vV/5acOk0\nTiadSybJpLkXSRdKrCQl1axZkyZNmnDjjTdG3L9v3z4aNGjAgw8+GLWP8ePHc9NNN3HyySdTv359\nRo0aVeq2v+zsbD799FMeeughunTpQqNGjWjYsCGDBg3izDPPLFnkt3v37mRnZ/Pkk0+SnZ0NeLe4\n/f3vf+eJJ57g1ltvZffu3dx3331cdtllXHTRRTzwwAOccsop1K9fnwYNGvDMM8/QsWPHOH5D8ffU\nU0/9/+3deZxkVXnw8d/DgCzOMBACLuxGwQUJoLghoKAsvnED11eDoEmMCxrQRF5RZ9o1fsy8JGow\nQJQlUVlUNBgUBVHkVURQFHBBEGQAwyrMDLvM8/5xbtl32qrqmq7qrupbv+/ncz99q+rcc5/b93RX\nPXXPPYdDDz2U3XffnVe/+tVsuOGGnHvuuZx33nlMTExw9NFH91TPtttuS0SwePHitnODTfWyl72M\nk08+mZtvvrnt8PXTWb58Odtvv/0f7rNr/TzttNM47bTTALj22mvZZhuny5MkSbMnvA9hdi1dujTn\nwzfGUssVV1zBxRdfzG233cY222zDfvvtt1aj+61atYoTTzyRZz/72ey6666zGKmaZNSuWI1SPDB6\nMY1aPDB6MY1aPDB6MY1aPDB6MY1iPCNoZEYnn/EVq4g4ZJCBZOYpg6xP0szstNNO7LTTTjPefuHC\nhRx++OEDjEiSJGn09dMV8CTWnBS4XyZWkiRJkualQdzNHWuxdCovSZoFdkUeTctW3NWY/TTpWJqk\nSedemi/6Sayur5bf9LDcANzNZBKV1XJD9fr1fcQhSepgYmJi2CGojWNWrWzMfpp0LE3SpHMvzRf9\nTBC83dpuExHbAQcD7wAeAfwceEVm+pWHJEmSpHlrTid2yczrMnMZsDPwQ+B5wDcjYvpZRCVJkiRp\nRA1lxszMvA14EbACeArwwWHEIUmSJEmDMJTECiAzbwE+Tbnv6o0RseGwYpEkSZKkfgwtsap8t/q5\nCNhnmIFIUhMtWbJk2CGojSMWLmrMfpp0LE3SpHMvzRfDTqxur61vO7QoJKmhHG59NL1j48WN2U+T\njqVJmnTupfli2InV5rV1v/aQJEmSNC8NO7F6aW391qFFMUVEfDsiVve4/HrY8UqSJEkarhnPY9Wv\niHgt8JraUxcNK5Y2WhMY91pWkiRJ0hibcWIVEdus5SbrAX9CmcPqlcC+lBEBE/hhZv5sprHMklZs\nL6nWO7lnbsKRJEmSNKr6uWJ1Hf1drWklK/cAb+6jnlmVmWcNOwZJmqmlS5c6gMUIWrbirjm58X8u\n9tOkY2mSJp17ab4YxD1WMcMF4HrgBZn5owHEIUmaYmJiYtghqI1jVq1szH6adCxN0qRzL80X/d5j\n1a2LXCd3AJcAXwA+l5l2pZMkSZI0r/WTWG2/luUfAFZk5t197FOSJEmSRs6ME6vM/M0gAxlVEfFV\nYDdgM2AlsBz4LvDpzPzJMGOTJEmSNBqGPY/VfHAg8AhKEropZVTDtwI/johPR8QGwwxOkiRJ0vAN\nbR6reeA24BzgUuAmyv1k2wF/ATyrKnMYsHVEHJCZq4cRpCR1s2TJkmGHoDaOWLioMftp0rE0SZPO\nvTRfmFi1dxRwSWY+1Oa1j0bEi4HPAhtS5uM6CvjwHMYnST1xqPXRNFdDVM/Ffpp0LE3SpHMvzRd2\nBWwjM3/QIalqvf4V4K+ZHDr+nRGx3lzFJ0mSJGm0THvFKiIOmYtAMvOUudjPoGTm5yPifcCOwGJg\nD+DbU8vtsssucxyZJM0/i448YtghrGHU4oHRi2nU4oHRi2nU4oHRi2nU4oHRi2nU4lFnkZndC0Ss\nBroXGoDMXDDb+xi0iPgU8EbK7+ctmflvU8ssXbo07YojSd3duOXWww7hD7a8cflIxQOjF9OoxQOj\nF9OoxQOjF9OoxQOjF9MoxjOCZjKv7qwYxj1W7Q5+1hO3WXJ7bX2ToUUhSZIkaah6vccqBri0JPM3\noWrZrLZ+59CikKQOvGI+mpatuKsx+2nSsTRJk869NF/0klhtOODlZcBVjNBluz7sXVv/5dCikKQO\nJiYmhh2C2jhm1crG7KdJx9IkTTr30nwxbVfAzLx/EDuKiN2BjzKZjCQluUrgc4PYx1yKiFcDj68e\nrgQuHGI4kiRJkoZo1odbj4jHRcQZwEWUpKreLfAbwG6Z+ZezHUevIuLwiHjaNGVeApxQPUzgY5n5\n4KwHJ0mSJGkkzdrgFRHxCGAp8PpqP/Wuf5cAR2Xmt2Zr/33YB/iXiPglcB5wJWWQigC2A14IPKsq\nm1WZj859mJIkSZJGxcATq4hYBLwLeDuwEZPd/QCuBt6TmacPer8DlsAOlDmqOr2ewPHAkZn5+7kK\nTJIkSdLoGVhiFRHrAW8B3k0ZLa+eUN0CfAA4fh4kIUcCZwHPAP4c2AL4U8rv6k7KwBsXAidm5tXD\nClKSerFkyZJhh6A2jli4qDH7adKxNEmTzr00XwwksYqI1wLvB7ZlzYRqFbAMWJaZdw9iX7MtM68F\nrgU+M+xYJKlfDrc+mt6x8eLG7KdJx9IkTTr30nzRV2IVEQcAHwF2Zs2E6vfAccAHMvPWviKUJEmS\npBE3o8QqIp5KGbDhOW1ePhU4urryI0mSJEmNt1aJVUQ8FvgwcHDrqdrL36SM9PfjAcUmSZIkSfNC\nT/NYRcQWEXEsZejxg5mchwrgUuD5mbm/SZUkjb68775hhyBJUuNMe8UqIt4P/B3wcNa8QnUNZej0\n02YpNklSn5YuXfpHA1jEBhtw45ZbDyegNra8cfmwQ5hzy1bcNSc3/s/Ffpp0LE3SpHMvzRe9XLF6\nD5NJVQI3A28FHm9SJUmjbWJiYtghqI1jVq1szH6adCxN0qRzL80Xa3OPVWvEv/UpydZ7IqJL8bWS\nmbnloCqTJEmSpLk0k1EBF1fLwLIqJpM2SZIkSZp3ek2sBplESZIkSVKj9JJY2UFfkiRJkrqYNrHK\nTBMrSZqnlixZMuwQ1MYRCxc1Zj9NOpYmadK5l+aLnuaxkiTNT1OHWtdomKshqudiP006liZp0rmX\n5gsTK0mSJEnqk4mVJEmSJPXJxEqSJEmS+mRiJUmSJEl9MrGSpAZz8IrRtGzFXY3ZT5OOpUmadO6l\n+cLESpIabGLCGTNG0TGrVjZmP006liZp0rnX6Mj77ht2CCOtlwmCJUmSJI252GADbtxy62GHsYYt\nb1w+7BD+wCtWkiRJktQnEytJkiRJ6pOJlSRJkiT1ycRKkhpsyZIlww5BbRyxcFFj9tOkY2mSJp17\nab4wsZKkBnO49dH0jo0XN2Y/TTqWJmnSuZfmCxMrSZIkSeqTiZUkSZIk9cnESpIkSZL6ZGIlSZIk\nSX0ysZKkBnPwitG0bMVdjdlPk46lSZp07qX5wsRKkhpsYmJi2CGojWNWrWzMfpp0LE3SpHMvzRcm\nVpIkSZLUJxMrSZIkSeqTiZUkSZIk9cnESpIkSZL6ZGIlSQ22ZMmSYYegNo5YuKgx+2nSsTRJk869\nNF+YWElSgznc+mh6x8aLG7OfJh1LkzTp3EvzhYmVJEmSJPXJxEqSJEmS+mRiJUmSJEl9MrGSJEmS\npD6ZWElSgzl4xWhatuKuxuynScfSJE0699J8YWIlSQ02MTEx7BDUxjGrVjZmP006liZp0rmX5gsT\nK0mSJEnqk4mVJEmSJPXJxErqIO+7b9gh/JFRi2nU4oHRi2nU4pEkSbNj3WEHMMoi4pXAa4FdgM2B\nO4CfAZ8HTsrMh4YYnmZZbLABN2659bDDWMOWNy4fdghrGNXf0SjFNGrnTJIkzQ4TqzYiYhPgi8Bz\nq6ey+vkI4JHAPsCbIuKlmemnJkkja8mSJcMOQW0csXBRY/bTpGNpkiade2m+MLGaIiLWA/4LeDYl\noVoOHA9cDWwFvB54ArAbcHZEPDMzVw0pXEnqyuHWR9M7Nl7cmP006ViapEnnXpovTKz+2JuZTKou\nBZ6fmX+YqCEiPgl8BdgfeCLwXuBdQ4hTkiRJ0ohw8IqaiFgAvLt6mMAh9aQKIDMfAA4B7gYCODwi\nNp3TQCVJkiSNFBOrNe1DGaQigfMy8xftCmXmrcCp1cP1gRfPTXiSJEmSRpGJ1Zr2q61/fZqy9dcP\nmIVYJEmSJM0TJlZr2qm2fuk0ZS/psJ0kjQwHrxhNy1bcNX2hebKfJh1LkzTp3EvzhYnVmnaorV83\nTdkbgIco91k9brYCkqR+TExMDDsEtXHMqpWN2U+TjqVJmnTupfnCxGpNm9TWb+tWsJoceEX1cN2I\n2GjWopIkSZI00kys1rSwtn5fD+Xvra07S54kSZI0pkysJEmSJKlPJlZrWlVb36CH8hvW1u1oLEmS\nJI2pyMxhxzAyIuIaYHvKPFbbZ+b1XcouoHQXXAA8kJltE7GI+HfKQBeSJEmSBuu6zDxp2EEArDvs\nAEbMVZTECmA7oGNiBWxFSaoSuLpTocz8q0EFJ0mSJGk02RVwTVfU1p8yTdmndthOkiRJ0pgxsVrT\nObX1/acpe0Bt/euzEIskSZKkecJ7rGqq+6ZuAjYHVgNPzsyftym3BXAN8HDKkOtbZebv5jJWSZIk\nSaPDK1Y11aS/H6oeBnBKRNQnDSYi1gdOpiRVCXyiXVIVEa+MiLMiYnlE3BcRN0XEuRHxhiqB0xiK\niI0j4uURcWxEXBQRt0XEAxFxR0RcFhH/GhFPnb6mNeo8ICJOjYjrIuLeiLg5Ii6MiL9z4urxExHn\nRMTq2nJIj9vZjsZYRDwrIj4REZdHxO0RcU/VFr4bER+KiD16qMM2NKYi4hkR8anqfex3EfFg9fMn\nEXFcL+1nSn22pYaIiHUi4kkR8bqI+HhEfC8i7q69R71vBnUOrH1UbffTEXF1FdftEXFJRBwdEZut\ndWxesVpTRKwHnAvsWT21HDiOMkDFVsAbgCdUr10B7JGZK2vbbwJ8EXhu9VT9FxzVzx8BL83M5bNx\nDBpNEfH3wPuB9aun2v3xtdrIfwJvzMx725Rp1fcwSpL/yjb1teq5BjgoMy+fadyaPyLidcCJrNkW\nDsvMU7psYzsaY9UHh38DDq6e6vR/6bLM3K1DHbahMVV92fwZ4NXVU93e106l/D+6v0t9tqWGiYgv\nAi+d8nT9vE5k5vt7rGug7SMi/i/w9mrbqW03gJuB/52Z5/cSH5hYtRURi4EvAPu0nqq93PqFXUo5\ncTfUtlsPOA94dlVuOXA8k0nZ6ylJWQBXAs/MzPrcWWqwiDiBkpgnZcTJb1La0W3ApsC+lA83Cyht\n5JzMPLBLfacCr6jqu53S1i4H/hR4LfC0qp6bgKdn5o2zcmAaCRGxOfBzSlu6G1hIaRvTJVa2ozFV\ndWv/FvBEyvn/OfBlygi5q4DNgJ2AA4GVmdl2UCfb0PiKiNOAlzP52egs4NuUc70F8Mzq9db72umZ\n+aou9dmWGiYizgReVHvqDsq53YFyntcmsRpY+4iIfwT+oarrbuDfgR9S3jsPBp5f1bUS2DMzf9rT\nAWemS4eF8s/gvygJ0r3VifomJUFap035t1PuzXoIuBhYPOX1hwFfq5X56LCP0WVO29PxwFeBvbuU\n2QNYUbWPh4DXdSj34lo7uhbYsk2ZT9fKnDbs43eZ3QU4rTrfl1C+0Wud+0O6bGM7GuMF+E51bh8A\n3jRN2T9qG9XztqExXYA/r53XB4B9O5TbpXpfa5Xd2bY0PgtwFOU2m4OAbavnXlc7j+/rsZ6BtQ9g\n19rnrDuAJ7Up875aXRf1fLzD/oU3ZaF8G3NzdRJ+Dzy+Q7nNKdnvauAeYNNhx+4yZ21kkx7LvaX2\nx3x+hzI/qpXZv0OZDYDrauWeOOzfgcvsLJRvA1cDDwK7UboD9pJY2Y7GdAH+tnZOD++jHtvQmC7A\nW2vn9NRpyn6sVvYtHcrYlsZkmWFiNbD2AZxZK/PGLvu8qFbuwF7idPCKwdmHkjQlcF5m/qJdocy8\nldLPGMq9Ni+em/A0bJl5Z49Fz6h+BvDkqS9GxGMp3wAm8KvMPGdqmWp/9wEn1J56Re/Rar6IiEXA\nsUwOpvOjHrezHY23I6uf12TmJ2ZSgW1o7C2srf9qmrJX1dYfPvVF25K6GWT7iIiFTE6ZtILSw6OT\n+v/GV3YsVWNiNTj71danm9eq/voBHUtpXK2srW/Y5vX6HGtt/7nU2Naa72PAoyldlt+7FtvZjsZU\nROwJPJbyIeVzfVRlGxpvV9TWHzdN2frrfzSNDbYldTfI9rE35cJGAhdUyVgn9X311NZMrAZnp9r6\npdOUvaTDdhJMtokEftPldZi+rV1GuYQdlBvU1SARsRfw15S28tbMvHstNrcdja+9ausXR3FYRHw7\nIm6thi++LiI+FxHP71KPbWi8fY2SJAVwUEQ8r12hiNgNeGP18Crg7DbFbEvqZpDto+e6MvM2yuew\nADaPiD+dLlATq8HZobZ+3TRlb2DypE/3LY/Gzxtr619t83rPbS3L3GytUXEeHhGP7i80jYpqmONW\nl4cvZWa7ttKN7Wh81efKuxu4gHLT957An1AGWtoaeBVwTkScHhHtrp7bhsZYdU5fQLn3ZQHwjYj4\nSjWX0Csi4q0R8TngB5Rug1cAf1FtN5VtSd0Msn2szed1WPML7h06lqqs20OF6k19IuHbuhXMzIci\nYgVlWOR1I2KjzLxnVqPTvBARzwIOrR7eB/xzm2I9t7XK7cA2tW1vmml8GilLKV/MrADeNoPtbUfj\n65G19eMoHxZ+R0nULwPWo1zV+stq/WXVz6lz0diGxlxm/iYinklpIx8EXlgtdbcARwOf7dLtyrak\nbgbZPmZSV7tt2zKxGpz6TZzd+mu23EtJrAAWUUYI1BiLiEdShsxeh9K16z2Z2e7NYiZtrWXRzCPU\nqIiIXYB3UNrJuzPztzOoxnY0vjZhct6hHSjds547pR39R0QcB5wLbAy8KCJekZmn18rYhgRlGO3/\nA2xP+wmCtwDeRempc1KHOmxL6maQ7WNW25pdAaUREBEbAV8BtqS8MX01M48ZblQaRRGxDqXb1rrA\nxZl57JBD0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"text/plain": [ "<matplotlib.figure.Figure at 0x10d725b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cutoffs = bar_plot(d, \"Weighted Total\", offset=-2)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': (77.829829749501982, 100), 'C+': (56.885981768822631, 60.376623098935859), 'C': (53.395340438709404, 56.885981768822631), 'B': (63.867264429049087, 74.339188419388762), 'C-': (39.432775118256501, 53.395340438709404), 'F': (0, 39.432775118256501), 'B-': (60.376623098935859, 63.867264429049087), 'B+': (74.339188419388762, 77.829829749501982)}\n" ] } ], "source": [ "print cutoffs" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Written!\n" ] } ], "source": [ "def assign_grade(pts):\n", " for g, c in cutoffs.items():\n", " if c[0] < pts <= c[1]:\n", " return g\n", "\n", "#d = load_data(\"gc_CENG114_WI16_Ong_fullgc_2016-03-21-15-47-06.csv\") #use revised gc\n", " \n", "d[\"Final_Assigned_Egrade\"] = map(assign_grade, d[\"Weighted Total\"])\n", "d.to_csv(\"Overall grades_OLD.csv\")\n", "print(\"Written!\")" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
EdwardJKim/nbgrader
nbgrader/tests/apps/files/side-effects.ipynb
8
307
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"side-effect.txt\", \"w\") as fh:\n", " fh.write(\"a side effect\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
ML4DS/ML4all
P5.Data preprocessing/Intro5_DataNormalization_professor.ipynb
1
80283
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "N3MgtUPKg1qq", "slideshow": { "slide_type": "slide" } }, "source": [ "# Data preprocessing methods: Normalization\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ " Notebook version:\n", "\n", " * 1.0 (Sep 15, 2020) - First version\n", " * 1.1 (Sep 15, 2021) - Exercises\n", "\n", " Authors: Jesús Cid Sueiro ([email protected])" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "-VPh26VrkBRD", "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Some libraries that will be used along the notebook.\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Data preprocessing\n", "\n", "## 1.1. The dataset.\n", "\n", "A key component of any data processing method or any machine learning algorithm is the **dataset**, i.e., the set of data that will be the input to the method or algorithm. \n", "\n", "The dataset collects information extracted from a population (of objects, entities, individuals,...). For instance, we can measure the weight and height of students from a class and collect this information in a dataset ${\\cal S} = \\{{\\bf x}_k, k=0, \\ldots, K-1\\}$ where $K$ is the number of students, and each sample is a 2 dimensional vector, ${\\bf x}_k= (x_{k0}, x_{k1})$, with the height and the weight in the first and the second component, respectively. These components are usually called **features**. In other datasets, the number of features can be arbitrarily large." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dBTW556Vjz39", "slideshow": { "slide_type": "slide" } }, "source": [ "### 1.1. Data preprocessing\n", "\n", "The aim of [data preprocessing methods](https://scikit-learn.org/stable/modules/preprocessing.html) is to transform the data into a form that is ready to apply machine learning algorithms. This may include:\n", "\n", " * [Data normalization](https://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling): transform the individual features to ensure a proper range of variation\n", " * [Data imputation](https://scikit-learn.org/stable/modules/impute.html): assign values to features that may be missed for some data samples\n", " * [Feature extraction](https://scikit-learn.org/stable/modules/feature_extraction.html): transform the original data to compute new features that are more appropiate for a specific prediction task\n", " * [Dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction): remove features that are not relevant for the prediction task.\n", " * [Outlier removal](https://scikit-learn.org/stable/modules/outlier_detection.html): remove samples that may contain errors and are not reliable for the prediction task.\n", " * [Clustering](https://scikit-learn.org/stable/modules/clustering.html): partition the data into smaller subsets, that could be easier to process.\n", " \n", "In this notebook we will focus on data normalization." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 2. Data normalization \n", "\n", "All samples in the dataset can be arranged by rows in a $K \\times m$ **data matrix** ${\\bf X}$, where $m$ is the number of features (i.e. the dimension of the vector space containing the data). Each one of the $m$ data features may represent variables of very different nature (e.g. time, distance, price, volume, pixel intensity,...). Thus, the scale and the range of variation of each feature can be completely different.\n", "\n", "As an illustration, consider the 2-dimensional dataset in the figure" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_blobs\n", "X, y = make_blobs(n_samples=300, centers=4, random_state=0, cluster_std=0.60)\n", "X = X @ np.array([[30, 4], [-8, 1]]) + np.array([90, 10])\n", "\n", "plt.figure(figsize=(12, 3))\n", "plt.scatter(X[:, 0], X[:, 1], s=50);\n", "plt.axis('equal')\n", "plt.xlabel('$x_0$')\n", "plt.ylabel('$x_1$')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "We can see that the first data feature ($x_0$) has a much large range of variation than the second ($x_1$). In practice, this may be problematic: the convergence properties of some machine learning algorithms may depend critically on the feature distributions and, in general, features sets ranging over similar scales use to offer a better performance.\n", "\n", "For this reason, transforming the data in order to get similar range of variations for all features is desirable. This can be done in several ways." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.1. Standard scaling.\n", "\n", "A common normalization method consists on applying an affine transformation\n", "$$\n", "{\\bf t}_k = {\\bf D}({\\bf x}_k - {\\bf m})\n", "$$\n", "\n", "where ${\\bf D}$ is a diagonal matrix, in such a way that the transformed dataset ${\\cal S}' = \\{{\\bf t}_k, k=0, \\ldots, K-1\\}$ has zero sample mean, i.e.,\n", "\n", "$$\n", "\\frac{1}{K} \\sum_{k=0}^{K-1} {\\bf t}_k = 0\n", "$$\n", "\n", "and unit sample variance, i.e., \n", "\n", "$$\n", "\\frac{1}{K} \\sum_{k=0}^{K-1} t_{ki}^2 = 1\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "It is not difficult to verify that this can be done by taking ${\\bf m}$ equal to the sample mean\n", "$$\n", "{\\bf m} = \\frac{1}{K} \\sum_{k=0}^{K-1} {\\bf x}_k\n", "$$\n", "\n", "and taking the diagonal components of ${\\bf D}$ equal to the inverse of the standard deviation of each feature, i.e.,\n", "\n", "$$\n", "d_{ii} = \\frac{1}{\\sqrt{\\frac{1}{K} \\sum_{k=0}^{K-1} (x_{ki} - m_i)^2}}\n", "$$\n", "\n", "Using the data matrix ${\\bf X}$ and the *broadcasting* property of the basic mathematical operators in Python, the implementation of this normalization is straightforward." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Exercise 1**: Apply a standard scaling to the data matrix. To do so:\n", "\n", " 1. Compute the mean, and store it in variable `m` (you can use method `mean` from `numpy`)\n", " 2. Compute the standard deviation of each feature, and store the result in variable `s` (you can use method `std` from `numpy`)\n", " 3. Take advangate of the broadcasting property to normalize the data matrix in a single line of code. Save the result in variable `T`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sample mean is m = [58.06760405 13.94251037]\n" ] } ], "source": [ "# Compute the sample mean\n", "# m = <FILL IN>\n", "m = np.mean(X, axis=0) # Compute the sample mean\n", "print(f'The sample mean is m = {m}')\n", "\n", "# Compute the standard deviation of each feature\n", "# s = <FILL IN>\n", "s = np.std(X, axis=0) # Compute the standard deviation of each feature\n", "\n", "# Normalize de data matrix\n", "# T = <FILL IN>\n", "T = (X-m)/s # Normalize" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can test if the transformed features have zero-mean and unit variance:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- The mean of the transformed features are: [ 5.81016716e-16 -6.03961325e-16]\n", "- The standard deviation of the transformed features are: [1. 1.]\n" ] } ], "source": [ "# Testing mean\n", "print(f\"- The mean of the transformed features are: {np.mean(T, axis=0)}\")\n", "print(f\"- The standard deviation of the transformed features are: {np.std(T, axis=0)}\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "(note that the results can deviate from 0 or 1 due to finite precision errors)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 288x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Now you can verify if your solution satisfies\n", "plt.figure(figsize=(4, 4))\n", "plt.scatter(T[:, 0], T[:, 1], s=50);\n", "plt.axis('equal')\n", "plt.xlabel('$x_0$')\n", "plt.ylabel('$x_1$')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### 2.1.1. Implementation in `sklearn`\n", "\n", "The `sklearn` package contains a method to perform the standard scaling over a given data matrix.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sample mean is m = [58.06760405 13.94251037]\n" ] }, { "data": { "image/png": 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05CS+lYUl/PLb5hpZB53i/wlLBDd7t2aTu3WIhpz36csSYcywqrzsL2RKPxHhH369Hysap6L1TCe2HmkHAMwaXxPocZ9WKY2fsojQSoX3omhMbTUUL0CRixMOzEKm9MeTacSTwKKX9qFpyWw03hRz/D2CAItJgNDL4XCrd2s2drcOdhB59orVi3I5RSKZxt+u2Ye519YVvN7Jj3CeSUAwyuGY1VDrSu/WbNysBl61s9V2b1dgYDvKQg1B700JvN5yFo9tOIzpT27BntaPCvI+foXFJCD8du/7mpEQIYBtb7e70rs1m3z7uFqD8k78UlL6H7lzvG6SWb6oJeqVAqW1Dgso21vO4LENzUhp7Cm6elP4w4E23D6+Fl+fdjXOdyVwWUUEY/OIfpjBja2DglN+n8poCI03jcL4+kvxred2F8THo1BIx7cfYTHxOXq9SbPZcfQsdhw9CwCOJqLpoWwd8qmzMYtTfp9sB/a02OXYcfScA9apE8RuafnAYuJjjHqTauFmU2Jl6/Czre9gxfZjmlmg+eKE3yfXgV0o34lCELul5QOLiY8x6k1qRDotsGzdQdRcEi1oRzUBYPXO444KSUilF4iWKJrpHKeX1FcogtgtLR9YTHxMvr1JuxNprNt/qj8VPbsVgFX0HtgNB04hkXLO91AelnHHpOGovaTMMN3fqOWBQiHD2PMmDce2ljMFqXcKEqXzkwaQ2BWViMiU1ydosm+5kM/Wx+iBNTPqMyQRIiFCMiUMz02kUrjuqstw95SrdFdSVubOFCqMXRGRccvYGiz/yuTAdEsrFBwa9jEzG2odX4pbncdipkeJInp6LLxlFPYsvR1L75wAo4z7ZBpYvvmIYa6GlbkzdsPYhEyxZDSk/vMpWxml3mnx3AbcM21EyQkJwGLia7YdadesdQlJQDQkWc6XsBphMPPAzptcrzvAuzxM/VXL990Uw68emjEgHyas8iOaydWwUkKgN5BdD4HMqoqIUBaWXMvhCSIsJj6m9VynZh5EMg3cPr5Ws0pXC6sRBjMPbHPbRWh5TMpCEu6bMQo/2/pO/1BuJQL08JxxuHVsDUbVVGmKot5KysrcGSWMHdFYYejRnUijJ5GGTISH547DoltH49G7JqBpyWxfdpD3CpZUH6I4O5vbLmr6TMrDEl5tbrccQbEaYTCq+am7NIr7X2gaVHoPZLYHRAKrdh0f5GsBgOWvvG2Yn6K3krI6d2ZabAh2/HAWbnlqm61kNYFMC8fFcxssv7YUYDHxmNwoSf1l5Vi0Zp/hQ5YSAjKZX1iWh2VIkvUIg9ED25NMa6b5J1ICmY6Hn/paAOC+lbs1R26oUXeZ+gxitaQ5tUhK7u/4F41TB/yOoyEJ8WS6//9alFoSmlVYTDwkN0pSHpYN+42WhyVIEmH2+GFYt1/fkRqRCSDgjmvrMGP0FbYiDHoP7OI5DXh8w2Hd/qdq6I3cUEVob02ULZNWJEWr+/yzC6ag7UJ3/2tmjqvFtrfb8fJbH2Lnu2dVV4OlloRmFRYTj1ALaxoJidJn9N8XTMGp89149fBp1dWLTMCM0UNx1+Q6R0KUag/szHG1mPn0a5aFBLCeLNZ2Qb9tolbnOL3Q8aI1+waFyO+ZNgJ3XleP6U9uKVjT7WKGHbAeYSeJKi0yjXgWrdmn23KgLCLj3++d4miIMjf0ufVIu+0kML2RG7nksxqwEjpWyG5VwJEba/BvxiPySaKKJ9JYvvkInl0wZcDe383My3zs1xu5kUs+qwG73eeMtk6MOvzb8Yh82ggm0wLr9n+AzYc+xLPfmoK2892u3/RW7C8PS+hOpHVHbuQ6QZ0Qxny6zxXLyE438cXgciKaA+DfAMgAnhNC/FTv/GIYXK43YNwKZgaOmymEs4pZ+/XqbHIHditOUKeEUc/GQg9qL1b0Bpd7LiZEJANoAXA7gJMA9gD4hhBCs+tOMYgJoF4Sb9U5WRGR8ehdEzQ/RfVm6eabcKVcO55I99cAqbHo1tGe5WYU8ucvRfTExA+yfAOAo0KIYwBARGsBzAfgTgsvD8ndmx9uu4DXW85auobe3t9KIVw+9i9bdxDr9n+gWuHsdTiV/R/u4Yff6JUA3s/6/iSA6bknEdFDAB4CgBEjimcvm703X9t0Am8cPaf7KZ+L3sPqxiydymgI/zT/Wmw+9CGSKtsJP4RT2f/hDn4IDavFCAc9AkKIFUKIqUKIqTU1NS6Y5Q4d8STWNp3ATzc1oyeZBlkc6aD3sLac/sSVWTocTmUAf6xMTgK4Ouv7qwDk14bch2Q7QeuqyyCI8JcTH2PTwTZIAHqSQnf4dS4hiRANS5oP657Wj7Bm9wnN1zu9/eDtBOOHv/QeAGOIaBSADwB8HcA3vTXJPmqRk+a2i6aaLpsXEmD+9fV4bP61qg+r4ivRqzMpxPaDtxOljediIoRIEtHfAXgFmdDwSiHEIY/NsoVa5OCxDYeQFlCtqrVLNCxrCglgnF0bCRFvPxjHsX03EdFiIcRyJ4wQQrwM4GUnruUVepETpyAA4RDh2QVTUBkN2Zo7DACNN8Y4LMo4jmkxIaJfZ38L4HoAjohJMeDG3F0BQCYJi9bsw+I5DVi++YitucNjhlUV1lCmJLESzbkohPha339fBbClUEYFEbfm7nYnMu0Ml6075Ku5wwxjRUyeyPl+qZOGBB135+5qk+/c4exQtdJmkWHMYJhOT0T/CuAfhNd591n4MZ1erw6kLCxBpkwGiZXVS1VURoeN2h0lfT239sUoVMup54wReun0ZlYmHQDWE1Fl38X+OxG94aSBxYBe4tbqB6ejaelsPHrXBNw6tka1G3suFREZY4ddYtmO7PwRK+MXzIy0YBg9DB2wQohHiOibAF4jojiATgAPF9yyAGKUuHXPtBF472wnXm85Y3gtImBifTX+fOK8JRvs+kTcSL1nihvDz0giug3Ad5ARkRoAfy+E+FOhDQsqldEQ7ryuHiOvqMB7ZzvxhwOnBvgdzPpWnl0wBRPrL9Wcm5NLvunrdhsJMYyCmbtuKYB/FELsIKJJAP4vEf1PIcTWAtsWSIxGac5sqMWy9QcNr9N6thNfnnIVHt94GHo7jIhMuGn0UMydNDyv9PV8GgkxDGBiZSKEmCWE2NH39VsA5gL4SaENCyJGfoft75zBrKdfA6nWNg7kpd3HAWScn3pT+3pTAuPrqvPu96o38Y7DyYwZLFcNCyHaANxWAFsCj57fIZ0GFv5yDzrjKVMDoFpOd+CGJ7bgj4dPY/qoIdDSE6dWDVz5y+SLrTtECNHttCHFgJ7foTuRMhzYnUtXbwo/335M9xwnVw25DuS6S6MQIPyx+TTebe9wpN0jU7zwneEgen4HiaDaicwuhepEr4ST9Xw/4+uqHe8pywQfz3vA2sGPSWuAc02i9XDK4aqHUQKeRABAnNhWguSbtMaYRM3v4DROOVz10PP99CTS6OpNc2IbMwgWE4dR/A6L5zT0fYI7ixthWjtFi1oT8pjSgcWkAFRGQ4iGJEhasdY8cCNMa6dokRPbGBaTPNCqsO2IJ7HpYJulLvO5hPqWNUoGrJthWr2cEz2ExWbYTHHBDlibaFXYKk2LEsm05YFaABCWCTf3OVidnnBnhT2tH+G+lbvR1Ws+BFUZkdG0lKfkFTN+H8IVOPRaNC5bl1/72kSWgxWAZ8V102JDsHjOeDyx8bBpURTggsBShrc5Nihki0Y/1cG0Xei2tLpiv0lpw2Jig0K2aPRTHYxVR6yfhJBxHxYTGxSiRaMf62CsOmL9JISM+7CY2MButEOLkAQ8PKcBTUtmD8oi9bInq1bxX1lYQllY4oJAZgD8l7eB8pAteG4XepP5O08iIRnRsDToQTTqjeIGWt3jAPAoUGYAHBrOg/aLPbjlqW2mWgoYoTSBVtCrj6mMymhawiFYxn24NqdA1FaX4aWF0wdsA3T6GGmi5rg005PVK3gcBqMGf7TlSe42oP2THvzhQBt6U+ZXK2qOS7d6smqNGNXCD1svxp+wmNhA7QFUErVOX+zBhjfbTF0nIhPCIUnVcelGT9ZcYSgPS1i2/iDmXluHGddcMUhY9JL17n+hibdeJQ7/5S2i98kMZB4qM4RlwiPzxuPuz12t+gDOm1yPxzceVn2tEyFYNWHoTmRWU+v2n8LGN9vw6PpD+EXjVPy3sTUAeBwGow/7TCyg1zD6vud3Y8Evdmn2eC0PDyzY+4/v3IjGGaM0P8kL3ZPVKIs3mRaIJ9NoXJlphA3wOAxGH16ZWEDvAexKaPtIIjLhjkl1qL2kzFIY1WioVz5YyeJ94IUm3DcjhvNdCZSHpf4VTDac/cqwmFjAbhp9b0rg8orIgNCvWZSerE6j55PJJZUGVr7RivKwrCokAGe/MrzNsUQ+afQfd/U6bI0+RuFbO1m83YlPhSd328bZrwz/9S2g5xQ14vKKiMPWaGMmfKv4ZO5f2YROi6ut8rCMOyYNt7xtY4obXplYQM0pGjGRpRYNSRgzrKrQ5gEwniqY3fR5WmwIfjhnnIn5ggPpTqRQe0kZFs9tKGhjayZYsJhYRHGKPnrXBCy6dTQeuXM8Kg22PgICb3/4iSvZolYyZzviSWw90m652SI7Wxk1PP1IIaKvAvgxgPEAbhBCeF9wY4Jsp2hHPIlT53vw/BvvQQgM6PsalgmJlACBsPKNVleyRc2Gb5WtUMJGXRE7Wxk1vF6ZHATwZQDbPbbDFntaP8L0J7dg1a7jSKQEJMq0E7h17FA03jiyvzu9knfixowZPSexsqLI3gpZ6aQmEdjZymjiqZgIIZqFEG97aYNd1HwTvSmBZBrYe/xjjK6tgqwxOKeQhXp6URplRbHhwCmkbXTOv2tynWrPFYYBvF+ZBBY930Q8kcaa3cc9yRY1kzm789g5zXwRLWQCvn/bWF6RMJoU/M4goi0AhqscWiqEWGfhOg8BeAgARozwpv4ju8Cvue2iplgk0wJH2zs0r+OUA1Or4lcvc7YjnsSmt8wVImaTEsDMp1/HY/MnonFGLG/bmeKj4GIihJjt0HVWAFgBZJojOXFNheyHsq66DIIIH17oHvCAbm85g4Wr9iCdzmxlwgZrOr1dhBMOTKNcEq3M2Wf++I6teT4Ky9YdwpyJw1FbXZaP+UwRUvJr1tyHMhvlAf3+bWPxxMvNA45Z2SVEZEJvSvQP6srXgWm3FUBHPInn33jP9vsqLN98BE9/7fq8r8MUF16Hhv8KwM8A1ADYSET7hRBfdOv91R7KbJQHNFdIcpFIfyVy0+ihGF9X7Vi2qN1WABsOnLKcoKbGsTOdAKw3VmKKG0//8kKI3wP4vVfv79Qwrc/UVuHYmQ6opWxURGTMnTTc0WI9u60AWs915rXFUbimppI7rjGDKOlojhPDtEISsGD6SETD6rkdhUjwMpNLopBd8Nd+MY5yDTut8ODnY6ZT9pnSoaTXpFbK8LWQJMJXplyFCfXVqoPMC5HgpVdwmEoLtJzOpO7XX1aORWv2DWjLaDUknEtYJjy/o5U7rjGDKGkxyacKWOG5+6ah0iAc6zT9Fb9Z4hUNSf2Zts/vUHqPDBTJbCFRjisp/2ZJpATePcMd15jBlLSYZD+UPb0p6D1TIYkQliV0J1IISQRZokx/1DE1/ecUqpGRGtni1XK6A6t3tQL4NHU/V0iyKQ9LuGPScKTSAn84YC0TtyIiY3RNJVpOf1LQZtdM8ChpMQE+fSj/evVe7Dh6TvO8b04fgYn11b6aYKeI19qmEyAQYLL+tzuRxmXlEaxpOq4roGoQAYvnNGDzoQ81j3MRYGlS8mICZB7KedfVY0/rx6rNoKMhCRPrq33rB2g5/YnlqYIfXuhCjwn/ieJnyfYB1VaXDdpmFdJHxAQD/qv3ofhP1AIRIZl8/Wl7vith+TX7T17QPS71CUPbhW7V1ZibPiImGPBfvg81p2ZQPm0vqwhbfs0H53t0j988emj/vBwt3PQRMf7Hv0+IB/j501Yv23TssEsGRHPMYORhuX1ibX4GMyWH90+Jz/Djp61RtunMhlosW3/Q0jUFAFnKjLHIpSws4e7PXe2M8UzJUNIZsEHAqEH09nfOYNbTr/VFc8wTkQn3z4ghIhOUHk5hmVARkbD6wem+WI0xwYLvGJ+jVz+UTgMLf7kHvUk79TYCa/e8D1nKVDSHpExY9+f3TuXaGsYWvDLxOXr1Q92JFFI2C/dSaaCzN9WfFZtMA71JgUUv7ePaGsYWLCY+x2iKoN0iYK3XFbI/LVPcsJj4HDtjPPOBa2sYu7CY+Bwl/yUacudPxbU1jF1YTALAtNgQfOtGd8LVXFvD2IXFJCCMqb1E13dih4hMmuMwGMYqfNcEBCd6r+TSOCOGMcOqfJftywQTvnMCglrtUFlIQo+NWcEKIZl8l+3LBBcWE5+TW5Oz7QdfwLa327Hz3XN4+WAbIjJgt+vkqp2t+N6sMbwaYRyB7yIfk1uTUx6W8ej6Q7htfC22NJ+2mfmaDXG/VsYxWEx8itpMH6UV48tvqXc5swrnlDBOwtEcn+LUTB89OKeEcRIWE5/ixEwfI9JCcE4J4xgsJj7FqCaHYfwGi4lPcaMmRyLioj7GMdgB6yNyw8DPLpiCRWv2IZ3WnoMTkQkCsDRIS4EdsIyTsJj4BLXWjETAs9+agrbz3fjDgTbsOHp20OsemTcB4+uqseC53ei1mMDGDljGSXib4wP0WjMuemkfZo6rxV/e/1j1tU9sbMbIIRW410YhIBf1MU7CYuID9MLAQgDLNx/RPB5PpnHz8j/iaHsHtLoUREMSIiEu6mMKC99JPkAvDNzVm9IdFA4AiRTwesvgLZBCSCZs+8FMbHu7nYv6mILBd5MPUMLAWoPA9QaF61EeliBJ1D/Sk9PmmULC2xwfoBcGVgaFWw0ThyTCHZPq0LRkNnebZ1yBxcQn3HvjSISkTKgXGOjXUAaFW2ndmEwL1F5SxlsZxjX4TvOY7S1n8J1Ve5FKCyTTQFjODMNqnDFyQHuAabEh+NMPZ+LzT201VS3MYV/GbXhl4iHbW86gcWUT4sk0kumMQCRSAomUwOpdxwedX1tdhjULb0RlVDZMteewL+M2nq5MiOifAdwFoBfAuwAeEEKc99Imt+iIJ7Fw1R7N48mUUO01kjtcXUBg9c7jEBiY7MZhX8ZtvL7bXgXwIyFEkoiWA/gRgMUe2+QKGw6c0p3GF0+m8c7pDtVjucPVvzdrTL+4cNiX8QpP7zghxH9lfbsLwFe8ssVtWs91Gk7j+7ir19S1csWFYbzATz6TbwPYpHWQiB4ior1EtPfMmTMumlUYYldUQjYI915eEXHHGIZxgIKLCRFtIaKDKv/NzzpnKYAkgDVa1xFCrBBCTBVCTK2pqSm02QVn3uR6yDpqIkvA1UM4GsMEh4Jvc4QQs/WOE9F9AOYBuE2IQjcq9A9V0RCea5yGxpVNqsdTaWD55mZMqK/mpDMmEHi6zSGiOcg4XL8khCiZxhod8STWNp3A/3v3LL59cwwRWf3P0NWb7qsmTrpsIcNYx2uX/zMAogBepUy++C4hxN94a1JhUetbAgiEJOrPNclGCPA4CiYQeB3N+YyX7+82auMrjIr3uBsaExT8FM0peuyMr+C0eCYosJi4iJ3xFZwWzwQFr30mJYVe35JoSIKAQEiSOC2eCSR8l7rIvMn1eHzjYdVj3A2NCTp8p7pIVTSEFx+4QbULPXdDY4IOi4nL5Fb98gqEKRb4DnaY3EFa8ybXoypHKJTCPOXcn219R/NchgkKFMQM9qlTp4q9e/d6bcYgtAZpvfjADYNS4q2cyzB+gYj2CSGmqh3j0LBD6A3Syk2Jt3IuwwQFFhOHMBqklT0g3Mq5DBMUWEwcwmiQVnZKvJVzGSYosJg4hJKQpkZuSryVcxkmKLCYOITRIK3slHgr5zJMUGAxcQglIS17DIXWgHAr5zJMUODQsMN0xpOmE9KsnMswfkAvNMx3rsNY6RTPXeWZYoK3OQzDOAKLCcMwjsBiwjCMI7CYMAzjCIGM5hDRGQDHbbx0KICzDpvjBkG0m212DzftHimEUJ2CF0gxsQsR7dUKa/mZINrNNruHX+zmbQ7DMI7AYsIwjCOUmpis8NoAmwTRbrbZPXxhd0n5TBiGKRyltjJhGKZAsJgwDOMIJScmRPTPRHSEiN4kot8T0WVe22QEEX2ViA4RUZqIPA8B6kFEc4jobSI6SkQPe22PGYhoJRG1E9FBr20xCxFdTUTbiKi57974H17bVHJiAuBVANcKIa4D0ALgRx7bY4aDAL4MYLvXhuhBRDKA/w1gLoAJAL5BRBO8tcoULwKY47URFkkC+IEQYjyAGwF81+vfdcmJiRDiv4QQSvv3XQCu8tIeMwghmoUQb3tthwluAHBUCHFMCNELYC2A+R7bZIgQYjuAj7y2wwpCiDYhxJ/7vv4EQDOAK720qeTEJIdvA9jktRFFxJUA3s/6/iQ8vsFLASKKAfgsgN1e2lGUzZGIaAuA4SqHlgoh1vWdsxSZpeIaN23TwozNAUCtsy3nHhQQIqoC8FsA3xdCXPTSlqIUEyHEbL3jRHQfgHkAbhM+SbQxsjkgnARwddb3VwHgIUAFgojCyAjJGiHE77y2p+S2OUQ0B8BiAF8SQvCAGmfZA2AMEY0iogiArwNY77FNRQkREYDnATQLIf7Fa3uAEhQTAM8AuATAq0S0n4h+7rVBRhDRXxHRSQAzAGwkole8tkmNPsf23wF4BRmH4K+FEIe8tcoYIvoVgJ0AxhHRSSJ60GubTHAzgHsBzOq7j/cT0R1eGsTp9AzDOEIprkwYhikALCYMwzgCiwnDMI7AYsIwjCOwmDAM4wgsJgzDOAKLCVNQ+srkb+/7+idE9L+8tokpDEWZTs/4ikcBPEZEtcgUo33JY3uYAsFJa0zBIaLXAVQB+IIQ4hMiqgTwfwD0AnhNCOGLYksmP3ibwxQUIpoEoA5AvK/vBpBp9PQbIcR3wCuVooHFhCkYRFSHTIuH+QA6ieiLfYeuwqd9T1Je2MY4D4sJUxCIqALA75BpLdgM4HEAP+47fBKfdrjje7BIYJ8J4zp9PpNnAPQA2ME+k+KAxYRhGEfgJSbDMI7AYsIwjCOwmDAM4wgsJgzDOAKLCcMwjsBiwjCMI7CYMAzjCCwmDMM4AosJwzCO8P8BErksSRXWYu4AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 288x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "scaler.fit(X)\n", "print(f'The sample mean is m = {scaler.mean_}')\n", "\n", "T2 = scaler.transform(X)\n", "plt.figure(figsize=(4, 4))\n", "plt.scatter(T2[:, 0], T2[:, 1], s=50);\n", "plt.axis('equal')\n", "plt.xlabel('$x_0$')\n", "plt.ylabel('$x_1$')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Note that, once we have defined the scaler object in Python, you can apply the scaling transformation to other datasets. This will be useful in further topics, when the dataset may be split in several matrices and we may be interested in defining the transformation using some matrix, and apply it to others" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.2. Other normalizations.\n", "\n", "The are some alternatives to the standard scaling that may be interesting for some datasets. Here we show some of them, available at the [preprocessing](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing) module in `sklearn`:\n", "\n", " * [preprocessing.MaxAbsScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler): Scale each feature by its maximum absolute value. As a result, all feature values will lie in the interval [-1, 1].\n", " * [preprocessing.MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler): Transform features by scaling each feature to a given range. Also, all feature values will lie in the specified interval.\n", " * [preprocessing.Normalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer): Normalize samples individually to unit norm. That is, it applies the transformation ${\\bf t}_k = \\frac{1}{\\|{\\bf x}_k\\|} {\\bf x}_k$\n", " * [preprocessing.PowerTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer): Apply a power transform featurewise to make data more Gaussian-like.\n", " * [preprocessing.QuantileTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer): Transform features using quantile information. The transformed features follow a specific target distribution (uniform or normal). \n", " * [preprocessing.RobustScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler): Scale features using statistics that are robust to outliers. This way, anomalous values in one or very few samples cannot have a strong influence in the normalization.\n", "\n", "You can find more detailed explanation of these transformations `sklearn` [documentation](https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing).\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Exercise 2**: Use `sklearn` to transform the data matrix `X` into a matrix `T24`such that the minimum feature value is 2 and the maximum is 4.\n", "\n", "(Hint: select and import the appropriate preprocessing module from `sklearn` an follow the same steps used in the code cell above for the scandard scaler)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 288x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Write your solution here\n", "# <SOL>\n", "from sklearn.preprocessing import MinMaxScaler\n", "scaler = MinMaxScaler(feature_range=(2, 4))\n", "scaler.fit(X)\n", "T24 = scaler.transform(X)\n", "# </SOL>\n", "\n", "# We can visually check that the transformed data features lie in the selected range.\n", "plt.figure(figsize=(4, 4))\n", "plt.scatter(T24[:, 0], T24[:, 1], s=50);\n", "plt.axis('equal')\n", "plt.xlabel('$x_0$')\n", "plt.ylabel('$x_1$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Intro3_Working_with_Data_solution.ipynb", "provenance": [], "version": "0.3.2" }, "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
Neuroglycerin/hail-seizure
ipynotebooks/Naive Bayes with Totally Random Tree Embedding.ipynb
1
39144
{ "metadata": { "name": "", "signature": "sha256:329ea422fd21114ae7c3f0c3550407fec033f2f3bef285b3e3bc95c6455e84ff" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This might be an interesting type of classifier to deal with.\n", "Essentially the plan is in the title, transform the data with a Totally Random Tree embedding into a sparse binary representation.\n", "After doing this, could go through the data and test for mutual information, removing features that are correlated to preserve the independence assumption of Naive Bayes.\n", "Then, run Naive Bayes to classify the data.\n", "\n", "Unfortunately, this can't easily be plugged into the existing code, so it's probably best to prototype it quickly to see if it will be interesting.\n", "Going to use the same features as used by the current best performing classifier, the SVC.\n", "Specifically, just one of its features, that seems to be contributing the most to its performance: `mvar_csp`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = 8, 12\n", "plt.rcParams['axes.grid'] = True\n", "plt.set_cmap('brg')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x7f6e740a2588>" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading the global training set\n", "===============================\n", "\n", "This should be able to transform a tiled composite training set into a more workable form." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cd .." ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/home/gavin/repositories/hail-seizure\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from python import utils" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "with open(\"probablygood.gavin.json\") as f:\n", " settings = utils.json.load(f)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "settings['FEATURES'] = [feature for feature in settings['FEATURES'] if 'mvar' in feature]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "data = utils.get_data(settings)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "with open(\"segmentMetadata.json\") as f:\n", " meta = utils.json.load(f)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "da = utils.DataAssembler(settings,data,meta)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "X,y = da.composite_tiled_training()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "(7454, 24600)" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting together the pipeline\n", "=============================\n", "\n", "First, we will fill the missing data with means, then we will apply a standard scaler to the data.\n", "After doing that, we can apply the totally random tree embedding.\n", "Then, it will be interesting to look at mutual information.\n", "\n", "After the totally random tree embedding we can perform classification with Naive Bayes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.preprocessing\n", "import sklearn.pipeline\n", "import sklearn.ensemble" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "imputer = sklearn.preprocessing.Imputer()\n", "scaler = sklearn.preprocessing.StandardScaler()\n", "hasher = sklearn.ensemble.RandomTreesEmbedding(n_estimators=3000,random_state=7,max_depth=5,n_jobs=-1)\n", "pipe = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('hsh',hasher)])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "%time pipe.fit(X,y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "Pipeline(steps=[('imp', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('scl', StandardScaler(copy=True, with_mean=True, with_std=True)), ('hsh', RandomTreesEmbedding(max_depth=5, max_leaf_nodes=None, min_density=None,\n", " min_samples_leaf=1, min_samples_split=2, n_estimators=3000,\n", " n_jobs=1, random_state=7, sparse_output=True, verbose=0))])" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "X_hashed = pipe.transform(X)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "X_hashed.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "(7454, 63231)" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.metrics" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "scores = []\n", "for i in range(X_hashed.shape[1]):\n", " scores.append(sklearn.metrics.mutual_info_score(y,list(X_hashed[:,i].todense().flat)))\n", " if i>0:\n", " if i%int(X_hashed.shape[1]/100) == 0:\n", " print(i)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "632\n", "1264" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1896" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2528" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "3160" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "3792" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "4424" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "5056" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "5688" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "6320" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "6952" ] }, { "output_type": "stream", 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}, { "cell_type": "code", "collapsed": false, "input": [ "h=plt.hist(scores,log=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f6e476961d0>" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.naive_bayes" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "nb = sklearn.naive_bayes.BernoulliNB()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "pipe = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('hsh',hasher),('cls',nb)])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "cv = utils.Sequence_CV(da.composite_training_segments,meta)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time \n", "for train,test in cv:\n", " pipe.fit(X[train],y[train])\n", " prds = pipe.predict_proba(X[test])\n", " print(sklearn.metrics.roc_auc_score(y[test],prds[:,1]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.596453302652\n", "0.664414776902" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.618333286127" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.608484229462" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.594321714882" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.597103873542" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.618142108576" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.545518998143" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.636693822854" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.623119719408" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "CPU times: user 2min 33s, sys: 2min 6s, total: 4min 39s\n", "Wall time: 4min 39s\n" ] } ], "prompt_number": 63 } ], "metadata": {} } ] }
apache-2.0
CompPhysics/ThesisProjects
doc/MSc/msc_students/former/AudunHansen/Audun/Notebooks/PreviousVersions/ccalgebra.ipynb
1
149115
{ "metadata": { "name": "ccalgebra.ipynb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Coupled Cluster Algebra\n", "###Audun Skau Hansen | Comp-Phys UiO | September 2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class included in this notebook (cell below) is intended to facilitate simple evaluation of hamiltonian operators combining with cluster operators. It consist of a main operator class (Operator) and some complementary functions to simplify the initialization of the normal-ordered hamiltonian and the cluster operators of any order.\n", "\n", "The code is pretty much untested and should currently be considered unreliable. A lot of functionality remains to be included, such as the two final rules for CC-diagram interpretation from Shavitt-Bartlett (Chapter 10) and the possibility to return function objects that may potentially be used directly in a CC-implementation. The class could also very well be rewritten to take advantage of Sympy or something similar. \n", "\n", "* Dependencies: itertools, numpy, matplotlib.pyplot\n", "\n", "Some simple examples are given below the initialization cell." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# <!-- collapse=True -->\n", "from IPython.display import display, Math, Latex \n", "#%matplotlib inline \n", "#from sympy.interactive import printing\n", "#printing.init_printing()\n", "\n", "from numpy import *\n", "from itertools import *\n", "from matplotlib.pyplot import *\n", "\n", "class Operator():\n", " #Normal ordered operator for cluster algebra (diagrammatic)\n", " def __init__(self, q_create, q_annihilate):\n", " self.q_c = q_create\n", " self.q_a = q_annihilate\n", " self.diagrams = []\n", " self.contracted = []\n", " self.I = []\n", " self.T_operator = []\n", " self.T_vertices = []\n", " self.vertexform = []\n", " self.vertexform_locked = []\n", " self.labels_p = ['h','g','f','e','d','c','b','a']\n", " self.labels_h = ['p','o','n','m','l','k','j','i']\n", " self.enable_printing = False\n", " \n", " self.assess_excitation()\n", " \n", " def combine(self, ops, excitation = None):\n", " #Assosciate a list of T-operators (ops) to the current operator instance \n", " #Find all possible ways to combine the operators using self.distinct_combinations() \n", " T = []\n", " for i in ops:\n", " T.append(i.q_c)\n", " self.T_vertices.append(i.vertexform)\n", " self.T_operator = T\n", " \n", " #Finding acceptable combinations of internal contractions between the operators\n", " self.I = self.distinct_combinations(self.q_a, T)\n", " \n", " #Find excitation level of combination\n", " self.assess_excitation()\n", " def assess_excitation(self):\n", " #Assess current excitation level (also if combined operator)\n", " self.E = self.q_c.count(-1) + self.q_c.count(1) - (self.q_a.count(1) + self.q_a.count(-1))\n", " for i in range(len(self.T_operator)):\n", " self.E += self.T_operator[i].count(1) + self.T_operator[i].count(-1) \n", " #fill in contracted elements\n", " self.E/= 2.0\n", " def scan_extract(L, e):\n", " #Exctract element e from list L\n", " ret = None\n", " for i in range(len(L)):\n", " if L[i] == e:\n", " L = delete(L, i)\n", " ret = e\n", " def nloops(self,x,y):\n", " #returns number of loops in budget\n", " return (x+y - abs(x-y))//2\n", " \n", " def assess_contributions(self, excitation_level = None):\n", " #self.label_vertices()\n", " #returns the contribution to the CC-energy or amplitude eq. in symbolic form\n", " \n", " enable_printing = self.enable_printing #True #self.enable_printing\n", " self.excitation_level = excitation_level #Setting target excitation level\n", "\n", " \n", " self.loops = []\n", " self.holes = []\n", " self.equivalents = []\n", " self.stringforms = []\n", " for i in self.I:\n", " #for each distinct diagram, generate the expression for the contribution\n", " \"\"\"\n", " Following S-B, ch. 10, this section evaluates the contribution to the CC-eqs. using the diagrammatic rules:\n", " (1) Label internal and external lines with particle and hole labels\n", " (2) Associate f(i,a) with every 1-p operator vertex\n", " (3) Associate <lout rout!!lin rin> with every 2-p operator vertex\n", " (4) Associate t(ab,ij) for every amplitude\n", " (5) Sum over all internal lines\n", " (6) Multiply by 1/2 for each pair of equivalent internal lines\n", " (7) Multiply by 1/2 for each pair of equivalent T-vertices\n", " (8) Include a phase factor (-)**(holes-loops)\n", " (9) Not yet implemented\n", " (10) Not yet implemented\n", " \"\"\"\n", " \n", " H_labels = []\n", " H_ins = []\n", " H_outs = []\n", " T_labels = []\n", " prefactor = 1.0 #this factor is multiplied to the sum and adjusted according to the rules laid out above\n", " \n", " #Comparing distribution of lines\n", " i_budget = self.itemcount(i) #internal distribution\n", " t_budget = self.itemcount(self.T_operator) #all lines from T\n", " t_budget_external = self.itemcount(self.T_operator) #distribution of external lines in T\n", " for e in range(len(t_budget_external)):\n", " #Subtracting internal lines \n", " t_budget_external[e][0] -= i_budget[e][0]\n", " t_budget_external[e][1] -= i_budget[e][1]\n", " \n", " t_external_equiv = self.find_identical(t_budget_external) #len of this list yields number of identical external distribution in the Ts\n", " t_equiv = self.find_identical(i_budget) #len of this line yields the number of identical internal distributions in the Ts\n", " n_equivalent_t = 0\n", " for e in range(len(t_external_equiv)):\n", " if t_external_equiv[e] == t_equiv[e]:\n", " n_equivalent_t += 1\n", "\n", " \n", " #Counting number of equivalent lines, holes and loops\n", " n_equi_lines = 0\n", " n_loops = 0\n", " n_loops_external = 0 \n", " n_holes = 0\n", " \n", " #Counting equivalent lines\n", " for e in i_budget:\n", " n_equi_lines += e[0]//2\n", " n_equi_lines += e[1]//2\n", "\n", " \n", " #Counting loops\n", " for e in range(len(i_budget)):\n", " n_loops += self.nloops(i_budget[e][0], i_budget[e][1])\n", " n_loops_external += self.nloops(t_budget_external[e][0], t_budget_external[e][1]) #So-called quasi-loops (S-B, ch. 10)\n", "\n", " \n", " #Counting holes\n", " for e in range(len(i_budget)):\n", " n_holes += i_budget[e][1] + t_budget_external[e][1]\n", " n_holes += self.q_c.count(-1)\n", "\n", " \n", " \n", " #labelling all lines\n", " \n", " #Setting up a mapping to keep track of connected lines in T\n", " t_mapping = []\n", " for e in range(len(self.T_operator)):\n", " t_mapping.append([])\n", " for u in range(len(self.T_operator[e])):\n", " t_mapping[e].append(0) #a 0 implies an external line\n", " \n", " for e in range(len(i)):\n", " for u in range(len(i[e])):\n", " linetype = i[e][u] #+1 = particle / -1 = hole\n", " \n", " for l in range(len(self.T_operator[e])):\n", " if self.T_operator[e][l] == linetype:\n", " if t_mapping[e][l] == 0:\n", " #Connect and label line\n", " t_mapping[e][l] = 1 #1 implies a connected line\n", " break\n", " \n", "\n", " #Labeling all looped lines:\n", " plabels = ['h','g','f','e','d','c','b','a']\n", " hlabels = ['p','o','n','m','l','k','j','i']\n", " \n", " i_connections = []\n", " i_labels = []\n", " for e in range(len(i)):\n", " i_connections.append([])\n", " i_labels.append([]) \n", " for u in range(len(i[e])):\n", " i_connections[e].append(0)\n", " i_labels[e].append(0) \n", "\n", " h_vertices = [] #list containing pairs of operators\n", " t_vertices = []\n", " for e in range(len(self.T_operator)):\n", " t_vertices.append([])\n", "\n", " \n", " #Connect all loops connecting at the hamiltonian\n", " for e in range(len(i)):\n", " for u in range(len(i[e])):\n", " if i_connections[e][u] == 0:\n", " linetype = i[e][u]\n", " found = False\n", " for ee in range(len(i)):\n", " for uu in range(len(i[ee])):\n", " if i_connections[e][u] == 0 and i_connections[ee][uu] == 0 and i[ee][uu] == -linetype:\n", " #Connect\n", " i_connections[e][u] = 2\n", " i_connections[ee][uu] = 2 #2 denotes a looped connection vertex\n", " if linetype == -1:\n", " i_labels[e][u] = hlabels.pop()\n", " i_labels[ee][uu] = plabels.pop()\n", " if e == ee:\n", " t_vertices[e].append([i_labels[e][u], i_labels[ee][uu]])\n", " if e != ee:\n", " t_vertices[e].append([i_labels[e][u], -1])\n", " t_vertices[ee].append([1,i_labels[ee][uu]])\n", " if linetype == 1:\n", " i_labels[e][u] = plabels.pop()\n", " i_labels[ee][uu] = hlabels.pop()\n", " if e == ee:\n", " t_vertices[e].append([i_labels[e][u], i_labels[ee][uu]])\n", " if e != ee:\n", " t_vertices[e].append([i_labels[e][u], -1])\n", " t_vertices[ee].append([1,i_labels[ee][uu]])\n", " #set in/out in vertex n\n", " h_vertices.append([i_labels[e][u],i_labels[ee][uu]])\n", "\n", " \n", " found = True\n", " break\n", " if found:\n", " break\n", "\n", " #Connect all lines passing through the hamiltonian\n", " for e in range(len(i)):\n", " for u in range(len(i[e])):\n", " if i_connections[e][u] == 0:\n", " linetype = i[e][u]\n", " found = False\n", " for ee in range(len(self.q_c)):\n", " if self.q_c[ee] == linetype:\n", " #add vertex, connect\n", " i_connections[e][u] = 1\n", " if linetype == 1:\n", " i_labels[e][u] = plabels.pop()\n", " t_vertices[e].append([i_labels[e][u], -1])\n", " if linetype == -1:\n", " i_labels[e][u] = hlabels.pop()\n", " t_vertices[e].append([1, i_labels[e][u]])\n", " h_vertices.append([i_labels[e][u], i_labels[e][u]])\n", " \n", " found = True\n", " if found:\n", " break\n", " \n", " #Label remaining lines in the T_operator\n", " for e in range(len(t_vertices)):\n", " for u in range(len(t_vertices[e])):\n", " if t_vertices[e][u][0] == 1:\n", " t_vertices[e][u][0] = plabels.pop()\n", " if t_vertices[e][u][1] == -1:\n", " t_vertices[e][u][1] = hlabels.pop()\n", " \n", " #Add unconnected vertices to T_operator\n", " for e in range(len(self.T_operator)):\n", " while len(self.T_operator[e])/2 > len(t_vertices[e]):\n", " t_vertices[e].append([plabels.pop(), hlabels.pop()])\n", " \n", " \n", " stringform = '' #The stringform will contain the CC contribution in latex format \n", " if enable_printing: \n", " print \"=== Distinct contribution with excitation %i ===\" % self.E\n", " print \"Connection pattern:\", i\n", " prefactor = ((-1)**(n_holes - (n_loops+n_loops_external)))\n", " predivisor = (2**n_equi_lines)*(2**n_equivalent_t)\n", " if enable_printing:\n", " print \"Multiplier:\", prefactor, \"/\", predivisor\n", "\n", " stringform+=' \\\\frac{%i}{%i} ' % (prefactor, predivisor)\n", " summingover = ''\n", " \n", " summation_indices = []\n", " for e in range(len(h_vertices)):\n", " summation_indices.append(h_vertices[e][0])\n", " summation_indices.append(h_vertices[e][1])\n", " summation_indices2 = set(summation_indices) #only unique values\n", " #print summation_indices\n", "\n", " for e in summation_indices2:\n", " summingover+= e\n", " stringform+= '\\sum_{%s} ' % summingover\n", " if enable_printing:\n", " print \"Sum over :\", summation_indices\n", " \n", " H_tensor = ['','']\n", " \n", " for e in range(len(h_vertices)):\n", " H_tensor[1]+=h_vertices[e][0]\n", " H_tensor[0]+=h_vertices[e][1]\n", " if enable_printing:\n", " print \"H-tensor : <%s||%s> \" % (H_tensor[0], H_tensor[1])\n", " \n", " stringform += ' \\langle %s|H|%s \\\\rangle '% (H_tensor[0], H_tensor[1])\n", "\n", " \n", " T_tensor = []\n", " for e in range(len(t_vertices)):\n", " T_tensor.append(['', ''])\n", " for u in range(len(t_vertices[e])):\n", " T_tensor[e][1]+=t_vertices[e][u][0]\n", " T_tensor[e][0]+=t_vertices[e][u][1]\n", " if enable_printing:\n", " print \"T-tensor(s):\"\n", " for e in T_tensor:\n", " if enable_printing:\n", " print \" T(%s,%s)\" % (e[0], e[1])\n", " stringform += ' t_{%s}^{%s} ' % (e[0], e[1])\n", " stringform += \" (excitation:%i) \" % self.E\n", " if excitation_level == None:\n", " self.stringforms.append(stringform)\n", " else:\n", " if excitation_level == self.E:\n", " self.stringforms.append(stringform)\n", " \n", " \n", " if enable_printing: \n", " print \" \"\n", " if len(self.I) == 0:\n", " #self.stringforms.append('No contributions found.')\n", " if enable_printing:\n", " print \"=== No distinct contribution found ===\"\n", " print \" \"\n", " #print self.I\n", " for e in range(len(self.I)):\n", " if excitation_level == None:\n", " hh,tt = self.visualize(self.q_a, self.q_c, [0,e*2.6], self.I[e], T = self.T_operator, t = 0)\n", " self.diagrams.append([hh,tt])\n", " else:\n", " if excitation_level == self.E:\n", " hh,tt = self.visualize(self.q_a, self.q_c, [0,e*2.6], self.I[e], T = self.T_operator, t = 0)\n", " self.diagrams.append([hh,tt])\n", " \n", " #self.hhvis, self.tvis = self.visualize(self.q_a, self.q_c, [0,0], self.I[0], T = None, t = 0)\n", " return 0\n", "\n", " def nozeroedges(self,i):\n", " #Assert that there are no borders in the endpoints of the connection pattern i\n", " ret = True\n", " try:\n", " if i[0] == 0 or i[-1] == 0:\n", " ret = False\n", " except:\n", " ret = False\n", " return ret\n", " \n", " def nozerocontact(self,i):\n", " #Assert that there are no neighbouring borders in the connection pattern i\n", " ret = True\n", " for e in range(len(i) - 1):\n", " if i[e+1] == 0 and i[e] == 0:\n", " ret = False\n", " return ret\n", " \n", " def splitlist(self,L, d):\n", " #Returns a split list HL from a list L into constituents, d denotes barrier\n", " HL = [[]]\n", " n = 0\n", " for i in range(len(L)):\n", " if L[i] != d:\n", " HL[n].append(L[i]) \n", " if L[i] == d:\n", " HL.append([])\n", " n += 1 \n", " return HL\n", " \n", " def itemcount(self,T):\n", " #Count number of particle- and hole lines in each constituent part of T\n", " #Returns a list object of the form [[#particles, #holes], ...]\n", " itemnumber = []\n", " for i in range(len(T)):\n", " itemnumber.append([])\n", " itemnumber[i].append(T[i].count(1)) #number of q-particles\n", " itemnumber[i].append(T[i].count(-1)) #number of q-holes\n", " return itemnumber\n", " \n", " def contractable(self,L,T):\n", " #Asserts that the number of contractions in each p- and hline of L does not superseed # of p-h in T \n", " #input two itemcount items lists, returns bool\n", " ret = True\n", " for i in range(len(T)):\n", " for e in range(len(T[i])):\n", " if T[i][e]<L[i][e]:\n", " #print T[i][e],L[i][e]\n", " ret = False\n", " return ret\n", " \n", " def find_identical(self,T):\n", " #Find identical operators in the list of operators T\n", " #returns a list of pairs of indices that denotes permutations that does not alter the T operator\n", " identicals = []\n", " for i in range(len(T)):\n", " for e in range(i,len(T)):\n", " if T[i] == T[e] and i!=e:\n", " identicals.append([i,e])\n", " return identicals\n", " \n", " def permute_elements(self,e1,e2,L):\n", " #Returns a list where elements at indices e1,e2 in L is permuted\n", " L_ret = [] \n", " for i in L:\n", " L_ret.append(i)\n", " L_ret[e1] = L[e2]\n", " L_ret[e2] = L[e1]\n", " return L_ret\n", " \n", " def acceptable(self,i, T, excluded, excluded_budgets):\n", " #Test if a potential connection pattern is distinct\n", " #Returns bool\n", " ret = False\n", " identicals = self.find_identical(T)\n", " T_budget = self.itemcount(T)\n", " \n", " if self.nozeroedges(i) and self.nozerocontact(i):\n", " I = self.splitlist(i, 0)\n", " I_budget = self.itemcount(I)\n", " if I_budget not in excluded_budgets:\n", " excluded_budgets.append(I_budget)\n", " for e in identicals:\n", " excluded_budgets.append(self.permute_elements(e[0], e[1], I_budget))\n", " if self.contractable(I_budget,T_budget):\n", " if I not in excluded:\n", " excluded.append(I)\n", " for e in identicals:\n", " excluded.append(self.permute_elements(e[0], e[1], I))\n", " ret = True\n", " return ret\n", " \n", " \n", " \n", " def distinct_combinations(self,H,T):\n", " #Returns all possible combinations of H and T\n", " #I - all q-particle annihilation operators\n", " #T list of list with T operators. ex. [[-1,1],[-1,1]] = T_1 T_1\n", " lenH = len(H)\n", " lenT = len(T)\n", " lenTi = []\n", " for i in range(lenT):\n", " lenTi.append(len(T[i]))\n", " H+=[0 for i in range(lenT-1)] #adding zeros to denote separations in the cluster-operators\n", " \n", " #Creating countlist for T-operator to keep track of q-operators in each clusteroperator\n", " T_budget = self.itemcount(T)\n", " \n", " \n", " #Create all permutations\n", " H_permuted = permutations(H)\n", " \n", " #Sort out indistinct diagrams and cancelling terms\n", " excluded = []\n", " excluded_budgets = []\n", " accepted = []\n", " for i in H_permuted:\n", " if self.acceptable(i, T, excluded, excluded_budgets):\n", " #print \"Accepted\"\n", " accepted.append(self.splitlist(i,0)) \n", " self.combined = accepted\n", " return accepted\n", " def printout(self):\n", " for i in self.stringforms:\n", " display(Math(r'%s' % i))\n", " def plot_diagrams(self):\n", " figure(figsize = (2, len(self.diagrams)+1), dpi = 60, edgecolor = 'k',facecolor='white')\n", " \n", " p2 =0.0\n", " for i in self.diagrams:\n", " pos2 = [0,p2]\n", " for e in i[0]:\n", " e.draw()\n", " for e in i[1]:\n", " for u in e:\n", " u.draw()\n", " p2 += 1.8\n", " #axisbg='red'\n", " #set_cmap('hot')\n", " axis('off')\n", " plot(-.1,-.1,'w.')\n", " axes().set_aspect('equal', 'datalim')\n", " \n", " if self.excitation_level == None:\n", " show()\n", " else:\n", " if self.excitation_level == self.E:\n", " show()\n", " \n", "\n", "\n", " def visualize(self,h_below, h_above, pos, I, T = None, t = 0):\n", " #create operator vxnode objects from Ob-object\n", " #NV = len(O.L)/2\n", " NV = (len(h_below) + len(h_above))/2\n", " Nbelow = len(h_below)\n", " Nabove = len(h_above)\n", " \n", " c_below = []\n", " for i in range(Nbelow):\n", " c_below.append(0) #A zero implies a \"free\" line below the interaction line\n", " \n", " c_above = []\n", " for i in range(Nabove):\n", " c_above.append(0) #A zero implies a \"free\" line above the interaction line\n", " ncount = NV\n", " #(1) Identify lines passing through the interaction line\n", " vnodes = []\n", " for i in range(Nabove):\n", " for e in range(Nbelow):\n", " if c_below[e] == 0 and c_above[i] == 0:\n", " if h_above[i]== h_below[e]:\n", " #Append the operator to vnodes\n", " #print \"Found a line passing through the interaction.\"\n", " c_above[i] = 1\n", " c_below[e] = 1\n", " ncount -= 1\n", " if h_above[i] == 1:\n", " c1,c2,c3,c4 = [0,None],[1,None],[0,None],[1,None]\n", " nd = vxnode([pos[0] + i, pos[1]+1.5], [c1,c2,c3,c4])\n", " vnodes.append(nd)\n", " \n", " if h_above[i] == -1:\n", " c1,c2,c3,c4 = [1,None],[0,None],[1,None],[0,None]\n", " nd = vxnode([pos[0] + i, pos[1]+1.5], [c1,c2,c3,c4])\n", " vnodes.append(nd)\n", " \n", " \n", " \n", " #(2) Identify lines annihilating at the interaction \n", " for i in range(Nbelow):\n", " for e in range(Nbelow):\n", " if c_below[e] == 0 and c_below[i] == 0 and i!= e:\n", " if h_below[i]== -1*h_below[e]:\n", " #Append the operator to vnodes\n", " #print \"Found a line annihilating at the interaction.\"\n", " c_below[i] = 1\n", " c_below[e] = 1\n", " ncount -= 1\n", " c1,c2,c3,c4 = [0,None],[0,None],[1,None],[1,None]\n", " nd = vxnode([pos[0] + i, pos[1]+1.5], [c1,c2,c3,c4])\n", " vnodes.append(nd)\n", " \n", " #(3) Identify lines created at the interaction \n", " for i in range(Nabove):\n", " for e in range(Nabove):\n", " if c_above[e] == 0 and c_above[i] == 0 and i!= e:\n", " if h_above[i]== -1*h_above[e]:\n", " #Append the operator to vnodes\n", " #print \"Found a line creating at the interaction\"\n", " c_above[i] = 1\n", " c_above[e] = 1\n", " ncount -= 1\n", " c1,c2,c3,c4 = [1,None],[1,None],[0,None],[0,None]\n", " nd = vxnode([pos[0] + i, pos[1]+1.5], [c1,c2,c3,c4])\n", " vnodes.append(nd)\n", " #print \"C_above:\", c_above\n", " #print \"C_below:\", c_below\n", " \n", " \n", " #if NV%2 != 0:\n", " # print \"Warning: non-binary operator.\"\n", " \n", " \n", " for i in range(len(vnodes)-1):\n", " if t == 0:\n", " vnodes[i].opconnect(vnodes[i+1].pos)\n", " if t == 1:\n", " vnodes[i].tconnect(vnodes[i+1].pos)\n", " \n", " tnodes = []\n", " #print \"lenT:\", len(T), T\n", " \n", " if T != None:\n", " \n", " for t in range(len(T)):\n", " tnodes.append([])\n", " \n", " for i in range(len(T[t])/2):\n", " c1,c2,c3,c4 = [1,None],[1,None],[0,None],[0,None]\n", " #nd = vxnode([pos[0] + i, pos[1]], [c1,c2,c3,c4])\n", " tnodes[t].append(vxnode([pos[0] + i, pos[1]], [c1,c2,c3,c4]))\n", " \n", " for i in range(len(tnodes[t])-1): \n", " tnodes[t][i].tconnect(tnodes[t][i+1].pos)\n", " \n", " p = 0\n", " for i in range(len(tnodes)):\n", " #print \"Tlen:\", len(tnodes[i])\n", " for e in range(len(tnodes[i])):\n", " tnodes[i][e].pos[0] = pos[0] + p\n", " p += 1\n", " for i in range(len((vnodes))):\n", " vnodes[i].pos[0] += .5\n", "\n", "\n", " \n", " #Contract T\n", " #I contains a recipe for the contractions in the diagram.\n", " #Iterate over each element in I and match up the contractions\n", "\n", " #For every element in I, match up corresponding elements in H and T\n", "\n", " for i in range(len(tnodes)):\n", "\n", " for e in range(len(I[i])):\n", "\n", " cn = I[i][e]\n", "\n", " self.m = 0\n", " nn = 0\n", " cond = True\n", " while cond:\n", " cond = self.probe(tnodes[i],vnodes,cn) #probe and perform a possible connection\n", "\n", " nn += 1\n", " if self.m != 0:\n", "\n", " break\n", " if nn>10:\n", " break\n", " for i in range(len(tnodes)):\n", " pass \n", " return [vnodes, tnodes]\n", " \n", " \n", " def probe(self,T,H,cn):\n", " cond = True\n", " if cn == -1:\n", " #hole line\n", " for i in range(len(T)):\n", " for e in range(len(H)):\n", " if T[i].config[0][0] == 1 and T[i].config[0][1] == None:\n", " if H[e].config[2][0] == 1 and H[e].config[2][1] == None:\n", " #perform connection\n", " H[e].config[2][1] = T[i].pos\n", " T[i].config[0][0] = 0\n", " self.m = 1\n", " cn = 0\n", " break\n", " if self.m == 1:\n", " break\n", " \n", " if cn == 1:\n", " #particle line\n", " for i in range(len(T)):\n", " for e in range(len(H)):\n", "\n", " if T[i].config[1][0] == 1 and T[i].config[1][1] == None:\n", " if H[e].config[3][0] == 1 and H[e].config[3][1] == None:\n", " #perform connection\n", " H[e].config[3][1] = T[i].pos\n", " T[i].config[1][0] = 0\n", " \n", " self.m = 1\n", "\n", " cn = 0\n", " \n", " break\n", " if self.m == 1:\n", " break\n", " if self.m == 1:\n", " cond = False\n", " return cond \n", "\n", " def nconnect(self,n1,n2,S,order=\"l0\", p_h = None):\n", " N = 60\n", " \n", " if n1.x==n2.x and n1.y == n2.y:\n", " Cx = n1.x + S\n", " Cy = n1.y\n", " \n", " X = Cx + S*cos(linspace(0,2*pi,N))\n", " Y = Cy + S*sin(linspace(0,2*pi,N))\n", " #S = -1\n", " else:\n", " Phx = (n1.x+n2.x)/2.0\n", " Phy = (n1.y+n2.y)/2.0\n", " \n", " lP = sqrt((n2.x-n1.x)**2 + (n2.y-n1.y)**2)\n", " dPx = (n2.x-n1.x)/lP \n", " dPy = (n2.y-n1.y)/lP\n", " \n", " Cx = Phx - S*dPy\n", " Cy = Phy + S*dPx \n", " \n", " \n", " lC = sqrt((S*dPy)**2 + (S*dPx)**2)\n", " #node(Phx,Phy, c=\"blue\")\n", " #node(Cx,Cy, c=\"red\")\n", " R = sqrt((Cx-n1.x)**2 + (Cy-n1.y)**2)\n", " \n", " lPC0 = sqrt(((Cx+R)-n1.x)**2 + (Cy - n1.y)**2)\n", " lPC1 = sqrt(((Cx+R)-n2.x)**2 + (Cy - n2.y)**2)\n", " \n", " dalpha = arccos((2*R**2 - lP**2)/(2.0*R**2))\n", " \n", " \n", " CPx = n1.x - Cx\n", " CPy = n1.y - Cy\n", " X,Y = 0,0\n", " if order == \"0\":\n", " X = [n1.x, n2.x]\n", " Y = [n1.y, n2.y]\n", " if order == \"l0\":\n", " if S<0:\n", " dalpha = 2*pi - dalpha\n", " A = linspace(0,dalpha, N)\n", " X,Y = rotate_v(CPx,CPy,A)\n", " X+=Cx\n", " Y+=Cy\n", " if order == \"r0\":\n", " if S>0:\n", " dalpha = 2*pi - dalpha \n", " A = linspace(0,-dalpha, N)\n", " X,Y = rotate_v(CPx,CPy,A)\n", " X+=Cx\n", " Y+=Cy\n", " msize = 10\n", " if p_h == 1:\n", " draw_arrow([X[len(X)/2],Y[len(X)/2]], [-dPx,-dPy])\n", " #X[len(X)/2],Y[len(X)/2]\n", " #plot(X[len(X)/2],Y[len(X)/2], \"^\", color = \"black\", markersize = msize)\n", " if p_h == -1:\n", " draw_arrow([X[len(X)/2],Y[len(X)/2]], [-dPx,-dPy])\n", " #plot(X[len(X)/2],Y[len(X)/2], \"v\", color = \"black\", markersize = msize)\n", " plot(X,Y, color = \"black\")\n", " \n", " def ncon(self,n1,n2,order = 0, p_h = None):\n", " if order == 0:\n", " nconnect(n1,n2,1,\"0\", p_h)\n", " if order > 0:\n", " nconnect(n1,n2,(-2+order),\"l0\", p_h)\n", " if order < 0:\n", " nconnect(n1,n2,(-2-order),\"r0\", p_h)\n", " \n", " \n", "def rotate_v(x,y,alpha):\n", " ca = cos(alpha)\n", " sa = sin(alpha)\n", " return ca*x - sa*y, sa*x + ca*y\n", " \n", "\n", "class vxnode():\n", " def __init__(self, pos, config):\n", " #config = [[1,None],[1,None],[1,None],[1,None]]\n", " self.pos = pos\n", " self.hole_up = 0 #config[0] #Outgoing Q-particle creation operators\n", " self.part_up = 0 #config[1] #Outgoing Q-particle creation operators\n", " self.hole_down = 0#config[2] #Outgoing Q-particle creation operators\n", " self.part_down = 0#config[3] #Outgoing Q-particle creation operators\n", " self.config = config\n", " self.subline = False\n", " \n", " self.Opconnect = []\n", " self.Tconnect = []\n", " self.vconnect_h = []\n", " self.c = \"black\"\n", " def opconnect(self, pos):\n", " #connect horizontal to another vxnode\n", " self.Opconnect.append(pos)\n", " def tconnect(self, pos):\n", " #connect horizontal to another vxnode\n", " self.Tconnect.append(pos) \n", " def ttype(self):\n", " #Draw a solid, horizontal line through the operator\n", " self.subline = True\n", " def draw(self, pos2 = None):\n", " if pos2 != None:\n", " self.pos[0] += pos2[0]\n", " self.pos[1] += pos2[1] \n", " \n", " msize = 10\n", " c= self.c\n", " hold('on')\n", " sx = .4\n", " sy = 2.0\n", " #if len(self.Tconnect) != 0:\n", " #sy *= 1.5\n", " #sx *= 1.3\n", " config = self.config\n", " plot(self.pos[0],self.pos[1], \".\", color = c,markersize = 10)\n", " if config[0][0] == 1:\n", " if config[0][1] == None:\n", " #Draw straight line hole up\n", " plot([self.pos[0], self.pos[0]-sx],[self.pos[1], self.pos[1]+sy],color = c)\n", "\n", " #print \"DRAWING ARROW\"\n", " draw_arrow([(self.pos[0] + self.pos[0]-sx)/2.0,(self.pos[1] + self.pos[1]+sy)/2.0], [sx,-sy])\n", " \n", " #plot((self.pos[0] + self.pos[0]-sx)/2.0,(self.pos[1] + self.pos[1]+sy)/2.0,\"v\", color = c, markersize = msize) \n", " if config[0][1] != None:\n", " #connect to node config[0][1] in hole up manner\n", " order = -1\n", " #if config[1][1] != None:\n", " # order = 0\n", " self.ncon(node(self.pos),node(config[0][1]), order, -1)\n", " \n", " if config[1][0] == 1:\n", " if config[1][1] == None:\n", " #Draw straight line particle up\n", " plot([self.pos[0], self.pos[0]+sx],[self.pos[1], self.pos[1]+sy],color = c)\n", " \n", " #print \"DRAWING ARROW\"\n", " draw_arrow([(self.pos[0] + self.pos[0]+sx)/2.0,(self.pos[1] + self.pos[1]+sy)/2.0], [sx,sy])\n", " #plot((self.pos[0] + self.pos[0]+sx)/2.0,(self.pos[1] + self.pos[1]+sy)/2.0,\"^\", color = c, markersize = msize) \n", " if config[1][1] != None:\n", " #connect to node config[0][1] in particle up manner\n", " order = -1\n", " #if config[0][1] != None:\n", " # order = 0\n", " self.ncon(node(config[1][1]),node(self.pos),order,1)\n", " \n", " if config[2][0] == 1:\n", " if config[2][1] == None:\n", " #Draw straight line hole down\n", " plot([self.pos[0], self.pos[0]-sx],[self.pos[1], self.pos[1]-sy],color = c)\n", " \n", " #print \"DRAWING ARROW\"\n", " draw_arrow([(self.pos[0] + self.pos[0]-sx)/2.0,(self.pos[1] + self.pos[1]-sy)/2.0], [-sx,-sy])\n", " #plot((self.pos[0] + self.pos[0]-sx)/2.0,(self.pos[1] + self.pos[1]-sy)/2.0,\"v\", color = c, markersize = msize) \n", " if config[2][1] != None:\n", " #connect to node config[0][1] in hole down manner\n", " #print \"Active\"\n", " order = -1\n", " #if config[3][1] != None:\n", " # order = 0\n", " self.ncon(node(config[2][1]), node(self.pos),order, -1)\n", " \n", " if config[3][0] == 1:\n", " if config[3][1] == None:\n", " plot([self.pos[0], self.pos[0]+sx],[self.pos[1], self.pos[1]-sy],color = c)\n", " \n", " #print \"DRAWING ARROW\"\n", " draw_arrow([(self.pos[0] + self.pos[0]+sx)/2.0,(self.pos[1] + self.pos[1]-sy)/2.0], [-sx,sy])\n", " #plot((self.pos[0] + self.pos[0]+sx)/2.0,(self.pos[1] + self.pos[1]-sy)/2.0,\"^\", color = c, markersize = msize)\n", " #Draw straight line particle down\n", "\n", " if config[3][1] != None:\n", " #connect to node config[0][1] in particle down manner\n", " order = -1\n", " #if config[2][1] != None:\n", " # order = 0\n", " self.ncon(node(self.pos), node(config[3][1]), order, 1)\n", " for i in range(len(self.Opconnect)):\n", " plot([self.pos[0],self.Opconnect[i][0]],[self.pos[1],self.Opconnect[i][1]], ls = \"dotted\", color = c)\n", " \n", " for i in range(len(self.Tconnect)):\n", " plot([self.pos[0],self.Tconnect[i][0]],[self.pos[1],self.Tconnect[i][1]], color = c)\n", " \n", " if self.subline:\n", " plot([self.pos[0]-sx, self.pos[0]+sx], [self.pos[1], self.pos[1]], color = c) \n", " def ncon(self,n1,n2,order = 0, p_h = None):\n", " if order == 0:\n", " self.nconnect(n1,n2,1,\"0\", p_h)\n", " if order > 0:\n", " self.nconnect(n1,n2,(-2+order),\"l0\", p_h)\n", " if order < 0:\n", " self.nconnect(n1,n2,(-2-order),\"r0\", p_h)\n", " def nconnect(self,n1,n2,S,order=\"l0\", p_h = None):\n", " N = 60\n", " \n", " if n1.x==n2.x and n1.y == n2.y:\n", " Cx = n1.x + S\n", " Cy = n1.y\n", " \n", " X = Cx + S*cos(linspace(0,2*pi,N))\n", " Y = Cy + S*sin(linspace(0,2*pi,N))\n", " #S = -1\n", " else:\n", " Phx = (n1.x+n2.x)/2.0\n", " Phy = (n1.y+n2.y)/2.0\n", " \n", " lP = sqrt((n2.x-n1.x)**2 + (n2.y-n1.y)**2)\n", " dPx = (n2.x-n1.x)/lP \n", " dPy = (n2.y-n1.y)/lP\n", " \n", " Cx = Phx - S*dPy\n", " Cy = Phy + S*dPx \n", " \n", " \n", " lC = sqrt((S*dPy)**2 + (S*dPx)**2)\n", " #node(Phx,Phy, c=\"blue\")\n", " #node(Cx,Cy, c=\"red\")\n", " R = sqrt((Cx-n1.x)**2 + (Cy-n1.y)**2)\n", " \n", " lPC0 = sqrt(((Cx+R)-n1.x)**2 + (Cy - n1.y)**2)\n", " lPC1 = sqrt(((Cx+R)-n2.x)**2 + (Cy - n2.y)**2)\n", " \n", " dalpha = arccos((2*R**2 - lP**2)/(2.0*R**2))\n", " \n", " \n", " CPx = n1.x - Cx\n", " CPy = n1.y - Cy\n", " X,Y = 0,0\n", " if order == \"0\":\n", " X = [n1.x, n2.x]\n", " Y = [n1.y, n2.y]\n", " if order == \"l0\":\n", " if S<0:\n", " dalpha = 2*pi - dalpha\n", " A = linspace(0,dalpha, N)\n", " X,Y = rotate_v(CPx,CPy,A)\n", " X+=Cx\n", " Y+=Cy\n", " if order == \"r0\":\n", " if S>0:\n", " dalpha = 2*pi - dalpha \n", " A = linspace(0,-dalpha, N)\n", " X,Y = rotate_v(CPx,CPy,A)\n", " X+=Cx\n", " Y+=Cy\n", " msize = 10\n", " if p_h == 1:\n", " draw_arrow([X[len(X)/2],Y[len(X)/2]], [-dPx,-dPy])\n", " #X[len(X)/2],Y[len(X)/2]\n", " #plot(X[len(X)/2],Y[len(X)/2], \"^\", color = \"black\", markersize = msize)\n", " if p_h == -1:\n", " draw_arrow([X[len(X)/2],Y[len(X)/2]], [-dPx,-dPy])\n", " #plot(X[len(X)/2],Y[len(X)/2], \"v\", color = \"black\", markersize = msize)\n", " plot(X,Y, color = \"black\")\n", "\n", "def draw_arrow(pos, point, s = .2, h = .1):\n", " #Draw an arrow at pos, pointing in the direction of point.\n", " #normalize direction\n", " p2 = sqrt(point[0]**2 + point[1]**2)\n", " point[0] /= p2\n", " point[1] /= p2\n", " \n", " #pi/2 degree rotation\n", " p_rotx, p_roty = point[1], -point[0]\n", "\n", " x0, y0 = pos[0], pos[1]\n", " x1, y1 = pos[0] - s*point[0], pos[1] - s*point[1]\n", " \n", " #plot the arrow\n", " plot([x0, x1+h*p_rotx],[y0, y1+h*p_roty], color = \"black\")\n", " plot([x0, x1-h*p_rotx],[y0, y1-h*p_roty], color = \"black\")\n", "\n", "class node():\n", " def __init__(self, V, c= \"black\"):\n", " self.x = V[0]\n", " self.y = V[1]\n", " #plot(x,y,\".\", color = c,markersize = 15)\n", " \n", "def normal_ordered_hamiltonian():\n", " F1 = Operator([1],[1])\n", " F2 = Operator([-1],[-1])\n", " F3 = Operator([1,-1],[])\n", " F4 = Operator([],[1,-1])\n", " \n", " V1 = Operator([1,1],[1,1])\n", " V2 = Operator([-1,-1],[-1,-1])\n", " V3 = Operator([1,-1],[1,-1])\n", " \n", " V4 = Operator([1,1,-1],[1])\n", " V5 = Operator([1],[1,1,-1])\n", " \n", " V6 = Operator([1,-1,-1],[-1])\n", " V7 = Operator([-1],[1,-1,-1])\n", " V8 = Operator([1,1,-1,-1],[])\n", " V9 = Operator([],[1,1,-1,-1])\n", " return [F1,F2,F3,F4,V1,V2,V3,V4,V5,V6,V7,V8,V9]\n", "\n", "def cluster_operator(configuration):\n", " #configuration =[]\n", " T1= Operator([1,-1],[])\n", " T2= Operator([1,1,-1,-1],[])\n", " T3= Operator([1,1,1,-1,-1,-1],[])\n", " T4= Operator([1,1,1,1,-1,-1,-1,-1],[])\n", " T5= Operator([1,1,1,1,1,-1,-1,-1,-1,-1],[])\n", " T_list = [T1, T2, T3, T4, T5]\n", " ret = []\n", " for i in configuration:\n", " ret.append(T_list[i-1])\n", " return ret\n", "\n", "def generate_all_combinations(H,T, excitation_level = None, print_alg = 0, print_diag = 0):\n", " for i in H:\n", " i.combine(T)\n", " i.assess_contributions(excitation_level)\n", " if print_alg:\n", " i.printout()\n", " if print_diag:\n", " i.plot_diagrams()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell below shows how to quickly assemble a list containing all normal-ordered parts of the hamiltonian. (line 1) The list contains a total of 13 elements, where the first 4 are the one particle term and the following 9 are the two-particle interaction.\n", "\n", "In the second line a cluster operator $\\hat{T}_1^2 \\hat{T}_2$ is created. \n", "\n", "The final line finds all contribution to the CC equations (energy and amplitudes) from the connected terms obtained by combining H and T. Excitation level is only outputted to the screen, but could potentially be implemented in such a way that only preferred exitations are displayed. (If your aim is to find contributions only to the energy or certain amplitudes)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "H = normal_ordered_hamiltonian()\n", "T = cluster_operator([1,1,2])\n", "generate_all_combinations(H,T, excitation_level = 2, print_alg = 1, print_diag = 0)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "generate_all_combinations() got an unexpected keyword argument 'print_alg'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-48-2297e94bbeb3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormal_ordered_hamiltonian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcluster_operator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mgenerate_all_combinations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexcitation_level\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprint_alg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprint_diag\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: generate_all_combinations() got an unexpected keyword argument 'print_alg'" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "All operators and combinations of operators may be represented visually as diagrams" ] }, { "cell_type": "code", "collapsed": false, "input": [ "T = cluster_operator([2,2])\n", "V9 = Operator([],[1,1,-1,-1])\n", "V9.combine(T) \n", "V9.assess_contributions()\n", "V9.plot_diagrams()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4AqwQhCPACkE4AqwQhGMg9+/fx6effqpybN++fSZbwOj69eusd8ZTZ9OmTUZtn62MIBwD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\nPZqIjo6m48ePcx6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"text": [ "<matplotlib.figure.Figure at 0x2ba2310>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example below gives a better picture of the inner workings of the algorithm. Any operator is called with two sets of particle and hole definitions. (Particles are 1, holes are -1) The first set relates to lines above the interaction (created virtual particles) and the second to lines below the interaction. As such, the two operators below represents the contribution to the energy arising from combining the two q-particle annihilation filament of the hamiltonian with the $\\hat{T}_2$ cluster operator. As expected, the final excitation is 0." ] }, { "cell_type": "code", "collapsed": false, "input": [ "T2 = Operator([1,1,-1,-1],[])\n", "V9 = Operator([],[1,1,-1,-1])\n", "V9.combine([T2]) #input operator must be a list, as a product of cluster operators may potentially be involved\n", "V9.assess_contributions()\n", "V9.printout()\n", "V9.plot_diagrams()" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$ \\frac{1}{4} \\sum_{aibj} \\langle ij|H|ab \\rangle t_{ij}^{ab} (excitation:0) $$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Math at 0x3404990>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x340fa10>" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, to complete the investigation above, the two other contributions to the CC-energy is confirmed to have excitation level 0 in the example below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "T1= Operator([1,-1],[])\n", "T2= Operator([1,1,-1,-1],[])\n", "F4 = Operator([],[1,-1]) \n", "V9 = Operator([],[1,1,-1,-1])\n", "\n", "V9.combine([T1,T1]) #input operator must be a list, as a product of cluster operators may potentially be involved\n", "V9.assess_contributions()\n", "V9.printout()\n", "V9.plot_diagrams()\n", "\n", "F4.combine([T1])\n", "F4.assess_contributions()\n", "F4.printout()\n", "F4.plot_diagrams()\n" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$ \\frac{1}{2} \\sum_{aibj} \\langle ij|H|ab \\rangle t_{i}^{a} t_{j}^{b} (excitation:0) $$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Math at 0x2e2c610>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2bf06d0>" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part II: Solving the CCD amplitude equation using CCAlgebra\n", "\n", "Here, we demonstrate how to calculate the CCD contributions to the T2 amplitude using CCAlgebra. \n", "\n", "We first initialize the normal-ordered hamiltonian, and proceed by generating all combinations between the operators:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "H = normal_ordered_hamiltonian()\n", "T = cluster_operator([2])\n", "generate_all_combinations(H,T, excitation_level = 1, print_alg = 1, print_diag = 1)\n", "#Too much spacing beneath" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$ \\frac{1}{1} \\sum_{ai} \\langle i|H|a \\rangle t_{ij}^{ab} (excitation:1) $$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Math at 0x3a2d450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x389f710>" ] }, { "latex": [ "$$ \\frac{1}{2} \\sum_{aij} \\langle ij|H|aj \\rangle t_{ij}^{ab} (excitation:1) $$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Math at 0x2bd0890>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAALYAAABZCAYAAACT4t5TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAQhJREFUeJzt0kENACAQwDDAv+dDBSFZWgV7bM/MLIg5vwPgBWOTZGyS\njE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmb\nJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknG\nJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2S\nsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJF0eZQSu\nRf4zdgAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x34077d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2bd0fd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAALYAAABZCAYAAACT4t5TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAQhJREFUeJzt0kENACAQwDDAv+dDBSFZWgV7bM/MLIg5vwPgBWOTZGyS\njE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmb\nJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknG\nJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2S\nsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJF0eZQSu\nRf4zdgAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x2e15bd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAALYAAABZCAYAAACT4t5TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAQhJREFUeJzt0kENACAQwDDAv+dDBSFZWgV7bM/MLIg5vwPgBWOTZGyS\njE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmb\nJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknG\nJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2S\nsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJBmbJGOTZGySjE2SsUkyNknGJsnYJF0eZQSu\nRf4zdgAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x2aa4a10>" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "import IPython\n", "print IPython.sys_info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'codename': 'An Afternoon Hack',\n", " 'commit_hash': '02da31c',\n", " 'commit_source': 'installation',\n", " 'default_encoding': 'UTF-8',\n", " 'ipython_path': '/Users/kinealicegulbrandsen/anaconda/python.app/Contents/lib/python2.7/site-packages/IPython',\n", " 'ipython_version': '1.0.0',\n", " 'os_name': 'posix',\n", " 'platform': 'Darwin-13.3.0-x86_64-i386-64bit',\n", " 'sys_executable': '/Users/kinealicegulbrandsen/anaconda/python.app/Contents/MacOS/python',\n", " 'sys_platform': 'darwin',\n", " 'sys_version': '2.7.5 |Anaconda 1.7.0 (x86_64)| (default, Jun 28 2013, 22:20:13) \\n[GCC 4.0.1 (Apple Inc. build 5493)]'}\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List of bugs / unphysical combinations\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "T2 = Operator([1,1,-1,-1],[])\n", "V9 = Operator([],[1,-1])\n", "V9.combine([T2]) #input operator must be a list, as a product of cluster operators may potentially be involved\n", "V9.assess_contributions()\n", "V9.printout()\n", "V9.plot_diagrams()" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$ \\frac{1}{1} \\sum_{ai} \\langle i|H|a \\rangle t_{ij}^{ab} (excitation:1) $$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Math at 0x3f3a510>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x3f3a3d0>" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "T = cluster_operator([2,2,2])\n", "V9 = Operator([],[1,1,-1,-1])\n", "V9.combine(T) \n", "V9.assess_contributions()\n", "V9.plot_diagrams()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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y9PSkkydPkomJCb148YKEQiGlp6fT559/Tg4ODgSADhw40GxmWRnl5eXk4+ND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\nwbVr18Dn8zF8+HDExcVpchrA5XLF9id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"text": [ "<matplotlib.figure.Figure at 0x2e1a150>" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
cc0-1.0
IIPBC/Material
Notebooks/Python BootCamp 2017 - Display Data.ipynb
1
1209846
null
mit
wehlutyk/brainscopypaste
data/notebooks/Model(time=Time.discrete, source=Source.majority, past=Past.all, durl=Durl.exclude_past, max_distance=2) - susceptibility.ipynb
1
1415114
null
gpl-3.0
ES-DOC/esdoc-jupyterhub
notebooks/test-institute-1/cmip6/models/sandbox-3/toplevel.ipynb
1
112371
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Toplevel \n", "**MIP Era**: CMIP6 \n", "**Institute**: TEST-INSTITUTE-1 \n", "**Source ID**: SANDBOX-3 \n", "**Sub-Topics**: Radiative Forcings. \n", "**Properties**: 85 (42 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/toplevel?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 16:54:43" ], "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', 'test-institute-1', 'sandbox-3', 'toplevel')" ], "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; Flux Correction](#2.-Key-Properties---&gt;-Flux-Correction) \n", "[3. Key Properties --&gt; Genealogy](#3.-Key-Properties---&gt;-Genealogy) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Key Properties --&gt; Coupling](#5.-Key-Properties---&gt;-Coupling) \n", "[6. Key Properties --&gt; Tuning Applied](#6.-Key-Properties---&gt;-Tuning-Applied) \n", "[7. Key Properties --&gt; Conservation --&gt; Heat](#7.-Key-Properties---&gt;-Conservation---&gt;-Heat) \n", "[8. Key Properties --&gt; Conservation --&gt; Fresh Water](#8.-Key-Properties---&gt;-Conservation---&gt;-Fresh-Water) \n", "[9. Key Properties --&gt; Conservation --&gt; Salt](#9.-Key-Properties---&gt;-Conservation---&gt;-Salt) \n", "[10. Key Properties --&gt; Conservation --&gt; Momentum](#10.-Key-Properties---&gt;-Conservation---&gt;-Momentum) \n", "[11. Radiative Forcings](#11.-Radiative-Forcings) \n", "[12. Radiative Forcings --&gt; Greenhouse Gases --&gt; CO2](#12.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-CO2) \n", "[13. Radiative Forcings --&gt; Greenhouse Gases --&gt; CH4](#13.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-CH4) \n", "[14. Radiative Forcings --&gt; Greenhouse Gases --&gt; N2O](#14.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-N2O) \n", "[15. Radiative Forcings --&gt; Greenhouse Gases --&gt; Tropospheric O3](#15.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-Tropospheric-O3) \n", "[16. Radiative Forcings --&gt; Greenhouse Gases --&gt; Stratospheric O3](#16.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-Stratospheric-O3) \n", "[17. Radiative Forcings --&gt; Greenhouse Gases --&gt; CFC](#17.-Radiative-Forcings---&gt;-Greenhouse-Gases---&gt;-CFC) \n", "[18. Radiative Forcings --&gt; Aerosols --&gt; SO4](#18.-Radiative-Forcings---&gt;-Aerosols---&gt;-SO4) \n", "[19. Radiative Forcings --&gt; Aerosols --&gt; Black Carbon](#19.-Radiative-Forcings---&gt;-Aerosols---&gt;-Black-Carbon) \n", "[20. Radiative Forcings --&gt; Aerosols --&gt; Organic Carbon](#20.-Radiative-Forcings---&gt;-Aerosols---&gt;-Organic-Carbon) \n", "[21. Radiative Forcings --&gt; Aerosols --&gt; Nitrate](#21.-Radiative-Forcings---&gt;-Aerosols---&gt;-Nitrate) \n", "[22. Radiative Forcings --&gt; Aerosols --&gt; Cloud Albedo Effect](#22.-Radiative-Forcings---&gt;-Aerosols---&gt;-Cloud-Albedo-Effect) \n", "[23. Radiative Forcings --&gt; Aerosols --&gt; Cloud Lifetime Effect](#23.-Radiative-Forcings---&gt;-Aerosols---&gt;-Cloud-Lifetime-Effect) \n", "[24. Radiative Forcings --&gt; Aerosols --&gt; Dust](#24.-Radiative-Forcings---&gt;-Aerosols---&gt;-Dust) \n", "[25. Radiative Forcings --&gt; Aerosols --&gt; Tropospheric Volcanic](#25.-Radiative-Forcings---&gt;-Aerosols---&gt;-Tropospheric-Volcanic) \n", "[26. Radiative Forcings --&gt; Aerosols --&gt; Stratospheric Volcanic](#26.-Radiative-Forcings---&gt;-Aerosols---&gt;-Stratospheric-Volcanic) \n", "[27. Radiative Forcings --&gt; Aerosols --&gt; Sea Salt](#27.-Radiative-Forcings---&gt;-Aerosols---&gt;-Sea-Salt) \n", "[28. Radiative Forcings --&gt; Other --&gt; Land Use](#28.-Radiative-Forcings---&gt;-Other---&gt;-Land-Use) \n", "[29. Radiative Forcings --&gt; Other --&gt; Solar](#29.-Radiative-Forcings---&gt;-Other---&gt;-Solar) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the 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", "### *Top level overview of coupled model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.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 coupled model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.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": [ "# 2. Key Properties --&gt; Flux Correction \n", "*Flux correction properties of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Details\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how flux corrections are applied in the model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.flux_correction.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": [ "# 3. Key Properties --&gt; Genealogy \n", "*Genealogy and history of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Year Released\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Year the model was released*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.genealogy.year_released') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. CMIP3 Parent\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *CMIP3 parent if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP3_parent') \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. CMIP5 Parent\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *CMIP5 parent if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP5_parent') \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. Previous Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Previously known as*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.genealogy.previous_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. Key Properties --&gt; Software Properties \n", "*Software properties of model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. 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.toplevel.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&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.toplevel.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&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.toplevel.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": [ "### 4.4. Components Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how model realms are structured into independent software components (coupled via a coupler) and internal software components.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.software_properties.components_structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.5. Coupler\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Overarching coupling framework for model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.software_properties.coupler') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OASIS\" \n", "# \"OASIS3-MCT\" \n", "# \"ESMF\" \n", "# \"NUOPC\" \n", "# \"Bespoke\" \n", "# \"Unknown\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Coupling \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of coupling in the model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.coupling.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": [ "### 5.2. Atmosphere Double Flux\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the atmosphere passing a double flux to the ocean and sea ice (as opposed to a single one)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_double_flux') \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": [ "### 5.3. Atmosphere Fluxes Calculation Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Where are the air-sea fluxes calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_fluxes_calculation_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Atmosphere grid\" \n", "# \"Ocean grid\" \n", "# \"Specific coupler grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Atmosphere Relative Winds\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are relative or absolute winds used to compute the flux? I.e. do ocean surface currents enter the wind stress calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_relative_winds') \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 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/diagnostics retained. Document the relative weight given to climate performance metrics/diagnostics versus process oriented metrics/diagnostics, 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.toplevel.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/diagnostics of the global mean state used in tuning model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.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/diagnostics of mean state (e.g THC, AABW, regional means etc) 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.toplevel.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/diagnostics used in tuning model/component (such as 20th century)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Energy Balance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how energy balance was obtained in the full system: in the various components independently or at the components coupling stage?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.energy_balance') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Fresh Water Balance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how fresh_water balance was obtained in the full system: in the various components independently or at the components coupling stage?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.fresh_water_balance') \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. Key Properties --&gt; Conservation --&gt; Heat \n", "*Global heat convervation properties of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Global\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how heat is conserved globally*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.global') \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. Atmos Ocean Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how heat is conserved at the atmosphere/ocean coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_ocean_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Atmos Land Interface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how heat is conserved at the atmosphere/land coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_land_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Atmos Sea-ice Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how heat is conserved at the atmosphere/sea-ice coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_sea-ice_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.5. Ocean Seaice Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how heat is conserved at the ocean/sea-ice coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.ocean_seaice_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.6. Land Ocean Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how heat is conserved at the land/ocean coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.land_ocean_interface') \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. Key Properties --&gt; Conservation --&gt; Fresh Water \n", "*Global fresh water convervation properties of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Global\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how fresh_water is conserved globally*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.global') \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. Atmos Ocean Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how fresh_water is conserved at the atmosphere/ocean coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_ocean_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Atmos Land Interface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how fresh water is conserved at the atmosphere/land coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_land_interface') \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. Atmos Sea-ice Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how fresh water is conserved at the atmosphere/sea-ice coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_sea-ice_interface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Ocean Seaice Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how fresh water is conserved at the ocean/sea-ice coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.ocean_seaice_interface') \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. Runoff\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how runoff is distributed and conserved*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.runoff') \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. Iceberg Calving\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how iceberg calving is modeled and conserved*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.iceberg_calving') \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. Endoreic Basins\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how endoreic basins (no ocean access) are treated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.endoreic_basins') \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. Snow Accumulation\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how snow accumulation over land and over sea-ice is treated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.snow_accumulation') \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. Key Properties --&gt; Conservation --&gt; Salt \n", "*Global salt convervation properties of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Ocean Seaice Interface\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how salt is conserved at the ocean/sea-ice coupling interface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.salt.ocean_seaice_interface') \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. Key Properties --&gt; Conservation --&gt; Momentum \n", "*Global momentum convervation properties of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how momentum is conserved in the model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.key_properties.conservation.momentum.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. Radiative Forcings \n", "*Radiative forcings of the model for historical and scenario (aka Table 12.1 IPCC AR5)*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of radiative forcings (GHG and aerosols) implementation in model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.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": [ "# 12. Radiative Forcings --&gt; Greenhouse Gases --&gt; CO2 \n", "*Carbon dioxide forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.additional_information') \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. Radiative Forcings --&gt; Greenhouse Gases --&gt; CH4 \n", "*Methane forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.additional_information') \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. Radiative Forcings --&gt; Greenhouse Gases --&gt; N2O \n", "*Nitrous oxide forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.additional_information') \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. Radiative Forcings --&gt; Greenhouse Gases --&gt; Tropospheric O3 \n", "*Troposheric ozone forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.additional_information') \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. Radiative Forcings --&gt; Greenhouse Gases --&gt; Stratospheric O3 \n", "*Stratospheric ozone forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.additional_information') \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. Radiative Forcings --&gt; Greenhouse Gases --&gt; CFC \n", "*Ozone-depleting and non-ozone-depleting fluorinated gases forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Equivalence Concentration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Details of any equivalence concentrations used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.equivalence_concentration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"Option 1\" \n", "# \"Option 2\" \n", "# \"Option 3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.additional_information') \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. Radiative Forcings --&gt; Aerosols --&gt; SO4 \n", "*SO4 aerosol forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.additional_information') \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. Radiative Forcings --&gt; Aerosols --&gt; Black Carbon \n", "*Black carbon aerosol forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Radiative Forcings --&gt; Aerosols --&gt; Organic Carbon \n", "*Organic carbon aerosol forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.additional_information') \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. Radiative Forcings --&gt; Aerosols --&gt; Nitrate \n", "*Nitrate forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.additional_information') \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. Radiative Forcings --&gt; Aerosols --&gt; Cloud Albedo Effect \n", "*Cloud albedo effect forcing (RFaci)*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Aerosol Effect On Ice Clouds\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Radiative effects of aerosols on ice clouds are represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.aerosol_effect_on_ice_clouds') \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": [ "### 22.3. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Forcings --&gt; Aerosols --&gt; Cloud Lifetime Effect \n", "*Cloud lifetime effect forcing (ERFaci)*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Aerosol Effect On Ice Clouds\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Radiative effects of aerosols on ice clouds are represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.aerosol_effect_on_ice_clouds') \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": [ "### 23.3. RFaci From Sulfate Only\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Radiative forcing from aerosol cloud interactions from sulfate aerosol only?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.RFaci_from_sulfate_only') \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": [ "### 23.4. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Radiative Forcings --&gt; Aerosols --&gt; Dust \n", "*Dust forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Radiative Forcings --&gt; Aerosols --&gt; Tropospheric Volcanic \n", "*Tropospheric volcanic forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Historical Explosive Volcanic Aerosol Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How explosive volcanic aerosol is implemented in historical simulations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Type A\" \n", "# \"Type B\" \n", "# \"Type C\" \n", "# \"Type D\" \n", "# \"Type E\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Future Explosive Volcanic Aerosol Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How explosive volcanic aerosol is implemented in future simulations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.future_explosive_volcanic_aerosol_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Type A\" \n", "# \"Type B\" \n", "# \"Type C\" \n", "# \"Type D\" \n", "# \"Type E\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.4. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Radiative Forcings --&gt; Aerosols --&gt; Stratospheric Volcanic \n", "*Stratospheric volcanic forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Historical Explosive Volcanic Aerosol Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How explosive volcanic aerosol is implemented in historical simulations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Type A\" \n", "# \"Type B\" \n", "# \"Type C\" \n", "# \"Type D\" \n", "# \"Type E\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Future Explosive Volcanic Aerosol Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How explosive volcanic aerosol is implemented in future simulations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.future_explosive_volcanic_aerosol_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Type A\" \n", "# \"Type B\" \n", "# \"Type C\" \n", "# \"Type D\" \n", "# \"Type E\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Radiative Forcings --&gt; Aerosols --&gt; Sea Salt \n", "*Sea salt forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Radiative Forcings --&gt; Other --&gt; Land Use \n", "*Land use forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"M\" \n", "# \"Y\" \n", "# \"E\" \n", "# \"ES\" \n", "# \"C\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Crop Change Only\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Land use change represented via crop change only?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.crop_change_only') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Radiative Forcings --&gt; Other --&gt; Solar \n", "*Solar forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Provision\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *How solar forcing is provided*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.provision') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"N/A\" \n", "# \"irradiance\" \n", "# \"proton\" \n", "# \"electron\" \n", "# \"cosmic ray\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Additional Information\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.additional_information') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
LSSTC-DSFP/LSSTC-DSFP-Sessions
Sessions/Session10/Day0/TooBriefVisualization.ipynb
1
31242
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction to Visualization:\n", "Density Estimation and Data Exploration\n", "========\n", "\n", "##### Version 0.1\n", "\n", "There are many flavors of data analysis that fall under the \"visualization\" umbrella in astronomy. Today, by way of example, we will focus on 2 basic problems.\n", "\n", "***\n", "By AA Miller \n", "16 September 2017\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem 1) Density Estimation\n", "\n", "Starting with 2MASS and SDSS and extending through LSST, we are firmly in an era where data and large statistical samples are cheap. With this explosion in data volume comes a problem: we do not know the underlying probability density function (PDF) of the random variables measured via our observations. Hence - density estimation: an attempt to recover the unknown PDF from observations. In some cases theory can guide us to a parametric form for the PDF, but more often than not such guidance is not available. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "There is a common, simple, and very familiar tool for density estimation: histograms. \n", "\n", "But there is also a problem:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "HISTOGRAMS LIE!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We will \"prove\" this to be the case in a series of examples. For this exercise, we will load the famous Linnerud data set, which tested 20 middle aged men by measuring the number of chinups, situps, and jumps they could do in order to compare these numbers to their weight, pulse, and waist size. To load the data (just chinups for now) we will run the following:\n", "\n", " from sklearn.datasets import load_linnerud\n", " linnerud = load_linnerud()\n", " chinups = linnerud.data[:,0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "from sklearn.datasets import load_linnerud\n", "\n", "linnerud = load_linnerud()\n", "chinups = linnerud.data[:,0]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Problem 1a** \n", "\n", "Plot the histogram for the number of chinups using the default settings in pyplot." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "plt.hist( # complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Already with this simple plot we see a problem - the choice of bin centers and number of bins suggest that there is a 0% probability that middle aged men can do 10 chinups. Intuitively this seems incorrect, so lets examine how the histogram changes if we change the number of bins or the bin centers.\n", "\n", "**Problem 1b** \n", "\n", "Using the same data make 2 new histograms: (i) one with 5 bins (`bins = 5`), and (ii) one with the bars centered on the left bin edges (`align = \"left\"`).\n", "\n", "*Hint - if overplotting the results, you may find it helpful to use the `histtype = \"step\"` option*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "plt.hist( # complete\n", "# complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "These small changes significantly change the output PDF. With fewer bins we get something closer to a continuous distribution, while shifting the bin centers reduces the probability to zero at 9 chinups. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "What if we instead allow the bin width to vary and require the same number of points in each bin? You can determine the bin edges for bins with 5 sources using the following command:\n", "\n", " bins = np.append(np.sort(chinups)[::5], np.max(chinups))\n", "\n", "**Problem 1c** \n", "\n", "Plot a histogram with variable width bins, each with the same number of points.\n", "\n", "*Hint - setting `normed = True` will normalize the bin heights so that the PDF integrates to 1.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# complete\n", "plt.hist(# complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "*Ending the lie* \n", "\n", "Earlier I stated that histograms lie. One simple way to combat this lie: show all the data. Displaying the original data points allows viewers to somewhat intuit the effects of the particular bin choices that have been made (though this can also be cumbersome for very large data sets, which these days is essentially all data sets). The standard for showing individual observations relative to a histogram is a \"rug plot,\" which shows a vertical tick (or other symbol) at the location of each source used to estimate the PDF.\n", "\n", "**Problem 1d** Execute the cell below to see an example of a rug plot. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "plt.hist(chinups, histtype = 'step')\n", "\n", "# this is the code for the rug plot\n", "plt.plot(chinups, np.zeros_like(chinups), '|', color='k', ms = 25, mew = 4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Of course, even rug plots are not a perfect solution. Many of the chinup measurements are repeated, and those instances cannot be easily isolated above. One (slightly) better solution is to vary the transparency of the rug \"whiskers\" using `alpha = 0.3` in the whiskers plot call. But this too is far from perfect. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To recap, histograms are not ideal for density estimation for the following reasons: \n", "\n", "* They introduce discontinuities that are not present in the data\n", "* They are strongly sensitive to user choices ($N_\\mathrm{bins}$, bin centering, bin grouping), without any mathematical guidance to what these choices should be\n", "* They are difficult to visualize in higher dimensions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Histograms are useful for generating a quick representation of univariate data, but for the reasons listed above they should never be used for analysis. Most especially, functions should not be fit to histograms given how greatly the number of bins and bin centering affects the output histogram." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Okay - so if we are going to rail on histograms this much, there must be a better option. There is: [Kernel Density Estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) (KDE), a nonparametric form of density estimation whereby a normalized kernel function is convolved with the discrete data to obtain a continuous estimate of the underlying PDF. As a rule, the kernel must integrate to 1 over the interval $-\\infty$ to $\\infty$ and be symmetric. There are many possible kernels (gaussian is highly popular, though Epanechnikov, an inverted parabola, produces the minimal mean square error). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "KDE is not completely free of the problems we illustrated for histograms above (in particular, both a kernel and the width of the kernel need to be selected), but it does manage to correct a number of the ills. We will now demonstrate this via a few examples using the `scikit-learn` implementation of KDE: [`KernelDensity`](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html#sklearn.neighbors.KernelDensity), which is part of the [`sklearn.neighbors`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors) module. \n", "\n", "*Note* There are many implementations of KDE in Python, and Jake VanderPlas has put together [an excellent description of the strengths and weaknesses of each](https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/). We will use the `scitkit-learn` version as it is in many cases the fastest implementation." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To demonstrate the basic idea behind KDE, we will begin by representing each point in the dataset as a block (i.e. we will adopt the tophat kernel). Borrowing some code from Jake, we can estimate the KDE using the following code:\n", "\n", " from sklearn.neighbors import KernelDensity\n", " def kde_sklearn(data, grid, bandwidth = 1.0, **kwargs):\n", " kde_skl = KernelDensity(bandwidth = bandwidth, **kwargs)\n", " kde_skl.fit(data[:, np.newaxis])\n", " log_pdf = kde_skl.score_samples(grid[:, np.newaxis]) # sklearn returns log(density)\n", " \n", " return np.exp(log_pdf)\n", " \n", "The two main options to set are the bandwidth and the kernel. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# execute this cell\n", "from sklearn.neighbors import KernelDensity\n", "def kde_sklearn(data, grid, bandwidth = 1.0, **kwargs):\n", " kde_skl = KernelDensity(bandwidth = bandwidth, **kwargs)\n", " kde_skl.fit(data[:, np.newaxis])\n", " log_pdf = kde_skl.score_samples(grid[:, np.newaxis]) # sklearn returns log(density)\n", "\n", " return np.exp(log_pdf)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 1e** \n", "\n", "Plot the KDE of the PDF for the number of chinups middle aged men can do using a bandwidth of 0.1 and a tophat kernel.\n", "\n", "*Hint - as a general rule, the grid should be smaller than the bandwidth when plotting the PDF.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "grid = # complete\n", "PDFtophat = kde_sklearn( # complete\n", "plt.plot( # complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In this representation, each \"block\" has a height of 0.25. The bandwidth is too narrow to provide any overlap between the blocks. This choice of kernel and bandwidth produces an estimate that is essentially a histogram with a large number of bins. It gives no sense of continuity for the distribution. Now, we examine the difference (relative to histograms) upon changing the the width (i.e. kernel) of the blocks. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 1f** \n", "\n", "Plot the KDE of the PDF for the number of chinups middle aged men can do using bandwidths of 1 and 5 and a tophat kernel. How do the results differ from the histogram plots above? " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "PDFtophat1 = # complete\n", "\n", "# complete\n", "# complete\n", "# complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "It turns out blocks are not an ideal representation for continuous data (see discussion on histograms above). Now we will explore the resulting PDF from other kernels. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 1g** Plot the KDE of the PDF for the number of chinups middle aged men can do using a gaussian and Epanechnikov kernel. How do the results differ from the histogram plots above? \n", "\n", "*Hint - you will need to select the bandwidth. The examples above should provide insight into the useful range for bandwidth selection. You may need to adjust the values to get an answer you \"like.\"*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "PDFgaussian = # complete\n", "PDFepanechnikov = # complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "So, what is the *optimal* choice of bandwidth and kernel? Unfortunately, there is no hard and fast rule, as every problem will likely have a different optimization. Typically, the choice of bandwidth is far more important than the choice of kernel. In the case where the PDF is likely to be gaussian (or close to gaussian), then [Silverman's rule of thumb](https://en.wikipedia.org/wiki/Kernel_density_estimation#A_rule-of-thumb_bandwidth_estimator) can be used: \n", "\n", "$$h = 1.059 \\sigma n^{-1/5}$$\n", "\n", "where $h$ is the bandwidth, $\\sigma$ is the standard deviation of the samples, and $n$ is the total number of samples. Note - in situations with bimodal or more complicated distributions, this rule of thumb can lead to woefully inaccurate PDF estimates. The most general way to estimate the choice of bandwidth is via cross validation (we will cover cross-validation later today). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "*What about multidimensional PDFs?* It is possible using many of the Python implementations of KDE to estimate multidimensional PDFs, though it is very very important to beware the curse of dimensionality in these circumstances." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem 2) Data Exploration\n", "\n", "Now a more open ended topic: data exploration. In brief, data exploration encompases a large suite of tools (including those discussed above) to examine data that live in large dimensional spaces. There is no single best method or optimal direction for data exploration. Instead, today we will introduce some of the tools available via python. \n", "\n", "As an example we will start with a basic line plot - and examine tools beyond `matplotlib`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "x = np.arange(0, 6*np.pi, 0.1)\n", "y = np.cos(x)\n", "\n", "plt.plot(x,y, lw = 2)\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", "plt.xlim(0, 6*np.pi)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Seaborn\n", "\n", "[`Seaborn`](https://stanford.edu/~mwaskom/software/seaborn/index.html) is a plotting package that enables many useful features for exploration. In fact, a lot of the functionality that we developed above can readily be handled with `seaborn`." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To begin, we will make the same plot that we created in matplotlib. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import seaborn as sns\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", "ax.plot(x,y, lw = 2)\n", "ax.set_xlabel('X')\n", "ax.set_ylabel('Y')\n", "ax.set_xlim(0, 6*np.pi)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "These plots look identical, but it is possible to change the style with `seaborn`. \n", "\n", "`seaborn` has 5 style presets: `darkgrid`, `whitegrid`, `dark`, `white`, and `ticks`. You can change the preset using the following: \n", "\n", " sns.set_style(\"whitegrid\")\n", " \n", "which will change the output for all subsequent plots. Note - if you want to change the style for only a single plot, that can be accomplished with the following: \n", "\n", " with sns.axes_style(\"dark\"):\n", "\n", "with all ploting commands inside the `with` statement. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 3a** \n", "\n", "Re-plot the sine curve using each `seaborn` preset to see which you like best - then adopt this for the remainder of the notebook. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "sns.set_style( # complete\n", "# complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The folks behind `seaborn` have thought a lot about color palettes, which is a good thing. Remember - the choice of color for plots is one of the most essential aspects of visualization. A poor choice of colors can easily mask interesting patterns or suggest structure that is not real. To learn more about what is available, see the [`seaborn` color tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html). \n", "\n", "Here we load the default:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# default color palette\n", "\n", "current_palette = sns.color_palette()\n", "sns.palplot(current_palette)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "which we will now change to `colorblind`, which is clearer to those that are colorblind." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# set palette to colorblind\n", "sns.set_palette(\"colorblind\")\n", "\n", "current_palette = sns.color_palette()\n", "sns.palplot(current_palette)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Now that we have covered the basics of `seaborn` (and the above examples truly only scratch the surface of what is possible), we will explore the power of `seaborn` for higher dimension data sets. We will load the famous Iris data set, which measures 4 different features of 3 different types of Iris flowers. There are 150 different flowers in the data set." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "*Note - for those familiar with `pandas` `seaborn` is designed to integrate easily and directly with `pandas DataFrame` objects. In the example below the Iris data are loaded into a `DataFrame`. `iPython` notebooks also display the `DataFrame` data in a nice readable format.* " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "iris = sns.load_dataset(\"iris\")\n", "iris" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Now that we have a sense of the data structure, it is useful to examine the distribution of features. Above, we went to great pains to produce histograms, KDEs, and rug plots. `seaborn` handles all of that effortlessly with the `distplot` function.\n", "\n", "**Problem 3b** \n", "\n", "Plot the distribution of petal lengths for the Iris data set. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# note - hist, kde, and rug all set to True, set to False to turn them off \n", "with sns.axes_style(\"dark\"):\n", " sns.distplot(iris['petal_length'], bins=20, hist=True, kde=True, rug=True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Of course, this data set lives in a 4D space, so plotting more than univariate distributions is important (and as we will see tomorrow this is particularly useful for visualizing classification results). Fortunately, `seaborn` makes it very easy to produce handy summary plots. \n", "\n", "At this point, we are familiar with basic scatter plots in matplotlib.\n", "\n", "**Problem 3c** \n", "\n", "Make a matplotlib scatter plot showing the Iris petal length against the Iris petal width." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "plt.scatter( # complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Of course, when there are many many data points, scatter plots become difficult to interpret. As in the example below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "with sns.axes_style(\"darkgrid\"):\n", " xexample = np.random.normal(loc = 0.2, scale = 1.1, size = 10000)\n", " yexample = np.random.normal(loc = -0.1, scale = 0.9, size = 10000)\n", "\n", " plt.scatter(xexample, yexample)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Here, we see that there are many points, clustered about the origin, but we have no sense of the underlying density of the distribution. 2D histograms, such as `plt.hist2d()`, can alleviate this problem. I prefer to use `plt.hexbin()` which is a little easier on the eyes (though note - these histograms are just as subject to the same issues discussed above). " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# hexbin w/ bins = \"log\" returns the log of counts/bin\n", "# mincnt = 1 displays only hexpix with at least 1 source present\n", "with sns.axes_style(\"darkgrid\"):\n", " plt.hexbin(xexample, yexample, bins = \"log\", cmap = \"viridis\", mincnt = 1)\n", " plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "While the above plot provides a significant improvement over the scatter plot by providing a better sense of the density near the center of the distribution, the binedge effects are clearly present. An even better solution, like before, is a density estimate, which is easily built into `seaborn` via the `kdeplot` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "with sns.axes_style(\"darkgrid\"):\n", " sns.kdeplot(xexample, yexample,shade=False)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This plot is much more appealing (and informative) than the previous two. For the first time we can clearly see that the distribution is not actually centered on the origin. Now we will move back to the Iris data set. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Suppose we want to see univariate distributions in addition to the scatter plot? This is certainly possible with `matplotlib` and you can find examples on the web, however, with `seaborn` this is really easy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "sns.jointplot(x=iris['petal_length'], y=iris['petal_width'])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "But! Histograms and scatter plots can be problematic as we have discussed many times before. \n", "\n", "**Problem 3d** \n", "\n", "Re-create the plot above but set `kind='kde'` to produce density estimates of the distributions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "sns.jointplot( # complete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "That is much nicer than what was presented above. However - we still have a problem in that our data live in 4D, but we are (mostly) limited to 2D projections of that data. One way around this is via the `seaborn` version of a `pairplot`, which plots the distribution of every variable in the data set against each other. (Here is where the integration with `pandas DataFrame`s becomes so powerful.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "sns.pairplot(iris[[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For data sets where we have classification labels, we can even color the various points using the `hue` option, and produce KDEs along the diagonal with `diag_type = 'kde'`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "sns.pairplot(iris, vars = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"],\n", " hue = \"species\", diag_kind = 'kde')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Even better - there is an option to create a `PairGrid` which allows fine tuned control of the data as displayed above, below, and along the diagonal. In this way it becomes possible to avoid having symmetric redundancy, which is not all that informative. In the example below, we will show scatter plots and contour plots simultaneously. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "g = sns.PairGrid(iris, vars = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"],\n", " hue = \"species\", diag_sharey=False)\n", "g.map_lower(sns.kdeplot)\n", "g.map_upper(plt.scatter, edgecolor='white')\n", "g.map_diag(sns.kdeplot, lw=3)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Note - one disadvantage to the plot above is that the contours do not share the same color scheme as the KDE estimates and the scatter plot. I have not been able to figure out how to change this in a satisfactory way. (One potential solution is detailed [here](http://stackoverflow.com/questions/32889590/seaborn-pairgrid-using-kdeplot-with-2-hues), however, it is worth noting that this solution restricts your color choices to a maximum of ~5 unless you are a colormaps wizard, and I am not.)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
nwhidden/ND101-Deep-Learning
batch-norm/Batch_Normalization_Exercises.ipynb
1
33908
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization – Practice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional neural network with 20 convolutional layers, followed by a fully connected layer. We'll use it to classify handwritten digits in the MNIST dataset, which should be familiar to you by now.\n", "\n", "This is **not** a good network for classfying MNIST digits. You could create a _much_ simpler network and get _better_ results. However, to give you hands-on experience with batch normalization, we had to make an example that was:\n", "1. Complicated enough that training would benefit from batch normalization.\n", "2. Simple enough that it would train quickly, since this is meant to be a short exercise just to give you some practice adding batch normalization.\n", "3. Simple enough that the architecture would be easy to understand without additional resources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook includes two versions of the network that you can edit. The first uses higher level functions from the `tf.layers` package. The second is the same network, but uses only lower level functions in the `tf.nn` package.\n", "\n", "1. [Batch Normalization with `tf.layers.batch_normalization`](#example_1)\n", "2. [Batch Normalization with `tf.nn.batch_normalization`](#example_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell loads TensorFlow, downloads the MNIST dataset if necessary, and loads it into an object named `mnist`. You'll need to run this cell before running anything else in the notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization using `tf.layers.batch_normalization`<a id=\"example_1\"></a>\n", "\n", "This version of the network uses `tf.layers` for almost everything, and expects you to implement batch normalization using [`tf.layers.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the following function to create fully connected layers in our network. We'll create them with the specified number of neurons and a ReLU activation function.\n", "\n", "This version of the function does not include batch normalization." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def fully_connected(prev_layer, num_units):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", " layer = tf.layers.dense(prev_layer, num_units, activation=tf.nn.relu)\n", " return layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the following function to create convolutional layers in our network. They are very basic: we're always using a 3x3 kernel, ReLU activation functions, strides of 1x1 on layers with odd depths, and strides of 2x2 on layers with even depths. We aren't bothering with pooling layers at all in this network.\n", "\n", "This version of the function does not include batch normalization." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def conv_layer(prev_layer, layer_depth):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_depth*4, 3, strides, 'same', activation=tf.nn.relu)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Run the following cell**, along with the earlier cells (to load the dataset and define the necessary functions). \n", "\n", "This cell builds the network **without** batch normalization, then trains it on the MNIST dataset. It displays loss and accuracy data periodically while training." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 0: Validation loss: 0.69160, Validation accuracy: 0.09760\n", "Batch: 25: Training loss: 0.49741, Training accuracy: 0.09375\n", "Batch: 50: Training loss: 0.33376, Training accuracy: 0.06250\n", "Batch: 75: Training loss: 0.32579, Training accuracy: 0.10938\n", "Batch: 100: Validation loss: 0.32595, Validation accuracy: 0.09860\n", "Batch: 125: Training loss: 0.32429, Training accuracy: 0.14062\n", "Batch: 150: Training loss: 0.32398, Training accuracy: 0.12500\n", "Batch: 175: Training loss: 0.32605, Training accuracy: 0.10938\n", "Batch: 200: Validation loss: 0.32558, Validation accuracy: 0.09860\n", "Batch: 225: Training loss: 0.32687, Training accuracy: 0.01562\n", "Batch: 250: Training loss: 0.32609, Training accuracy: 0.04688\n", "Batch: 275: Training loss: 0.32545, Training accuracy: 0.09375\n", "Batch: 300: Validation loss: 0.32537, Validation accuracy: 0.09900\n", "Batch: 325: Training loss: 0.32435, Training accuracy: 0.17188\n", "Batch: 350: Training loss: 0.32629, Training accuracy: 0.09375\n", "Batch: 375: Training loss: 0.32599, Training accuracy: 0.10938\n", "Batch: 400: Validation loss: 0.32505, Validation accuracy: 0.11260\n", "Batch: 425: Training loss: 0.32421, Training accuracy: 0.14062\n", "Batch: 450: Training loss: 0.32546, Training accuracy: 0.03125\n", "Batch: 475: Training loss: 0.32324, Training accuracy: 0.20312\n", "Batch: 500: Validation loss: 0.32582, Validation accuracy: 0.09860\n", "Batch: 525: Training loss: 0.32595, Training accuracy: 0.10938\n", "Batch: 550: Training loss: 0.32834, Training accuracy: 0.04688\n", "Batch: 575: Training loss: 0.32400, Training accuracy: 0.09375\n", "Batch: 600: Validation loss: 0.32519, Validation accuracy: 0.11260\n", "Batch: 625: Training loss: 0.32431, Training accuracy: 0.15625\n", "Batch: 650: Training loss: 0.32273, Training accuracy: 0.10938\n", "Batch: 675: Training loss: 0.32511, Training accuracy: 0.06250\n", "Batch: 700: Validation loss: 0.32606, Validation accuracy: 0.11260\n", "Batch: 725: Training loss: 0.32464, Training accuracy: 0.06250\n", "Batch: 750: Training loss: 0.32820, Training accuracy: 0.07812\n", "Batch: 775: Training loss: 0.32467, Training accuracy: 0.15625\n", "Final validation accuracy: 0.11260\n", "Final test accuracy: 0.11350\n", "Accuracy on 100 samples: 0.14\n" ] } ], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", " \n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define loss and training operations\n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " # Create operations to test accuracy\n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, labels: batch_ys})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly.\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]]})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this many layers, it's going to take a lot of iterations for this network to learn. By the time you're done training these 800 batches, your final test and validation accuracies probably won't be much better than 10%. (It will be different each time, but will most likely be less than 15%.)\n", "\n", "Using batch normalization, you'll be able to train this same network to over 90% in that same number of batches.\n", "\n", "\n", "# Add batch normalization\n", "\n", "We've copied the previous three cells to get you started. **Edit these cells** to add batch normalization to the network. For this exercise, you should use [`tf.layers.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization) to handle most of the math, but you'll need to make a few other changes to your network to integrate batch normalization. You may want to refer back to the lesson notebook to remind yourself of important things, like how your graph operations need to know whether or not you are performing training or inference. \n", "\n", "If you get stuck, you can check out the `Batch_Normalization_Solutions` notebook to see how we did things." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Modify `fully_connected` to add batch normalization to the fully connected layers it creates. Feel free to change the function's parameters if it helps." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(prev_layer, num_units, is_training):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", " layer = tf.layers.dense(prev_layer, num_units, use_bias = False, activation=None)\n", " layer = tf.layers.batch_normalization(layer, training = is_training)\n", " layer = tf.nn.relu(layer)\n", " return layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Modify `conv_layer` to add batch normalization to the convolutional layers it creates. Feel free to change the function's parameters if it helps." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_depth, is_training):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_depth*4, 3, strides, 'same', use_bias = False, activation=None)\n", " conv_layer = tf.layers.batch_normalization(conv_layer, training = is_training)\n", " conv_layer = tf.nn.relu(conv_layer)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Edit the `train` function to support batch normalization. You'll need to make sure the network knows whether or not it is training, and you'll need to make sure it updates and uses its population statistics correctly." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 0: Validation loss: 0.69110, Validation accuracy: 0.09900\n", "Batch: 25: Training loss: 0.59087, Training accuracy: 0.07812\n", "Batch: 50: Training loss: 0.49259, Training accuracy: 0.14062\n", "Batch: 75: Training loss: 0.42057, Training accuracy: 0.09375\n", "Batch: 100: Validation loss: 0.37618, Validation accuracy: 0.09860\n", "Batch: 125: Training loss: 0.36034, Training accuracy: 0.07812\n", "Batch: 150: Training loss: 0.34570, Training accuracy: 0.10938\n", "Batch: 175: Training loss: 0.32702, Training accuracy: 0.12500\n", "Batch: 200: Validation loss: 0.27629, Validation accuracy: 0.35600\n", "Batch: 225: Training loss: 0.20708, Training accuracy: 0.59375\n", "Batch: 250: Training loss: 0.17623, Training accuracy: 0.65625\n", "Batch: 275: Training loss: 0.15114, Training accuracy: 0.71875\n", "Batch: 300: Validation loss: 0.08008, Validation accuracy: 0.86420\n", "Batch: 325: Training loss: 0.05558, Training accuracy: 0.92188\n", "Batch: 350: Training loss: 0.15270, Training accuracy: 0.75000\n", "Batch: 375: Training loss: 0.01340, Training accuracy: 0.98438\n", "Batch: 400: Validation loss: 0.11828, Validation accuracy: 0.81340\n", "Batch: 425: Training loss: 0.06374, Training accuracy: 0.92188\n", "Batch: 450: Training loss: 0.00155, Training accuracy: 1.00000\n", "Batch: 475: Training loss: 0.07377, Training accuracy: 0.90625\n", "Batch: 500: Validation loss: 0.04924, Validation accuracy: 0.93380\n", "Batch: 525: Training loss: 0.02573, Training accuracy: 0.96875\n", "Batch: 550: Training loss: 0.01876, Training accuracy: 0.96875\n", "Batch: 575: Training loss: 0.07208, Training accuracy: 0.93750\n", "Batch: 600: Validation loss: 0.03775, Validation accuracy: 0.95340\n", "Batch: 625: Training loss: 0.02607, Training accuracy: 0.96875\n", "Batch: 650: Training loss: 0.04171, Training accuracy: 0.95312\n", "Batch: 675: Training loss: 0.06097, Training accuracy: 0.89062\n", "Batch: 700: Validation loss: 0.05849, Validation accuracy: 0.91840\n", "Batch: 725: Training loss: 0.03696, Training accuracy: 0.93750\n", "Batch: 750: Training loss: 0.08440, Training accuracy: 0.90625\n", "Batch: 775: Training loss: 0.01877, Training accuracy: 0.96875\n", "Final validation accuracy: 0.94040\n", "Final test accuracy: 0.94300\n", "Accuracy on 100 samples: 0.92\n" ] } ], "source": [ "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", " \n", " # training boolean\n", " is_training = tf.placeholder(tf.bool)\n", " \n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i, is_training)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100, is_training)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define loss and training operations\n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " # Tell TensorFlow to update the population statistics while training\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " # Create operations to test accuracy\n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, labels: batch_ys, is_training: True})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " is_training: False})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys, is_training: False})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " is_training: False})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels,\n", " is_training: False})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly.\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]],\n", " is_training: False})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With batch normalization, you should now get an accuracy over 90%. Notice also the last line of the output: `Accuracy on 100 samples`. If this value is low while everything else looks good, that means you did not implement batch normalization correctly. Specifically, it means you either did not calculate the population mean and variance while training, or you are not using those values during inference.\n", "\n", "# Batch Normalization using `tf.nn.batch_normalization`<a id=\"example_2\"></a>\n", "\n", "Most of the time you will be able to use higher level functions exclusively, but sometimes you may want to work at a lower level. For example, if you ever want to implement a new feature – something new enough that TensorFlow does not already include a high-level implementation of it, like batch normalization in an LSTM – then you may need to know these sorts of things.\n", "\n", "This version of the network uses `tf.nn` for almost everything, and expects you to implement batch normalization using [`tf.nn.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization).\n", "\n", "**Optional TODO:** You can run the next three cells before you edit them just to see how the network performs without batch normalization. However, the results should be pretty much the same as you saw with the previous example before you added batch normalization. \n", "\n", "**TODO:** Modify `fully_connected` to add batch normalization to the fully connected layers it creates. Feel free to change the function's parameters if it helps.\n", "\n", "**Note:** For convenience, we continue to use `tf.layers.dense` for the `fully_connected` layer. By this point in the class, you should have no problem replacing that with matrix operations between the `prev_layer` and explicit weights and biases variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(prev_layer, num_units):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", " layer = tf.layers.dense(prev_layer, num_units, activation=tf.nn.relu)\n", " return layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Modify `conv_layer` to add batch normalization to the fully connected layers it creates. Feel free to change the function's parameters if it helps.\n", "\n", "**Note:** Unlike in the previous example that used `tf.layers`, adding batch normalization to these convolutional layers _does_ require some slight differences to what you did in `fully_connected`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_depth):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", "\n", " in_channels = prev_layer.get_shape().as_list()[3]\n", " out_channels = layer_depth*4\n", " \n", " weights = tf.Variable(\n", " tf.truncated_normal([3, 3, in_channels, out_channels], stddev=0.05))\n", " \n", " bias = tf.Variable(tf.zeros(out_channels))\n", "\n", " conv_layer = tf.nn.conv2d(prev_layer, weights, strides=[1,strides, strides, 1], padding='SAME')\n", " conv_layer = tf.nn.bias_add(conv_layer, bias)\n", " conv_layer = tf.nn.relu(conv_layer)\n", "\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Edit the `train` function to support batch normalization. You'll need to make sure the network knows whether or not it is training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", " \n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define loss and training operations\n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " # Create operations to test accuracy\n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, labels: batch_ys})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly.\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]]})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, the model with batch normalization should reach an accuracy over 90%. There are plenty of details that can go wrong when implementing at this low level, so if you got it working - great job! If not, do not worry, just look at the `Batch_Normalization_Solutions` notebook to see what went wrong." ] } ], "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ES-DOC/esdoc-jupyterhub
notebooks/messy-consortium/cmip6/models/sandbox-1/atmoschem.ipynb
1
102089
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmoschem \n", "**MIP Era**: CMIP6 \n", "**Institute**: MESSY-CONSORTIUM \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Atmoschem \n", "**Sub-Topics**: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \n", "**Properties**: 84 (39 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmoschem?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:10" ], "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', 'sandbox-1', 'atmoschem')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&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; Timestep Framework --&gt; Split Operator Order](#4.-Key-Properties---&gt;-Timestep-Framework---&gt;-Split-Operator-Order) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Grid](#6.-Grid) \n", "[7. Grid --&gt; Resolution](#7.-Grid---&gt;-Resolution) \n", "[8. Transport](#8.-Transport) \n", "[9. Emissions Concentrations](#9.-Emissions-Concentrations) \n", "[10. Emissions Concentrations --&gt; Surface Emissions](#10.-Emissions-Concentrations---&gt;-Surface-Emissions) \n", "[11. Emissions Concentrations --&gt; Atmospheric Emissions](#11.-Emissions-Concentrations---&gt;-Atmospheric-Emissions) \n", "[12. Emissions Concentrations --&gt; Concentrations](#12.-Emissions-Concentrations---&gt;-Concentrations) \n", "[13. Gas Phase Chemistry](#13.-Gas-Phase-Chemistry) \n", "[14. Stratospheric Heterogeneous Chemistry](#14.-Stratospheric-Heterogeneous-Chemistry) \n", "[15. Tropospheric Heterogeneous Chemistry](#15.-Tropospheric-Heterogeneous-Chemistry) \n", "[16. Photo Chemistry](#16.-Photo-Chemistry) \n", "[17. Photo Chemistry --&gt; Photolysis](#17.-Photo-Chemistry---&gt;-Photolysis) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmospheric chemistry model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of atmospheric chemistry model code.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Chemistry Scheme Scope\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Atmospheric domains covered by the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.chemistry_scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basic approximations made in the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Form of prognostic variables in the atmospheric chemistry component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/mixing ratio for gas\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of advected tracers in the atmospheric chemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Atmospheric chemistry calculations (not advection) generalized into families of species?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Coupling With Chemical Reactivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Atmospheric chemistry transport scheme turbulence is couple with chemical reactivity?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.coupling_with_chemical_reactivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&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.atmoschem.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "**Is Required:** FALSE&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.atmoschem.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "**Is Required:** FALSE&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.atmoschem.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestep Framework \n", "*Timestepping in the atmospheric chemistry model*" ], "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 evolution of a given variable*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Operator splitting\" \n", "# \"Integrated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for chemical species advection (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for physics (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Split Operator Chemistry Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for chemistry (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_chemistry_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Split Operator Alternate Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_alternate_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.6. Integrated Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the atmospheric chemistry model (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.7. Integrated Scheme Type\n", "**Is Required:** TRUE&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.atmoschem.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Timestep Framework --&gt; Split Operator Order \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Turbulence\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for turbulence scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.turbulence') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Convection\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for convection scheme This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.convection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Precipitation\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for precipitation scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.4. Emissions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for emissions scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.emissions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.5. Deposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for deposition scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.6. Gas Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for gas phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.gas_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.7. Tropospheric Heterogeneous Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for tropospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.tropospheric_heterogeneous_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.8. Stratospheric Heterogeneous Phase Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for stratospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.stratospheric_heterogeneous_phase_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.9. Photo Chemistry\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for photo chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.photo_chemistry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.10. Aerosols\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Call order for aerosols scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.aerosols') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for atmospheric chemistry 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. &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.atmoschem.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&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.atmoschem.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Regional Metrics Used\n", "**Is Required:** FALSE&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.atmoschem.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Trend Metrics Used\n", "**Is Required:** FALSE&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.atmoschem.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid \n", "*Atmospheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the atmopsheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### * Does the atmospheric chemistry grid match the atmosphere grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.grid.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Resolution \n", "*Resolution in the atmospheric chemistry grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. 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.atmoschem.grid.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Canonical Horizontal Resolution\n", "**Is Required:** FALSE&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.atmoschem.grid.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Number Of Horizontal Gridpoints\n", "**Is Required:** FALSE&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.atmoschem.grid.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Number Of Vertical Levels\n", "**Is Required:** FALSE&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.atmoschem.grid.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.5. Is Adaptive Grid\n", "**Is Required:** FALSE&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.atmoschem.grid.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Transport \n", "*Atmospheric chemistry transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview of transport implementation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Use Atmospheric Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is transport handled by the atmosphere, rather than within atmospheric cehmistry?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.use_atmospheric_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Transport Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If transport is handled within the atmospheric chemistry scheme, describe it.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.transport.transport_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Emissions Concentrations \n", "*Atmospheric chemistry emissions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview atmospheric chemistry emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Emissions Concentrations --&gt; Surface Emissions \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of the chemical species emitted at the surface that are taken into account in the emissions scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Soil\" \n", "# \"Sea surface\" \n", "# \"Anthropogenic\" \n", "# \"Biomass burning\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Methods used to define chemical species emitted directly into model layers above the surface (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Climatology\" \n", "# \"Spatially uniform mixing ratio\" \n", "# \"Spatially uniform concentration\" \n", "# \"Interactive\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and prescribed via a climatology, and the nature of the climatology (E.g. CO (monthly), C2H6 (constant))*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.5. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.6. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted at the surface and specified via any other method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Emissions Concentrations --&gt; Atmospheric Emissions \n", "*TO DO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of chemical species emitted in the atmosphere that are taken into account in the emissions scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Aircraft\" \n", "# \"Biomass burning\" \n", "# \"Lightning\" \n", "# \"Volcanos\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Methods used to define the chemical species emitted in the atmosphere (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Climatology\" \n", "# \"Spatially uniform mixing ratio\" \n", "# \"Spatially uniform concentration\" \n", "# \"Interactive\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and prescribed via a climatology (E.g. CO (monthly), C2H6 (constant))*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of chemical species emitted in the atmosphere 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.atmoschem.emissions_concentrations.atmospheric_emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Emissions Concentrations --&gt; Concentrations \n", "*TO DO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. 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.atmoschem.emissions_concentrations.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Prescribed Upper Boundary\n", "**Is Required:** FALSE&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.atmoschem.emissions_concentrations.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Gas Phase Chemistry \n", "*Atmospheric chemistry transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview gas phase atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Species included in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HOx\" \n", "# \"NOy\" \n", "# \"Ox\" \n", "# \"Cly\" \n", "# \"HSOx\" \n", "# \"Bry\" \n", "# \"VOCs\" \n", "# \"isoprene\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Number Of Bimolecular Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of bi-molecular reactions in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_bimolecular_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Number Of Termolecular Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of ter-molecular reactions in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_termolecular_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Number Of Tropospheric Heterogenous Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_tropospheric_heterogenous_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.6. Number Of Stratospheric Heterogenous Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_stratospheric_heterogenous_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.7. Number Of Advected Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of advected species in the gas phase chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_advected_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.8. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of gas phase species for which the concentration is updated in the chemical solver assuming photochemical steady state*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.9. Interactive Dry Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.interactive_dry_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.10. Wet Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is wet deposition included? Wet deposition describes the moist processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.11. Wet Oxidation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is wet oxidation included? Oxidation describes the loss of electrons or an increase in oxidation state by a molecule*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_oxidation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Stratospheric Heterogeneous Chemistry \n", "*Atmospheric chemistry startospheric heterogeneous chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview stratospheric heterogenous atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Gas Phase Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Gas phase species included in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.gas_phase_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Cly\" \n", "# \"Bry\" \n", "# \"NOy\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Aerosol Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Aerosol species included in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.aerosol_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule))\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of steady state species in the stratospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Sedimentation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is sedimentation is included in the stratospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.sedimentation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Coagulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is coagulation is included in the stratospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.coagulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Tropospheric Heterogeneous Chemistry \n", "*Atmospheric chemistry tropospheric heterogeneous chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview tropospheric heterogenous atmospheric chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Gas Phase Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of gas phase species included in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.gas_phase_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Aerosol Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Aerosol species included in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.aerosol_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon/soot\" \n", "# \"Polar stratospheric ice\" \n", "# \"Secondary organic aerosols\" \n", "# \"Particulate organic matter\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Number Of Steady State Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of steady state species in the tropospheric heterogeneous chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.number_of_steady_state_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Interactive Dry Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.interactive_dry_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Coagulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is coagulation is included in the tropospheric heterogeneous chemistry scheme or not?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.coagulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Photo Chemistry \n", "*Atmospheric chemistry photo chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview atmospheric photo chemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Number Of Reactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of reactions in the photo-chemistry scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.number_of_reactions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Photo Chemistry --&gt; Photolysis \n", "*Photolysis scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Photolysis scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Offline (clear sky)\" \n", "# \"Offline (with clouds)\" \n", "# \"Online\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Environmental Conditions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any environmental conditions taken into account by the photolysis scheme (e.g. whether pressure- and temperature-sensitive cross-sections and quantum yields in the photolysis calculations are modified to reflect the modelled conditions.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.environmental_conditions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
magwenelab/mini-term-2016
ode-modeling3.ipynb
1
2583
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading Questions\n", "\n", "**Paper**: A synthetic oscillatory network of transcriptional regulators. Elowitz MB, Leibler S. Nature. 2000 Jan 20;403(6767):335-8. [DOI Link](http://dx.doi.org/10.1038/35002125)\n", "\n", "1. How was the repressilator network that Elowitz and Liebler explored and constructed? How did they monitor the behavior of the network in E. coli cells?\n", "\n", "2. Elowitz and Liebler developed a mathematical model of the repressilator to understand the space of dynamical behaviors of the system. What sort of parameters did they consider important in their model? What sort of outcomes did their model predict?\n", "\n", "3. Did the in vivo experiments that Elowitz and Liebler carried out agree with their modeling results? What other types of phenomena did Elowitz and Liebler observe in their experiments?\n", "\n", "![repressilator](http://people.duke.edu/~pm21/repressilator-fig.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modeling the Repressilator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implement a model of the repressilator using the logic approximation framework we explored in class sessions 4 and 5." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## define the differential equations for each gene here" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### define the simulations here" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## generate some plots here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Questions, Hybrid models\n", "\n", "1. For what range of parameters do you get oscillatory behaviors vs is stable fixed points?\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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
dvav/eQTLseq
notebooks/test_realdata.ipynb
1
40501
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings as _wrn\n", "_wrn.filterwarnings('always')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys as _sys\n", "import itertools as _itr\n", "import pickle as _pkl\n", "\n", "import numpy as _nmp\n", "import numpy.random as _rnd\n", "import matplotlib.pyplot as _plt\n", "import pandas as _pnd\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "\n", "_plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_sys.path.append('../')\n", "import eQTLseq as _assoc" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ... load real data\n", "\n", "data = {\n", " 'G': _pnd.read_table('/home/dimitris/WTCHG/Projects/eQTLseq/data/geuvadis/genotypes.TF.common.HIGH.no_missing.txt', index_col=0, header=None),\n", " 'Z': _pnd.read_table('/home/dimitris/WTCHG/Projects/eQTLseq/data/geuvadis/counts_miRNAs.txt', index_col=0)\n", "}\n", "\n", "samples = data['G'].index & data['Z'].columns\n", "\n", "data['Z'] = data['Z'][samples].values\n", "data['G'] = data['G'].loc[samples].values" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(452, 37) (408, 452)\n" ] }, { "data": { "image/png": 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tpmW0jprRsVasWKGbr41O2LVrV7zyyitq+je/+Y3YnrSOWlBQkFjH1XXUtNdp\nVVWVS3WIiG5HWVlZOHDgAOLi4jB9+vR6k7nCwkKEh4er6fDwcHHplezsbGRnZwOoW3szIiKi5Tr+\nX76+vs1+nCv//a9zu1fq76ruc+UngwEAUR8cktv97z6G7egc25W2W1pLnOeW5G39BbyzzxI++khE\nRETUBGPGjMHkyZMB1P2It2XLFnF9WFdYrVZYrVY1bbSmbHOJiIhoseO40q7zPu72xZV2WuN8Slry\nPLcEb+sv4B191t4gMMJHH4mIiIiaoFOnTvDx8YGPjw9GjRqF8+fP19vHYrHg2rVravratWuwWCyt\n2U0i8jKcqBERERE1QVFRkbp95MgRdOvWrd4+PXv2xKVLl5Cfn4/q6mocOnQISUlJrdlNIvIyfPSR\niIiIyEVr167FqVOnUFpaijlz5mDKlCk4efIkLl68CEVREBkZidmzZwOoey9t48aNWLp0KUwmE2bO\nnImVK1eitrYWDz30kO6EjojIjhM1IiIiIhctWLCgXt7IkSN197VYLFi6dKmaTkxM1F1fjYhIDx99\nJCIiIiIi8jAec0dNCi+vKIpb7WlDnTd0HOnYWrW1tS635wptPUVR1LRRe+4cqzF1tPtK422uY7nC\nqA/S59va/XCH1D9tvs1mq5duSnuNKbPTXpdERERE1Lp4R42IiIiIiMjDcKJGRERERETkYThRIyIi\nIiIi8jCcqBERERHRbaNm1iTUzJrU4nWImooTNSIiIiIiIg/T6lEftdENtRHlampqdPevrq4W26qo\nqBDLbt26pZvvHMHPZrM1Kaqf1G+9Y2lpx+Xr66umXa3jrLUiHRr1wehcGJXZPyubzSZ+blIdPb6+\nzXtZS2OuqqoS67gTcdH5HGnTRufPHUafo532umyJPhARERGRjHfUiIiIiIiIPAwnakRERERERB6G\nEzUiIiIiIiIP0+rvqBERNcaGDRuQl5eH0NBQpKWlAQD+/Oc/48svv4Svry+ioqIwd+5cBAcHIz8/\nHwsXLkR0dDQAoHfv3pg9e3Zbdp+IiIjILZyoEZFHGzFiBMaNG4eMjAw1LyEhAU888QRMJhPeeust\nfPDBB5g2bRoAoEuXLli9enVbdZeIiIioWfDRRyLyaPHx8QgJCXHI+9GPfgSTyQQAuOuuu1BYWNgW\nXSMiIiJqMR5zR00KWe7jI88ljcpaS3OHxfcUTVmyoLm4e25b6zNRFMWtelL/3G3vdrd3714MHjxY\nTefn5+MhaQ9uAAAgAElEQVTZZ5+F2WzG448/jr59++rWy87ORnZ2NgAgNTUVERERLh1Pu/yDq3U8\nla+vr9ePwa69jKW9jANoX2MhImoLDU7U9N4P2bFjB3JyctCxY0cAwNSpU5GYmNiyPSUicvL+++/D\nZDJh2LBhAICwsDBs2LABHTp0wDfffIPVq1cjLS0NQUFB9eparVZYrVY1XVBQ4NIxtV88Xa3jqSIi\nIrx+DHbtZSztZRyAZ4zF/r4qEZE3anCipvd+CABMmDABkyZNarGOEREZ2bdvH7788kssW7ZMvRvp\n5+cHPz8/AEBcXByioqJw6dIl9OzZsy27SkRETVAzq+77pulPH7ZxT4haV4PPDuq9H0JE1JaOHTuG\nnTt34rnnnkNAQICaf/36dfWx3StXruDSpUuIiopqq24SERERuc3td9SysrJw4MABxMXFYfr06eJk\nzvk9kIMHDwIA+vTpo24DQExMjHgcSWRkpFj285//XDc/ODjYIW0ymdS8nJwcsT3p/aHQ0FCxjlHf\nte+5KIqipleuXCnWsd8paAmKosDf319Njx8/Xne/ESNGiG0Y9e+TTz4Ry8xmMwCgR48e2LBhg5rf\nrVs3sc7u3bvFMumzMnrvTjv22NhYbN26VU1L19nw4cPF9ozek5Ouda2goCCHx4m3bdtmuK8eo3c4\ntdefRHtdAlCDd7S2tWvX4tSpUygtLcWcOXMwZcoUfPDBB6iursZLL70E4H9h+E+dOoUdO3bAZDLB\nx8cHs2bN4g9NRERE5JXcmqiNGTMGkydPBgBs374dW7Zswdy5c3X3dX4PZOjQoQCAgwcPqtsA8NVX\nX+nWHzt2rNiPp556Siz7y1/+opt/7Ngxh3RwcDDKy8sBAKNGjRLbs08mGmpPy6jv3333nbrt6+uL\n6upqAMBvf/tbsc6bb74pljU1gIa/vz9u3ryppnft2qW7n9F4X375ZbFs9OjRYllCQgKAuvchtddR\nenq6WGfcuHFiWWBgoG7+jRs3xDra9xi2bt2KJ598Uk3/+te/1q2zfft2sb3KykqxTJqMayeYiYmJ\nyMvLU9O/+c1vxPbuv/9+3XxpAgfUTX4aor0uAeDWrVu6+zn/+NHcFixYUC9v5MiRuvsOGjQIgwYN\natH+ENHtrTFrOzqbN28eAgMD4ePjA5PJhNTU1NbuPhF5Ebcmap06dVK3R40ahVWrVjVbh4iIiIg8\nVWPXdnS2fPlyNRgbEZERtyZqRUVFCAsLAwAcOXLE8BE1Z9pf6LXb7733nu7+ZWVlYlvSXTgAKC0t\n1c3fsWOHQzo5ORmZmZkAjO+CSHes3n33XbFOVVWVWLZz5051e9SoUepjl19//bVYx95PPdJjfUaP\n+2nLxo8f73AX7fLly7p1Tp48Kbb3wQcfiGVGd7P+/e9/AwBu3rypbgPG4zW6LqQxV1RUiHXy8/PV\n7Vu3bjmk//GPf+jW+eabbxrdB0C+W6mtExcXh48++khNX716VWzv7NmzuvlGjzdqrz+pH6NHj3Z4\nZFW6nqdPny62RUTU3sTHxzv8PwKoW9vR7q677sLhw4dbu1tE1A41OFHTez/k5MmTuHjxIhRFQWRk\nJGbPnt0afSUiIiLyaM5rOzqzP/4+evRoh1dDtNxd57EpWmLduyv//a9zu1fq76ruo1fHOU+vfmPa\ncd7XFdJYGsvb1hf0tv4C3tlnSYMTtca8H0JERER0u3Je29HZSy+9BIvFgpKSEqxYsQLR0dGIj4+v\nt5+76zw2RUuue+dKu8776NVprnYa015z1NHyhPUFG8Pb+gt4R59dXeOxwfD8RERERGTMvrbj/Pnz\nxejDFosFQF3E6AEDBuDcuXOt2UUi8jKcqBERERE1gbS2o1ZlZaX6rnRlZSWOHz+O7t27t2Y3icjL\nuL2OGhEREdHtpjFrOxYWFmLjxo1YunQpSkpKsGbNGgBATU0Nhg4div79+7flUIjIw7X6RM0eUc5m\nszlEuZOiHRpFz/v+++/FMilC3YkTJxzSY8eOVfO0USidSYsHG0VB1K5L5uzLL79Utx988EE1XVxc\nLNbRrqvlKqMxaY0YMcJhjTRpXJ07dxbbOHr0qFhm9DnaIzjW1tY6RHM0eiSkpqZGLJPW+5LyAceI\nkLW1tQ5p6TpzJ0ooAHz77bcN9q+qqgoXLlxQ00afoxSh0yjq4xdffCGW2R/ZGTJkiMNnanT+iIhu\nF415d99isWDp0qUAgKioKKxevbpF+9ZSamZNUrdNf/rQI/rRlH0aU78tx0vERx+JiIiIiIg8DCdq\nREREREREHoYTNSIiIiIiIg/DiRoREREREZGH4USNiIiIiIjIw3CiRkRERERE5GFaPTy/v78/gLoQ\n4PZtAFi+fLnu/rt27RLbeuSRR8QyKcz9ypUrHdJBQUFq3tatW8X2zGazbv6KFSvEOu+++65Y9vLL\nL6vbiqKo6e+++86lOs6kcPBGYey1/Pz8HNr/4IMPdPczCsFv1L//9//+n1hmX0fGbDY7rCnz3HPP\niXX27t0rlkmLjfr5+Yl17rjjDnXb39/fYRHSadOm6dbZsWOH2J52mQFnc+fO1c3XLgERFRWFhQsX\nqunz58+L7Unr8AQHB4t1XnnlFbHMTntdAsbLTRARERFR8+IdNSIiIiIiIg/DiRoREREREZGH4USN\niIiIiIjIw3CiRkRERERE5GFaPZgIEREREbVfNbMmeUQbRN6Od9SIiIiIiIg8TKvfUauqqgJQF07e\nvg0A9913n+7+169fF9tas2ZNg8dxFhsb65DOzs6G1WoFABQWFortSeLi4sQyaYkAAIiOjla3s7Ky\nMHbs2Ab7kJOT0+j+uRqe/5NPPsHo0aPV9I0bN3T3q62tFdswCsFfXl4ultlD7ZeWljqE3R84cKBY\nR/p8AaC6uloskxQUFKjbFRUVOHHihJpetGhRo/tw69YtsWz8+PG6+b6+//tz/Pvf/46JEyeqaaNw\n/9q+amnD/Tt77733xDI77XUJyEtAXLlypcG2mmrDhg3Iy8tDaGgo0tLSANSdk/T0dFy9ehWRkZFY\nuHAhQkJCYLPZsHnzZhw9ehQBAQGYO3eu4d8pERERkSfiHTUi8ngjRozA888/75CXmZmJfv36Yf36\n9ejXrx8yMzMB1K31d/nyZaxfvx6zZ8/G66+/3hZdJiIiImoSTtSIyOPFx8cjJCTEIS83NxfDhw8H\nAAwfPhy5ubkAgC+++AI//vGPoSgK7rrrLpSXl6OoqKjV+0xERETUFAwmQkReqaSkBGFhYQCATp06\noaSkBEDd48MRERHqfuHh4SgsLFT3tcvOzkZ2djYAIDU11aGOEe0jqq7W8VS+vr5ePwa79jKW9jIO\noH2NhYioLXCiRkReT1EUKIrSqDpWq1V9PxVwfE/RiPaLp6t1PFVERITXj8GuvYylvYwD8IyxaN8H\nJyLyNnz0kYi8UmhoqPpIY1FRETp27AgAsFgsDl8Or127BovF0iZ9JCIiInIX76gRkVdKSkrC/v37\nkZycjP3792PAgAFq/u7duzFkyBCcPXsWQUFB9R57JCJyV2Oi0Drbt28f3n//fQDAY489hhEjRrRm\n14nIy7T6RE37eJJ2++6779bdPy8vT2yrc+fOYll+fr5u/j333OOQDgwMVPMOHjwotieFOu/bt69Y\n5/PPPxfLtMsRmM1mNW1Up3///mKZxChMvJbZbEZCQoKa/ve//627n1GYeKP+acPuO7N/9oGBgQ7X\ngclkEuucOXNGLJM+K6OlBbR1FEVxeA9JuhtjfydKT0VFhVjWvXt33XzteP39/dGtWzc1ff78ebG9\nwMDABttzJi2HoaW9LgHXl3poCWvXrsWpU6dQWlqKOXPmYMqUKUhOTkZ6ejr27t2rfjEC6saWl5eH\n+fPnw9/fH3Pnzm2zfhNR+zNixAiMGzcOGRkZap49Cm1ycjIyMzORmZmJadOmOdQrKyvDu+++i9TU\nVADAkiVLkJSUpDuhIyICXJioFRQUICMjA8XFxVAUBVarFePHj3f51yMioqZasGCBbv6yZcvq5SmK\ngqeeeqqlu0REt6n4+Ph6Pwbn5ubihRdeAFAXhfaFF16oN1E7duwYEhIS1O9KCQkJOHbsGIYOHdoq\n/SYi79PgRM1kMiElJQVxcXGoqKjAkiVLkJCQgH379jX46xERERFReydFodUqLCxEeHi4mrZYLCgs\nLNRtz92otE1hFKXzimbblb5ccUo713Eu1+6jV9YUNbMmiWXqMX8yuOF9dNqL+uBQo/vjbdFQva2/\ngHf2WdLgRC0sLEz9x8dsNiMmJgaFhYUu/XpEREREdDtxJwqtM3ej0jaFq1E63elLS7XbVE3tlzt9\n9oRoqI3hbf0FvKPPrkakbdQ7avn5+bhw4QJ69erl0q9HQP1fhezvYPXt29fhfazg4GDd+jdu3BD7\n4+/vL5ZJ72YFBQU5pHv06IGNGzcCMH7/SvpHV+o3AJSXl4tlHTp0ULe7d++OdevWNVjH6FgSm83m\n0n49evTAhg0b1PTNmzd19zN6z8tsNotlpaWlYpn9Hau4uDhs27atoa4CAKqqqsQyV8cs6dOnDw4d\n+t+vZNJ1ZvTOltF5ktrTXmPO58JovNK1afRFwfnvQI/2uiQiIpk9Cm1YWJhDFFoti8WCU6dOqenC\nwkLEx8e3ZjeJyMu4PFGrrKxEWloaZsyYUe9LntGvR86/Cg0cOBBAXdAM+zYAPPDAA7r1jYKJdO3a\nVSyTgokkJiY6pDdu3Ihf/epXANwLJqIdgzOjwCDac7Ju3To888wzDdaRzpERV4OJbNiwwSHoQlsE\nE9m2bRsef/xxNd/dYCLSBMrVYCKHDh3C4MH/ewwiJiZGt467wUTuvPNO3XzteJ3PRXMHE0lKShLL\n7LTXJSCf1z179jTYFhFReyZFodXq378/3n77bfX/o//85z/xxBNPtHZXiciLuDRRq66uRlpaGoYN\nG6ZOTFz59UiP/QuxoigOX46XLl2qu7/2y6qzQYMGiWW7du3SzV+zZo1Dulu3bmreqFGjxPakOxD2\n0Lx6Hn74YbFMW69bt25q+umnn3apjjOjSYhEe+epW7duSE9PV9OZmZm6dc6dOye299xzz4llRhNa\n7YRCu/3aa6+JdUaOHCmWSaHYjSZPkZGR6nZAQIDDZOqRRx7RrWMPsaynS5cuYtmiRYt087V3MSMj\nIx0mzqtWrRLbkybwRnfUli9fLpbZaa9LwPiuHhHR7aIxUWjPnz+PTz75BHPmzEFISAh++tOfqt93\nJk+ezCBsRGSowYmazWbDq6++ipiYGEycOFHNd+XXIyIiIqL2pDFRaHv27ImePXuq6ZEjRxr+0EhE\npNXgRO306dM4cOAAunfvjsWLFwMApk6dKv56RERERERERE3T4EStT58+2LFjh26Z3q9HRERERERE\n1DT6ETKIiIiIiIiozXCiRkRERERE5GE4USMiIiIiIvIwjVrwujnY12Ky2WwO6zLNmjWr0W3l5OSI\nZdL6YdrIlQCQlZWl5hktyiyVTZgwQaxjtM7WuHHj1O2PP/5Y7cPly5fFOmPHjhXLpHXeXF1Hbffu\n3Q59ktZLM1rk2WitNKPQ7vY10aqqqhzWRzOKjCWtHQbI59BoCYOioiJ1u7Ky0qEfmzdvFutJvvvu\nO7Fs/vz5uvnahbAHDRqE559/Xk0brV/3zjvv6Ob7+fmJdT777DOxzE57XQLy+fvPf/7TYFtEREQ1\nsya1dReIvArvqBEREREREXkYTtSIiIiIiIg8DCdqREREREREHoYTNSIiIiIiIg/DiRoREREREZGH\n4USNiIiIiIjIw7R6eH6bzaa7HRAQoLv/1atXxba04cydSeHgIyIiHNI+Pj4wm80AAEVRxPYkHTp0\nEMuKi4vFMm3fFUVR00Yh1Y1C0mvPpZavr/wRa+soiuLQvhSK3Sjcv/QZAkB1dbVYZl9awHnJhrCw\nMLGO0TIGFotFN//atWtiHZPJpG4riuJw3qRzW15eLrZnpGPHjrr52uP4+Pg4nE+j8PzBwcGN7oPR\n346d9rp07h8RERERtSzeUSMiIiIiIvIwrX5HjYioOfzwww9IT09X0/n5+ZgyZQrKy8uRk5Oj3rmc\nOnUqEhMT26qbRERERG7hRI2IvFJ0dDRWr14NoO5R3V/96ld44IEH8Pe//x0TJkzApEmT2riHRERE\nRO7jRI2IvN6JEyfQpUsXREZGtnVXiIjIDTWz+OMakTNO1IjI63366acYMmSIms7KysKBAwcQFxeH\n6dOnIyQkpF6d7OxsZGdnAwBSU1PrBRqSaAPNuFrHU/n6+nr9GOzay1jayziA9jUWIqK24DETNSmq\nnVGkOSmyIwCH6IFazpH6amtr1TyjY9kjEzozisYnRU50rldbW6umb968KdYpLS0Vy5qqtrYWN27c\nUNMVFRW6+xlFfTSKWNnQsfW2pT447+dMiu4YFBQk1qmsrFS3bTabw7UgXWfSNQYYRxCVxqUdU01N\njcPnYTRe6TORrlnA+Fqy9117XQKeG/WxuroaX375JZ544gkAwJgxYzB58mQAwPbt27FlyxbMnTu3\nXj2r1Qqr1aqmCwoKXDqe9ounq3U8VUREhNePwa69jKW9jAPwjLFER0e36fGJiJrCYyZqRETuOHr0\nKGJjY9GpUycAUP8LAKNGjcKqVavaqmtEdBuRAhxNmDBBzTt58iR+//vfo3PnzgCAgQMHqj8sERE5\n40SNiLya82OPRUVF6hp8R44cQbdu3dqqa0R0G5ECHDnr27cvlixZ0trdIyIvxIkaEXmtyspKHD9+\nHLNnz1bz3nrrLVy8eBGKoiAyMtKhjIioNTDAERE1B07UiMhrBQYG4o033nDIe/rpp1vt+PYoZaY/\nfdhqxyQiz+d8p1/rzJkzWLx4McLCwpCSkqJ719/dYEdNYRT85Ypm25W+XHFK2+s457c1V/pltI87\nn4u3Bdnxtv4C3tlnCSdqRERERM3EOcCRVmxsLDZs2IDAwEDk5eVh9erVWL9+fb393A121BSuBn9x\npy9tHVRG0tTxujMuTwiy0xje1l/AO/rsaqAjOSwcERERETWKc4AjraCgIAQGBgIAEhMTUVNTg+vX\nr7d2F4nIS3jMHTXpFmVRUZFYJyAgQCyTwtzHxMQ4pP38/NS8wsJCsT0p3Lpze1pG7d1xxx0OfbCn\ntWHinbVkmGF/f3+H9vPz83X3MwqZrx2TM6NfNrRh5LXbRs/2G10XJpNJN9/o3EZFRanbfn5+Dmkp\nLP3Vq1fF9rRrbTmzB7ow4uvri/DwcDVt1Pfg4GDdfKOQ/q5cS9rrEqj7lZiIiIwZPfZYXFyM0NBQ\nKIqCc+fOoba2Fh06dGjlHhKRt2hwolZQUICMjAwUFxdDURRYrVaMHz8eO3bsQE5ODjp27AgAmDp1\nKhITE1u8w0RERESeSC/A0Z49ewDUrfF4+PBh7NmzByaTCf7+/liwYIHhuptEdHtrcKJmMpmQkpKC\nuLg4VFRUYMmSJUhISAAATJgwAZMmTWrxThIRERF5Or0AR2PGjFG3x40bh3HjxrV2t1pEY4Ip2ff1\nNK70y52+a+sw2BQ1RYMTtbCwMPVRLbPZjJiYGMNH+oiIiIiIiKhpGvWOWn5+Pi5cuIBevXrh66+/\nRlZWFg4cOIC4uDhMnz4dISEh9eo4h5jNzc0FULfgo30bkN83q6qqEvsjvYsEADU1Nbr5ZrPZIR0X\nF4etW7cCMH7/SuLcnpZRe0FBQep2bGys2gej8Rq9k9dU2j4AwK1bt3T3M3rvyd/fXywzOhf2xz76\n9OmDQ4cOqflG4zV6Z0t6jER61wyoex/Lrnfv3ti1a5e4r52772xpjyXp1asXdu7cqaalzwNwfK/P\nVa5cS87XhNH5IyIiIqLm5fJErbKyEmlpaZgxYwaCgoIwZswYTJ48GQCwfft2bNmyBXPnzq1XzznE\n7IABAwAAubm56jZQ9+VYz9mzZ8U+hYaGimWlpaW6+f369XNIb926FU8++SSAugUqJdKX/3vvvVes\nY9Se/fFR5z5cuHBBrBMbGyuWNZW2D4B7wUS6d+8ulhmdC3vgjUOHDmHw4MFq/p133inWOXPmTIPt\nOTOaaGiDh+zatQvjx49vsJ67wUS6dOkiltnt3LkTjz76qJr+/vvvxX31fiABjCfVrlxLzteENDE1\n+myJiIiIyD0u/RRfXV2NtLQ0DBs2DAMHDgQAdOrUCT4+PvDx8cGoUaNw/vz5Fu0oERERERHR7aLB\nO2o2mw2vvvoqYmJiMHHiRDW/qKhIfXftyJEj6Natm2sH/O+dBkVRHO46pKSk6O6flpYmtjVs2DCx\n7MiRI7r5M2fOdEhHRESoeUuWLBHbkx4Vk/oNAC+88IJYtmDBAnW7c+fOanrt2rVinWeeeUYsk+6e\nuPp4XmRkJH7961+r6X/84x+6+xnd2Zk2bZpYtmjRIrHMYrEAqHt0UrvcwSOPPCLW2bx5s1gm3QEz\neqzUuY42rY3epbV9+3axvfLycrFM+3ekpb1r26lTJ4fxv/POO2J70jIGUth+AA6ftaRz585YuHCh\nmjZ63JSIiIiImleDE7XTp0/jwIED6N69OxYvXgygLhT/p59+iosXL0JRFERGRopfZomIiIiIiKhx\nGpyo9enTBzt27KiXzzXTiIiIiIiIWkbjw8URERERERFRi+JEjYiIiIiIyMNwokZERERERORhGrXg\ndXOwR9Oz2WwOkfWkBYaN1oK6ePGiWCZFO3R+327MmDFqnlFUwJs3b+rmG0XjM2pPGzFw8ODBavrb\nb791qY6rjPqgjTI4fPhwh/a/+eYb3TpGkf/03mV0pR8lJSUA6hYpt28DwPvvvy/WMSJFXJQWQQcc\n10Srrq52SEvn3Siq4nfffSeWSde6NrLo7Nmz1YXiAaCsrExsT3vOtLSLqjvbtm2bWGb34IMPOuwn\n/U0xkBARERFR82v1iRoRERERUXtRM2sSAMD0pw/FMtUHh1qjS9RO8NFHIiIiIiIiD8OJGhERERER\nkYfhRI2IiIiIiMjDcKJGRERERETkYRhMhIi82rx58xAYGAgfHx+YTCakpqairKwM6enpuHr1KiIj\nI7Fw4UKEhIS0dVeJiIiIXNbqEzVtiHTtthRS/caNG2Jb2hDqzkpLS3Xzb9265ZC22Wz18vRoQ9lr\nGYV81y4/4Oz69esObdjTRn2RxmTEqA/O+2lD70vLIhi1ZxRC3mhcFRUV6jHt2wDQpUsXsY5R+HuJ\n9BkCgK+vr5iWrk2jPkRGRjayd47h7202m0PaaJkKf3//Rh9Le/058/Gpu9FeU1Pj8JlKS1R4guXL\nl6Njx45qOjMzE/369UNycjIyMzORmZmJadOmtWEPiYiIiBqHjz4SUbuTm5uL4cOHA6hbIzA3N7eN\ne0RERETUOHz0kYi83sqVKwEAo0ePhtVqRUlJCcLCwgAAnTp10l0UPDs7W11UPDU1FRERES4dy/nu\nKwCX63oaX19fr+27s/YylvYyDqB9jcVVeo9ia9lsNmzevBlHjx5FQEAA5s6di7i4uDbqLRF5Ok7U\niMirvfTSS7BYLCgpKcGKFSsQHR3tUK4oiu5jr1arFVarVU0XFBS4dDy9L56u1vU0ERERXtt3Z+1l\nLO1lHIBnjMX534PW4PwottbRo0dx+fJlrF+/HmfPnsXrr7+Ol19+uZV7SETego8+EpFXs1gsAIDQ\n0FAMGDAA586dQ2hoKIqKigAARUVF4pcmIqLW9MUXX+DHP/4xFEXBXXfdhfLycvXfKiIiZ7yjRkRe\nq7KyEjabDWazGZWVlTh+/DgmT56MpKQk7N+/H8nJydi/fz8GDBjQ1l0lotuE86PYWoWFhQ535cPD\nw1FYWKg+qm3n7qPZTaH3qOqVnwyut599nytOaYd6zd47zySdC6Pxe9sjwd7WX8A7+yzhRI2IvFZJ\nSQnWrFkDoC5K5dChQ9G/f3/07NkT6enp2Lt3rxqen4iopek9ih0fH9/odtx9NLspXH1U1Xmftn68\ntS25cy6qq6u96px5wiPMjeUNfXb1sexWn6iZTCbd7UWLFunuP2/ePLGtoUOHimU5OTm6+S+++KJD\nOjo6Ws372c9+JrYnhUBfvny5WMcoHLj2BeOYmBg1/dvf/lass2LFCrFMCptvFE5fWxYTE6P+CggA\nu3bt0q3z7bffiu3NnTtXLBs/frxY1r17dwB15/jOO+9U86VrAgDmz58vlkmPuWlD/zvT/prp5+fn\nsDTAxIkTdetI56gh0uf473//W92OiIjAzJkz1fQf//hHsT3pS4Be0Au75557rqFuIiYmxuHdiaqq\nqgbrtLaoqCisXr26Xn6HDh2wbNmyNugREd3O9B7F1v4bbbFYHL5AXrt2Ta1DROSM76gRERERNVFl\nZaX6g6D9UWz7D5F2SUlJOHDgAGw2G86cOYOgoKB6jz0SEdnx0UciIiKiJpIexd6zZw8AYMyYMbjv\nvvuQl5eH+fPnw9/f3/BJFCIiTtSIiIiImkh6FHvMmDHqtqIoeOqpp1qzW0TkxfjoIxERERERkYfh\nRI2IiIiIiMjDtPqjj4qiqP+1bwOAj4/+nFHKB4CAgACxzCjincTVCImu1tGOz5l2XIqiqGmj9ozO\nhVSvpqbG5f5p07W1tbp1bt265Vb/jD4Pe/RPRVEcIoHevHlTrCNF4QTkcyGNqSHS52h0/VVXV4tl\n2uiOWtqIlwEBAQ5po4iL0rHcvZbstNelq3WIiIiIqHk0OJu5efMmli9fjurqatTU1GDQoEGYMmUK\n8vPzsXbtWpSWliIuLg5PP/20W5MjIiIiIiIictTgzMrPzw/Lly9HYGAgqqursWzZMvTv3x8ff/wx\nJkyYgCFDhuC1117D3r17HV6YJSIiIiIiIvc0OFFTFAWBgYEA6h6jq6mpgaIoOHnyJJ555hkAwIgR\nI/DOO+9wokZERER0G6qZNamtu9DqmjJmbV3Tnz50u747dcl7uPSsYm1tLZ577jlcvnwZY8eORVRU\nFB45WP8AABw3SURBVIKCgtT3iSwWCwoLC3XrZmdnIzs7GwCQmpqKw4cPAwD69OmjbgNAjx49dOvv\n379f7FenTp3EssWLF+vmx8bGOqSDg4MxaNCgBo8lvZ+jfY/I2d69e8Wy3r17q9tmsxkJCQkAgNde\ne02s07VrV7HM6H0kVwQFBSExMVFNx8XF6e5n9K5UVFSUWPb3v/9dLLO/bxYXF4dt27ap+ZGRkWId\n+2emR/qsjN7X0z6226tXL+zcuVNNS9fZ7NmzxfaMPo+IiAjdfO07bx07dnT44SMrK0tsz2w2i2WS\n6OjoBvfRXpeA++/4EREREVHjuTRR8/HxwerVq1FeXo41a9bghx9+cPkAVqsVVqtVTdu/YB8+fNjh\ny/amTZt068+bN09se9Ik+ZcM+wKTzt566y2H9KBBg9QJ4+TJk8X2pOAVb775plhn+vTpYtlf//pX\ndTshIQHHjx8HACxZskSs88orr4hl7gQT0UpMTEReXp6a/uijj3T3u3DhgtjGwoULxbKJEyeKZd26\ndQMAbNu2DY8//riab7QQ6PPPPy+WSUE+bty4IdYJDw9Xt3fu3IlHH31UTT/yyCO6dew/QOgxCiYy\nc+ZM3XztpH/MmDEO1/CCBQvE9vr166ebrw3M4mzZsmVimZ32ugTkSfpDDz3UYFtERERE1DiNCuMW\nHByMe+65B2fOnMGNGzfUSUBhYSEsFkuLdJCIiIiIiOh20+AdtevXr8NkMiE4OBg3b97E8ePH8eij\nj+Kee+7B4cOHMWTIEOzbtw9JSUkuHdB+p8FmszncdVixYoXu/hUVFWJbu3fvFsvKysp08xctWuSQ\n3r59u5pndMdFKnv22WfFOqWlpWKZ9k7h1q1b1fTZs2fFOr/5zW/EsqbeUdu2bZtD+1evXtXdz+hO\n0fnz58Uy6fPQ1quqqnJoY9WqVW61J5UZPbpXWVmpbt+6dQvff/+9mn7nnXca3QejY/3xj3/Uzdfe\nscrKynK4i2Z0d0x6rDQsLEysY3Sn2k57XQLyZ3/ixIkG2yIiIiKixmlwolZUVISMjAzU1tbCZrPh\nwQcfxP3334+uXbti7dq12LZtG2JjYzFy5MjW6C8REREREVG71+BErUePHvj9739fLz8qKsrwnSki\nIiIiIiJyT6PeUSMiIiIiIqKWx4kaERERERGRh+FEjYiIiIiIyMNwokZERERERORhXFrwujXEx8fr\n5huFq+/QoYNYVlxcrJufmJjokA4KClLzzpw5I7bn5+enm3///feLdc6dOyeW9e/fX902m81qOj8/\n36U6zqTw/K4KCgpyGIt03i9fviy2YdQ/oxDugYGBAABFUdRtAHjggQfEOlLIfKBuvT89t27dcqmO\nj48PQkJC1HRkZKRunZKSErE9aYF0QL7WteHvzWazw0LWUgh+QF5w2tXrz5n9WjKbzfjRj36k5ru6\n1AMREXmPmlmTAABXNHmmP33Y6Prk2rm48pPBYr3GnHej4ze1HfIcHjNRIyJqjIKCAmRkZKC4uBiK\nosBqtWL8+PHYsWMHcnJy0LFjRwDA1KlT6/1A09z4P0ciIiJqbpyoEZFXMplMSElJQVxcHCoqKrBk\nyRIkJCQAACZMmIBJk/grLxEREXkvTtSIyCuFhYUhLCwMQN1jmjExMSgsLGzjXhHR7Uq6y6918uRJ\n/P73v0fnzp0BAAMHDsTkyZPbortE5AU4USMir5efn48LFy6gV69e+Prrr5GVlYUDBw4gLi4O06dP\nd3jn0C47OxvZ2dkAgNTUVERERLh0LF9f+Z9NV9vwFL6+vl7XZ0l7GUt7GQfQvsbiCukuf9euXR32\n69u3L5YsWdJGvSQib8KJGhF5tcrKSqSlpWHGjBkICgrCmDFj1F+ot2/fji1btmDu3Ln16lmtVlit\nVjVdUFDg0vGMvni62oaniIiI8Lo+S9rLWNrLOADPGEt0dHSrHUu6y+88USMiclWrT9QURdHdDgoK\n0t1firZoVAeQo+5powoCdRH+tFEHJT4++isZBAQENLoO4Bhl0GQyqWmTySTWMRpvU6M++vj4OLQv\n3TUwupsgRVu0ty+xj1lRFIfxG30eRteFO32ora0V09K4jD4PI9I5dP4MtefC/j9/PVJ0x759+4p1\njD4r7fG1d6Ju3rzZYJ3WVl1djbS0NAwbNgwDBw4EAHTq1EktHzVqFFatWtVW3SOi25T2Lr+zM2fO\nYPHixQgLC0NKSgq6detWbx937/i744pOnv14jS2jpmnM52w//9o6enlN5Y13xr2xzxLeUSMir2Sz\n2fDqq68iJiYGEydOVPOLiorUie2RI0d0vwQREbUU57v8WrGxsdiwYQMCAwORl5eH1atXY/369fXa\ncPeOf3MxOl5b3yVtz9w5t3p1mvMz8oQ7443lDX1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ssaUuncONAUCj0fD2tbH14Ovx48ex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\nbAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa148ef320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# prepare data\n", "\n", "Z = data['Z']\n", "G = data['G']\n", "\n", "Z = Z[Z.mean(1) > 10, :] # keep only expressed tags\n", "# Z = Z[Z.sum(1) > _nmp.percentile(Z.sum(1), 40), :]\n", "G = G[:, _nmp.std(G, 0) > 0] # keep only non-monomorphic loci\n", "\n", "Z = Z / _assoc.calculate_norm_factors(Z)\n", "Z = _assoc.transform_data(Z, kind='arcsin')\n", "\n", "print(G.shape, Z.shape)\n", "\n", "# take a quick look at the data\n", "\n", "_plt.figure(figsize=(15,10));\n", "_plt.subplot(2,3,1); _plt.imshow(G.T.dot(G), cmap=_plt.cm.Greys_r);\n", "_plt.subplot(2,3,2); _plt.hist(Z[:, 0], bins=100);\n", "_plt.subplot(2,3,3); _plt.hist(Z[0, :], bins=100);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# _rnd.seed(0)\n", "res = _assoc.run(Z.T, G, n_iters = 1000, model='Normal', n_threads=8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# with open('/home/dimitris/WTCHG/Projects/eQTLseq/results/geuvadis/raw/TF.common.HIGH.tfs.339.NBinomial.none.1.pkl', 'rb') as fh:\n", "# res = _pkl.load(fh)\n", "\n", "##\n", "_plt.figure(figsize = (15,10));\n", "\n", "_plt.subplot(4,1,1); _plt.plot(res['state'][100:]); _plt.xlabel('iteration'); _plt.ylabel('state')\n", "_plt.subplot(4,1,2); _plt.vlines(range(res['beta'].size), 0, res['beta']); _plt.xlabel('markers x genes'); _plt.ylabel('effect size')\n", "_plt.axhline(linestyle='--', color='k');\n", "\n", "\n", "##\n", "tmp = res['beta'][_nmp.abs(res['beta'])>0.25 * _nmp.max(_nmp.abs(res['beta']))]\n", "print(_nmp.min(_nmp.abs(tmp)), tmp.size)\n", "_plt.subplot(4,1,3); _plt.hist(tmp.ravel(), 100);\n", "\n", "##\n", "_nmp.transpose((_nmp.abs(res['beta']) > 1e-6).nonzero())\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cp eQTLseq/*.py /home/dimitris/WTCHG/Projects/eQTLseq/eQTLseq/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G.ravel() in (0,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_nmp.min(res['zeta'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('/home/dimitris/WTCHG/Projects/eQTLseq/data/geuvadis/N1000.M100.G1000.P1_1_0_0.S2.R1.2.1.pkl', 'rb') as fh: \n", " tmp = _pkl.load(fh)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_nmp.min(tmp['phi']), tmp['poisson'].sum(), tmp['outliers'].sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tmp.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "100/1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['G'].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['G'][:,:8]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_nmp.any(data['G'] == -1, 0).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "75/100*452" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cnts = _pnd.read_table('/home/dimitris/Data/Geuvadis/counts/counts_miRNAs.txt', index_col=0).values\n", "flt = cnts[cnts.mean(1) > 0,:]\n", "p = (flt + 1*0) / (flt.sum(0) + 0*flt.shape[0])\n", "arcsin = _nmp.arcsin(_nmp.sqrt(p))\n", "logit = _nmp.log(p / (1 - p))\n", "log = _nmp.log(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_plt.plot(arcsin.mean(1), arcsin.std(1), '.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_plt.hist(arcsin.ravel(), 100);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "3.14/2" ] }, { "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 }
mit
rsignell-usgs/notebook
iris_snippets_debug.ipynb
1
28585
{ "metadata": { "kernelspec": { "codemirror_mode": { "name": "ipython", "version": 3 }, "display_name": "IOOS (Python 2)", "language": "python", "name": "ioos_python2" }, "name": "", "signature": "sha256:af4ddc42aaacc7423eaa3d18494f8bc910804117a6fefc8567f28388faf0bdb0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import time\n", "import contextlib\n", "from datetime import datetime, timedelta\n", "\n", "import numpy as np\n", "import numpy.ma as ma\n", "import matplotlib.tri as tri\n", "import matplotlib.pyplot as plt\n", "from scipy.spatial import Delaunay\n", "\n", "import iris\n", "from iris.unit import Unit\n", "from iris.exceptions import CoordinateNotFoundError\n", "\n", "import cartopy.crs as ccrs\n", "from cartopy.feature import NaturalEarthFeature, COLORS\n", "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", "\n", "LAND = NaturalEarthFeature('physical', 'land', '10m', edgecolor='face',\n", " facecolor=COLORS['land'])\n", "\n", "iris.FUTURE.netcdf_promote = True\n", "iris.FUTURE.cell_datetime_objects = True # <- TODO!\n", "\n", "\n", "def time_coord(cube):\n", " \"\"\"Return the variable attached to time axis and rename it to time.\"\"\"\n", " try:\n", " cube.coord(axis='T').rename('time')\n", " except CoordinateNotFoundError:\n", " pass\n", " timevar = cube.coord('time')\n", " return timevar\n", "\n", "\n", "def z_coord(cube):\n", " \"\"\"Heuristic way to return the dimensionless vertical coordinate.\"\"\"\n", " try:\n", " z = cube.coord(axis='Z')\n", " except CoordinateNotFoundError:\n", " z = cube.coords(axis='Z')\n", " for coord in cube.coords(axis='Z'):\n", " if coord.ndim == 1:\n", " z = coord\n", " return z\n", "\n", "\n", "def time_near(cube, datetime):\n", " \"\"\"Return the nearest index to a `datetime`.\"\"\"\n", " timevar = time_coord(cube)\n", " try:\n", " time = timevar.units.date2num(datetime)\n", " idx = timevar.nearest_neighbour_index(time)\n", " except IndexError:\n", " idx = -1\n", " return idx\n", "\n", "\n", "def time_slice(cube, start, stop=None):\n", " \"\"\"TODO: Re-write to use `iris.FUTURE.cell_datetime_objects`.\"\"\"\n", " istart = time_near(cube, start)\n", " if stop:\n", " istop = time_near(cube, stop)\n", " if istart == istop:\n", " raise ValueError('istart must be different from istop!'\n", " 'Got istart {!r} and '\n", " ' istop {!r}'.format(istart, istop))\n", " return cube[istart:istop, ...]\n", " else:\n", " return cube[istart, ...]\n", "\n", "\n", "def plot_surface(cube, model='', unstructure=False, **kw):\n", " projection = kw.pop('projection', ccrs.PlateCarree())\n", " figsize = kw.pop('figsize', (8, 6))\n", " cmap = kw.pop('cmap', plt.cm.rainbow)\n", "\n", " fig, ax = plt.subplots(figsize=figsize,\n", " subplot_kw=dict(projection=projection))\n", " ax.set_extent(get_bbox(cube))\n", " ax.add_feature(LAND)\n", " ax.coastlines(resolution='10m')\n", " gl = ax.gridlines(draw_labels=True)\n", " gl.xlabels_top = gl.ylabels_right = False\n", " gl.xformatter = LONGITUDE_FORMATTER\n", " gl.yformatter = LATITUDE_FORMATTER\n", "\n", " z = z_coord(cube)\n", " if z:\n", " positive = z.attributes.get('positive', None)\n", " if positive == 'up':\n", " idx = np.unique(z.points.argmax(axis=0))[0]\n", " else:\n", " idx = np.unique(z.points.argmin(axis=0))[0]\n", " c = cube[idx, ...].copy()\n", " else:\n", " idx = None\n", " c = cube.copy()\n", " c.data = ma.masked_invalid(c.data)\n", " t = time_coord(cube)\n", " t = t.units.num2date(t.points)[0]\n", " if unstructure:\n", " # The following lines would work if the cube is note bbox-sliced.\n", " # lon = cube.mesh.nodes[:, 0]\n", " # lat = cube.mesh.nodes[:, 1]\n", " # nv = cube.mesh.faces\n", " lon = cube.coord(axis='X').points\n", " lat = cube.coord(axis='Y').points\n", " nv = Delaunay(np.c_[lon, lat]).vertices\n", " triang = tri.Triangulation(lon, lat, triangles=nv)\n", " # http://matplotlib.org/examples/pylab_examples/ tricontour_smooth_delaunay.html\n", " if False: # TODO: Test this.\n", " subdiv = 3\n", " min_circle_ratio = 0.01\n", " mask = tri.TriAnalyzer(triang).get_flat_tri_mask(min_circle_ratio)\n", " triang.set_mask(mask)\n", " refiner = tri.UniformTriRefiner(triang)\n", " tri_ref, data_ref = refiner.refine_field(cube.data, subdiv=subdiv)\n", " cs = ax.tricontourf(triang, c.data, cmap=cmap, **kw)\n", " else:\n", " cs = ax.pcolormesh(c.coord(axis='X').points,\n", " c.coord(axis='Y').points,\n", " c.data, cmap=cmap, **kw)\n", " title = (model, t, c.name(), idx)\n", " ax.set_title('{}: {}\\nVariable: {} level: {}'.format(*title))\n", " return fig, ax, cs\n", "\n", "\n", "def get_bbox(cube):\n", " xmin = cube.coord(axis='X').points.min()\n", " xmax = cube.coord(axis='X').points.max()\n", " ymin = cube.coord(axis='Y').points.min()\n", " ymax = cube.coord(axis='Y').points.max()\n", " return [xmin, xmax, ymin, ymax]\n", "\n", "\n", "@contextlib.contextmanager\n", "def timeit(log=None):\n", " t = time.time()\n", " yield\n", " elapsed = time.strftime(\"%H:%M:%S\", time.gmtime(time.time()-t))\n", " if log:\n", " log.info(elapsed)\n", " else:\n", " print(elapsed)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "model = 'NECOFS_FVCOM'\n", "\n", "start = datetime.utcnow() - timedelta(days=7)\n", "\n", "bbox = [-70.8, 41.4, -69.9, 42.3]\n", "\n", "units = Unit('Kelvin')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### No horizontal subset works fine." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with timeit():\n", " url = \"http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/\"\n", " url += \"Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc\"\n", "\n", " cube = iris.load_cube(url, 'sea_water_potential_temperature')\n", " cube = time_slice(cube, start, None)\n", " cube.convert_units(units)\n", " print(cube)\n", " \n", "fig, ax, cs = plot_surface(cube, model, unstructure=True)\n", "cbar = fig.colorbar(cs, extend='both', shrink=0.75)\n", "t = cbar.ax.set_title(cube.units)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/cf.py:1004: UserWarning: Ignoring variable u'siglay' referenced by variable u'ww': Dimensions (u'siglay', u'node') do not span (u'time', u'siglay', u'nele')\n", " warnings.warn(msg)\n", "/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/cf.py:1004: UserWarning: Ignoring variable u'siglay' referenced by variable u'u': Dimensions (u'siglay', u'node') do not span (u'time', u'siglay', u'nele')\n", " warnings.warn(msg)\n", "/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/cf.py:1004: UserWarning: Ignoring variable u'siglay' referenced by variable u'v': Dimensions (u'siglay', u'node') do not span (u'time', u'siglay', u'nele')\n", " warnings.warn(msg)\n", "/home/usgs/anaconda/lib/python2.7/site-packages/cartopy/io/__init__.py:261: DownloadWarning: Downloading: http://www.nacis.org/naturalearth/10m/physical/ne_10m_land.zip\n", " warnings.warn('Downloading: {}'.format(url), DownloadWarning)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "sea_water_potential_temperature / (Kelvin) (-- : 10; -- : 98432)\n", " Auxiliary coordinates:\n", " ocean_sigma_coordinate x x\n", " latitude - x\n", " longitude - x\n", " sea_floor_depth_below_geoid - x\n", " sea_surface_height_above_geoid - x\n", " Scalar coordinates:\n", " time: 2014-09-16 00:00:00\n", " Attributes:\n", " Conventions: CF-1.0\n", " CoordinateProjection: init=nad83:1802\n", " CoordinateSystem: Cartesian\n", " GroundWater_Forcing: GROUND WATER FORCING IS OFF!\n", " River_Forcing: THERE ARE 18 RIVERS IN THIS MODEL.\n", "RIVER INFLOW IS ON THE nodes WHERE TEMPERATURE...\n", " Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Surface_PrecipEvap_Forcing: FVCOM periodic surface precip forcing:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Surface_Wind_Forcing: FVCOM variable surface Wind forcing:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!\n", " _DODS_Unlimited_Dimension: time\n", " cdm_data_type: any\n", " coverage_content_type: modelResult\n", " grid: fvcom_grid\n", " history: Fri Sep 19 09:31:25 2014: ncrcat -O -v x,y,lat,lon,xc,yc,lonc,latc,siglay,siglev,nv,nbe,aw0,awx,awy,h,temp,salinity,u,v,ww,ua,va,zeta,Times...\n", " institution: School for Marine Science and Technology\n", " location: node\n", " mesh: fvcom_mesh\n", " nco_openmp_thread_number: 1\n", " references: http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu\n", " source: FVCOM_3.0\n", " summary: Latest forecast from the FVCOM Northeast Coastal Ocean Forecast System...\n", " title: NECOFS Massachusetts (FVCOM) - Massachusetts Coastal - Latest Forecast\n", " type: data\n", "00:00:05\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/home/usgs/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:239: FormatterWarning: Exception in image/png formatter: HTTP Error 404: Not Found\n", " FormatterWarning,\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x7f4189009e90>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### If forcing the `X` and `Y` the subset works." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with timeit():\n", " url = \"http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/\"\n", " url += \"Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc\"\n", "\n", " cube = iris.load_cube(url, 'sea_water_potential_temperature')\n", " cube = time_slice(cube, start, None)\n", " cube.convert_units(units)\n", " print(cube.coord(axis='Y'))\n", " print(cube.coord(axis='X'))\n", " print(cube.coord(axis='Z'))\n", " print(\"\\n\")\n", " cube = cube.intersection(longitude=(bbox[0], bbox[2]),\n", " latitude=(bbox[1], bbox[3]))\n", " print(cube)\n", " \n", "fig, ax, cs = plot_surface(cube, model, unstructure=True)\n", "cbar = fig.colorbar(cs, extend='both', shrink=0.75)\n", "t = cbar.ax.set_title(cube.units)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "AuxCoord(array([ 43.30540466, 43.29943466, 43.29204559, ..., 42.37276459,\n", " 42.37341309, 42.37305069], dtype=float32), standard_name=u'latitude', units=Unit('degrees'), long_name=u'nodal latitude', var_name='lat')\n", "AuxCoord(array([-70.56564331, -70.54589081, -70.52242279, ..., -71.13265991,\n", " -71.13302612, -71.13330841], dtype=float32), standard_name=u'longitude', units=Unit('degrees'), long_name=u'nodal longitude', var_name='lon')" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "AuxCoord(array([[-0.05000001, -0.04501463, -0.03064007, ..., -0.05000001,\n", " -0.05000001, -0.05000001],\n", " [-0.15000001, -0.13504389, -0.09192021, ..., -0.15000001,\n", " -0.15000001, -0.15000001],\n", " [-0.25000003, -0.22507316, -0.15320036, ..., -0.25000003,\n", " -0.25000003, -0.25000003],\n", " ..., \n", " [-0.75000006, -0.73931712, -0.70851445, ..., -0.75000006,\n", " -0.75000006, -0.75000006],\n", " [-0.85000002, -0.84359032, -0.82510877, ..., -0.85000002,\n", " -0.85000002, -0.85000002],\n", " [-0.95000005, -0.94786352, -0.94170296, ..., -0.95000005,\n", " -0.95000005, -0.95000005]], dtype=float32), standard_name=u'ocean_sigma_coordinate', units=Unit('unknown'), long_name=u'Sigma Layers', var_name='siglay', attributes={'positive': 'up'})" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "\n", "sea_water_potential_temperature / (Kelvin) (-- : 10; -- : 44575)\n", " Auxiliary coordinates:\n", " ocean_sigma_coordinate x x\n", " latitude - x\n", " longitude - x\n", " sea_floor_depth_below_geoid - x\n", " sea_surface_height_above_geoid - x\n", " Scalar coordinates:\n", " time: 2014-09-16 00:00:00\n", " Attributes:\n", " Conventions: CF-1.0\n", " CoordinateProjection: init=nad83:1802\n", " CoordinateSystem: Cartesian\n", " GroundWater_Forcing: GROUND WATER FORCING IS OFF!\n", " River_Forcing: THERE ARE 18 RIVERS IN THIS MODEL.\n", "RIVER INFLOW IS ON THE nodes WHERE TEMPERATURE...\n", " Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Surface_PrecipEvap_Forcing: FVCOM periodic surface precip forcing:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Surface_Wind_Forcing: FVCOM variable surface Wind forcing:\n", "FILE NAME:wrf_for.nc\n", "SOURCE:wrf2fvcom...\n", " Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!\n", " _DODS_Unlimited_Dimension: time\n", " cdm_data_type: any\n", " coverage_content_type: modelResult\n", " grid: fvcom_grid\n", " history: Fri Sep 19 09:31:25 2014: ncrcat -O -v x,y,lat,lon,xc,yc,lonc,latc,siglay,siglev,nv,nbe,aw0,awx,awy,h,temp,salinity,u,v,ww,ua,va,zeta,Times...\n", " institution: School for Marine Science and Technology\n", " location: node\n", " mesh: fvcom_mesh\n", " nco_openmp_thread_number: 1\n", " references: http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu\n", " source: FVCOM_3.0\n", " summary: Latest forecast from the FVCOM Northeast Coastal Ocean Forecast System...\n", " title: NECOFS Massachusetts (FVCOM) - Massachusetts Coastal - Latest Forecast\n", " type: data" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "00:00:05\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x7f4187b31b50>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Trying to subset directly takes forever..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with timeit():\n", " url = \"http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/\"\n", " url += \"Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc\"\n", "\n", " cube = iris.load_cube(url, 'sea_water_potential_temperature')\n", " cube = time_slice(cube, start, None)\n", " cube.convert_units(units)\n", " cube = cube.intersection(longitude=(bbox[0], bbox[2]),\n", " latitude=(bbox[1], bbox[3]))\n", " print(cube)\n", " \n", "fig, ax, cs = plot_surface(cube, model, unstructure=True)\n", "cbar = fig.colorbar(cs, extend='both', shrink=0.75)\n", "t = cbar.ax.set_title(cube.units)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "NetCDF: I/O failure", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-5-856ee578308c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mcube\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m cube = cube.intersection(longitude=(bbox[0], bbox[2]),\n\u001b[1;32m----> 9\u001b[1;33m latitude=(bbox[1], bbox[3]))\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcube\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/iris/cube.pyc\u001b[0m in \u001b[0;36mintersection\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2051\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_intersect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2052\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2053\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_intersect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2054\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2055\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/iris/cube.pyc\u001b[0m in \u001b[0;36m_intersect\u001b[1;34m(self, name_or_coord, minimum, maximum, min_inclusive, max_inclusive)\u001b[0m\n\u001b[0;32m 2071\u001b[0m \u001b[0mminimum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaximum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2072\u001b[0m \u001b[0mmin_inclusive\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2073\u001b[1;33m max_inclusive)\n\u001b[0m\u001b[0;32m 2074\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2075\u001b[0m \u001b[1;31m# By this point we have either one or two subsets along the relevant\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/iris/cube.pyc\u001b[0m in \u001b[0;36m_intersect_modulus\u001b[1;34m(self, coord, minimum, maximum, min_inclusive, max_inclusive)\u001b[0m\n\u001b[0;32m 2153\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoord\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2154\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoord\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2156\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmodulus\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2157\u001b[0m raise ValueError(\"coordinate's range greater than coordinate's\"\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/iris/coords.pyc\u001b[0m in \u001b[0;36mpoints\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1491\u001b[0m \u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_points\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1492\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbiggus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1493\u001b[1;33m \u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\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 1494\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_points\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1495\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/biggus/__init__.pyc\u001b[0m in \u001b[0;36mndarray\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 780\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 781\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mndarray\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--> 782\u001b[1;33m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_keys\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 783\u001b[0m \u001b[1;31m# We want the shape of the result to match the shape of the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 784\u001b[0m \u001b[1;31m# Array, so where we've ended up with an array-scalar,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/biggus/__init__.pyc\u001b[0m in \u001b[0;36m_apply_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 877\u001b[0m \"\"\"\n\u001b[0;32m 878\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_apply_keys\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--> 879\u001b[1;33m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcrete\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 880\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/netcdf.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, keys)\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[0mvariable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 248\u001b[0m \u001b[1;31m# Get the NetCDF variable data and slice.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 250\u001b[0m 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mit
searchs/bigdatabox
pulsar_stars.ipynb
1
39660
{ "cells": [ { "cell_type": "markdown", "id": "8d9bd88d", "metadata": {}, "source": [ "# Data Science samples" ] }, { "cell_type": "code", "execution_count": 1, "id": "28d8db6f", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import train_test_split, cross_val_score" ] }, { "cell_type": "code", "execution_count": 38, "id": "490188a1", "metadata": {}, "outputs": [], "source": [ "def cleanup_names(name):\n", " return str(name).strip().replace(\" \",\"_\")" ] }, { "cell_type": "markdown", "id": "c7bae041", "metadata": {}, "source": [ "## Classifiers and Regressors" ] }, { "cell_type": "markdown", "id": "575beb62", "metadata": {}, "source": [ "### Pulsar stars" ] }, { "cell_type": "code", "execution_count": 6, "id": "2f18dd21", "metadata": {}, "outputs": [], "source": [ "col_names = [\"Mean of the integrated profile\",\"Standard deviation of the integrated profile\",\n", "\"Excess kurtosis of the integrated profile\",\n", "\"Skewness of the integrated profile\",\n", "\"Mean of the DM-SNR curve\",\n", "\"Standard deviation of the DM-SNR curve\",\n", "\"Excess kurtosis of the DM-SNR curve\",\n", "\"Skewness of the DM-SNR curve\",\n", "\"Class\"]" ] }, { "cell_type": "code", "execution_count": 7, "id": "8581612d", "metadata": {}, "outputs": [], "source": [ "col_names = [cleanup_names(col) for col in col_names]" ] }, { "cell_type": "code", "execution_count": 8, "id": "5669320e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Mean_of_the_integrated_profile', 'Standard_deviation_of_the_integrated_profile', 'Excess_kurtosis_of_the_integrated_profile', 'Skewness_of_the_integrated_profile', 'Mean_of_the_DM-SNR_curve', 'Standard_deviation_of_the_DM-SNR_curve', 'Excess_kurtosis_of_the_DM-SNR_curve', 'Skewness_of_the_DM-SNR_curve', 'Class']\n" ] } ], "source": [ "print(col_names)" ] }, { "cell_type": "code", "execution_count": 9, "id": "8ba0f07b", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"HTRU_2.csv\", sep=\",\", names=col_names)" ] }, { "cell_type": "code", "execution_count": 10, "id": "b9c187a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 17898 entries, 0 to 17897\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Mean_of_the_integrated_profile 17898 non-null float64\n", " 1 Standard_deviation_of_the_integrated_profile 17898 non-null float64\n", " 2 Excess_kurtosis_of_the_integrated_profile 17898 non-null float64\n", " 3 Skewness_of_the_integrated_profile 17898 non-null float64\n", " 4 Mean_of_the_DM-SNR_curve 17898 non-null float64\n", " 5 Standard_deviation_of_the_DM-SNR_curve 17898 non-null float64\n", " 6 Excess_kurtosis_of_the_DM-SNR_curve 17898 non-null float64\n", " 7 Skewness_of_the_DM-SNR_curve 17898 non-null float64\n", " 8 Class 17898 non-null int64 \n", "dtypes: float64(8), int64(1)\n", "memory usage: 1.2 MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "id": "37730083", "metadata": {}, "source": [ "1. Mean of the integrated profile.\n", "2. Standard deviation of the integrated profile.\n", "3. Excess kurtosis of the integrated profile.\n", "4. Skewness of the integrated profile.\n", "5. Mean of the DM-SNR curve.\n", "6. Standard deviation of the DM-SNR curve.\n", "7. Excess kurtosis of the DM-SNR curve.\n", "8. Skewness of the DM-SNR curve.\n", "9. Class" ] }, { "cell_type": "code", "execution_count": 11, "id": "0d8236e9", "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>Mean_of_the_integrated_profile</th>\n", " <th>Standard_deviation_of_the_integrated_profile</th>\n", " <th>Excess_kurtosis_of_the_integrated_profile</th>\n", " <th>Skewness_of_the_integrated_profile</th>\n", " <th>Mean_of_the_DM-SNR_curve</th>\n", " <th>Standard_deviation_of_the_DM-SNR_curve</th>\n", " <th>Excess_kurtosis_of_the_DM-SNR_curve</th>\n", " <th>Skewness_of_the_DM-SNR_curve</th>\n", " <th>Class</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>140.562500</td>\n", " <td>55.683782</td>\n", " <td>-0.234571</td>\n", " <td>-0.699648</td>\n", " <td>3.199833</td>\n", " <td>19.110426</td>\n", " <td>7.975532</td>\n", " <td>74.242225</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>102.507812</td>\n", " <td>58.882430</td>\n", " <td>0.465318</td>\n", " <td>-0.515088</td>\n", " <td>1.677258</td>\n", " <td>14.860146</td>\n", " <td>10.576487</td>\n", " <td>127.393580</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>103.015625</td>\n", " <td>39.341649</td>\n", " <td>0.323328</td>\n", " <td>1.051164</td>\n", " <td>3.121237</td>\n", " <td>21.744669</td>\n", " <td>7.735822</td>\n", " <td>63.171909</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean_of_the_integrated_profile \\\n", "0 140.562500 \n", "1 102.507812 \n", "2 103.015625 \n", "\n", " Standard_deviation_of_the_integrated_profile \\\n", "0 55.683782 \n", "1 58.882430 \n", "2 39.341649 \n", "\n", " Excess_kurtosis_of_the_integrated_profile \\\n", "0 -0.234571 \n", "1 0.465318 \n", "2 0.323328 \n", "\n", " Skewness_of_the_integrated_profile Mean_of_the_DM-SNR_curve \\\n", "0 -0.699648 3.199833 \n", "1 -0.515088 1.677258 \n", "2 1.051164 3.121237 \n", "\n", " Standard_deviation_of_the_DM-SNR_curve \\\n", "0 19.110426 \n", "1 14.860146 \n", "2 21.744669 \n", "\n", " Excess_kurtosis_of_the_DM-SNR_curve Skewness_of_the_DM-SNR_curve Class \n", "0 7.975532 74.242225 0 \n", "1 10.576487 127.393580 0 \n", "2 7.735822 63.171909 0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(3)" ] }, { "cell_type": "code", "execution_count": 12, "id": "75ac4bba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17898" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df.info()\n", "len(df)" ] }, { "cell_type": "code", "execution_count": 13, "id": "d6fa0459", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 14, "id": "0e3be332", "metadata": {}, "outputs": [], "source": [ "X = df.iloc[:, 0:8]\n", "y = df.iloc[:,8]" ] }, { "cell_type": "code", "execution_count": 15, "id": "257f0b3e", "metadata": {}, "outputs": [], "source": [ "def clf_model(model):\n", " clf = model\n", " \n", " scores = cross_val_score(clf, X, y)\n", " print(f\"Scores: {scores}\")\n", " print(f\"Mean Score: {scores.mean()}\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "90a2d5e8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.97458101 0.97988827 0.98128492 0.97736798 0.9782062 ]\n", "Mean Score: 0.9782656745353482\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] } ], "source": [ "clf_model(LogisticRegression())" ] }, { "cell_type": "code", "execution_count": 17, "id": "d21afba1", "metadata": {}, "outputs": [], "source": [ "from sklearn.naive_bayes import GaussianNB" ] }, { "cell_type": "code", "execution_count": 18, "id": "f1bddfcd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.96061453 0.92374302 0.94273743 0.92847164 0.96451523]\n", "Mean Score: 0.9440163679814436\n" ] } ], "source": [ "clf_model(GaussianNB())" ] }, { "cell_type": "code", "execution_count": 19, "id": "768a03fe", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.96955307 0.96927374 0.97318436 0.9706622 0.97289746]\n", "Mean Score: 0.9711141653437728\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "clf_model(KNeighborsClassifier())" ] }, { "cell_type": "code", "execution_count": 20, "id": "e38f14ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.96955307 0.96620112 0.96703911 0.96507404 0.96982397]\n", "Mean Score: 0.9675382624590059\n" ] } ], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "clf_model(DecisionTreeClassifier())" ] }, { "cell_type": "code", "execution_count": 21, "id": "2c4afff0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.97709497 0.98184358 0.98072626 0.97513272 0.9779268 ]\n", "Mean Score: 0.9785448636599906\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "clf_model(RandomForestClassifier())" ] }, { "cell_type": "code", "execution_count": 37, "id": "07cd734f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17898" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Mean_of_the_integrated_profile.count()" ] }, { "cell_type": "code", "execution_count": 23, "id": "46ed5ab0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17898" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Class.count()" ] }, { "cell_type": "code", "execution_count": 24, "id": "be425190", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1639" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.Class == 1].Class.count()" ] }, { "cell_type": "code", "execution_count": 25, "id": "e649cb49", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.09157447759526204" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.Class == 1].Class.count()/df.Class.count()" ] }, { "cell_type": "code", "execution_count": 26, "id": "e5cb0aeb", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "from sklearn.metrics import confusion_matrix" ] }, { "cell_type": "code", "execution_count": 27, "id": "1c1af69e", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25)" ] }, { "cell_type": "code", "execution_count": 30, "id": "1d60c212", "metadata": {}, "outputs": [], "source": [ "def confusion(model):\n", " clf = model\n", " clf.fit(X_train, y_train)\n", " y_pred = clf.predict(X_test)\n", " print(f'Confusion Matrix: {y_test, y_pred}')\n", " print(f'Classification Report: {classification_report(y_test,y_pred)}')\n", " return clf" ] }, { "cell_type": "code", "execution_count": 31, "id": "3918d8db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (8413 0\n", "4087 1\n", "11325 0\n", "48 0\n", "10967 0\n", " ..\n", "14159 0\n", "469 0\n", "14196 0\n", "7359 0\n", "14277 0\n", "Name: Class, Length: 4475, dtype: int64, array([0, 1, 0, ..., 0, 0, 0]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.98 1.00 0.99 4061\n", " 1 0.95 0.85 0.90 414\n", "\n", " accuracy 0.98 4475\n", " macro avg 0.97 0.92 0.94 4475\n", "weighted avg 0.98 0.98 0.98 4475\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(LogisticRegression())" ] }, { "cell_type": "code", "execution_count": 32, "id": "4e0a3cf1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (8413 0\n", "4087 1\n", "11325 0\n", "48 0\n", "10967 0\n", " ..\n", "14159 0\n", "469 0\n", "14196 0\n", "7359 0\n", "14277 0\n", "Name: Class, Length: 4475, dtype: int64, array([0, 1, 0, ..., 0, 0, 0]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.99 1.00 0.99 4061\n", " 1 0.95 0.87 0.91 414\n", "\n", " accuracy 0.98 4475\n", " macro avg 0.97 0.93 0.95 4475\n", "weighted avg 0.98 0.98 0.98 4475\n", "\n" ] }, { "data": { "text/plain": [ "RandomForestClassifier()" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(RandomForestClassifier())" ] }, { "cell_type": "code", "execution_count": 34, "id": "2909e47b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.97430168 0.97988827 0.98128492 0.97597094 0.97708857]\n", "Mean Score: 0.977706874833175\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n" ] } ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "clf_model(AdaBoostClassifier())" ] }, { "cell_type": "code", "execution_count": 35, "id": "51a34a29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (8413 0\n", "4087 1\n", "11325 0\n", "48 0\n", "10967 0\n", " ..\n", "14159 0\n", "469 0\n", "14196 0\n", "7359 0\n", "14277 0\n", "Name: Class, Length: 4475, dtype: int64, array([0, 1, 0, ..., 0, 0, 0]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.98 0.99 0.99 4061\n", " 1 0.94 0.84 0.89 414\n", "\n", " accuracy 0.98 4475\n", " macro avg 0.96 0.92 0.94 4475\n", "weighted avg 0.98 0.98 0.98 4475\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "AdaBoostClassifier()" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(AdaBoostClassifier())" ] }, { "cell_type": "markdown", "id": "42f24366", "metadata": {}, "source": [ "## Customer Churn" ] }, { "cell_type": "code", "execution_count": 49, "id": "8397a2ad", "metadata": {}, "outputs": [], "source": [ "churnDF = pd.read_csv(\"CHURN.csv\")" ] }, { "cell_type": "code", "execution_count": 50, "id": "eb72f4ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 No\n", "1 No\n", "2 Yes\n", "Name: Churn, dtype: object" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "churnDF.Churn.head(3)" ] }, { "cell_type": "code", "execution_count": 51, "id": "4b9e70e3", "metadata": {}, "outputs": [], "source": [ "churnDF['Churn'] = churnDF['Churn']. \\\n", "replace(to_replace=['No', 'Yes'], value=[0,1])" ] }, { "cell_type": "code", "execution_count": 52, "id": "f20af190", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 1\n", "3 0\n", "4 1\n", "Name: Churn, dtype: int64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "churnDF.Churn.head()" ] }, { "cell_type": "code", "execution_count": 53, "id": "e9a79ba6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(churnDF.columns)" ] }, { "cell_type": "code", "execution_count": 54, "id": "fcdb4971", "metadata": {}, "outputs": [], "source": [ "X = churnDF.iloc[:, 0:20]\n", "y = churnDF.iloc[:,20]" ] }, { "cell_type": "code", "execution_count": 55, "id": "c281e0f0", "metadata": {}, "outputs": [], "source": [ "X = pd.get_dummies(X)" ] }, { "cell_type": "code", "execution_count": 67, "id": "601c7c79", "metadata": {}, "outputs": [], "source": [ "def clf_models(model, cv=3):\n", " clf = model\n", " \n", " scores = cross_val_score(clf, X, y, cv=cv)\n", " print(f\"Scores: {scores}\")\n", " print(f\"Mean Score: {scores.mean()}\")" ] }, { "cell_type": "code", "execution_count": 68, "id": "1f4c47e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.78833049 0.79131175 0.79463144]\n", "Mean Score: 0.7914245643731398\n" ] } ], "source": [ "clf_models(RandomForestClassifier())" ] }, { "cell_type": "code", "execution_count": 69, "id": "6e367f62", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.78109029 0.76192504 0.77290158]\n", "Mean Score: 0.7719723028927428\n" ] } ], "source": [ "clf_models(KNeighborsClassifier())" ] }, { "cell_type": "code", "execution_count": 70, "id": "8e927c28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.27309454 0.2624904 0.25858513]\n", "Mean Score: 0.2647233557045052\n" ] } ], "source": [ "clf_models(LinearRegression())" ] }, { "cell_type": "code", "execution_count": 71, "id": "1d158003", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.80366269 0.80451448 0.80059651]\n", "Mean Score: 0.8029245594131428\n" ] } ], "source": [ "clf_models(AdaBoostClassifier())\n", "# GaussianNB\n", "# DecisionTree\n", "# X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25)" ] }, { "cell_type": "code", "execution_count": 72, "id": "a8df6e71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.27725724 0.28109029 0.27652322]\n", "Mean Score: 0.2782902503153228\n" ] } ], "source": [ "clf_models(GaussianNB())" ] }, { "cell_type": "code", "execution_count": 73, "id": "e426ab54", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.7649063 0.77001704 0.77801449]\n", "Mean Score: 0.7709792751968454\n" ] } ], "source": [ "clf_models(DecisionTreeClassifier())" ] }, { "cell_type": "code", "execution_count": 74, "id": "29c22524", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25)" ] }, { "cell_type": "code", "execution_count": 75, "id": "e35aa5a8", "metadata": {}, "outputs": [], "source": [ "# def confusion_churn(model):\n", "# clf = model\n", "# clf.fit(X_train, y_train)\n", "# y_pred = clf.predict(X_test)\n", "# print(f'Confusion Matrix: {y_test, y_pred}')\n", "# print(f'Classification Report: {classification_report(y_test,y_pred)}')\n", "# return clf" ] }, { "cell_type": "code", "execution_count": 78, "id": "3ba7d9af", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (5906 1\n", "3648 0\n", "1563 0\n", "5549 1\n", "3740 0\n", " ..\n", "3870 0\n", "3083 0\n", "5923 0\n", "2027 0\n", "6590 1\n", "Name: Churn, Length: 1761, dtype: int64, array([1, 0, 1, ..., 0, 0, 1]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.85 0.90 0.88 1314\n", " 1 0.65 0.54 0.59 447\n", "\n", " accuracy 0.81 1761\n", " macro avg 0.75 0.72 0.73 1761\n", "weighted avg 0.80 0.81 0.80 1761\n", "\n" ] }, { "data": { "text/plain": [ "AdaBoostClassifier(n_estimators=250)" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(AdaBoostClassifier(n_estimators=250))" ] }, { "cell_type": "code", "execution_count": 77, "id": "7d6428eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (5906 1\n", "3648 0\n", "1563 0\n", "5549 1\n", "3740 0\n", " ..\n", "3870 0\n", "3083 0\n", "5923 0\n", "2027 0\n", "6590 1\n", "Name: Churn, Length: 1761, dtype: int64, array([1, 0, 1, ..., 1, 0, 1]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.83 0.90 0.86 1314\n", " 1 0.62 0.45 0.52 447\n", "\n", " accuracy 0.79 1761\n", " macro avg 0.72 0.68 0.69 1761\n", "weighted avg 0.77 0.79 0.78 1761\n", "\n" ] }, { "data": { "text/plain": [ "RandomForestClassifier()" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(RandomForestClassifier())" ] }, { "cell_type": "code", "execution_count": 79, "id": "c38153ba", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scores: [0.79258944 0.79940375 0.80017043]\n", "Mean Score: 0.7973878720088496\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] } ], "source": [ "clf_models(LogisticRegression())" ] }, { "cell_type": "code", "execution_count": 80, "id": "56c15690", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: (5906 1\n", "3648 0\n", "1563 0\n", "5549 1\n", "3740 0\n", " ..\n", "3870 0\n", "3083 0\n", "5923 0\n", "2027 0\n", "6590 1\n", "Name: Churn, Length: 1761, dtype: int64, array([1, 0, 1, ..., 1, 0, 1]))\n", "Classification Report: precision recall f1-score support\n", "\n", " 0 0.85 0.90 0.87 1314\n", " 1 0.64 0.54 0.58 447\n", "\n", " accuracy 0.81 1761\n", " macro avg 0.74 0.72 0.73 1761\n", "weighted avg 0.80 0.81 0.80 1761\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion(LogisticRegression())" ] }, { "cell_type": "code", "execution_count": null, "id": "a875ef3d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "8768e4b8", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }
mit
transcranial/keras-js
notebooks/layers/convolutional/Conv2DTranspose.ipynb
1
48677
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.models import Model\n", "from keras.layers import Input\n", "from keras.layers.convolutional import Conv2DTranspose\n", "from keras import backend as K\n", "import json\n", "from collections import OrderedDict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def format_decimal(arr, places=6):\n", " return [round(x * 10**places) / 10**places for x in arr]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DATA = OrderedDict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conv2DTranspose" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.0] 4 3x3 filters on 4x4x2 input, strides=(1,1), padding='valid', data_format='channels_last', activation='linear', use_bias=False**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (3, 3, 4, 2)\n", "W: [0.817168, -0.484057, 0.75531, 0.477931, 0.396153, 0.034417, 0.904219, 0.827289, -0.843651, 0.564641, -0.772669, 0.2817, -0.840474, -0.536068, -0.228097, -0.52653, 0.996766, -0.221318, 0.447674, -0.096508, -0.046254, 0.090528, -0.13441, 0.179412, -0.790058, 0.722203, -0.383929, -0.026572, -0.579839, 0.240175, -0.317323, -0.010873, -0.385402, -0.164789, 0.520344, -0.918119, -0.000266, 0.625388, 0.040686, -0.136983, 0.477384, 0.485779, 0.761687, 0.965039, -0.560193, -0.813418, -0.708546, -0.178286, -0.283609, 0.943543, 0.314461, 0.343435, 0.480156, 0.274123, 0.794715, -0.361677, -0.763759, -0.54021, -0.794172, 0.943047, -0.073416, -0.57276, -0.091824, 0.998847, -0.802122, -0.525324, -0.731307, 0.449204, -0.313752, -0.823668, 0.671953, -0.761801]\n", "\n", "in shape: (4, 4, 2)\n", "in: [-0.929961, -0.63673, 0.945405, -0.573858, 0.000985, 0.681999, -0.080736, 0.575131, -0.614662, -0.588668, 0.80306, 0.256132, -0.307227, -0.077013, 0.21169, -0.678163, 0.451697, -0.680262, -0.125876, -0.335162, -0.258573, 0.105624, -0.63008, -0.384056, -0.816483, 0.552606, 0.332199, -0.201029, -0.480198, 0.2547, 0.712057, -0.072584]\n", "out shape: (6, 6, 4)\n", "out: [-0.45172, -1.006722, -0.390321, -1.367648, 1.475372, 0.978995, 1.477713, 0.927486, -2.23697, -0.920318, -0.477727, 0.662369, 1.109235, 0.883865, -0.474294, -0.186551, 0.242899, 0.159019, -0.178758, -0.162181, -0.207762, -0.091648, 0.0558, 0.114037, 0.05754, -0.371645, 0.12254, -0.740767, 0.020394, 0.791051, 0.075158, 1.143817, -1.760339, -1.358067, -0.118497, 0.199239, 1.999675, -0.057364, 0.894701, -0.96255, -0.582588, -0.395852, -0.002741, 0.131907, 0.601939, 0.653744, -0.493777, -0.195456, 0.42184, -0.24343, -0.250519, -0.461656, -0.576741, -0.652102, -0.118883, 0.070342, 0.822911, -1.271066, 2.509899, -0.533334, -0.3811, 0.117875, -1.214318, -0.122343, -0.728397, 1.58929, -0.317988, 0.795561, -1.008794, -0.420824, -0.020967, -0.505686, -2.163961, -0.90339, -1.186228, -0.692625, 1.966407, 2.131711, 0.919721, 0.198514, -1.511084, -1.281079, 0.304343, -0.06039, 0.418964, 0.248102, 0.714929, 1.515878, -0.451173, -1.498029, -0.16266, -0.667201, 0.424917, -0.984223, 1.118021, 1.065053, 0.274201, 0.207203, 0.636561, 0.858086, -0.442072, -2.209343, 0.309056, -0.911916, 0.792328, -0.450646, 0.746286, 0.925489, -0.17717, -0.779708, -0.427246, -0.540969, 0.472643, 0.691941, 0.276602, -0.246223, 1.01182, 0.760581, 0.174175, -0.622395, 0.752969, -0.066968, -0.240558, -0.848737, 0.041179, 1.204985, -0.152167, 0.963653, 0.596008, 0.328399, -0.268989, -1.674655, -0.202127, 0.487297, 0.272727, 1.267001, -0.253252, -0.168362, -0.069828, -0.654585, -0.533027, -0.553337, -0.163624, 0.533764]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(4, (3,3), strides=(1,1), \n", " padding='valid', data_format='channels_last',\n", " activation='linear', use_bias=False)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(150)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "# print('b shape:', weights[1].shape)\n", "# print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.0'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.1] 4 3x3 filters on 4x4x2 input, strides=(1,1), padding='valid', data_format='channels_last', activation='linear', use_bias=True**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (3, 3, 4, 2)\n", "W: [0.539589, -0.646357, -0.378579, 0.600628, 0.148989, 0.539942, 0.901522, 0.505578, -0.106529, 0.052912, 0.212682, 0.154012, 0.907638, -0.157428, -0.539244, 0.908274, -0.805752, 0.802096, 0.832403, 0.18143, 0.805057, -0.915723, -0.056526, 0.826083, -0.930412, 0.568274, 0.688561, 0.945088, 0.215499, -0.855602, 0.23233, -0.568225, 0.855135, 0.162787, 0.965045, 0.048574, 0.23368, -0.215715, 0.303608, -0.981652, 0.943344, -0.172658, 0.245129, 0.753963, -0.941923, 0.131806, 0.860396, 0.286018, 0.92336, 0.270758, -0.859684, -0.482492, -0.702331, -0.68649, -0.945042, -0.458865, -0.130765, -0.706792, 0.634812, 0.665099, 0.984552, -0.442333, -0.395297, 0.281599, -0.587822, 0.298219, -0.52161, 0.871918, 0.986648, -0.139607, -0.149301, -0.75598]\n", "b shape: (4,)\n", "b: [0.539589, -0.646357, -0.378579, 0.600628]\n", "\n", "in shape: (4, 4, 2)\n", "in: [0.148989, 0.539942, 0.901522, 0.505578, -0.106529, 0.052912, 0.212682, 0.154012, 0.907638, -0.157428, -0.539244, 0.908274, -0.805752, 0.802096, 0.832403, 0.18143, 0.805057, -0.915723, -0.056526, 0.826083, -0.930412, 0.568274, 0.688561, 0.945088, 0.215499, -0.855602, 0.23233, -0.568225, 0.855135, 0.162787, 0.965045, 0.048574]\n", "out shape: (6, 6, 4)\n", "out: [0.270987, -0.378457, -0.064845, 1.007927, 0.711954, -0.569145, 0.078946, 2.079053, 0.691656, -0.082662, -0.00171, 0.942018, 0.24807, 0.193279, -0.105947, 1.342425, 0.653358, -0.656478, -0.344002, 0.675557, 0.491752, -0.441377, -0.34839, 0.715833, 1.299308, -0.471645, -0.758222, 1.067098, -0.779649, 1.540695, 0.56013, -0.621367, -0.135992, 2.207442, 0.141102, 1.219351, 2.733081, -0.390491, -3.184827, 4.184479, 1.850421, -0.740053, -0.911002, 0.861553, 0.188444, 0.247709, -0.054618, 0.930493, 0.915697, -1.413564, -0.898085, 0.775201, 2.177532, 0.691494, -0.785763, -1.500629, -0.373408, 1.864835, -0.188503, -1.502463, -0.922299, -0.107305, -0.775615, 1.52532, 1.50623, 0.532773, -0.122982, 1.15573, 1.417657, 0.462458, -1.261492, 1.962332, 0.734929, -2.257271, -0.380853, 0.284189, 1.765985, 0.809511, 0.064657, -0.020006, 0.494605, -1.540584, -0.662995, 0.002932, 0.244839, 0.712761, -2.104895, -1.262, 0.123499, 2.987249, 1.769682, -1.910215, -0.147863, 0.771113, 0.625813, 1.187515, 0.348288, -1.556858, 0.463134, 0.796249, 0.758945, -1.304971, 1.062863, 0.981071, -1.734376, -0.478499, 0.291964, 2.721394, 1.63561, 0.014866, -2.812089, -0.2382, 2.109688, 2.666155, -1.685004, 1.331514, 1.318664, 0.091707, -0.733749, 0.627571, 0.506911, -0.418796, 0.057432, 0.789578, 1.176815, -1.004182, 0.43896, 0.315681, 1.362667, -2.548908, -0.278765, 0.080584, 0.910921, -1.464939, -0.011232, -0.230981, -0.075059, -0.305537, 1.371063, -0.01791, -0.013201, -1.107381, 0.566799, 0.419824]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(4, (3,3), strides=(1,1), \n", " padding='valid', data_format='channels_last',\n", " activation='linear', use_bias=True)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(151)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "print('b shape:', weights[1].shape)\n", "print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.1'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.2] 4 3x3 filters on 4x4x2 input, strides=(2,2), padding='valid', data_format='channels_last', activation='relu', use_bias=True**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (3, 3, 4, 2)\n", "W: [0.897456, 0.487288, -0.923342, -0.349253, -0.358135, -0.188918, -0.854928, -0.340478, 0.552639, -0.654335, 0.054774, -0.504375, -0.879382, -0.611747, -0.127514, 0.302488, -0.190093, 0.825193, 0.313868, 0.409695, 0.074585, -0.158004, -0.796628, -0.238881, -0.386481, 0.984118, 0.835142, -0.393954, -0.594307, -0.446792, -0.88912, 0.340353, -0.419182, 0.636229, 0.665132, -0.306754, 0.068433, -0.062402, -0.41091, 0.148379, -0.289494, -0.286142, -0.843449, -0.755613, 0.440714, -0.89768, -0.517294, -0.691731, -0.558247, 0.548046, -0.743271, 0.318663, -0.951589, -0.680511, 0.054639, -0.342901, 0.359072, 0.98732, 0.60034, -0.951034, 0.957127, -0.215193, -0.025811, -0.572621, 0.967642, -0.106668, 0.796646, 0.227034, 0.278936, 0.737666, 0.885642, -0.240247]\n", "b shape: (4,)\n", "b: [0.897456, 0.487288, -0.923342, -0.349253]\n", "\n", "in shape: (4, 4, 2)\n", "in: [-0.358135, -0.188918, -0.854928, -0.340478, 0.552639, -0.654335, 0.054774, -0.504375, -0.879382, -0.611747, -0.127514, 0.302488, -0.190093, 0.825193, 0.313868, 0.409695, 0.074585, -0.158004, -0.796628, -0.238881, -0.386481, 0.984118, 0.835142, -0.393954, -0.594307, -0.446792, -0.88912, 0.340353, -0.419182, 0.636229, 0.665132, -0.306754]\n", "out shape: (9, 9, 4)\n", "out: [0.483988, 0.883949, 0.0, 0.021249, 0.823152, 0.562957, 0.0, 0.0, 0.0, 1.205786, 0.0, 0.828004, 0.647776, 0.612188, 0.036754, 0.0, 0.95613, 0.0, 0.0, 0.163461, 1.63102, 0.847588, 0.0, 0.0, 0.055832, 0.518244, 0.0, 0.0, 1.257756, 0.744682, 0.0, 0.0, 0.470837, 0.29784, 0.0, 0.0, 0.84995, 0.262619, 0.0, 0.0, 0.927384, 0.307032, 0.0, 0.0, 1.050533, 0.352252, 0.0, 0.610939, 1.039204, 0.02309, 0.0, 0.0, 0.38485, 2.184956, 0.0, 0.0, 0.249492, 1.055586, 0.0, 0.0, 0.407169, 0.760034, 0.100396, 0.0, 0.553597, 0.678439, 0.0, 0.0, 1.025921, 0.822201, 0.0, 0.0, 0.0, 1.718903, 0.0, 0.656054, 0.496644, 0.712335, 0.0, 0.0, 0.557038, 0.17149, 0.0, 0.301692, 0.0, 0.138295, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.014735, 0.0, 0.28502, 2.393411, 0.423071, 0.0, 0.019816, 0.324521, 0.8104, 0.0, 0.022054, 1.282672, 0.682776, 0.0, 0.0, 0.635288, 0.0, 0.0, 0.224413, 0.876865, 0.090038, 0.0, 0.0, 1.674044, 1.465587, 0.0, 0.745138, 1.143359, 0.309685, 0.0, 0.0, 1.73337, 0.0, 0.0, 0.0, 1.502151, 0.10772, 0.0, 0.0, 0.998249, 0.124817, 0.0, 0.0, 1.026548, 0.570376, 0.0, 0.0, 0.689362, 0.0, 0.0, 0.0, 1.043046, 0.932281, 0.332907, 0.0, 0.12231, 0.624931, 0.0, 0.0, 0.0, 0.61668, 0.0, 0.0, 0.866382, 0.19991, 0.0, 0.0, 1.387174, 0.523872, 0.0, 0.0, 0.786401, 0.0, 0.0, 0.0, 2.117897, 0.068817, 0.0, 0.0, 2.133966, 0.530525, 0.0, 0.0, 0.673624, 0.931067, 0.0, 0.0, 0.713135, 0.611823, 0.0, 0.0, 0.765664, 0.585365, 0.0, 0.0, 0.993869, 0.0, 0.0, 0.348454, 1.079404, 0.030702, 0.0, 0.0, 2.314283, 0.629242, 0.0, 0.906654, 1.685585, 0.0, 0.0, 0.0, 0.01728, 0.922314, 0.0, 0.0, 0.296735, 1.163614, 0.0, 0.0, 0.768413, 0.080564, 0.0, 0.0, 0.018144, 1.086293, 0.0, 0.369215, 0.732151, 0.87513, 0.0, 0.0, 0.412466, 1.359331, 0.181926, 1.017571, 0.0, 0.015858, 0.0, 0.024425, 1.290881, 0.424503, 0.0, 0.0, 1.082357, 0.0, 0.0, 0.0, 0.788531, 0.0, 0.0, 0.0, 1.376671, 1.55447, 0.0, 0.0, 1.368029, 1.146247, 0.0, 0.028444, 0.687447, 0.166972, 0.0, 0.02709, 0.862316, 0.22905, 0.0, 0.0, 1.875925, 0.449533, 0.0, 1.173613, 1.486701, 0.0, 0.0, 0.066596, 1.845592, 0.379319, 0.0, 0.464495, 1.477957, 0.013311, 0.0, 0.0, 0.277812, 1.036429, 0.0, 0.0, 0.423479, 1.023786, 0.0, 0.0, 0.792679, 0.15807, 0.0, 0.0, 0.984363, 0.786642, 0.0, 0.0, 0.242929, 0.555416, 0.0, 0.0, 1.052915, 0.681713, 0.0, 0.0, 0.914235, 0.0, 0.0, 0.0, 0.583491, 0.370554, 0.0, 0.0, 1.375101, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.833422, 1.178326, 0.0, 0.0, 1.573786, 0.947519, 0.0, 0.313512]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(4, (3,3), strides=(2,2), \n", " padding='valid', data_format='channels_last',\n", " activation='relu', use_bias=True)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(152)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "print('b shape:', weights[1].shape)\n", "print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.2'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.3] 4 3x3 filters on 4x4x2 input, strides=(1,1), padding='same', data_format='channels_last', activation='relu', use_bias=True**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (3, 3, 4, 2)\n", "W: [-0.7046, 0.058369, -0.012661, -0.889136, -0.007435, 0.615048, -0.558658, -0.008729, 0.041294, -0.438168, 0.529679, 0.266543, 0.550212, -0.183091, 0.3534, 0.109289, -0.621224, -0.674319, 0.15753, -0.648414, 0.830683, -0.934728, 0.16761, 0.50837, 0.086898, -0.035362, -0.851074, -0.016054, -0.545162, -0.344549, 0.467416, 0.926628, -0.560706, -0.569233, 0.855837, -0.009762, 0.293612, -0.714253, -0.817222, 0.023038, -0.85581, 0.539004, 0.741369, -0.823095, -0.859253, -0.035201, 0.737323, 0.025245, 0.366923, -0.220199, 0.028247, 0.196676, 0.835236, 0.376875, 0.771537, -0.449685, 0.756239, 0.813555, 0.931644, 0.659618, 0.742881, -0.696761, 0.246132, -0.088558, -0.595887, 0.278583, -0.957392, -0.062303, -0.402006, 0.976563, -0.887975, 0.147616]\n", "b shape: (4,)\n", "b: [-0.7046, 0.058369, -0.012661, -0.889136]\n", "\n", "in shape: (4, 4, 2)\n", "in: [-0.007435, 0.615048, -0.558658, -0.008729, 0.041294, -0.438168, 0.529679, 0.266543, 0.550212, -0.183091, 0.3534, 0.109289, -0.621224, -0.674319, 0.15753, -0.648414, 0.830683, -0.934728, 0.16761, 0.50837, 0.086898, -0.035362, -0.851074, -0.016054, -0.545162, -0.344549, 0.467416, 0.926628, -0.560706, -0.569233, 0.855837, -0.009762]\n", "out shape: (4, 4, 4)\n", "out: [0.0, 0.662588, 0.25427, 0.0, 0.117289, 0.069607, 0.33525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.15034, 0.25208, 0.0, 0.0, 0.346514, 0.084998, 0.0, 0.0, 1.807031, 1.722262, 0.0, 0.132415, 0.0, 0.926277, 0.960665, 0.15685, 0.532586, 0.457428, 0.0, 0.0, 0.0, 1.834585, 0.0, 0.0, 2.389801, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.234034, 0.510985, 0.374524, 0.3355, 0.0, 0.0, 1.099249, 0.813507, 0.0, 0.565056, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.056894, 0.0]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(4, (3,3), strides=(1,1), \n", " padding='same', data_format='channels_last',\n", " activation='relu', use_bias=True)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(153)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "print('b shape:', weights[1].shape)\n", "print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.3'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.4] 5 3x3 filters on 4x4x2 input, strides=(2,2), padding='same', data_format='channels_last', activation='relu', use_bias=True**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (3, 3, 5, 2)\n", "W: [0.015448, 0.261101, 0.371131, 0.970987, 0.733096, 0.656737, 0.433335, 0.19508, 0.702874, 0.204501, -0.719665, 0.652834, -0.165006, 0.065511, 0.376121, 0.755842, 0.602262, -0.479869, -0.721179, 0.979413, 0.487952, 0.353365, 0.379881, -0.725052, 0.794477, -0.748911, 0.150616, -0.830063, 0.781869, -0.467526, -0.294391, -0.927268, 0.293802, 0.49534, 0.456641, 0.610365, -0.549942, 0.126156, 0.450587, -0.837003, 0.361856, 0.138717, -0.473225, 0.432004, 0.46321, -0.042096, -0.481635, -0.730053, 0.724533, 0.657809, -0.088475, 0.224629, -0.287917, -0.394062, -0.498412, -0.523992, 0.638287, -0.400103, 0.456819, -0.150058, -0.459513, 0.357662, 0.287583, -0.448432, 0.805097, 0.529896, -0.935158, -0.723581, -0.926538, -0.389357, 0.673302, 0.696614, -0.819421, 0.580246, 0.249636, -0.53035, -0.599139, -0.987449, -0.679439, 0.024594, -0.686301, -0.575677, -0.655428, -0.185761, 0.946483, 0.12546, -0.358746, 0.039991, -0.701225, -0.987664]\n", "b shape: (5,)\n", "b: [0.015448, 0.261101, 0.371131, 0.970987, 0.733096]\n", "\n", "in shape: (4, 4, 2)\n", "in: [0.656737, 0.433335, 0.19508, 0.702874, 0.204501, -0.719665, 0.652834, -0.165006, 0.065511, 0.376121, 0.755842, 0.602262, -0.479869, -0.721179, 0.979413, 0.487952, 0.353365, 0.379881, -0.725052, 0.794477, -0.748911, 0.150616, -0.830063, 0.781869, -0.467526, -0.294391, -0.927268, 0.293802, 0.49534, 0.456641, 0.610365, -0.549942]\n", "out shape: (8, 8, 5)\n", "out: [0.138737, 0.925599, 1.137169, 1.340109, 1.283317, 0.0, 0.181124, 0.945677, 1.15857, 0.683885, 0.675564, 0.951274, 1.17298, 0.931858, 1.324839, 0.333915, 0.274958, 0.975767, 0.751189, 1.280812, 0.174262, 0.0, 0.0, 0.365164, 0.553578, 0.0, 0.180212, 0.0, 1.439495, 0.0, 0.0, 0.942651, 1.442793, 1.849862, 1.654567, 0.0, 0.14257, 0.491958, 1.443345, 0.100677, 0.0, 0.6687, 0.935517, 0.664487, 0.666311, 0.313203, 0.137519, 0.657096, 0.338321, 1.493975, 0.0, 0.306731, 0.334833, 1.198184, 0.467674, 0.183539, 0.472428, 0.431906, 0.363894, 1.336795, 0.763193, 0.0, 0.0, 0.611028, 1.411248, 0.0, 0.0, 0.496153, 1.397886, 0.407861, 0.0, 0.595885, 0.843702, 1.00962, 1.366776, 0.228791, 0.0, 0.680476, 0.777022, 1.097554, 0.0, 0.645168, 1.424529, 0.145043, 0.078845, 0.957896, 0.0, 0.614186, 0.008579, 0.618674, 0.0, 0.108555, 2.296594, 0.204382, 0.0, 0.48565, 0.423824, 0.786565, 0.326262, 0.662603, 0.0, 0.0, 0.0, 0.523901, 0.0, 0.0, 0.0, 0.07827, 1.61616, 0.21619, 0.0, 1.700373, 2.109872, 1.423686, 1.510017, 0.0, 0.0, 1.358806, 1.098492, 0.057051, 0.0, 0.466656, 0.630617, 0.98241, 0.4478, 0.091327, 0.392586, 0.385643, 0.664846, 1.027977, 0.0, 0.614417, 0.854143, 0.522623, 0.543061, 0.372497, 0.163597, 0.695892, 0.167263, 1.676902, 0.893855, 0.0, 0.0, 1.385384, 1.375412, 0.0, 0.176635, 0.17921, 1.728608, 0.0, 0.0, 1.212909, 1.733265, 0.476176, 0.654994, 0.437541, 0.008416, 0.804264, 0.143036, 1.763692, 0.224515, 0.611281, 1.131711, 0.864802, 0.85201, 0.315262, 0.392242, 0.608048, 0.590861, 0.815057, 0.12495, 0.456729, 1.394459, 0.0, 0.0, 1.984358, 0.162893, 0.568199, 0.0, 1.535372, 0.0, 0.0, 0.0, 0.630684, 0.0, 0.0, 0.369296, 0.465976, 1.447306, 1.729015, 0.363531, 0.829857, 0.070725, 0.0, 0.0, 2.122601, 0.0, 0.635608, 0.0, 1.444042, 0.0, 0.553091, 0.764359, 0.824581, 0.574356, 0.196011, 0.25799, 0.518822, 0.52346, 1.23901, 0.0, 0.190179, 0.149787, 1.543508, 0.0, 0.0, 0.947431, 0.001836, 0.740187, 0.730386, 0.338871, 0.011358, 0.066153, 0.621182, 0.0, 0.0, 0.680571, 0.017888, 1.221731, 0.289562, 0.0, 0.560791, 0.763661, 0.987829, 0.0, 0.0, 0.991678, 0.0, 0.799968, 0.646009, 0.0, 0.0, 0.320843, 0.105635, 0.0, 0.662273, 0.24983, 0.0, 0.243855, 0.551188, 0.0, 0.0, 0.0, 0.792024, 0.0, 0.939838, 1.488467, 0.0, 0.0, 2.201744, 0.231932, 0.367778, 0.0, 1.774431, 0.67128, 0.0, 0.91035, 0.635753, 1.35016, 1.335651, 1.372691, 0.0, 0.0, 1.308948, 2.064499, 0.0, 1.258207, 0.0, 1.327754, 0.3375, 0.426062, 0.0, 0.0, 1.190959, 0.768841, 0.0, 0.355168, 0.166961, 1.411085, 0.200705, 0.0, 0.384817, 0.514308, 1.337366, 0.0, 0.0, 0.826831, 0.0, 1.203101, 0.254526, 0.0, 0.784026, 1.184253, 0.046772, 0.106399, 0.258033, 0.223965, 0.581355, 0.399041, 1.392369, 0.404455, 0.0, 0.0, 0.699408, 1.62618, 0.160026, 0.0, 0.677009, 1.0785, 0.813569]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(5, (3,3), strides=(2,2), \n", " padding='same', data_format='channels_last',\n", " activation='relu', use_bias=True)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(154)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "print('b shape:', weights[1].shape)\n", "print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.4'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[convolutional.Conv2DTranspose.5] 3 2x3 filters on 4x4x2 input, strides=(1,1), padding='same', data_format='channels_last', activation='relu', use_bias=True**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W shape: (2, 3, 3, 2)\n", "W: [0.304653, 0.842246, -0.653708, 0.24125, -0.786747, -0.629124, -0.519787, -0.038861, -0.715188, 0.71707, -0.180419, 0.054783, 0.458147, 0.631702, -0.028843, 0.586737, -0.21045, 0.463067, -0.16224, 0.36397, -0.958481, -0.793514, -0.985689, -0.380946, -0.946773, -0.259217, 0.266228, -0.671058, -0.358071, -0.200672, 0.343247, 0.883116, 0.945544, 8.8e-05, -0.43253, 0.375163]\n", "b shape: (3,)\n", "b: [0.304653, 0.842246, -0.653708]\n", "\n", "in shape: (4, 4, 2)\n", "in: [0.24125, -0.786747, -0.629124, -0.519787, -0.038861, -0.715188, 0.71707, -0.180419, 0.054783, 0.458147, 0.631702, -0.028843, 0.586737, -0.21045, 0.463067, -0.16224, 0.36397, -0.958481, -0.793514, -0.985689, -0.380946, -0.946773, -0.259217, 0.266228, -0.671058, -0.358071, -0.200672, 0.343247, 0.883116, 0.945544, 8.8e-05, -0.43253]\n", "out shape: (4, 4, 3)\n", "out: [0.0, 0.391419, 0.081638, 0.0, 0.303757, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.314941, 2.319324, 0.0, 0.157828, 1.216804, 0.0, 0.0, 0.0, 0.0, 0.0, 0.529495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.693646, 0.0, 0.0, 1.46094, 0.0, 0.569175, 3.562228, 0.609347, 0.929883, 2.602643, 0.0, 0.0, 0.812183, 0.165895, 0.532854, 0.453394, 0.0]\n" ] } ], "source": [ "data_in_shape = (4, 4, 2)\n", "conv = Conv2DTranspose(3, (2,3), strides=(1,1), \n", " padding='same', data_format='channels_last',\n", " activation='relu', use_bias=True)\n", "\n", "layer_0 = Input(shape=data_in_shape)\n", "layer_1 = conv(layer_0)\n", "model = Model(inputs=layer_0, outputs=layer_1)\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for w in model.get_weights():\n", " np.random.seed(155)\n", " weights.append(2 * np.random.random(w.shape) - 1)\n", "model.set_weights(weights)\n", "print('W shape:', weights[0].shape)\n", "print('W:', format_decimal(weights[0].ravel().tolist()))\n", "print('b shape:', weights[1].shape)\n", "print('b:', format_decimal(weights[1].ravel().tolist()))\n", "\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "result = model.predict(np.array([data_in]))\n", "data_out_shape = result[0].shape\n", "data_in_formatted = format_decimal(data_in.ravel().tolist())\n", "data_out_formatted = format_decimal(result[0].ravel().tolist())\n", "print('')\n", "print('in shape:', data_in_shape)\n", "print('in:', data_in_formatted)\n", "print('out shape:', data_out_shape)\n", "print('out:', data_out_formatted)\n", "\n", "DATA['convolutional.Conv2DTranspose.5'] = {\n", " 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n", " 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n", " 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### export for Keras.js tests" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "\n", "filename = '../../../test/data/layers/convolutional/Conv2DTranspose.json'\n", "if not os.path.exists(os.path.dirname(filename)):\n", " os.makedirs(os.path.dirname(filename))\n", "with open(filename, 'w') as f:\n", " json.dump(DATA, f)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"convolutional.Conv2DTranspose.0\": {\"input\": {\"data\": [-0.929961, -0.63673, 0.945405, -0.573858, 0.000985, 0.681999, -0.080736, 0.575131, -0.614662, -0.588668, 0.80306, 0.256132, -0.307227, -0.077013, 0.21169, -0.678163, 0.451697, -0.680262, -0.125876, -0.335162, -0.258573, 0.105624, -0.63008, -0.384056, -0.816483, 0.552606, 0.332199, -0.201029, -0.480198, 0.2547, 0.712057, -0.072584], \"shape\": [4, 4, 2]}, \"weights\": [{\"data\": [0.817168, -0.484057, 0.75531, 0.477931, 0.396153, 0.034417, 0.904219, 0.827289, -0.843651, 0.564641, -0.772669, 0.2817, -0.840474, -0.536068, -0.228097, -0.52653, 0.996766, -0.221318, 0.447674, -0.096508, -0.046254, 0.090528, -0.13441, 0.179412, -0.790058, 0.722203, -0.383929, -0.026572, -0.579839, 0.240175, -0.317323, -0.010873, -0.385402, -0.164789, 0.520344, -0.918119, -0.000266, 0.625388, 0.040686, -0.136983, 0.477384, 0.485779, 0.761687, 0.965039, -0.560193, -0.813418, -0.708546, -0.178286, -0.283609, 0.943543, 0.314461, 0.343435, 0.480156, 0.274123, 0.794715, -0.361677, -0.763759, -0.54021, -0.794172, 0.943047, -0.073416, -0.57276, -0.091824, 0.998847, -0.802122, -0.525324, -0.731307, 0.449204, -0.313752, -0.823668, 0.671953, -0.761801], \"shape\": [3, 3, 4, 2]}], \"expected\": {\"data\": [-0.45172, -1.006722, -0.390321, -1.367648, 1.475372, 0.978995, 1.477713, 0.927486, -2.23697, -0.920318, -0.477727, 0.662369, 1.109235, 0.883865, -0.474294, -0.186551, 0.242899, 0.159019, -0.178758, -0.162181, -0.207762, -0.091648, 0.0558, 0.114037, 0.05754, -0.371645, 0.12254, -0.740767, 0.020394, 0.791051, 0.075158, 1.143817, -1.760339, -1.358067, -0.118497, 0.199239, 1.999675, -0.057364, 0.894701, -0.96255, -0.582588, -0.395852, -0.002741, 0.131907, 0.601939, 0.653744, -0.493777, -0.195456, 0.42184, -0.24343, -0.250519, -0.461656, -0.576741, -0.652102, -0.118883, 0.070342, 0.822911, -1.271066, 2.509899, -0.533334, -0.3811, 0.117875, -1.214318, -0.122343, -0.728397, 1.58929, -0.317988, 0.795561, -1.008794, -0.420824, -0.020967, -0.505686, -2.163961, -0.90339, -1.186228, -0.692625, 1.966407, 2.131711, 0.919721, 0.198514, -1.511084, -1.281079, 0.304343, -0.06039, 0.418964, 0.248102, 0.714929, 1.515878, -0.451173, -1.498029, -0.16266, -0.667201, 0.424917, -0.984223, 1.118021, 1.065053, 0.274201, 0.207203, 0.636561, 0.858086, -0.442072, -2.209343, 0.309056, -0.911916, 0.792328, -0.450646, 0.746286, 0.925489, -0.17717, -0.779708, -0.427246, -0.540969, 0.472643, 0.691941, 0.276602, -0.246223, 1.01182, 0.760581, 0.174175, -0.622395, 0.752969, -0.066968, -0.240558, -0.848737, 0.041179, 1.204985, -0.152167, 0.963653, 0.596008, 0.328399, -0.268989, -1.674655, -0.202127, 0.487297, 0.272727, 1.267001, -0.253252, -0.168362, -0.069828, -0.654585, -0.533027, -0.553337, -0.163624, 0.533764], \"shape\": [6, 6, 4]}}, \"convolutional.Conv2DTranspose.1\": {\"input\": {\"data\": [0.148989, 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{\"data\": [0.897456, 0.487288, -0.923342, -0.349253], \"shape\": [4]}], \"expected\": {\"data\": [0.483988, 0.883949, 0.0, 0.021249, 0.823152, 0.562957, 0.0, 0.0, 0.0, 1.205786, 0.0, 0.828004, 0.647776, 0.612188, 0.036754, 0.0, 0.95613, 0.0, 0.0, 0.163461, 1.63102, 0.847588, 0.0, 0.0, 0.055832, 0.518244, 0.0, 0.0, 1.257756, 0.744682, 0.0, 0.0, 0.470837, 0.29784, 0.0, 0.0, 0.84995, 0.262619, 0.0, 0.0, 0.927384, 0.307032, 0.0, 0.0, 1.050533, 0.352252, 0.0, 0.610939, 1.039204, 0.02309, 0.0, 0.0, 0.38485, 2.184956, 0.0, 0.0, 0.249492, 1.055586, 0.0, 0.0, 0.407169, 0.760034, 0.100396, 0.0, 0.553597, 0.678439, 0.0, 0.0, 1.025921, 0.822201, 0.0, 0.0, 0.0, 1.718903, 0.0, 0.656054, 0.496644, 0.712335, 0.0, 0.0, 0.557038, 0.17149, 0.0, 0.301692, 0.0, 0.138295, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.014735, 0.0, 0.28502, 2.393411, 0.423071, 0.0, 0.019816, 0.324521, 0.8104, 0.0, 0.022054, 1.282672, 0.682776, 0.0, 0.0, 0.635288, 0.0, 0.0, 0.224413, 0.876865, 0.090038, 0.0, 0.0, 1.674044, 1.465587, 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0.883116, 0.945544, 8.8e-05, -0.43253], \"shape\": [4, 4, 2]}, \"weights\": [{\"data\": [0.304653, 0.842246, -0.653708, 0.24125, -0.786747, -0.629124, -0.519787, -0.038861, -0.715188, 0.71707, -0.180419, 0.054783, 0.458147, 0.631702, -0.028843, 0.586737, -0.21045, 0.463067, -0.16224, 0.36397, -0.958481, -0.793514, -0.985689, -0.380946, -0.946773, -0.259217, 0.266228, -0.671058, -0.358071, -0.200672, 0.343247, 0.883116, 0.945544, 8.8e-05, -0.43253, 0.375163], \"shape\": [2, 3, 3, 2]}, {\"data\": [0.304653, 0.842246, -0.653708], \"shape\": [3]}], \"expected\": {\"data\": [0.0, 0.391419, 0.081638, 0.0, 0.303757, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.314941, 2.319324, 0.0, 0.157828, 1.216804, 0.0, 0.0, 0.0, 0.0, 0.0, 0.529495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.693646, 0.0, 0.0, 1.46094, 0.0, 0.569175, 3.562228, 0.609347, 0.929883, 2.602643, 0.0, 0.0, 0.812183, 0.165895, 0.532854, 0.453394, 0.0], \"shape\": [4, 4, 3]}}}\n" ] } ], "source": [ "print(json.dumps(DATA))" ] }, { "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
deeplycloudy/brawl4d
notebooks/brawl4d.ipynb
2
1367
{ "metadata": { "name": "brawl4d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab\n", "from brawl4d.brawl4d import B4D_startup, get_demo_dataset, plot_demo_dataset" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: Qt4Agg].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "panels = B4D_startup()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "d = get_demo_dataset()\n", "post_filter_brancher, post_transform_branch_to_scatter_artists = plot_demo_dataset(d, panels)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-2-clause
sdpython/ensae_teaching_cs
_doc/notebooks/td2a_algo/gentry_integer_encryption.ipynb
1
11092
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cryptage homomorphic de Craig Gentry\n", "\n", "Un cryptage homomorphe pr\u00e9serve l'addition et la multiplication : une addition sur des nombres crypt\u00e9s est \u00e9gale au r\u00e9sultat crypt\u00e9 de l'addition sur les nombres non crypt\u00e9es. Craig Gentry a propos\u00e9 un tel cryptage dans son article [Fully Homomorphic Encryption over the Integers](https://eprint.iacr.org/2009/616.pdf). Le syst\u00e8me de cryptage encrypte et d\u00e9crypte des bits (0 ou 1)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", " var a = \"\" ;\n", " for ( ; n > 0 ; --n)\n", " a += \" \";\n", " return a;\n", "}\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", " var anchors = document.getElementsByClassName(\"section\");\n", " if (anchors.length == 0) {\n", " anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", " }\n", " var i,t;\n", " var text_menu = begin;\n", " var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", " var ind = \"\";\n", " var memo_level = 1;\n", " var href;\n", " var tags = [];\n", " var main_item = 0;\n", " var format_open = 0;\n", " for (i = 0; i <= llast; i++)\n", " tags.push(\"h\" + i);\n", "\n", " for (i = 0; i < anchors.length; i++) {\n", " text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", " var child = null;\n", " for(t = 0; t < tags.length; t++) {\n", " var r = anchors[i].getElementsByTagName(tags[t]);\n", " if (r.length > 0) {\n", "child = r[0];\n", "break;\n", " }\n", " }\n", " if (child == null) {\n", " text_memo += \"null\\n\";\n", " continue;\n", " }\n", " if (anchors[i].hasAttribute(\"id\")) {\n", " // when converted in RST\n", " href = anchors[i].id;\n", " text_memo += \"#1-\" + href;\n", " // passer \u00e0 child suivant (le chercher)\n", " }\n", " else if (child.hasAttribute(\"id\")) {\n", " // in a notebook\n", " href = child.id;\n", " text_memo += \"#2-\" + href;\n", " }\n", " else {\n", " text_memo += \"#3-\" + \"*\" + \"\\n\";\n", " continue;\n", " }\n", " var title = child.textContent;\n", " var level = parseInt(child.tagName.substring(1,2));\n", "\n", " text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", " if ((level < lfirst) || (level > llast)) {\n", " continue ;\n", " }\n", " if (title.endsWith('\u00b6')) {\n", " title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", " .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", " }\n", " if (title.length == 0) {\n", " continue;\n", " }\n", "\n", " while (level < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " format_open -= 1;\n", " memo_level -= 1;\n", " }\n", " if (level == lfirst) {\n", " main_item += 1;\n", " }\n", " if (keep_item != -1 && main_item != keep_item + 1) {\n", " // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", " continue;\n", " }\n", " while (level > memo_level) {\n", " text_menu += \"<ul>\\n\";\n", " memo_level += 1;\n", " }\n", " text_menu += repeat_indent_string(level-2);\n", " text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", " format_open += 1;\n", " }\n", " while (1 < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " memo_level -= 1;\n", " format_open -= 1;\n", " }\n", " text_menu += send;\n", " //text_menu += \"\\n\" + text_memo;\n", "\n", " while (format_open > 0) {\n", " text_menu += end_format;\n", " format_open -= 1;\n", " }\n", " return text_menu;\n", "};\n", "var update_menu = function() {\n", " var sbegin = \"\";\n", " var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", " var send = \"\";\n", " var begin_format = '<li>';\n", " var end_format = '</li>';\n", " var keep_item = -1;\n", " var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", " begin_format, end_format);\n", " var menu = document.getElementById(\"my_id_menu_nb\");\n", " menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## D\u00e9finition du cryptage\n", "\n", "$KeyGen(\\lambda)$\n", "\n", "* La cl\u00e9 secr\u00e8te $sk$ est un entier impair $p$ cod\u00e9 sur $\\eta$ bits : $p\\in (2\\mathbb{Z}+1) \\cap [2^{\\eta}, 2^{\\eta+1}[$.\n", "* La cl\u00e9 publique $pk$ est une s\u00e9quence de $\\tau+1$ entiers al\u00e9atoires tir\u00e9s selon un loi $\\mathcal{D}_{\\gamma,\\rho}(p)$. La s\u00e9quence $(x_0, ..., x_{\\tau})$ (doit v\u00e9rifier $x_0$ est impair et $r_p(x_0)$ est pair. Il faut recommencer si ce n'est pas le cas. Chaque entier est cod\u00e9 sur au plus $\\gamma$ bits.\n", "\n", "$Encrypt(pk, m\\in \\{0,1\\})$\n", "\n", "Choisir un ensemble al\u00e9atoire $S \\subset \\{1, ..., \\tau\\}$ et un entier al\u00e9atoire $r$ dans l'intervalle $]-2^{\\rho'}, 2^{\\rho'}[$. Calculer $c = (m + 2r + 2\\sum_{i \\in S} x_i) \\mod x_0$.\n", "\n", "$Evaluate(pk, C, c_1, ..., c_t)$\n", "\n", "La fonction $C$ effectue des op\u00e9rations sur $t$ bits. Le r\u00e9sultat est $c$.\n", "\n", "$Decrypt(sk, c)$\n", "\n", "Le r\u00e9sultat cherch\u00e9 est $(c \\mod p) \\mod 2$.\n", "\n", "**Avec :** (valeurs sugg\u00e9r\u00e9es par l'article mais d'autres sont possibles\n", "\n", "* $\\rho = \\lambda$\n", "* $\\rho' = 2\\lambda$\n", "* $\\eta \\sim O(\\lambda^2)$\n", "* $\\gamma \\sim O(\\lambda^5)$\n", "* $\\tau = \\gamma + \\lambda$\n", "* Pour simuler une loi $\\mathcal{D}_{\\gamma,\\rho}(p)$, choisir $q$ tel que $q \\in \\mathbb{Z} \\cap \\left[0, \\frac{2^{\\gamma}}{p}\\right[$, $r$ tel que $r \\in \\mathbb{Z} \\cap \\left]-2^{\\rho}, 2^{\\rho}\\right[$ et calculer $x = pq+r$.\n", "* $r_p(x)$ est le reste de la division enti\u00e8re de $x$ par $p$, reste choisi dans l'intervalle $\\left]-\\frac{p}{2}, \\frac{p}{2}\\right]$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 1 : impl\u00e9menter le cryptage" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 2 : v\u00e9rifier que le cryptage est stable par addition et multiplication" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 3 : impl\u00e9mententer l'addition enti\u00e8re " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 4 : impl\u00e9menter la multiplication enti\u00e8re" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "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
agdestine/machine-learning
notebook/Tour de SciKit-Learn.ipynb
1
1264004
null
mit
ghaslam/AM207
randomforest_explore.ipynb
1
7570
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###Random Forests\n", "\n", "Random forests are a type of machine learning technique in which an ensemble of decision trees are built and the predictions of the decision tree are averaged or the majority vote is taken as the final prediction. Each decision tree is trained with some stochasticity to decrease bias at the cost of variance.\n", "\n", "We do basic feature extraction in which we only keep 10 out of 39 features and convert these into 1-of-k encoding. Even with this basic feature extraction we get a prediction accuracy of 79%. \n", "\n", "By including more features and performing some more advanced feature engineering, we can reach prediction accuracies up to 83% (not shown in this notebook)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier as Tree\n", "from sklearn.ensemble import RandomForestClassifier as Forest\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import sklearn\n", "%matplotlib qt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train_data = pd.read_csv(\"WaterPump-training-values.csv\")\n", "train_labels = pd.read_csv(\"WaterPump-training-labels.csv\")\n", "N = train_data.shape[0]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#picking features that we want to keep\n", "features = ['longitude','latitude','gps_height','population','construction_year','water_quality','quantity','region_code',\n", " 'source','waterpoint_type']\n", "train = train_data[features]\n", "#converting categorical features to 1-of-k representation\n", "train1 = pd.concat([train_data, pd.get_dummies(train['water_quality']), pd.get_dummies(train['quantity']), \n", " pd.get_dummies(train['source']), pd.get_dummies(train['waterpoint_type'])], axis=1)\n", "#removing the categorical features after we converted them\n", "train1 = train1.drop(['water_quality','quantity','region_code', 'source', 'waterpoint_type'], axis=1, inPlace=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#separating dataset into training and testing for cross-validation\n", "test_idx = np.random.uniform(0, 1, len(train1)) <= 0.9\n", "train = train1[test_idx==True]\n", "trainLabels = train_labels[test_idx==True]\n", "test = train1[test_idx==False]\n", "testLabels = train_labels[test_idx==False]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, compute_importances=None,\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n", " min_samples_split=2, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#training the random forest\n", "forest = Forest(n_estimators=100,criterion='gini')\n", "forest.fit(train,trainLabels['status_group'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.790258449304\n" ] } ], "source": [ "#making predictions on the withheld data\n", "preds = forest.predict(test)\n", "accuracy = np.where(preds==testLabels['status_group'], 1, 0).sum() / float(len(test))\n", "#print \"Neighbors: %d, Accuracy: %3f\" % (n, accuracy)\n", "print accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above is the prediction accuracy of 79%!\n", "\n", "Below we can look at which features have the most predictive power. These tend to be nearer to the root of the decision trees. We can also steal these results for our model!" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#importance of each data feature that we kept\n", "importances = zip(forest.feature_importances_,list(train.columns.values))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(4.157248750361075e-05, 'dam'),\n", " (8.5802760153608722e-05, 'fluoride abandoned'),\n", " (0.00041373472890740434, 'unknown'),\n", " (0.00045397805951696829, 'cattle trough'),\n", " (0.00065079516735029065, 'fluoride'),\n", " (0.00079645670256850995, 'other'),\n", " (0.001171466300827778, 'salty abandoned'),\n", " (0.0012568216446781314, 'hand dtw'),\n", " (0.0012682113324976728, 'coloured'),\n", " (0.0014836707289752279, 'milky'),\n", " (0.0015663430362202501, 'improved spring'),\n", " (0.0021102186909650288, 'dam'),\n", " (0.0022838528373634297, 'unknown'),\n", " (0.0037050525654342709, 'salty'),\n", " (0.0037206671572899861, 'lake'),\n", " (0.0041189533059318925, 'rainwater harvesting'),\n", " (0.0057334878694563027, 'river'),\n", " (0.0065885452857120906, 'soft'),\n", " (0.0068209060906524446, 'seasonal'),\n", " (0.0070876581603355419, 'machine dbh'),\n", " (0.0086162268978782607, 'shallow well'),\n", " (0.0088090275722504281, 'communal standpipe multiple'),\n", " (0.0088500016424302025, 'spring'),\n", " (0.0088694981054384843, 'unknown'),\n", " (0.013998056932112398, 'insufficient'),\n", " (0.014753203455023909, 'communal standpipe'),\n", " (0.016176880044451083, 'hand pump'),\n", " (0.029274129586423212, 'enough'),\n", " (0.042610336816350937, 'other'),\n", " (0.070824578713865799, 'population'),\n", " (0.080924359030801168, 'dry'),\n", " (0.083194269443448848, 'construction_year'),\n", " (0.10541471600260641, 'gps_height'),\n", " (0.22798973110441112, 'longitude'),\n", " (0.22833678974016725, 'latitude')]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(importances,key=lambda x: x[0])" ] }, { "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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
timothydmorton/vespa-visualization
FPPbrowser/Untitled.ipynb
1
4917
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:PyMultiNest not available; only emcee fits will be possible.\n" ] } ], "source": [ "import isochrones" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:MultiNest not available; use_emcee being set to True\n" ] } ], "source": [ "a_star = isochrones.starmodel.StarModel(1)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "Must run MCMC (or load from file) before accessing samples", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-35-82809e449ed7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma_star\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtriangle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/ganeshravichandran/anaconda/lib/python2.7/site-packages/isochrones-0.9.0-py2.7.egg/isochrones/starmodel.pyc\u001b[0m in \u001b[0;36mtriangle\u001b[0;34m(self, params, query, extent, **kwargs)\u001b[0m\n\u001b[1;32m 800\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'mass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'feh'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 802\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 804\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/ganeshravichandran/anaconda/lib/python2.7/site-packages/isochrones-0.9.0-py2.7.egg/isochrones/starmodel.pyc\u001b[0m in \u001b[0;36msamples\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'sampler'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_samples\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 946\u001b[0m raise AttributeError('Must run MCMC (or load from file) '+\n\u001b[0;32m--> 947\u001b[0;31m 'before accessing samples')\n\u001b[0m\u001b[1;32m 948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_samples\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: Must run MCMC (or load from file) before accessing samples" ] } ], "source": [ "a_star.triangle()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'triangle' from '/Users/ganeshravichandran/anaconda/lib/python2.7/site-packages/triangle_plot-0.0.6-py2.7.egg/triangle.pyc'>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isochrones.starmodel.triangle" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
MachineLearningStudyGroup/Smart_Review_Summarization
ipynbs/Aspect_and_wordlist_generation.ipynb
1
102866
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymongo import MongoClient, ASCENDING\n", "from srs.database import connect_to_db\n", "from srs.utilities import Sentence, tokenize\n", "from nltk import pos_tag\n", "from collections import Counter\n", "import math\n", "import word2vec\n", "import os\n", "import numpy as np\n", "import random\n", "import copy\n", "import gzip\n", "import ast\n", "import operator\n", "# Loading Word2Vec model:\n", "current_directory = os.path.dirname(os.path.realpath(\"__file__\"))\n", "model_path = os.path.join(current_directory[:-6], 'srs/predictor_data/text8.bin')\n", "model = word2vec.load(model_path)\n", "\n", "# Define some generally used functions:\n", "def sort_list(list, sort_index, reverse = True):\n", " list_sorted = sorted(list, key=lambda tup: tup[sort_index], reverse = reverse)\n", " return list_sorted\n", "\n", "def get_excluded_words():\n", " f = open(\"Aspect_and_wordlist_txt/excluded_words.txt\",'r')\n", " excluded_words = eval(f.read())\n", " f.close()\n", " return excluded_words\n", "\n", "def get_excluded_words_wordlist(category_id):\n", " f = open(\"Aspect_and_wordlist_txt/excluded_words_wordlist.txt\",'r')\n", " dictionary = eval(f.read())\n", " if category_id in dictionary:\n", " excluded_words_dict = dictionary[category_id]\n", " is_apply_all = int(excluded_words_dict[\"apply_all\"])\n", " f.close()\n", " return is_apply_all, excluded_words_dict\n", " else:\n", " f.close()\n", " return -1, {}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obtain the prod_dict:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def parse(path):\n", " g = gzip.open(path, 'r')\n", " for l in g:\n", " yield ast.literal_eval(l)\n", "\n", "\n", "def construct_prod_dict(meta_file_path_list):\n", " \"\"\"return a dictionary for product metadata\"\"\"\n", " prod_dict = {}\n", " for meta_file_path in meta_file_path_list:\n", " metaParser = parse(meta_file_path)\n", " client, db = connect_to_db()\n", " i = 0 \n", " print \"Building the product dictionary for %s\" % meta_file_path\n", " for meta in metaParser:\n", " i+=1\n", " if i % 100000 == 0:\n", " print i\n", " product_id = meta['asin']\n", " category = meta['categories'][0]\n", " product_name = \"\"\n", " brand = \"\"\n", " if 'title' in meta:\n", " inter = meta['title'].split()\n", " if len (inter) > 1:\n", " product_name_short = inter[0] + ' ' + inter[1]\n", " else:\n", " product_name_short = inter[0]\n", " if 'brand' in meta:\n", " brand = meta['brand']\n", " prod_dict[product_id]={'category': category, 'product_name': product_name_short, 'brand': brand}\n", " print i\n", " return prod_dict" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building the product dictionary for ../../Datasets/Full_Reviews/meta_Electronics.json.gz\n", "100000\n", "200000\n", "300000\n", "400000\n", "498196\n", "Building the product dictionary for ../../Datasets/Full_Reviews/meta_Cell_Phones_and_Accessories.json.gz\n", "100000\n", "200000\n", "300000\n", "346793\n" ] } ], "source": [ "Electronics_Meta_Path = '../../Datasets/Full_Reviews/meta_Electronics.json.gz'\n", "Phone_Meta_Path = '../../Datasets/Full_Reviews/meta_Cell_Phones_and_Accessories.json.gz'\n", "\n", "prod_dict = construct_prod_dict([Electronics_Meta_Path,Phone_Meta_Path])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The following functions accumulate all the sentences by category" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_category_dict(prod_dict):\n", " \"\"\"Build a dictionary whose key is the category tuple, and the value is a list of product_ids:\"\"\"\n", " client, db = connect_to_db()\n", " cursor = db.product_collection.find()\n", " category_dict = {}\n", " i = 0\n", " for product in cursor:\n", " i += 1 \n", " if i % 100000 == 0:\n", " print i\n", " category = product['category']\n", " category_short = tuple(category[:4]) #generally category is 4-tuple. Now limit to the first three tuple\n", " product_id = product['product_id']\n", " product_name = \"\"\n", " brand = \"\"\n", " if product_id in prod_dict:\n", " product_info = prod_dict[product_id]\n", " if 'product_name' in product_info:\n", " product_name = product_info['product_name']\n", " if 'brand' in product_info:\n", " brand = product_info['brand']\n", "\n", " if category_short not in category_dict:\n", " category_dict[category_short] = {\"product_id\": [product_id], \"brand_list\": [], \"product_name_list\": []}\n", " else:\n", " category_dict[category_short]['product_id'].append(product_id)\n", " \n", " if len(product_name) > 0:\n", " category_dict[category_short]['product_name_list'].append(product_name)\n", " if len(brand) > 0:\n", " if brand not in category_dict[category_short]['brand_list']:\n", " category_dict[category_short]['brand_list'].append(brand)\n", " \n", " client.close()\n", " print i\n", " \n", " return category_dict\n", "\n", "\n", "def sort_category_dict(category_dict, isPrint = False):\n", " \"\"\"Sort the categories according to the number of products in that category, and print them from top\"\"\"\n", " category_list_sorted = []\n", " category_list = []\n", "\n", " for key in category_dict:\n", " length = len(category_dict[key]['product_id'])\n", " category_list.append([key,length,key[:3],0])\n", " category_list_sorted = sorted(category_list, key=lambda tup: (tup[2],tup[1]), reverse=True)\n", " \n", " category_list_sorted_dict = {}\n", " for Id in range(len(category_list_sorted)):\n", " category_list_sorted[Id][3]=Id\n", " category = category_list_sorted[Id][0]\n", " category_dict[category][\"category_id\"] = Id\n", " category_list_sorted_dict[Id] = category_list_sorted[Id][:3]\n", " \n", " if isPrint:\n", " for Id in range(len(category_list_sorted)):\n", " print Id, category_list_sorted_dict[Id][:2]\n", " \n", " return category_list_sorted_dict\n", "\n", "\n", "def combine_category_custom(category_dict_raw, category_list_sorted_dict):\n", " category_dict = copy.deepcopy(category_dict_raw)\n", " print \"Number of categories in original set: %g\"%len(category_dict_raw)\n", " print \"Combined category ID:\"\n", " f = open('Aspect_and_wordlist_txt/combined_dict.txt','r')\n", " for line in f:\n", " combine_info = eval(line)\n", " print combine_info\n", " if len(combine_info) > 0:\n", " Id_to_combine = combine_info[0]\n", " name_info = combine_info[1]\n", " category_name_combined = category_list_sorted_dict[name_info[0]][0][:name_info[1]]\n", " category_id = category_dict_raw[category_list_sorted_dict[name_info[0]][0]][\"category_id\"]\n", " new_prod_id_list = []\n", " new_product_name_list = []\n", " new_brand_list = []\n", " for Id in Id_to_combine:\n", " category_name = category_list_sorted_dict[Id][0]\n", " new_prod_id_list += category_dict[category_name][\"product_id\"]\n", " new_product_name_list += category_dict[category_name][\"product_name_list\"]\n", " new_brand_list += category_dict[category_name][\"brand_list\"]\n", " category_dict.pop(category_name, 0)\n", " category_dict[category_name_combined] = {\"category_id\": category_id,\"product_id\": new_prod_id_list,\\\n", " \"product_name_list\": new_product_name_list, \"brand_list\": new_brand_list}\n", " f.close()\n", " print \"Number of categories in the new dict: %g\"%len(category_dict)\n", " \n", " return category_dict\n", "\n", "\n", "def combine_small_category(category_dict_raw, category_list_sorted, prod_num_threshold = 100, shrink_level = 3):\n", " category_dict = copy.deepcopy(category_dict_raw)\n", " i = 0\n", " for i in range(len(category_list_sorted)):\n", " i += 1\n", " category_name = category_list_sorted[-i][1]\n", " prod_num = category_list_sorted[-i][0]\n", " if prod_num > prod_num_threshold:\n", " break\n", " if len(category_name) > shrink_level:\n", " category_name_shrink = category_name[:shrink_level]\n", " if category_name_shrink in category_dict:\n", " category_dict[category_name_shrink] += category_dict[category_name]\n", " category_dict.pop(category_name,0)\n", " print \"{0} combined into {1}\".format(category_name_shrink, category_name)\n", " else:\n", " print \"{0} not combined\".format(category_name_shrink)\n", " else:\n", " print \"{0} length not enough.\".format(category_name)\n", " \n", " return category_dict\n", "\n", "\n", "def save_category_dict_to_db(category_dict, dropPrevious = False):\n", " client, db = connect_to_db()\n", " db_category_data = db.category_data\n", " if dropPrevious == True:\n", " db_category_data.delete_many({})\n", " for category in category_dict:\n", " query = {\"category_id\": category_dict[category][\"category_id\"]}\n", " update_field = {\"category\": list(category),\\\n", " \"prod_id_list\": category_dict[category][\"product_id\"], \\\n", " \"brand_list\": category_dict[category][\"brand_list\"],\\\n", " \"product_name_list\": category_dict[category][\"product_name_list\"]}\n", " db_category_data.update_one(query, {\"$set\": update_field}, True)\n", " \n", " client.close()\n", "\n", "\n", "def show_category_dict_info(category_dict, min_prod_num = 1000):\n", " new_list = []\n", " for category in category_dict:\n", " new_list.append([len(category_dict[category][\"product_id\"]),category,category_dict[category][\"category_id\"]])\n", " \n", " new_list = sorted(new_list, key=lambda tup: tup[0], reverse=True)\n", " \n", " for item in new_list:\n", " if int(item[0]) < min_prod_num: \n", " break\n", " print \"{0},{1},{2}\".format(item[0],item[1],item[2])\n", "\n", "\n", "def get_sentence_from_category(category_list):\n", " \"\"\"Obtain all the review sentences from a list of category tuple:\"\"\"\n", " if isinstance(category_list, dict):\n", " category_lists = [category_list]\n", " else:\n", " category_lists = category_list\n", " \n", " category_content_list = []\n", " \n", " for category in category_lists:\n", " print \"{0}:\".format(category)\n", " client, db = connect_to_db()\n", " product_id_list = category_dict[category][\"product_id\"]\n", " category_contents = {\"category\": category,\"sentence_list\": [], \"brand_list\": category_dict[category][\"brand_list\"],\\\n", " \"product_name_list\": category_dict[category][\"product_name_list\"]}\n", " review_num = 0\n", " for product_id in product_id_list:\n", " query_res = list(db.product_collection.find({\"product_id\": product_id}))\n", " contents = query_res[0][\"contents\"]\n", " category_contents['sentence_list'] += contents\n", " review_num += len(query_res[0][\"review_ids\"])\n", " print \" ({0}, {1}, {2})\".format(len(product_id_list), review_num, len(category_contents['sentence_list'])) \n", " category_content_list.append(category_contents)\n", " \n", " client.close()\n", "\n", " return category_content_list\n", "\n", "\n", "def get_sentence_from_category_ensemble(category_dict, max_prod_chosen = 500, min_product_level = 500):\n", " client, db = connect_to_db()\n", " full_sentence_list = []\n", " print \"Getting product categories: (num_sentence_chosen, category):\"\n", " for category in category_dict:\n", " if len(category_dict[category]) < min_product_level:\n", " continue\n", " product_id_list = category_dict[category][\"product_id\"]\n", " random.shuffle(product_id_list)\n", " new_sentence = []\n", " for product_id in product_id_list[:max_prod_chosen]:\n", " query_res = list(db.product_collection.find({\"product_id\": product_id}))\n", " contents = query_res[0][\"contents\"]\n", " new_sentence += contents\n", " print len(new_sentence),category\n", " full_sentence_list += new_sentence\n", " client.close()\n", " print \"Number of sentences: {0}\".format(len(full_sentence_list))\n", " \n", " all_category_content = {\"sentence_list\": full_sentence_list}\n", " return all_category_content" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000\n", "200000\n", "300000\n", "400000\n", "500000\n", "600000\n", "700000\n", "793315\n" ] } ], "source": [ "category_dict_raw = get_category_dict(prod_dict)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of categories in original set: 512\n", "Combined category ID:\n", "[[16, 17], [17, 2]]\n", "[[20, 21, 22, 23, 26, 28, 29, 30, 31, 32], [22, 3]]\n", "[[38, 39, 40, 41, 42], [38, 3]]\n", "[[105, 106], [105, 3]]\n", "[[108, 109, 110, 111, 112], [112, 3]]\n", "[[139, 140, 141], [139, 3]]\n", "[[176, 177, 178, 179, 180, 181, 182, 183, 184], [176, 3]]\n", "[[277, 278, 279, 280], [277, 3]]\n", "[[282, 283, 284, 285, 286, 287, 288], [282, 3]]\n", "[[297, 298, 299, 300, 301], [297, 3]]\n", "[[302, 303, 304], [302, 3]]\n", "[[308, 309, 310, 311, 312, 313, 314], [308, 3]]\n", "[[316, 321], [316, 3]]\n", "[[322, 323, 324, 325, 326, 327, 328, 329, 330, 331], [322, 3]]\n", "[[356, 357, 358, 359, 360], [356, 3]]\n", "[[362, 363, 364, 365, 366], [366, 3]]\n", "[[367, 368, 369, 370], [367, 3]]\n", "[[371, 372, 373, 374, 375, 376, 377], [371, 3]]\n", "[[379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393], [379, 3]]\n", "[[399, 400], [399, 3]]\n", "[[417, 418, 419, 420, 421], [417, 3]]\n", "[[427, 428, 429, 430], [427, 2]]\n", "[[433, 434, 435, 436, 437, 438], [433, 2]]\n", "[[439, 440, 441, 442, 443, 444, 445], [439, 3]]\n", "[[462, 463, 464], [462, 3]]\n", "[[469, 470, 471], [469, 3]]\n", "Number of categories in the new dict: 396\n" ] } ], "source": [ "category_list_sorted_dict = sort_category_dict(category_dict_raw, isPrint = False)\n", "category_dict = combine_category_custom(category_dict_raw, category_list_sorted_dict)\n", "save_category_dict_to_db(category_dict, dropPrevious = False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "207742,(u'Cell Phones & Accessories', u'Cases', u'Waterproof Cases'),439\n", "25256,(u'Cell Phones & Accessories', u'Accessories', u'Accessory Kits'),474\n", "25245,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories', u'Cases & Sleeves'),129\n", "21726,(u'Electronics', u'Portable Audio & Video', u'MP3 Players & Accessories', u'MP3 Player Accessories'),78\n", "16275,(u'Electronics', u'Computers & Accessories', u'Laptop & Netbook Computer Accessories', u'Batteries'),155\n", "15453,(u'Electronics', u'Camera & Photo', u'Accessories', u'Batteries & Chargers'),332\n", "15195,(u'Cell Phones & Accessories', u'Accessories', u'Screen Protectors'),450\n", "15051,(u'Electronics', u'Computers & Accessories', u'Laptop & Netbook Computer Accessories', u'Chargers & Adapters'),156\n", "13548,(u'Electronics', u'Computers & Accessories', u'Laptop & Netbook Computer Accessories', u'Bags & Cases'),157\n", "13115,(u'Electronics', u'Accessories & Supplies', u'Audio & 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u'Telephone Accessories', u'Batteries'),353\n", "2804,(u'Electronics', u'Car & Vehicle Electronics', u'Vehicle Electronics Accessories'),231\n", "2625,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'Motherboards'),188\n", "2452,(u'Electronics', u'Car & Vehicle Electronics', u'Car Electronics', u'Car Safety & Security'),254\n", "2439,(u'Electronics', u'Computers & Accessories', u'Tablets'),136\n", "2379,(u'Electronics', u'Camera & Photo', u'Video', u'Camcorders'),271\n", "2353,(u'Electronics', u'Car & Vehicle Electronics', u'Car Electronics', u'Car Video'),255\n", "2343,(u'Electronics', u'Accessories & Supplies', u'Audio & Video Accessories', u'Headphone Accessories'),406\n", "2340,(u'Electronics', u'Accessories & Supplies', u'Blank Media'),379\n", "2298,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories', u'Bundles'),131\n", "2281,(u'Electronics', u'GPS & Navigation', u'GPS System Accessories', u'Vehicle Mounts'),114\n", "2190,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Hard Drive Enclosures'),209\n", "2187,(u'Electronics', u'Accessories & Supplies', u'Audio & Video Accessories', u'Media Storage & Organization'),407\n", "2096,(u'Electronics', u'Camera & Photo', u'Binoculars & Scopes'),316\n", "1955,(u'Electronics', u'Home Audio', u'Stereo Components', u'Receivers & Amplifiers'),91\n", "1929,(u'Electronics', u'Camera & Photo', u'Accessories', u'Flash Accessories'),336\n", "1854,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Computer Speakers'),210\n", "1840,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Keyboard & Mice Accessories'),211\n", "1824,(u'Electronics', u'Portable Audio & Video', u'CB & Two-Way Radios', u'Accessories'),83\n", "1813,(u'Electronics', u'Accessories & Supplies', u'Audio & Video Accessories', u'Distribution'),408\n", "1804,(u'Cell Phones & Accessories', u'Accessories', u'Chargers', u'Cell Phone Docks'),461\n", "1710,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'Power Supplies'),189\n", "1701,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Headsets & Microphones'),212\n", "1698,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'Computer Cases'),190\n", "1665,(u'Electronics', u'Accessories & Supplies'),423\n", "1579,(u'Electronics', u'Car & Vehicle Electronics', u'Vehicle Electronics Accessories', u'Vehicle Audio & Video Installation'),232\n", "1559,(u'Electronics', u'Computers & Accessories', u'Routers'),138\n", "1543,(u'Electronics', u'Camera & Photo'),352\n", "1513,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Memory Card Readers'),213\n", "1486,(u'Electronics', u'Computers & Accessories', u'Video Projectors'),128\n", "1460,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories', u'Chargers & Adapters'),132\n", "1459,(u'Electronics', u'Accessories & Supplies', u'Cord Management'),371\n", "1436,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories', u'Skins & Decals'),133\n", "1409,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Input Devices'),214\n", "1374,(u'Electronics', u'Computers & Accessories', u'Computer Components'),191\n", "1370,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories'),134\n", "1361,(u'Electronics', u'Camera & Photo', u'Accessories', u'Professional Video Accessories'),337\n", "1347,(u'Cell Phones & Accessories', u'Accessories'),475\n", "1302,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Monitor Accessories'),215\n", "1263,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Uninterrupted Power Supply (UPS)'),216\n", "1238,(u'Electronics', u'Computers & Accessories', u'Networking Products', u'Hubs'),143\n", "1211,(u'Electronics', u'Security & Surveillance', u'Home Security Systems'),58\n", "1204,(u'Electronics', u'Camera & Photo', u'Flashes'),302\n", "1162,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Keyboard & Mouse Combos'),217\n", "1151,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'I/O Port Cards'),192\n", "1134,(u'Electronics', u'Computers & Accessories', u'Webcams'),124\n", "1126,(u'Electronics', u'GPS & Navigation', u'Sports & Handheld GPS'),112\n", "1124,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'Internal Optical Drives'),193\n", "1105,(u'Electronics', u'Camera & Photo', u'Film Photography', u'Film Cameras'),305\n", "1088,(u'Electronics', u'eBook Readers & Accessories', u'Skins'),10\n", "1081,(u'Electronics', u'Computers & Accessories', u'Cables & Accessories', u'Surge Protectors'),218\n", "1066,(u'Electronics', u'Computers & Accessories', u'Computer Components', u'CPU Processors'),194\n", "1034,(u'Electronics', u'Accessories & Supplies', u'Audio & Video Accessories', u'Antennas'),409\n", "1030,(u'Electronics', u'Computers & Accessories', u'Networking Products', u'Switches'),144\n", "1022,(u'Electronics', u'Camera & Photo', u'Accessories', u'Tripod & Monopod Accessories'),338\n", "1012,(u'Electronics', u'Computers & Accessories', u'Touch Screen Tablet Accessories', u'Stands'),135\n", "1004,(u'Electronics', u'Camera & Photo', u'Underwater Photography'),277\n" ] } ], "source": [ "show_category_dict_info(category_dict, min_prod_num = 1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# all_category_content = get_sentence_from_category_ensemble(category_dict, max_prod_chosen = 1000, min_product_level = 0)\n", "# get_tf_idf(all_category_content, is_idf_db = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aspect extraction: the following functions collects sentences from one category, obtain each word's tf-idf score, and choose aspect candidates:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_category_word_scores(category_id_list, db_category_data = None, db_product_collection = None, db_word_score_list = None):\n", " \"\"\"Get tf-idf score for each word\n", " The dictionary records for each word as a key, the [num_word, num_doc] value, where num_word means the number of \n", " that word in the sentence_list, and num_doc means the number of sentences this word appears in.\n", " \"\"\"\n", " external_db = True\n", " if (not db_category_data) or (not db_product_collection) or (not db_word_score_list):\n", " external_db = False\n", " client, db = connect_to_db()\n", " if not db_category_data:\n", " db_category_data = db.category_data\n", " if not db_product_collection:\n", " db_product_collection = db.product_collection\n", " if not db_word_score_list:\n", " db_word_score_list = db.word_score_list\n", " \n", " if not isinstance(category_id_list, list):\n", " category_id_list = [category_id_list]\n", " \n", " # Collecting each word's data for that category\n", " for category_id in category_id_list:\n", " query_category = list(db_category_data.find({\"category_id\": category_id}))\n", " if len(query_category) == 0:\n", " print \"{0} not in db, skip.\".format(category_id)\n", " continue\n", " category_content = query_category[0]\n", " category = category_content[\"category\"]\n", " prod_id_list = category_content[\"prod_id_list\"]\n", " prod_num = len(prod_id_list) \n", " \n", " # Obtaining brand_list words and product_name words:\n", " brand_list = category_content[\"brand_list\"]\n", " product_name_list = category_content[\"product_name_list\"]\n", " brand_word_list = []\n", " product_name_word_list =[]\n", " for brand in brand_list: \n", " brand_word = tokenize(brand, stem = False)\n", " if len(brand_word) > 0:\n", " brand_word_list += brand_word[:1]\n", " brand_word_list = dict(Counter(brand_word_list))\n", " for product_name in product_name_list: \n", " product_name_word = tokenize(product_name, stem = False)\n", " if len(product_name_word) > 0:\n", " product_name_word_list += product_name_word[:1]\n", " product_name_word_list = dict(Counter(product_name_word_list))\n", " \n", " # Obtaining word_statistics: [word, word_freq, num_doc]\n", " \n", " word_statistics = {} \n", " i = 0\n", " for product_id in prod_id_list: \n", " query_res = list(db_product_collection.find({\"product_id\": product_id}))\n", " contents = query_res[0][\"contents\"]\n", " for sentence in contents:\n", " i += 1\n", " if i % 100000 == 0:\n", " print i\n", " tokens = tokenize(sentence, stem = False)\n", " tokens_count = Counter(tokens)\n", " for word in tokens_count: \n", " if word not in word_statistics:\n", " word_statistics[word] = [tokens_count[word], 1]\n", " else:\n", " word_statistics[word][0] += tokens_count[word]\n", " word_statistics[word][1] += 1\n", " \n", " total_num_doc = i\n", " print \"Id: {0}, num_prod: {1}, num_sentence: {2}\".format(category_id, prod_num, total_num_doc)\n", " print \"{0}\".format(category)\n", " word_scores = []\n", " \n", " max_word_freq = 0\n", " for word in word_statistics:\n", " if word_statistics[word][0] > max_word_freq:\n", " max_word_freq = word_statistics[word][0]\n", " \n", " # Calculating tf-idf for the category\n", " for word in word_statistics: \n", " word_rawdata = word_statistics[word]\n", " word_freq = word_rawdata[0]\n", " num_doc = word_rawdata[1]\n", " tf = float(word_freq) / max_word_freq \n", " \n", " idf_category = math.log(float(total_num_doc)/(num_doc))\n", " query_idf = list(db_word_score_list.find({\"word\": word}))\n", " if len(query_idf) > 0:\n", " idf = query_idf[0][\"full_word_score\"][2]\n", " else:\n", " idf = idf_category\n", " word_scores.append([word, tf * idf, tf, idf_category, idf, word_freq, num_doc])\n", " \n", " word_statistics.clear()\n", " word_scores.sort(key=lambda tup: tup[1], reverse=True)\n", " \n", " # Update database:\n", " query = {\"category_id\": category_id}\n", " update_field = {\"word_scores\": word_scores, \"brand_word_list\": brand_word_list,\\\n", " \"product_name_word_list\": product_name_word_list,\"total_num_sentence\":total_num_doc}\n", " db_category_data.update_one(query, {\"$set\": update_field}, True)\n", " \n", " # Get aspect_cadidate:\n", " if prod_num < 20:\n", " num_candidate = 80\n", " else:\n", " num_candidate = 60\n", " \n", " if external_db == False:\n", " client.close() \n", "\n", "\n", "\n", "def get_aspect_cadidate(category_id_list, tag_list = [\"NN\",\"NNS\",\"JJ\"], num_candidate = -1, rescan_word_scores = False):\n", " '''Get cadidate aspects from word_tf_idf. Only words whose tag belong to tag_list and score > threshold will pass''' \n", " # check if db cursor is given:\n", " client, db = connect_to_db()\n", " db_word_score_list = db.word_score_list\n", " db_product_collection = db.product_collection\n", " db_category_data = db.category_data\n", "\n", " for category_id in category_id_list: \n", " full_word_freq_thresh = 30\n", " full_num_doc_thresh = 10\n", " \n", " words_excluded = []\n", " query_category = list(db_category_data.find({\"category_id\": category_id}))\n", " if len(query_category) == 0:\n", " print \"{0} not in db, skip.\".format(category_id)\n", " continue\n", " category_content = query_category[0]\n", " category = category_content[\"category\"]\n", " num_prod = len(category_content[\"prod_id_list\"])\n", " if num_prod < 10:\n", " continue\n", " print \"{0}, {1}\".format(category_id, category)\n", " # Check if need to rescanning word_scores:\n", " reQuery = False\n", " if \"word_scores\" not in category_content or \"word_scores\" not in category_content or \"product_name_word_list\" not in category_content:\n", " print \"{0} don't have word_scores, constructing...\".format(category_id)\n", " get_category_word_scores(category_id, db_category_data, db_product_collection, db_word_score_list)\n", " reQuery = True\n", " elif rescan_word_scores == True:\n", " print \"rescanning word_scores...\".format(category_id)\n", " get_category_word_scores(category_id, db_category_data, db_product_collection, db_word_score_list)\n", " reQuery = True\n", " \n", " if reQuery == True:\n", " category_content = list(db_category_data.find({\"category_id\": category_id}))[0]\n", " word_scores = category_content[\"word_scores\"]\n", " brand_word_list = category_content[\"brand_word_list\"]\n", " product_name_word_list = category_content[\"product_name_word_list\"]\n", " \n", " \n", " #Setting num_candidate\n", " if num_candidate == -1:\n", " if num_prod >= 20:\n", " num_candidate2 = 70\n", " else:\n", " num_candidate2 = 50\n", " else:\n", " num_candidate2 = num_candidate\n", " \n", " if num_prod < 50:\n", " full_word_freq_thresh = int(num_prod/ 4)\n", " full_num_doc_thresh = int(num_prod / 8)\n", " \n", " aspect_candidate = []\n", " j = 0\n", " try:\n", " excluded_words = get_excluded_words()\n", " except:\n", " print \"Problem with excluded_words.txt, use previous.\"\n", " for word_data in word_scores:\n", " full_word_freq = 10000\n", " full_num_doc = 10000 # default setting\n", " word = word_data[0]\n", " # Various criterior to exclude the word:\n", " if len(word) == 1:\n", " continue\n", " if word_data[5] <= 1:\n", " continue\n", " if word in excluded_words:\n", " continue\n", " query_idf = list(db_word_score_list.find({\"word\": word}))\n", " if len(query_idf) > 0:\n", " full_word_score = query_idf[0][\"full_word_score\"]\n", " full_word_freq = full_word_score[4]\n", " full_num_doc = full_word_score[5]\n", " if full_word_freq < full_word_freq_thresh or full_num_doc < full_num_doc_thresh:\n", " words_excluded.append(word)\n", " continue \n", " if word in brand_word_list:\n", " if full_word_freq < 100:\n", " words_excluded.append(word)\n", " continue\n", " if word in product_name_word_list:\n", " if full_word_freq < 150:\n", " words_excluded.append(word)\n", " continue \n", " \n", " word_tag = pos_tag([word])[0][1]\n", " # If the tag is in tag_list:\n", " if word_tag in tag_list:\n", " j += 1\n", " word_data.append(word_tag)\n", " aspect_candidate.append(word_data)\n", " word1 = word\n", " if len(word1) <= 2:\n", " word1 += \" \"\n", " print \"%s \\t%0.2f\\t%0.2f\\t%0.2f\\t%0.2f\\t%g\\t%g\\t%s\"%(word1, word_data[1],word_data[2],word_data[3],word_data[4],\\\n", " word_data[5], word_data[6], word_data[7])\n", " else:\n", " words_excluded.append(word)\n", " if j > num_candidate2:\n", " break\n", " \n", " # Update database:\n", " query = {\"category_id\": category_id}\n", " update_field = {\"aspect_candidate\": aspect_candidate, \"words_excluded\": words_excluded}\n", " db_category_data.update_one(query, {\"$set\": update_field}, True) \n", " print \"Excluded words: {0}\".format(words_excluded)\n", " print\n", " client.close()\n", "\n", "\n", "def save_word_score_to_db(category_content_list, isRewrite = False):\n", " client, db = connect_to_db()\n", " db_word_score_list = db.word_score_list\n", " if isRewrite == True:\n", " db_word_score_list.delete_many({})\n", " db_word_score_list.create_index([(\"word\", ASCENDING)])\n", " db_word_score_list.create_index([(\"category\", ASCENDING)])\n", " \n", " if isinstance(category_content_list, dict):\n", " category_content_lists = [category_content_list]\n", " else:\n", " category_content_lists = category_content_list\n", " \n", " for category_content in category_content_lists:\n", " word_tf_idf = category_content[\"word_tf_idf\"]\n", " category = category_content[\"category\"]\n", " i = 0\n", " for word_data in word_tf_idf:\n", " i += 1\n", " if i % 50000 == 0:\n", " print i\n", " word = word_data[0]\n", " word_score = word_data[1:]\n", " query = {\"word\": word}\n", " update_field = {\"category\": category, \"word_score\": word_score}\n", "\n", " db_word_score_list.update_one(query, {\"$set\": update_field}, True) \n", " print \"{0}: Total number of words: {1}\".format(category, len(word_tf_idf))\n", " \n", " client.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wordlist generation and Database inferface:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_similarity(word1, word2):\n", " \"\"\"Find the similarity between two words, which equals the dot product of their vectors\"\"\"\n", " similarity = 0\n", " word1=word1.lower()\n", " word2=word2.lower()\n", " if word1 in model and word2 in model:\n", " word1_vec = model[word1]\n", " word2_vec = model[word2]\n", " similarity = np.dot(word1_vec, word2_vec)\n", " return similarity\n", "\n", "def get_wordlist_from_aspect_candidates(seed_word, word_tf_idf, similarity_threshold, score_threshold):\n", " \"\"\"Method 1: directly find the word list from all words whose similarity with the seed_word and tf-idf score are above \n", " certain threshold\"\"\"\n", " word_list = []\n", " for word_data in word_tf_idf:\n", " word = word_data[0]\n", " tf_idf = word_data[1]\n", " if tf_idf > score_threshold:\n", " similarity = get_similarity(seed_word, word)\n", " if similarity > similarity_threshold:\n", " word_list.append([word, similarity, tf_idf]) \n", " word_list_sorted = sorted(word_list, key=lambda tup: tup[1], reverse=True)\n", " return word_list_sorted\n", "\n", "\n", "def predict_aspect(token_list, wordlist_dict, predict_threshold = 1.05):\n", " \"\"\"\n", " sentence: a single labelled sentence obj \n", " returns a vector of length len(wordlist_dict)\n", " \"\"\"\n", " \n", " len_word = max(len(token_list),1) * 1.0;\n", " f_vec = copy.deepcopy(wordlist_dict) #speed up by putting copy and reset outside\n", " #reset to zero \n", " for key in f_vec.keys():\n", " for i in range(len(f_vec[key])):\n", " f_vec[key][i][1]=0\n", "\n", " for key in wordlist_dict.keys():\n", " for i in range(len(wordlist_dict[key])):\n", " count = token_list.count(wordlist_dict[key][i][0])\n", " f_vec[key][i][1]=(count/len_word)\n", " \n", " #multiply it with weights\n", " score_dict = dict.fromkeys(f_vec, 0)\n", " for key in f_vec.keys():\n", " dot_product = 0.0\n", " for i in range(len(wordlist_dict[key])):\n", " dot_product += np.exp(wordlist_dict[key][i][1]*f_vec[key][i][1])\n", "\n", " score_dict[key] = dot_product/len(wordlist_dict[key]) # min score is 1\n", " \n", " predicted_aspect = max(score_dict.iteritems(), key = operator.itemgetter(1))[0]\n", " if score_dict[predicted_aspect] <= predict_threshold: # no key word overlap\n", " #call word2vec similarity \n", " # score_dict_w2v = word2vec_predict(sentence,wordlist_dict,w2v_model)\n", " # predicted_aspect = max(score_dict_w2v.iteritems(), key=operator.itemgetter(1))[0]\n", " # if score_dict_w2v[predicted_aspect] < thres:\n", " predicted_aspect = \"no feature\"\n", "\n", " return predicted_aspect\n", "\n", "\n", "def test_prediction(category_id, wordlist_dict, aspect_to_show = [], predict_threshold = 1.05, num_show = 100, show_no_feature = False):\n", " client, db = connect_to_db()\n", " db_category_data = db.category_data\n", " db_product_collection = db.product_collection\n", " query_category = list(db_category_data.find({\"category_id\": category_id}))\n", " if len(query_category) == 0:\n", " print \"category {0} not in db.\".format(category_id)\n", " return\n", " category_content = query_category[0]\n", " category = category_content[\"category\"]\n", " prod_id_list = category_content[\"prod_id_list\"]\n", " random.shuffle(prod_id_list)\n", " num_prod = len(prod_id_list)\n", " \n", " i = 0\n", " for prod_id in prod_id_list:\n", " query_product = list(db_product_collection.find({\"product_id\": prod_id}))\n", " if len(query_product) == 0:\n", " print \"product {0} not in db, skip.\".format(prod_id)\n", " sentence_list = query_product[0][\"contents\"]\n", " random.shuffle(sentence_list)\n", " for sentence in sentence_list[:max(30,num_show / num_prod * 5)]: \n", " if i > num_show:\n", " break\n", " token_list = tokenize(sentence, stem = False)\n", " predicted_aspect = predict_aspect(token_list, wordlist_dict, predict_threshold)\n", " if show_no_feature == False:\n", " if predicted_aspect == \"no feature\":\n", " continue\n", " i += 1\n", " if len(aspect_to_show) == 0:\n", " print \"{0}:\\t{1}\".format(predicted_aspect, sentence)\n", " else:\n", " if predicted_aspect in aspect_to_show:\n", " print \"{0}:\\t{1}\".format(predicted_aspect, sentence)\n", " client.close() \n", "\n", "\n", "\n", "def get_word_statistics_from_seed_word(prod_id_list, seed_word_list):\n", " client, db = connect_to_db()\n", " db_product_collection = db.product_collection\n", " word_statistics_dict = {seed_word.lower(): {} for seed_word in seed_word_list}\n", " aspect_sentence_num_dict = {seed_word.lower(): 0 for seed_word in seed_word_list}\n", " aspect_sentence_num_dict[\"no feature\"] = 0\n", " sentence_num = 0\n", " for product_id in prod_id_list:\n", " query_product = list(db_product_collection.find({\"product_id\": product_id}))\n", " if len(query_product) == 0:\n", " print \"Product {0} doesn't exists, skip.\".format(product_id)\n", " continue\n", " if \"contents\" not in query_product[0]:\n", " print \"Product {0} doesn't have contents, skip.\".format(product_id)\n", " continue\n", " sentence_list = query_product[0][\"contents\"]\n", " for sentence in sentence_list: \n", " tokens_list = tokenize(sentence, stem = False) \n", " tokens_count = Counter(tokens_list)\n", " sentence_num += 1\n", " if sentence_num % 100000 == 0:\n", " print sentence_num\n", " has_feature = 0\n", " for seed_word in word_statistics_dict: \n", " if seed_word in tokens_count:\n", " has_feature = 1\n", " aspect_sentence_num_dict[seed_word] += 1\n", " for token in tokens_count:\n", " if token in word_statistics_dict[seed_word]:\n", " word_statistics_dict[seed_word][token][0] += tokens_count[token]\n", " word_statistics_dict[seed_word][token][1] += 1\n", " else:\n", " word_statistics_dict[seed_word][token] = [tokens_count[token], 1, 0, 0, 0, 0, 0, 0]\n", " if has_feature == 0:\n", " aspect_sentence_num_dict[\"no feature\"] += 1\n", " for seed_word in word_statistics_dict:\n", " word_statistics_tuple_sorted = sorted(word_statistics_dict[seed_word].items(), key=operator.itemgetter(1), reverse = True)\n", " word_statistics_list_sorted = []\n", " \n", " for i in range(len(word_statistics_tuple_sorted)):\n", " word_statistics_list_sorted.append(list(word_statistics_tuple_sorted[i]))\n", " \n", " # Calculate tf-idf:\n", " try:\n", " max_term_freq = word_statistics_list_sorted[0][1][0]\n", " except:\n", " print \"cannot access the first element\"\n", " print seed_word, word_statistics_list_sorted, word_statistics_tuple_sorted\n", " max_term_freq = 1\n", " for i in range(len(word_statistics_list_sorted)):\n", " word_data = word_statistics_list_sorted[i][1]\n", " tf_aspect = float(word_data[0]) / max_term_freq\n", " idf_aspect = math.log(float(sentence_num) / word_data[1])\n", " word_data[2:4] =[tf_aspect, idf_aspect] \n", " word_statistics_dict[seed_word] = word_statistics_list_sorted\n", " \n", " client.close()\n", " return word_statistics_dict, aspect_sentence_num_dict\n", " \n", " \n", "\n", "def get_word_statistics_from_wordlist_dict(prod_id_list, wordlist_dict, predict_threshold = 1):\n", " client, db = connect_to_db()\n", " db_product_collection = db.product_collection\n", " word_statistics_dict = {key: {} for key in wordlist_dict}\n", " aspect_sentence_num_dict = {aspect.lower(): 0 for aspect in wordlist_dict}\n", " aspect_sentence_num_dict[\"no feature\"] = 0\n", " sentence_num = 0\n", " for product_id in prod_id_list:\n", " query_product = list(db_product_collection.find({\"product_id\": product_id}))\n", " if len(query_product) == 0:\n", " print \"Product {0} doesn't exists, skip.\".format(product_id)\n", " continue\n", " if \"contents\" not in query_product[0]:\n", " print \"Product {0} doesn't have contents, skip.\".format(product_id)\n", " continue\n", " sentence_list = query_product[0][\"contents\"]\n", " for sentence in sentence_list: \n", " tokens = tokenize(sentence, stem = False) \n", " predicted_aspect = predict_aspect(tokens, wordlist_dict, predict_threshold)\n", " if predicted_aspect == \"no feature\":\n", " aspect_sentence_num_dict[\"no feature\"] += 1\n", " continue\n", " tokens_count = Counter(tokens)\n", " aspect_sentence_num_dict[predicted_aspect] += 1\n", " sentence_num += 1 \n", " if int(sentence_num) % 100000 == 0:\n", " print \"{0}, {1}: {2}\".format(sentence_num, predicted_aspect, sentence)\n", " for token in tokens_count:\n", " # Constructing aspect word_tf_idf_dict: \n", " if token in word_statistics_dict[predicted_aspect]:\n", " word_statistics_dict[predicted_aspect][token][0] += tokens_count[token]\n", " word_statistics_dict[predicted_aspect][token][1] += 1\n", " else:\n", " word_statistics_dict[predicted_aspect][token] = [tokens_count[token], 1, 0, 0, 0, 0, 0, 0]\n", " \n", " for aspect in word_statistics_dict:\n", " word_statistics_tuple_sorted = sorted(word_statistics_dict[aspect].items(), key=operator.itemgetter(1), reverse = True)\n", " word_statistics_list_sorted = []\n", " \n", " for i in range(len(word_statistics_tuple_sorted)):\n", " word_statistics_list_sorted.append(list(word_statistics_tuple_sorted[i]))\n", " \n", " # Calculate tf-idf:\n", " try:\n", " max_term_freq = word_statistics_list_sorted[0][1][0]\n", " except:\n", " print \"cannot access the first element\"\n", " print word_statistics_list_sorted[0]\n", " max_term_freq = 1\n", " \n", " for i in range(len(word_statistics_list_sorted)):\n", " word_data = word_statistics_list_sorted[i][1]\n", " tf_aspect = float(word_data[0]) / max_term_freq\n", " idf_aspect = math.log(float(sentence_num) / word_data[1])\n", " word_data[2:4] =[tf_aspect, idf_aspect] \n", " word_statistics_dict[aspect] = word_statistics_list_sorted\n", " \n", " client.close()\n", " return word_statistics_dict, aspect_sentence_num_dict\n", "\n", "\n", "def get_wordlist_dict(category_id_list, wordlist_dict, num_words_in_wordlist = 10, sim_slope = 1, sim_intercept = 0.2, predict_threshold = 1, isPrint = True):\n", " client, db = connect_to_db()\n", " db_category_data = db.category_data\n", " if not isinstance(category_id_list, list):\n", " isList = False\n", " category_id_list = [category_id_list]\n", " if isinstance(wordlist_dict, list):\n", " input_type = \"seed_word_list\"\n", " elif isinstance(wordlist_dict, dict):\n", " input_type = \"wordlist_dict\"\n", " else:\n", " print \"please input a seed_word_list or wordlist_dict!\"\n", " return\n", " \n", " for category_id in category_id_list:\n", " query_category = list(db_category_data.find({\"category_id\": category_id}))\n", " if len(query_category) == 0:\n", " print \"{0} not in db, skip.\".format(category_id)\n", " continue\n", " category_content = query_category[0]\n", " category = category_content[\"category\"]\n", " prod_id_list = category_content[\"prod_id_list\"]\n", " \n", " if \"word_scores\" not in category_content or \"total_num_sentence\" not in category_content:\n", " print \"word_scores not in category {0}, constructing...\".format(category_id)\n", " get_category_word_scores(category_id)\n", " word_scores_list = category_content[\"word_scores\"]\n", " word_scores_dict = {word_data[0]: word_data[1:] for word_data in word_scores_list}\n", " total_num_sentence = category_content[\"total_num_sentence\"]\n", " if input_type == \"seed_word_list\":\n", " word_statistics_dict, aspect_sentence_num_dict = get_word_statistics_from_seed_word(prod_id_list, seed_word_list)\n", " elif input_type == \"wordlist_dict\":\n", " word_statistics_dict, aspect_sentence_num_dict = get_word_statistics_from_wordlist_dict(prod_id_list, wordlist_dict, predict_threshold)\n", " total_prod_num = 0\n", " for aspect in aspect_sentence_num_dict:\n", " total_prod_num += aspect_sentence_num_dict[aspect]\n", " print \"total sentence num: {0}\".format(total_prod_num)\n", " for aspect in word_statistics_dict:\n", " word_statistics = word_statistics_dict[aspect]\n", " aspect_sentence_num = aspect_sentence_num_dict[aspect]\n", " print \"{0}: {1}\".format(aspect, aspect_sentence_num)\n", " for word_data in word_statistics:\n", " \n", " word = word_data[0]\n", " tf_aspect = word_data[1][2]\n", " idf_category = word_scores_dict[word][2]\n", " tf_ratio = (float(word_data[1][0]) / aspect_sentence_num) / (float(word_scores_dict[word][4]) / total_num_sentence)\n", " similarity = get_similarity(aspect, word)\n", " \n", " word_data[1][4] = idf_category\n", " word_data[1][5] = tf_ratio\n", " word_data[1][6] = similarity\n", " word_data[1][7] = math.log(1 + tf_aspect ** 0.8 * idf_category **2 * max(0.1, math.log(tf_ratio)) * max(0.01, sim_slope * similarity + sim_intercept)** 1.5)\n", " \n", " word_statistics.sort(key = lambda tup: tup[1][7], reverse = True)\n", " print \"no feature: {0}\".format(aspect_sentence_num_dict[\"no feature\"])\n", " wordlist_dict = {}\n", " for aspect in word_statistics_dict:\n", " wordlist = []\n", " for word_data in word_statistics_dict[aspect][:num_words_in_wordlist]:\n", " wordlist.append([word_data[0],word_data[1][7]])\n", " wordlist_dict[aspect] = wordlist\n", " \n", "\n", " update_field = {\"wordlist_dict\": wordlist_dict}\n", " db_category_data.update_one({\"category_id\": category_id}, {\"$set\": update_field}, True)\n", " try:\n", " update_field = {\"word_statistics_dict\": word_statistics_dict}\n", " db_category_data.update_one({\"category_id\": category_id}, {\"$set\": update_field}, True)\n", " except:\n", " print \"word_statistics too large, cannot save into db, skip.\"\n", " \n", " if isPrint == True:\n", " for aspect in wordlist_dict:\n", " print '\"{0}\": '.format(aspect)\n", " print ' ',\n", " for word_data in wordlist_dict[aspect]:\n", " print '\"%s\", %0.2f;'%(word_data[0], word_data[1]),\n", " print\n", " client.close()\n", " return wordlist_dict\n", "\n", "\n", "def prune_wordlist_dict(wordlist_dict, excluded_word_external = [], preserve_top = False, isPrint = True):\n", " word_location = {}\n", " excluded_words = [\"good\", \"great\", \"well\", \"bad\", \"worse\",\"better\"] + excluded_word_external\n", " # Obtaining each word's aspects and score in that aspect: \n", " if preserve_top == True:\n", " for aspect in wordlist_dict:\n", " for word_data in wordlist_dict[aspect]:\n", " word = word_data[0]\n", " score = word_data[1]\n", " if word in word_location:\n", " word_location[word].append([aspect, score])\n", " else:\n", " word_location[word]=[[aspect, score]] \n", "\n", " # Sort each word's aspect, and only keep the word in highest score aspect: \n", " wordlist_dict_pruned = {}\n", " for word in word_location:\n", " if word in excluded_words:\n", " continue\n", " aspect_sorted = sort_list(word_location[word], 1)\n", " aspect_chosen = aspect_sorted[0] # Choose the first one\n", " aspect = aspect_chosen[0]\n", " if aspect in wordlist_dict_pruned:\n", " wordlist_dict_pruned[aspect].append([word, aspect_chosen[1]])\n", " else:\n", " wordlist_dict_pruned[aspect] = [[word, aspect_chosen[1]]]\n", " else:\n", " wordlist_dict_pruned = {}\n", " for aspect in wordlist_dict:\n", " wordlist = []\n", " for word in wordlist_dict[aspect]:\n", " if word[0] not in excluded_words:\n", " wordlist.append(word) \n", " wordlist_dict_pruned[aspect] = wordlist\n", " \n", " # Sort each word\n", " for word in wordlist_dict_pruned:\n", " wordlist = wordlist_dict_pruned[word]\n", " wordlist_dict_pruned[word] = sort_list(wordlist, 1)\n", " \n", " if isPrint == True:\n", " for aspect in wordlist_dict_pruned:\n", " print '\"{0}\": '.format(aspect)\n", " print ' ',\n", " for word_data in wordlist_dict_pruned[aspect]:\n", " print '\"%s\", %0.2f;'%(word_data[0], word_data[1]),\n", " print\n", " \n", " return wordlist_dict_pruned\n", "\n", "\n", "def get_category_data_from_db(category_id, request_field_list):\n", " client, db = connect_to_db()\n", " db_category_data = db.category_data\n", " result = []\n", " if not isinstance(request_field_list, list):\n", " request_field_list = [request_field_list]\n", " isOnefield = True\n", " query = list(db_category_data.find({\"category_id\": category_id}))\n", " if len(query) > 0:\n", " query = query[0]\n", " for request_field in request_field_list: \n", " if request_field in query:\n", " result.append(query[request_field])\n", " else:\n", " print \"{0} not in category {1}\".format(request_field, category_id)\n", " result.append([])\n", " else:\n", " print \"category {0} not in db\".format(category_id)\n", " result = [[] for request_field in request_field_list]\n", " if isOnefield == True:\n", " result = result[0]\n", " return result\n", " \n", "\n", "def writeWordlistDictToDB(category_id, wordlist_dict, rewrite_wordlist_dict_list = False):\n", " # Update database: \n", " client, db = connect_to_db()\n", " query = {\"category_id\": category_id}\n", " query_res = list(db.category_data.find(query))\n", " if len(query_res) > 0:\n", " if \"wordlist_dict_list\" in query_res[0] and rewrite_wordlist_dict_list == False:\n", " wordlist_dict_list = query_res[0][\"wordlist_dict_list\"]\n", " wordlist_dict_list.append(wordlist_dict)\n", " else:\n", " wordlist_dict_list = []\n", " else:\n", " wordlist_dict_list = []\n", " wordlist_dict_list.append(wordlist_dict) \n", " \n", " update_field = {\"wordlist_dict\": wordlist_dict, \"wordlist_dict_list\": wordlist_dict_list}\n", " db.category_data.update_one(query, {\"$set\": update_field}, True)\n", " category = list(db.category_data.find({\"category_id\": category_id}))[0][\"category\"]\n", " \n", " query_category = {\"category_id\": category_id}\n", " update_field_category = {\"category\": category, \"wordlist_dict\": wordlist_dict}\n", " db.category_collection.update_one(query_category, {\"$set\": update_field_category}, True)\n", " client.close()\n", "\n", "\n", "def changeAspectNameinDB(category_id, aspect_old_name, aspect_new_name):\n", " client, db = connect_to_db()\n", " change_aspect_name_in_db(category_id, aspect_old_name, aspect_new_name, db.category_data)\n", " change_aspect_name_in_db(category_id, aspect_old_name, aspect_new_name, db.category_collection)\n", " client.close() \n", " \n", "def change_aspect_name_in_db(category_id, aspect_old_name, aspect_new_name, db_collection):\n", " query = {\"category_id\": category_id}\n", " query_res = list(db_collection.find(query))\n", " if len(query_res) == 0:\n", " print \"Category {0} do not exist!\".format(category_id)\n", " return\n", " category_content = query_res[0]\n", " if \"wordlist_dict\" not in category_content:\n", " print \"Category {0} do not have wordlist_dict!\".format(category_id)\n", " return\n", " wordlist_dict = category_content[\"wordlist_dict\"]\n", " if aspect_old_name not in wordlist_dict:\n", " print 'Category {0} do not have aspect \"{1}\"'.format(category_id, aspect_old_name)\n", " return\n", " wordlist_dict[aspect_new_name] = wordlist_dict.pop(aspect_old_name)\n", " \n", " #update db:\n", " db_collection.update_one(query,{\"$set\": {\"wordlist_dict\": wordlist_dict}},False)\n", " print \"Changing aspect name successful :D\"\n", "\n", " \n", "def addWordtoAspectinDB(category_id, word_data, aspect_to_update):\n", " client, db = connect_to_db()\n", " add_word_to_aspect(category_id, word_data, aspect_to_update, db.category_data)\n", " add_word_to_aspect(category_id, word_data, aspect_to_update, db.category_collection)\n", " client.close() \n", "\n", "def add_word_to_aspect(category_id, word_data, aspect_to_update, db_collection):\n", " query = {\"category_id\": category_id}\n", " query_res = list(db_collection.find(query))\n", " if len(query_res) == 0:\n", " print \"Category {0} do not exist!\".format(category_id)\n", " return\n", " category_content = query_res[0]\n", " if \"wordlist_dict\" not in category_content:\n", " print \"Category {0} do not have wordlist_dict!\".format(category_id)\n", " return\n", " wordlist_dict = category_content[\"wordlist_dict\"]\n", " if aspect_to_update not in wordlist_dict:\n", " print 'Category {0} do not have aspect \"{1}\"'.format(category_id, aspect_old_name)\n", " return\n", " \n", " wordlist_dict[aspect_to_update].append(word_data)\n", " wordlist_dict[aspect_to_update].sort(key = lambda tup: tup[1], reverse = True)\n", " \n", " db_collection.update_one(query,{\"$set\": {\"wordlist_dict\": wordlist_dict}},False)\n", " print 'Successfully add word {0} to category {1}\\'s aspect \"{2}\"'.format(word_data, aspect_to_update, category_id)\n", "\n", " \n", "def moveWordinAspect(category_id, word, previous_aspect, new_aspect):\n", " client, db = connect_to_db()\n", " move_word_in_aspect(category_id, word, previous_aspect, new_aspect, db.category_data)\n", " move_word_in_aspect(category_id, word, previous_aspect, new_aspect, db.category_collection)\n", " client.close()\n", "\n", "def move_word_in_aspect(category_id, word, previous_aspect, new_aspect, db_collection):\n", " query = {\"category_id\": category_id}\n", " query_res = list(db_collection.find(query))\n", " if len(query_res) == 0:\n", " print \"Category {0} do not exist!\".format(category_id)\n", " return\n", " category_content = query_res[0]\n", " if \"wordlist_dict\" not in category_content:\n", " print \"Category {0} do not have wordlist_dict!\".format(category_id)\n", " return\n", " wordlist_dict = category_content[\"wordlist_dict\"]\n", " if previous_aspect not in wordlist_dict:\n", " print 'Old aspect \"{0}\" not exists in category {1}'.format(previous_aspect, category_id)\n", " return\n", " if new_aspect not in wordlist_dict:\n", " print 'New aspect \"{0}\" not exists in category {1}'.format(new_aspect, category_id)\n", " return\n", " source_list = wordlist_dict[previous_aspect]\n", " target_list = wordlist_dict[new_aspect]\n", " isFound = False\n", " new_source_list = []\n", " for word_data in source_list:\n", " if word_data[0] == word:\n", " record = word_data\n", " isFound = True\n", " else:\n", " new_source_list.append(word_data)\n", " if isFound == False:\n", " print \"Word {0} not found in aspect {1}\".format(word, previous_aspect)\n", " return\n", " target_list.append(record)\n", " target_list.sort(key = lambda tup: tup[1], reverse = True)\n", " wordlist_dict[previous_aspect] = new_source_list\n", " wordlist_dict[new_aspect] = target_list\n", " db_collection.update_one(query,{\"$set\": {\"wordlist_dict\": wordlist_dict}},False)\n", " print 'Succesfully move word \"{0}\" from \"{1}\" to \"{2}\".'.format(word, previous_aspect, new_aspect)\n", " \n", " \n", "def deleteWordsFromDB(category_id_list, words_to_delete, aspects_to_update = []):\n", " client, db = connect_to_db()\n", " delete_words_from_collection(category_id_list, words_to_delete, db.category_collection, aspects_to_update)\n", " delete_words_from_collection(category_id_list, words_to_delete, db.category_data, aspects_to_update)\n", " client.close()\n", "\n", "\n", "def delete_words_from_collection(category_id_list, words_to_delete, db_collection, aspects_to_update = []):\n", " if not isinstance(words_to_delete, list):\n", " words_to_delete = [words_to_delete]\n", " if not isinstance(category_id_list, list):\n", " category_id_list = [category_id_list]\n", " if not isinstance(aspects_to_update, list):\n", " aspects_to_update = [aspects_to_update]\n", " for category_id in category_id_list:\n", " query_collection = list(db_collection.find({\"category_id\": category_id}))\n", " if len(query_collection) == 0:\n", " print \"{0} not in db, skip\".format(category_id)\n", " continue\n", " wordlist_dict = query_collection[0][\"wordlist_dict\"]\n", " isFound = False\n", " if len(aspects_to_update) > 0:\n", " for aspect in aspects_to_update:\n", " new_wordlist = []\n", " for word_data in wordlist_dict[aspect]:\n", " if word_data[0] not in words_to_delete:\n", " new_wordlist.append(word_data)\n", " else:\n", " isFound = True\n", " wordlist_dict[aspect] = new_wordlist\n", " else:\n", " for aspect in wordlist_dict:\n", " new_wordlist = []\n", " for word_data in wordlist_dict[aspect]:\n", " if word_data[0] not in words_to_delete:\n", " new_wordlist.append(word_data)\n", " else:\n", " isFound = True\n", " wordlist_dict[aspect] = new_wordlist\n", " if isFound == False:\n", " print \"words not found in the designated aspect\"\n", " return\n", " query_collection = {\"category_id\": category_id}\n", " update_field = {\"wordlist_dict\": wordlist_dict}\n", " db_collection.update_one(query_collection, {\"$set\": update_field}, False)\n", " print \"Delete sucessful.\"" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "category_id_list = [137, 73, 433, 399, 153, 297, 308, 136, 22, 154, 174, 187, 402, 176, 90, 253]\n", "category_seedword_dict = {\n", " 137: {\n", " \"category\": [u'Electronics', u'Computers & Accessories', u'Servers'], \\\n", " \"aspect_candidate\":[\"drive\",\"backup\",\"software\",\"network\",\"support\",\"console\",\"storage\",\"install\",\"stability\"]\n", " },\n", " 73:{\n", " \"category\": [u'Electronics', u'Portable Audio & Video', u'Portable DVD Players'], \\\n", " \"aspect_candidate\":[\"screen\", \"picture\",\"battery\",\"sound\",\"quality\",\"price\",\"video\",\"size\"]\n", " },\n", " 433:{\n", " \"category\": [u'Cell Phones & Accessories', u'Cell Phones'], \\\n", " \"aspect_candidate\":[\"screen\",\"battery\",\"camera\",\"sim\",\"call\",\"apps\",\"service\",\"quality\",\"wifi\",\"price\",\"plan\",\"design\"]\n", " },\n", " 399:{\n", " \"category\": [u'Electronics', u'Accessories & Supplies', u'Batteries, Chargers & Accessories'], \\\n", " \"aspect_candidate\":[\"power\",\"price\",\"quality\",\"plug\",\"protection\",\"adapter\"]\n", " },\n", " 153:{\n", " \"category\": [u'Electronics', u'Computers & Accessories', u'Monitors'],\\\n", " \"aspect_candidate\":[\"display\",\"color\",\"resolution\",\"price\",\"quality\",\"brightness\",\"contrast\",\"video\"]\n", " },\n", " 297:{\n", " \"category\": [\"Electronics\", \"Camera & Photo\", \"Lenses\" ],\\\n", " \"aspect_candidate\": [\"focus\",\"zoom\",\"quality\",\"sensor\",\"macro\",\"price\",\"aperture\",\"sharpness\",\"autofocus\"]\n", " },\n", " 308:{\n", " \"category\":[u'Electronics', u'Camera & Photo', u'Digital Cameras'],\\\n", " \"aspect_candidate\": [\"battery\",\"pictures\",\"price\",\"zoom\",\"easy\",\"detection\",\"design\",\"video\",\"quality\",\"screen\",\"size\"]\n", " },\n", " 136:{\n", " \"category\": [u'Electronics', u'Computers & Accessories', u'Tablets'],\\\n", " \"aspect_candidate\": [\"battery\",\"screen\",\"wifi\",\"apps\",\"camera\",\"video\",\"gb\",\"touch\",\"quality\",\"price\",\"size\"]\n", " },\n", " 22:{\n", " \"category\": [u'Electronics', u'Television & Video', u'Televisions'],\\\n", " \"aspect_candidate\": [\"picture\",\"sound\",\"screen\",\"price\",\"remote\",\"cable\",\"service\",\"audio\"]\n", " },\n", " 154:{\n", " \"category\":[u'Electronics', u'Computers & Accessories', u'Laptops'],\\\n", " \"aspect_candidate\": [\"screen\",\"keyboard\",\"battery\",\"drive\",\"price\",\"processor\",\"graphics\",\"touchpad\",\"support\"]\n", " },\n", " 174:{\n", " \"category\": [u'Electronics', u'Computers & Accessories', u'Desktops'],\\\n", " \"aspect_candidate\": [\"drive\",\"storage\",\"keyboard\",\"graphics\",\"software\",\"price\",\"memory\",\"monitor\",\"processor\",\"support\"]\n", " },\n", " 187:{\n", " \"category\": [u'Electronics', u'Computers & Accessories', u'Computer Components', u'Graphics Cards'],\\\n", " \"aspect_candidate\": [\"games\",\"video\",\"price\",\"speed\", \"fan\",\"drivers\"]\n", " },\n", " 402:{\n", " \"category\": [u'Electronics', u'Accessories & Supplies', u'Audio & Video Accessories', u'Headphones'],\\\n", " \"aspect_candidate\":[\"sound\",\"mic\",\"microphone\",\"comfortable\",\"price\",\"bass\",\"cord\"]\n", " },\n", " 176:{\n", " \"category\":[u'Electronics', u'Computers & Accessories', u'Data Storage'],\\\n", " \"aspect_candidate\": [\"storage size\",\"usb\", \"speed\",\"install\",\"performance\",\"price\",\"support\",\"quiet\"]\n", " },\n", " 90:{\n", " \"category\":[u'Electronics', u'Home Audio', u'Stereo Components', u'Speakers'],\\\n", " \"aspect_candidate\":[\"sound\",\"bass\",\"surround\",\"price\",\"setup\",\"size\"]\n", " },\n", " 253:{\n", " \"category\":[u'Electronics', u'Car & Vehicle Electronics', u'Car Electronics', u'Car Audio'],\\\n", " \"aspect_candidate\":[\"sound\",\"radio\",\"bass\",\"price\",\"quality\",\"install\",\"bluetooth\",\"power\",\"control\"]\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "changeAspectNameinDB(40)" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2 th iteration:\n", "100000, zoom: One of the few cameras with a 4x zoom, and 7 megapixels.\n", "200000, screen: I think you can buy or fashion some type of shade for the display.\n", "300000, screen: I substituted this for my entry level dslr, and was extremely impressed by the ease of use with the touch screen and camera ring.\n", "400000, pictures: It takes such sharp pictures.\n", "total sentence num: 1726360\n", "pictures: 51236\n", "price: 42169\n", "zoom: 74090\n", "detection: 3054\n", "batteries: 49902\n", "design: 5951\n", "easy: 46971\n", "video: 35338\n", "quality: 52544\n", "screen: 32742\n", "size: 37864\n", "word_statistics too large, cannot save into db, skip.\n", "\"pictures\": \n", " \"pictures\", 3.11; \"videos\", 1.55; \"beautiful\", 1.03; \"kids\", 0.93; \"grainy\", 0.75; \"download\", 0.72; \"takes\", 0.71; \"blurry\", 0.59; \"taken\", 0.53; \"crisp\", 0.45;\n", "\"price\": \n", " \"price\", 4.05; \"value\", 2.61; \"worth\", 2.26; \"money\", 2.03; \"paid\", 2.03; \"pay\", 1.89; \"reasonable\", 1.69; \"sale\", 1.59; \"range\", 1.58; \"affordable\", 0.92;\n", "\"zoom\": \n", " \"zoom\", 3.68; \"optical\", 2.53; \"lens\", 2.37; \"mm\", 2.37; \"kit\", 2.30; \"digital\", 2.21; \"x\", 2.10; \"cap\", 1.77; \"mp\", 1.76; \"vr\", 1.38;\n", "\"detection\": \n", " \"detection\", 5.72; \"tracking\", 4.73; \"correction\", 4.32; \"detect\", 3.85; \"phase\", 3.72; \"detects\", 3.64; \"blink\", 3.39; \"face\", 3.24; \"detected\", 3.12; \"removal\", 2.96;\n", "\"batteries\": \n", " \"batteries\", 3.98; \"battery\", 3.58; \"rechargeable\", 2.63; \"lithium\", 2.53; \"aa\", 2.30; \"extra\", 2.28; \"alkaline\", 2.28; \"recharge\", 2.10; \"ion\", 2.03; \"nimh\", 1.90;\n", "\"design\": \n", " \"design\", 5.42; \"flaw\", 3.75; \"layout\", 3.67; \"flaws\", 3.35; \"manufacturing\", 2.50; \"sleek\", 2.48; \"designs\", 2.36; \"retro\", 2.23; \"graphic\", 1.92; \"industrial\", 1.47;\n", "\"easy\": \n", " \"easy\", 3.89; \"use\", 1.89; \"learn\", 1.64; \"intuitive\", 1.46; \"quick\", 1.45; \"simple\", 1.41; \"navigate\", 1.33; \"understand\", 1.15; \"menus\", 1.09; \"handle\", 0.90;\n", "\"video\": \n", " \"video\", 4.03; \"hd\", 2.98; \"recording\", 2.93; \"audio\", 2.53; \"sound\", 2.38; \"clips\", 2.12; \"fps\", 2.00; \"stereo\", 1.81; \"record\", 1.65; \"stills\", 1.50;\n", "\"quality\": \n", " \"quality\", 3.48; \"excellent\", 2.23; \"picture\", 1.68; \"poor\", 1.62; \"high\", 1.28; \"ease\", 1.23; \"build\", 0.89; \"image\", 0.75; \"superb\", 0.60;\n", "\"screen\": \n", " \"screen\", 4.27; \"lcd\", 3.12; \"display\", 2.84; \"touch\", 2.76; \"bright\", 2.34; \"viewing\", 2.20; \"flip\", 2.09; \"sunlight\", 1.95; \"swivel\", 1.69; \"inch\", 1.58;\n", "\"size\": \n", " \"size\", 4.32; \"small\", 3.15; \"weight\", 3.14; \"smaller\", 2.47; \"larger\", 2.42; \"pocket\", 2.37; \"compact\", 2.35; \"sensor\", 1.93; \"large\", 1.63; \"fits\", 1.42;\n", "\n", "3 th iteration:\n", "100000, easy: It works great for normal pics, vids and is easy to use.\n", "200000, video: Now the negative: it's extremely inferior recording feature.\n", "300000, zoom: So The XF1 has a useful zoom range equivalent of about 25mm to 200mm.\n", "400000, design: Overall, I really like this camera and it's feel and layout.\n", "total sentence num: 1726360\n", "pictures: 48279\n", "price: 42309\n", "zoom: 70183\n", "detection: 3141\n", "batteries: 49322\n", "design: 5797\n", "easy: 50763\n", "video: 34408\n", "quality: 56969\n", "screen: 31807\n", "size: 40307\n", "word_statistics too large, cannot save into db, skip.\n", "\"pictures\": \n", " \"pictures\", 3.13; \"videos\", 1.37; \"beautiful\", 0.99; \"takes\", 0.67; \"grainy\", 0.67; \"kids\", 0.62; \"blurry\", 0.53; \"crisp\", 0.52; \"taken\", 0.49; \"wonderful\", 0.41;\n", "\"price\": \n", " \"price\", 4.04; \"value\", 2.64; \"money\", 2.50; \"worth\", 2.28; \"paid\", 2.02; \"pay\", 1.71; \"reasonable\", 1.70; \"range\", 1.33; \"waste\", 1.20; \"sale\", 1.14;\n", "\"zoom\": \n", " \"zoom\", 3.71; \"optical\", 2.54; \"kit\", 2.47; \"mm\", 2.37; \"lens\", 2.27; \"x\", 2.10; \"digital\", 1.93; \"cap\", 1.79; \"vr\", 1.69; \"mp\", 1.65;\n", "\"detection\": \n", " \"detection\", 5.73; \"tracking\", 4.72; \"correction\", 4.31; \"detect\", 4.13; \"phase\", 3.79; \"detects\", 3.44; \"blink\", 3.39; \"face\", 3.24; \"detected\", 3.10; \"removal\", 2.78;\n", "\"batteries\": \n", " \"batteries\", 4.00; \"battery\", 3.58; \"rechargeable\", 2.65; \"lithium\", 2.56; \"aa\", 2.33; \"alkaline\", 2.31; \"extra\", 2.17; \"recharge\", 2.10; \"ion\", 2.05; \"nimh\", 1.92;\n", "\"design\": \n", " \"design\", 5.43; \"flaw\", 3.76; \"layout\", 3.62; \"flaws\", 3.35; \"sleek\", 2.46; \"manufacturing\", 2.40; \"designs\", 2.06; \"retro\", 1.93; \"graphic\", 1.66; \"industrial\", 1.27;\n", "\"easy\": \n", " \"easy\", 3.89; \"use\", 1.84; \"simple\", 1.55; \"learn\", 1.50; \"intuitive\", 1.38; \"navigate\", 1.30; \"quick\", 1.11; \"understand\", 1.01; \"menus\", 0.94; \"operate\", 0.88;\n", "\"video\": \n", " \"video\", 4.04; \"hd\", 2.99; \"recording\", 2.94; \"audio\", 2.53; \"sound\", 2.31; \"clips\", 2.09; \"fps\", 1.82; \"stereo\", 1.76; \"record\", 1.58; \"stills\", 1.42;\n", "\"quality\": \n", " \"quality\", 3.49; \"excellent\", 2.18; \"picture\", 1.61; \"poor\", 1.56; \"ease\", 1.22; \"high\", 1.10; \"build\", 0.92; \"superb\", 0.73; \"image\", 0.69;\n", "\"screen\": \n", " \"screen\", 4.28; \"lcd\", 3.13; \"display\", 2.84; \"touch\", 2.78; \"bright\", 2.25; \"viewing\", 2.20; \"flip\", 2.09; \"sunlight\", 1.97; \"swivel\", 1.68; \"inch\", 1.53;\n", "\"size\": \n", " \"size\", 4.33; \"small\", 3.11; \"weight\", 3.08; \"pocket\", 2.39; \"smaller\", 2.37; \"compact\", 2.28; \"larger\", 2.16; \"fits\", 1.83; \"sensor\", 1.65; \"large\", 1.43;\n", "\n", "4 th iteration:\n", "100000, quality: Excellent.\n", "200000, screen: Granted, it does take decent enough pictures and the touch screen is great, but other negative factors outweigh the good.\n", "300000, zoom: You will probably need a tripod when maxing out the zoom.\n", "400000, size: It is very compact and convenient.\n", "total sentence num: 1726360\n", "pictures: 48177\n", "price: 43360\n", "zoom: 67755\n", "detection: 3155\n", "batteries: 49042\n", "design: 5703\n", "easy: 50219\n", "video: 34343\n", "quality: 56410\n", "screen: 31888\n", "size: 39629\n", "word_statistics too large, cannot save into db, skip.\n", "\"pictures\": \n", " \"pictures\", 3.13; \"videos\", 1.32; \"beautiful\", 0.99; \"takes\", 0.67; \"grainy\", 0.58; \"wonderful\", 0.58; \"crisp\", 0.52; \"blurry\", 0.52; \"taken\", 0.49; \"kids\", 0.43;\n", "\"price\": \n", " \"price\", 4.03; \"money\", 2.73; \"value\", 2.66; \"worth\", 2.32; \"paid\", 1.92; \"pay\", 1.70; \"reasonable\", 1.70; \"waste\", 1.49; \"range\", 1.29; \"sale\", 0.94;\n", "\"zoom\": \n", " \"zoom\", 3.73; \"optical\", 2.55; \"kit\", 2.54; \"mm\", 2.37; \"lens\", 2.27; \"x\", 2.11; \"cap\", 1.79; \"vr\", 1.75; \"digital\", 1.67; \"mp\", 1.64;\n", "\"detection\": \n", " \"detection\", 5.73; \"tracking\", 4.72; \"correction\", 4.31; \"detect\", 4.24; \"phase\", 3.80; \"detects\", 3.44; \"blink\", 3.39; \"face\", 3.24; \"detected\", 3.07; \"removal\", 2.48;\n", "\"batteries\": \n", " \"batteries\", 4.00; \"battery\", 3.58; \"rechargeable\", 2.65; \"lithium\", 2.56; \"aa\", 2.33; \"alkaline\", 2.31; \"extra\", 2.11; \"recharge\", 2.11; \"ion\", 2.05; \"nimh\", 1.92;\n", "\"design\": \n", " \"design\", 5.43; \"flaw\", 3.76; \"layout\", 3.48; \"flaws\", 3.35; \"sleek\", 2.46; \"manufacturing\", 2.23; \"designs\", 2.03; \"retro\", 1.73; \"graphic\", 1.66; \"industrial\", 1.28;\n", "\"easy\": \n", " \"easy\", 3.90; \"use\", 1.84; \"simple\", 1.58; \"intuitive\", 1.38; \"learn\", 1.37; \"navigate\", 1.30; \"operate\", 1.02; \"understand\", 1.00; \"menus\", 0.92; \"quick\", 0.84;\n", "\"video\": \n", " \"video\", 4.04; \"hd\", 2.99; \"recording\", 2.94; \"audio\", 2.52; \"sound\", 2.30; \"clips\", 2.08; \"fps\", 1.77; \"stereo\", 1.76; \"record\", 1.51; \"stills\", 1.42;\n", "\"quality\": \n", " \"quality\", 3.49; \"excellent\", 2.17; \"picture\", 1.60; \"poor\", 1.56; \"ease\", 1.21; \"high\", 1.05; \"build\", 0.93; \"superb\", 0.74; \"image\", 0.67;\n", "\"screen\": \n", " \"screen\", 4.28; \"lcd\", 3.13; \"display\", 2.84; \"touch\", 2.78; \"bright\", 2.25; \"viewing\", 2.20; \"flip\", 2.09; \"sunlight\", 1.96; \"swivel\", 1.68; \"inch\", 1.53;\n", "\"size\": \n", " \"size\", 4.33; \"small\", 3.10; \"weight\", 3.08; \"pocket\", 2.42; \"compact\", 2.28; \"smaller\", 2.23; \"larger\", 2.07; \"fits\", 2.00; \"sensor\", 1.54; \"fit\", 1.31;\n", "\n", "5 th iteration:\n", "100000, pictures: It takes GREAT pictures!\n", "200000, zoom: Second camera arrived and I am delighted with the clarity, zoom and ease of use.\n", "300000, video: I was once was making a video in my basement, and had to stay awake until 1am when everyone in my house was asleep... Why?\n", "400000, zoom: The zoom lens that comes with this kit is one of the best that Nikon has to offer.\n", "total sentence num: 1726360\n", "pictures: 48153\n", "price: 43598\n", "zoom: 67459\n", "detection: 3158\n", "batteries: 49121\n", "design: 5693\n", "easy: 50189\n", "video: 34333\n", "quality: 56349\n", "screen: 31908\n", "size: 39702\n", "word_statistics too large, cannot save into db, skip.\n", "\"pictures\": \n", " \"pictures\", 3.13; \"videos\", 1.31; \"beautiful\", 0.99; \"takes\", 0.67; \"wonderful\", 0.60; \"grainy\", 0.58; \"crisp\", 0.52; \"blurry\", 0.52; \"taken\", 0.49; \"kids\", 0.42;\n", "\"price\": \n", " \"price\", 4.03; \"money\", 2.75; \"value\", 2.67; \"worth\", 2.33; \"paid\", 1.92; \"pay\", 1.70; \"reasonable\", 1.69; \"waste\", 1.58; \"range\", 1.29; \"sale\", 0.93;\n", "\"zoom\": \n", " \"zoom\", 3.73; \"kit\", 2.55; \"optical\", 2.55; \"mm\", 2.37; \"lens\", 2.27; \"x\", 2.11; \"cap\", 1.79; \"vr\", 1.76; \"mp\", 1.62; \"digital\", 1.61;\n", "\"detection\": \n", " \"detection\", 5.73; \"tracking\", 4.72; \"correction\", 4.31; \"detect\", 4.25; \"phase\", 3.80; \"detects\", 3.45; \"blink\", 3.39; \"face\", 3.24; \"detected\", 3.07; \"removal\", 2.48;\n", "\"batteries\": \n", " \"batteries\", 4.00; \"battery\", 3.58; \"rechargeable\", 2.65; \"lithium\", 2.56; \"aa\", 2.33; \"alkaline\", 2.31; \"extra\", 2.11; \"recharge\", 2.11; \"ion\", 2.05; \"nimh\", 1.92;\n", "\"design\": \n", " \"design\", 5.43; \"flaw\", 3.76; \"layout\", 3.46; \"flaws\", 3.36; \"sleek\", 2.46; \"manufacturing\", 2.24; \"designs\", 2.03; \"retro\", 1.69; \"graphic\", 1.66; \"industrial\", 1.28;\n", "\"easy\": \n", " \"easy\", 3.90; \"use\", 1.84; \"simple\", 1.59; \"intuitive\", 1.38; \"learn\", 1.36; \"navigate\", 1.30; \"operate\", 1.03; \"understand\", 1.00; \"menus\", 0.92; \"quick\", 0.81;\n", "\"video\": \n", " \"video\", 4.04; \"hd\", 2.99; \"recording\", 2.94; \"audio\", 2.52; \"sound\", 2.30; \"clips\", 2.08; \"fps\", 1.77; \"stereo\", 1.76; \"record\", 1.50; \"stills\", 1.41;\n", "\"quality\": \n", " \"quality\", 3.49; \"excellent\", 2.17; \"picture\", 1.60; \"poor\", 1.56; \"ease\", 1.22; \"high\", 1.04; \"build\", 0.93; \"superb\", 0.74; \"image\", 0.67;\n", "\"screen\": \n", " \"screen\", 4.28; \"lcd\", 3.13; \"display\", 2.84; \"touch\", 2.78; \"bright\", 2.25; \"viewing\", 2.20; \"flip\", 2.09; \"sunlight\", 1.96; \"swivel\", 1.67; \"inch\", 1.53;\n", "\"size\": \n", " \"size\", 4.33; \"small\", 3.11; \"weight\", 3.08; \"pocket\", 2.49; \"compact\", 2.28; \"smaller\", 2.17; \"larger\", 2.03; \"fits\", 2.02; \"fit\", 1.72; \"sensor\", 1.35;\n" ] } ], "source": [ "category_id = 308 #Digital Cameras\n", "sim_slope = 1 \n", "sim_intercept = 0.2\n", "seed_word_list = [\"battery\",\"pictures\",\"price\",\"zoom\",\"easy\",\"detection\",\"design\",\"video\",\"quality\",\"screen\",\"size\"]\n", "wordlist_dict = get_wordlist_dict(category_id, seed_word_list, 10, sim_slope, sim_intercept, isPrint = False)\n", "wordlist_dict = prune_wordlist_dict(wordlist_dict, isPrint = True)\n", "writeWordlistDictToDB(category_id, wordlist_dict, rewrite_wordlist_dict_list = True)\n", "for i in range(4):\n", " print\n", " print \"{0} th iteration:\".format(i + 2)\n", " wordlist_dict = get_wordlist_dict(category_id, wordlist_dict, 10, sim_slope, sim_intercept, predict_threshold = 1.05, isPrint = False)\n", " wordlist_dict = prune_wordlist_dict(wordlist_dict, isPrint = True)\n", " writeWordlistDictToDB(category_id, wordlist_dict, rewrite_wordlist_dict_list = False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wordlist_dict = get_category_data_from_db(308, \"wordlist_dict\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ease of use:\tIt worked great and was very easy to use.\n", "ease of use:\tThe camera is fun and easy to use.\n", "size:\tI like the size, however, the camera is rather cheap.\n", "price:\tI would not spend close to $100 for this - I'm surprised that this is what this camera is worth.\n", "video:\tI got some video and great pictures of the sea turtles too!\n", "ease of use:\tThe camera is easy to use and great quality photos.\n", "pictures:\tAll my pictures and videos came out great.\n", "screen:\tI would estimate that I use the LCD screen ~1/2 the time.\n", "screen:\tEspecially at night in the woods, the LCD screen is bright enough to be distracting, and since I was aiming my shots through the viewfinder, I didn't need it activated.\n", "price:\tBuying this unit at any price is a gamble...\n", "zoom:\tCamera has no light, zoom features when taking movies2.\n", "size:\tI am now upgrading to one of those credit-card size cameras with 4 MP but my brother is buying the HP from me.\n", "ease of use:\tThe pros:-Very good pictures.-Easy to use buttons.\n", "batteries:\tAnd about the battery life... it's funny, but it seemed to get better with time.\n", "ease of use:\tIt is very easy to use.\n", "batteries:\tI received this camera today 6/18/12, set it up with the batteries it came with.\n", "price:\tThis one was the right price.\n", "quality:\tUsed it at my son;s prom and the picture quality was good.\n", "ease of use:\tSeems very easy to use for this amateur.\n", "pictures:\tAll the pictures are out of focus, over-exposed, and lack color.\n", "zoom:\tKodak Easyshare C1505 12 MP Digital Camera with 5x Digital Zoom - BlackKodak Easyshare C1505 12 MP Digital Camera with 5x Digital Zoom - Silver\n", "price:\ti like it very much,it is light weight and very simple to use.it was great value for the money it cost\n", "size:\tFrom a glance, it is a small cheap digital camera.\n", "zoom:\tIt's a great kit.\n", "zoom:\tI would highly recommend it to all who need a great zoom lens and set up.\n", "zoom:\tThis is a great accessory kit for anyone with a D3000 camera.\n", "quality:\tExcellent and great value.\n", "ease of use:\tSuper lightweight, easy to use.\n", "pictures:\tIt gives excellent pictures.\n", "zoom:\tIt also comes with the newer improved 18-55mm EF-IS STM lens.\n", "size:\tThis is my first DSLR and I think for the size and price of the package, that this was an amazing deal.\n", "ease of use:\tIt is very easy to use, and you can quickly access all the settings that a seasoned photographer will ever need with ease.\n", "size:\tThe photos are clear and sharp and the lower weight was a fit for me.\n", "zoom:\tI also purchased a 50mm prime lens.\n", "zoom:\tYou can seriously take awesome photos with this and the right lens.\n", "pictures:\tI've tried the different modes and some pictures are overexposed.\n", "ease of use:\tThis camera takes nice pictures, is compact enough to take everywhere and is easy to use.\n", "ease of use:\tEasy to use.\n", "pictures:\tIt's a pretty light weight camera and takes awesome pictures.\n", "size:\tIt is compact and fits anywhere.\n", "quality:\tThis price begins to accurately reflect the quality of the camera.\n", "batteries:\tI wanted not short battery life and not too long.\n", "zoom:\tThe zoom button often doesn't respond.\n", "zoom:\tnot high zoom.\n", "size:\tThis is everything we want in a compact camera.\n", "size:\tThe touchscreen is wonderful, although small and perhaps a bit low in resolution.\n", "quality:\tThis camera brings lots of quality and value to the party, and you are sure to enjoy it.\n", "zoom:\tThere&#8217;s also a lens cap that is included, but the tabs are so small that it is difficult to manipulate at times.\n", "size:\tI love how light and how clear and sharp the photos comes out of this tiny pocket size camera, the smart mode is great!\n", "price:\tBasically, I was extremely disappointed and found this to be a huge waste of money and time.\n", "ease of use:\tI can't find any more use of this camera.\n", "batteries:\tI had no problem with battery life.\n", "pictures:\tIt's perfect for someone who is not interested in taking picture after picture.\n", "size:\tIf you are looking for a small camera for your kids or for yourself, I do NOT suggest this one.\n", "ease of use:\tI use it all of the time.\n", "video:\tI rated it four stars because of the video recording with no audio.\n", "price:\tI guess you get what you pay for.\n", "quality:\tThe service from Amazon was excellent.\n", "batteries:\tIt wont power up when new batteries are put in and when it did work, the battery life was like about 2 or 3 hours.\n", "video:\tAfter finding out that this camera was cheap and flimsy and had no audio when recording a video.\n", "quality:\tThe picture quality is not bad either.\n", "quality:\tThe written instructions for using it are poor.\n", "price:\tI wasted my money.\n", "batteries:\tI bought rechargeable batteries with it and it works fine.\n", "batteries:\tThey look the same as the Sony battery.\n", "batteries:\tI immediately charged the batteries and tried them in the camera.\n", "batteries:\tDelivery was amazingly quick; the batteries and charger were delivered within two days.\n", "batteries:\tI was a bit confused about which way the battery would fit into the charger - there was no obvious marking/guide so I had to figure out how to match up the + and - (out of the 4 holes on the batteries).\n", "batteries:\tThe charger has a green and red light to indicate when the battery has been fully charged.\n", "batteries:\tNo disappointments here, batteries fit and look like OEM and work perfectly.\n", "price:\tAwesome price for a great battery and charger.\n", "price:\tThank you Seller for providing such a great product with a very reasonable price.\n", "design:\tThat bad design is the reason I'm knocking off one star.\n", "batteries:\tThis is much better than plugging in the camera to charge up the batteries.\n", "batteries:\tFirst battery &#34;Exhausted&#34; after two charges.\n", "quality:\tMy parents purchased this camera, and we are so impressed with the features and quality of the camera that we are going to purchase it for ourselves now.\n", "quality:\tWe are really impressed with it's quality.\n", "price:\tvERY GOOD FOR THE PRICE.\n", "ease of use:\tThe menus are easy to understand and to navigate.\n", "ease of use:\tCamera was purchased as a refurb and it works very well and is easy to use.\n", "price:\tFor the price is was THE RIGHT CHOICE AGAIN ----HP.\n", "screen:\tThe screen is large, and what you see in the screen is the you picture get.\n", "price:\tSpread the word ... great purchase for the money.\n", "size:\tIt is small but has all of the features of larger cameras.\n", "price:\tThese are amazing cameras for the money.\n", "ease of use:\tPictures are easy to take.\n", "price:\tgood price and a great camera, i love everything about it .\n", "zoom:\tZoom is a finger touch at the exposure button.\n", "ease of use:\tEverything working and as easy as the Nikon.\n", "video:\tthe zoom and video on this camera is absolutely amazing\n", "ease of use:\tPictures are nice and the camera is easy to use and carry.\n", "design:\tThe design is so beautiful, the weight is so light that I can carry it eassily in my pocket.\n", "quality:\tI am frankly a little puzzed by others' comments about the image quality of this camera.\n", "design:\tVery small and compact design.\n", "zoom:\tThe Zoom button lets you zoom out to view multiple thumbnails in a photo album view (swipe-and-pause to scroll left/right/up/down) and then zoom in for the full view of a selected picture.\n", "video:\tI also don't recall it being able to take Video, but I could be wrong about that also.\n", "batteries:\tBattery life is surprisingly good for such a small battery and the USB charging option is very nice.\n", "price:\tFor the price and the convenience I can easily recommend this camera.\n", "zoom:\t)SHAKE REDUCTIONThe ASR is digital, not optical.\n", "quality:\tThis camera has great image quality and I love the new toch sensitive buttons.\n", "quality:\tHowever, the shots I have made thusfar have been excellent.\n" ] } ], "source": [ "test_prediction(308,wordlist_dict, aspect_to_show = [], predict_threshold = 1.05, num_show = 100, show_no_feature = 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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Kaggle/learntools
notebooks/ethics/raw/ex4.ipynb
1
22526
{ "cells": [ { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.014709, "end_time": "2021-02-18T15:58:13.692777", "exception": false, "start_time": "2021-02-18T15:58:13.678068", "status": "completed" }, "tags": [] }, "source": [ "In the tutorial, you learned about different ways of measuring fairness of a machine learning model. In this exercise, you'll train a few models to approve (or deny) credit card applications and analyze fairness. Don't worry if you're new to coding: this exercise assumes no programming knowledge.\n", "\n", "# Introduction\n", "\n", "We work with a **synthetic** dataset of information submitted by credit card applicants. \n", "\n", "To load and preview the data, run the next code cell. When the code finishes running, you should see a message saying the data was successfully loaded, along with a preview of the first five rows of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:13.725506Z", "iopub.status.busy": "2021-02-18T15:58:13.724665Z", "iopub.status.idle": "2021-02-18T15:58:15.144776Z", "shell.execute_reply": "2021-02-18T15:58:15.143989Z" }, "papermill": { "duration": 1.439784, "end_time": "2021-02-18T15:58:15.144909", "exception": false, "start_time": "2021-02-18T15:58:13.705125", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Set up feedback system\n", "from learntools.core import binder\n", "binder.bind(globals())\n", "from learntools.ethics.ex4 import *\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Load the data, separate features from target\n", "data = pd.read_csv(\"../input/synthetic-credit-card-approval/synthetic_credit_card_approval.csv\")\n", "X = data.drop([\"Target\"], axis=1)\n", "y = data[\"Target\"]\n", "\n", "# Break into training and test sets\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)\n", "\n", "# Preview the data\n", "print(\"Data successfully loaded!\\n\")\n", "X_train.head()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.012323, "end_time": "2021-02-18T15:58:15.170550", "exception": false, "start_time": "2021-02-18T15:58:15.158227", "status": "completed" }, "tags": [] }, "source": [ " The dataset contains, for each applicant:\n", "- income (in the `Income` column),\n", "- the number of children (in the `Num_Children` column),\n", "- whether the applicant owns a car (in the `Own_Car` column, the value is `1` if the applicant owns a car, and is else `0`), and\n", "- whether the applicant owns a home (in the `Own_Housing` column, the value is `1` if the applicant owns a home, and is else `0`)\n", "\n", "When evaluating fairness, we'll check how the model performs for users in different groups, as identified by the `Group` column: \n", "- The `Group` column breaks the users into two groups (where each group corresponds to either `0` or `1`). \n", "- For instance, you can think of the column as breaking the users into two different races, ethnicities, or gender groupings. If the column breaks users into different ethnicities, `0` could correspond to a non-Hispanic user, while `1` corresponds to a Hispanic user. \n", "\n", "\n", "Run the next code cell without changes to train a simple model to approve or deny individuals for a credit card. The output shows the performance of the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:15.209442Z", "iopub.status.busy": "2021-02-18T15:58:15.204279Z", "iopub.status.idle": "2021-02-18T15:58:16.472303Z", "shell.execute_reply": "2021-02-18T15:58:16.471611Z" }, "papermill": { "duration": 1.289337, "end_time": "2021-02-18T15:58:16.472414", "exception": false, "start_time": "2021-02-18T15:58:15.183077", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn import tree\n", "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "# Train a model and make predictions\n", "model_baseline = tree.DecisionTreeClassifier(random_state=0, max_depth=3)\n", "model_baseline.fit(X_train, y_train)\n", "preds_baseline = model_baseline.predict(X_test)\n", "\n", "# Function to plot confusion matrix\n", "def plot_confusion_matrix(estimator, X, y_true, y_pred, display_labels=[\"Deny\", \"Approve\"],\n", " include_values=True, xticks_rotation='horizontal', values_format='',\n", " normalize=None, cmap=plt.cm.Blues):\n", " cm = confusion_matrix(y_true, y_pred, normalize=normalize)\n", " disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels)\n", " return cm, disp.plot(include_values=include_values, cmap=cmap, xticks_rotation=xticks_rotation,\n", " values_format=values_format)\n", "\n", "# Function to evaluate the fairness of the model\n", "def get_stats(X, y, model, group_one, preds):\n", " \n", " y_zero, preds_zero, X_zero = y[group_one==False], preds[group_one==False], X[group_one==False]\n", " y_one, preds_one, X_one = y[group_one], preds[group_one], X[group_one]\n", " \n", " print(\"Total approvals:\", preds.sum())\n", " print(\"Group A:\", preds_zero.sum(), \"({}% of approvals)\".format(round(preds_zero.sum()/sum(preds)*100, 2)))\n", " print(\"Group B:\", preds_one.sum(), \"({}% of approvals)\".format(round(preds_one.sum()/sum(preds)*100, 2)))\n", " \n", " print(\"\\nOverall accuracy: {}%\".format(round((preds==y).sum()/len(y)*100, 2)))\n", " print(\"Group A: {}%\".format(round((preds_zero==y_zero).sum()/len(y_zero)*100, 2)))\n", " print(\"Group B: {}%\".format(round((preds_one==y_one).sum()/len(y_one)*100, 2)))\n", " \n", " cm_zero, disp_zero = plot_confusion_matrix(model, X_zero, y_zero, preds_zero)\n", " disp_zero.ax_.set_title(\"Group A\")\n", " cm_one, disp_one = plot_confusion_matrix(model, X_one, y_one, preds_one)\n", " disp_one.ax_.set_title(\"Group B\")\n", " \n", " print(\"\\nSensitivity / True positive rate:\")\n", " print(\"Group A: {}%\".format(round(cm_zero[1,1] / cm_zero[1].sum()*100, 2)))\n", " print(\"Group B: {}%\".format(round(cm_one[1,1] / cm_one[1].sum()*100, 2)))\n", " \n", "# Evaluate the model \n", "get_stats(X_test, y_test, model_baseline, X_test[\"Group\"]==1, preds_baseline)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.014937, "end_time": "2021-02-18T15:58:16.502916", "exception": false, "start_time": "2021-02-18T15:58:16.487979", "status": "completed" }, "tags": [] }, "source": [ "The confusion matrices above show how the model performs on some test data. We also print additional information (calculated from the confusion matrices) to assess fairness of the model. For instance,\n", "- The model approved 38246 people for a credit card. Of these individuals, 8028 belonged to Group A, and 30218 belonged to Group B.\n", "- The model is 94.56% accurate for Group A, and 95.02% accurate for Group B. These percentages can be calculated directly from the confusion matrix; for instance, for Group A, the accuracy is (39723+7528)/(39723+500+2219+7528).\n", "- The true positive rate (TPR) for Group A is 77.23%, and the TPR for Group B is 98.03%. These percentages can be calculated directly from the confusion matrix; for instance, for Group A, the TPR is 7528/(7528+2219).\n", "\n", "# 1) Varieties of fairness\n", "\n", "Consider three different types of fairness covered in the tutorial:\n", "- **Demographic parity**: Which group has an unfair advantage, with more representation in the group of approved applicants? (Roughly 50% of applicants are from Group A, and 50% of applicants are from Group B.)\n", "- **Equal accuracy**: Which group has an unfair advantage, where applicants are more likely to be correctly classified? \n", "- **Equal opportunity**: Which group has an unfair advantage, with a higher true positive rate?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:16.539264Z", "iopub.status.busy": "2021-02-18T15:58:16.538376Z", "iopub.status.idle": "2021-02-18T15:58:16.542379Z", "shell.execute_reply": "2021-02-18T15:58:16.541835Z" }, "papermill": { "duration": 0.02343, "end_time": "2021-02-18T15:58:16.542486", "exception": false, "start_time": "2021-02-18T15:58:16.519056", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Check your answer (Run this code cell to get credit!)\n", "q_1.check()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.015592, "end_time": "2021-02-18T15:58:16.573553", "exception": false, "start_time": "2021-02-18T15:58:16.557961", "status": "completed" }, "tags": [] }, "source": [ "Run the next code cell without changes to visualize the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:16.629492Z", "iopub.status.busy": "2021-02-18T15:58:16.628402Z", "iopub.status.idle": "2021-02-18T15:58:17.055538Z", "shell.execute_reply": "2021-02-18T15:58:17.054922Z" }, "papermill": { "duration": 0.466948, "end_time": "2021-02-18T15:58:17.055684", "exception": false, "start_time": "2021-02-18T15:58:16.588736", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def visualize_model(model, feature_names, class_names=[\"Deny\", \"Approve\"], impurity=False):\n", " plot_list = tree.plot_tree(model, feature_names=feature_names, class_names=class_names, impurity=impurity)\n", " [process_plot_item(item) for item in plot_list]\n", "\n", "def process_plot_item(item):\n", " split_string = item.get_text().split(\"\\n\")\n", " if split_string[0].startswith(\"samples\"):\n", " item.set_text(split_string[-1])\n", " else:\n", " item.set_text(split_string[0])\n", "\n", "plt.figure(figsize=(20, 6))\n", "plot_list = visualize_model(model_baseline, feature_names=X_train.columns)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.016028, "end_time": "2021-02-18T15:58:17.088356", "exception": false, "start_time": "2021-02-18T15:58:17.072328", "status": "completed" }, "tags": [] }, "source": [ "The flowchart shows how the model makes decisions:\n", "- `Group <= 0.5` checks what group the applicant belongs to: if the applicant belongs to Group A, then `Group <= 0.5` is true.\n", "- Entries like `Income <= 80210.5` check the applicant's income.\n", "\n", "To follow the flow chart, we start at the top and trace a path depending on the details of the applicant. If the condition is true at a split, then we move down and to the left branch. If it is false, then we move to the right branch.\n", "\n", "For instance, consider an applicant in Group B, who has an income of 75k. Then, \n", "- We start at the top of the flow chart. the applicant has an income of 75k, so `Income <= 80210.5` is true, and we move to the left.\n", "- Next, we check the income again. Since `Income <= 71909.5` is false, we move to the right.\n", "- The last thing to check is what group the applicant belongs to. The applicant belongs to Group B, so `Group <= 0.5` is false, and we move to the right, where the model has decided to approve the applicant.\n", "\n", "# 2) Understand the baseline model\n", "\n", "Based on the visualization, how can you explain one source of unfairness in the model?\n", "\n", "**Hint**: Consider the example applicant, but change the group membership from Group B to Group A (leaving all other characteristics the same). Is this slightly different applicant approved or denied by the model?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:17.127089Z", "iopub.status.busy": "2021-02-18T15:58:17.126182Z", "iopub.status.idle": "2021-02-18T15:58:17.129843Z", "shell.execute_reply": "2021-02-18T15:58:17.129226Z" }, "papermill": { "duration": 0.024547, "end_time": "2021-02-18T15:58:17.129966", "exception": false, "start_time": "2021-02-18T15:58:17.105419", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Check your answer (Run this code cell to get credit!)\n", "q_2.check()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.016484, "end_time": "2021-02-18T15:58:17.163351", "exception": false, "start_time": "2021-02-18T15:58:17.146867", "status": "completed" }, "tags": [] }, "source": [ "Next, you decide to remove group membership from the training data and train a new model. Do you think this will make the model treat the groups more equally?\n", "\n", "Run the next code cell to see how this new **group unaware** model performs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:17.215558Z", "iopub.status.busy": "2021-02-18T15:58:17.214801Z", "iopub.status.idle": "2021-02-18T15:58:18.228587Z", "shell.execute_reply": "2021-02-18T15:58:18.227943Z" }, "papermill": { "duration": 1.048652, "end_time": "2021-02-18T15:58:18.228696", "exception": false, "start_time": "2021-02-18T15:58:17.180044", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Create new dataset with gender removed\n", "X_train_unaware = X_train.drop([\"Group\"],axis=1)\n", "X_test_unaware = X_test.drop([\"Group\"],axis=1)\n", "\n", "# Train new model on new dataset\n", "model_unaware = tree.DecisionTreeClassifier(random_state=0, max_depth=3)\n", "model_unaware.fit(X_train_unaware, y_train)\n", "\n", "# Evaluate the model\n", "preds_unaware = model_unaware.predict(X_test_unaware)\n", "get_stats(X_test_unaware, y_test, model_unaware, X_test[\"Group\"]==1, preds_unaware)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.018716, "end_time": "2021-02-18T15:58:18.266646", "exception": false, "start_time": "2021-02-18T15:58:18.247930", "status": "completed" }, "tags": [] }, "source": [ "# 3) Varieties of fairness, part 2\n", "\n", "How does this model compare to the first model you trained, when you consider **demographic parity**, **equal accuracy**, and **equal opportunity**? Once you have an answer, run the next code cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:18.309527Z", "iopub.status.busy": "2021-02-18T15:58:18.308525Z", "iopub.status.idle": "2021-02-18T15:58:18.311751Z", "shell.execute_reply": "2021-02-18T15:58:18.311124Z" }, "papermill": { "duration": 0.026228, "end_time": "2021-02-18T15:58:18.311867", "exception": false, "start_time": "2021-02-18T15:58:18.285639", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Check your answer (Run this code cell to get credit!)\n", "q_3.check()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.018855, "end_time": "2021-02-18T15:58:18.349740", "exception": false, "start_time": "2021-02-18T15:58:18.330885", "status": "completed" }, "tags": [] }, "source": [ "You decide to train a third potential model, this time with the goal of having each group have even representation in the group of approved applicants. (This is an implementation of group thresholds, which you can optionally read more about [here](https://pair-code.github.io/what-if-tool/ai-fairness.html).) \n", "\n", "Run the next code cell without changes to evaluate this new model. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:18.397959Z", "iopub.status.busy": "2021-02-18T15:58:18.396916Z", "iopub.status.idle": "2021-02-18T15:58:19.060073Z", "shell.execute_reply": "2021-02-18T15:58:19.059484Z" }, "papermill": { "duration": 0.691332, "end_time": "2021-02-18T15:58:19.060260", "exception": false, "start_time": "2021-02-18T15:58:18.368928", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Change the value of zero_threshold to hit the objective\n", "zero_threshold = 0.11\n", "one_threshold = 0.99\n", "\n", "# Evaluate the model\n", "test_probs = model_unaware.predict_proba(X_test_unaware)[:,1]\n", "preds_approval = (((test_probs>zero_threshold)*1)*[X_test[\"Group\"]==0] + ((test_probs>one_threshold)*1)*[X_test[\"Group\"]==1])[0]\n", "get_stats(X_test, y_test, model_unaware, X_test[\"Group\"]==1, preds_approval)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.021832, "end_time": "2021-02-18T15:58:19.103892", "exception": false, "start_time": "2021-02-18T15:58:19.082060", "status": "completed" }, "tags": [] }, "source": [ "# 4) Varieties of fairness, part 3\n", "\n", "How does this final model compare to the previous models, when you consider **demographic parity**, **equal accuracy**, and **equal opportunity**?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2021-02-18T15:58:19.152282Z", "iopub.status.busy": "2021-02-18T15:58:19.151543Z", "iopub.status.idle": "2021-02-18T15:58:19.154790Z", "shell.execute_reply": "2021-02-18T15:58:19.154237Z" }, "papermill": { "duration": 0.029371, "end_time": "2021-02-18T15:58:19.154900", "exception": false, "start_time": "2021-02-18T15:58:19.125529", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Check your answer (Run this code cell to get credit!)\n", "q_4.check()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.021224, "end_time": "2021-02-18T15:58:19.197870", "exception": false, "start_time": "2021-02-18T15:58:19.176646", "status": "completed" }, "tags": [] }, "source": [ "This is only a short exercise to explore different types of fairness, and to illustrate the tradeoff that can occur when you optimize for one type of fairness over another. We have focused on model training here, but in practice, to really mitigate bias, or to make ML systems fair, we need to take a close look at every step in the process, from data collection to releasing a final product to users. \n", "\n", "For instance, if you take a close look at the data, you'll notice that on average, individuals from Group B tend to have higher income than individuals from Group A, and are also more likely to own a home or a car. Knowing this will prove invaluable to deciding what fairness criterion you should use, and to inform ways to achieve fairness. (*For instance, it would likely be a bad aproach, if you did not remove the historical bias in the data and then train the model to get equal accuracy for each group.*)\n", "\n", "In this course, we intentionally avoid taking an opinionated stance on how exactly to minimize bias and ensure fairness in specific projects. This is because the correct answers continue to evolve, since AI fairness is an active area of research. This lesson was a hands-on introduction to the topic, and you can continue your learning by reading blog posts from the [Partnership on AI](https://www.partnershiponai.org/research-lander/) or by following conferences like the [ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)](https://facctconference.org/)." ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.021672, "end_time": "2021-02-18T15:58:19.241234", "exception": false, "start_time": "2021-02-18T15:58:19.219562", "status": "completed" }, "tags": [] }, "source": [ "# Keep going\n", "\n", "Continue to **[learn how to use model cards](#$NEXT_NOTEBOOK_URL$)** to make machine learning models transparent to large audiences." ] } ], "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" }, "papermill": { "duration": 10.573914, "end_time": "2021-02-18T15:58:19.370982", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2021-02-18T15:58:08.797068", "version": "2.1.0" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
ofanoyi/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Chapter3_MCMC/IntroMCMC.ipynb
1
1225780
null
mit
jarvis-fga/Projetos
Problema 3/Alexandre-Treating_data/Problem 3.ipynb
1
8268
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Descrição do Problema" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tratamento dos dados" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Aux Functions\n", "\n", "def labels_dictionary():\n", " file = open('data/communities.names_edit.txt', 'r')\n", " labels = dict()\n", " for index, line in enumerate(file):\n", " line = line[:-1]\n", " labels[index] = line # Allow to read the label by the index\n", " labels[line] = index # Allow to read the index by the label\n", " return labels \n", "\n", "def get_labels(labels_dic):\n", " labels = []\n", " for i in range (128):\n", " labels.append(labels_dic[i])\n", " return labels\n", "\n", "def columns_to_remove(communities, missing_data_percentage):\n", " columns = []\n", " incomplete_values = communities.isnull().sum()\n", " incomplete_percent = (incomplete_values/communities.shape[0]*100)\n", " for i in range (communities.shape[1]):\n", " if incomplete_percent[i] > missing_data_percentage:\n", " columns.append(i)\n", " return columns\n", "\n", "\n", "\n", "\n", "# incomplete_values = communities.isnull().sum()\n", "# incomplete_values_percent = (incomplete_values/communities.shape[0]*100)\n", "# print(\"Percent of Incomple values\")\n", "# print(list(map(lambda x: x>75, incomplete_values_percent)))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "More than 75% incomplete:\n", "['LemasSwornFT numeric', 'LemasSwFTPerPop numeric', 'LemasSwFTFieldOps numeric', 'LemasSwFTFieldPerPop numeric', 'LemasTotalReq numeric', 'LemasTotReqPerPop numeric', 'PolicReqPerOffic numeric', 'PolicPerPop numeric', 'RacialMatchCommPol numeric', 'PctPolicWhite numeric', 'PctPolicBlack numeric', 'PctPolicHisp numeric', 'PctPolicAsian numeric', 'PctPolicMinor numeric', 'OfficAssgnDrugUnits numeric', 'NumKindsDrugsSeiz numeric', 'PolicAveOTWorked numeric', 'PolicCars numeric', 'PolicOperBudg numeric', 'LemasPctPolicOnPatr numeric', 'LemasGangUnitDeploy numeric', 'PolicBudgPerPop numeric']\n", "----\n", "\n", "X new shape: (1994, 104) \n", "\n" ] } ], "source": [ "import pandas\n", "import numpy\n", "\n", "labels_dic = labels_dictionary()\n", "communities = pandas.read_csv('data/communities.data.txt', sep=\",\", names=get_labels(labels_dic), encoding='utf-8')\n", "\n", "\n", "communities = communities.replace('?', numpy.NaN)\n", "col_to_remove = columns_to_remove(communities, 75) # Columns with more than 75% of missing data are removed\n", "\n", "\n", "print(\"More than 75% incomplete:\")\n", "list_of_incomplete = list(map(lambda x: labels_dic[x], col_to_remove))\n", "print(list_of_incomplete)\n", "print(\"----\\n\")\n", "\n", "X = communities.iloc[:, 0:127] # OR .drop(labels='ViolentCrimesPerPop numeric', axis=1)\n", "X = X.drop(labels=list_of_incomplete, axis=1)\n", "X = X.drop(labels=['communityname string'], axis=1) #Temporary, change later\n", "print(\"X new shape: \", X.shape, \"\\n\")\n", "Y = communities.iloc[:, [127]]\n", "\n", "\n", "# df = pandas.DataFrame(X, columns=list(set(X['communityname string'])) )\n", "# dummies = pandas.get_dummies(df)\n", "# X.join(dummies)\n", "# print(X.shape)\n", "\n", "# X_val = X.values\n", "# Y_val = Y.values\n", "\n", "#print(X_val)\n", "#print(Y_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### * Removing all Rows With Missing Values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.652390305044\n", "0.0188594742411\n" ] } ], "source": [ "X_rem = X\n", "X_rem.dropna(inplace=True, axis='columns')\n", "X_rem = X_rem.values\n", "Y_rem = Y.values\n", "#print(X_rem.shape)\n", "\n", "\n", "from sklearn import linear_model\n", "lm = linear_model.LinearRegression()\n", "\n", "from sklearn.cross_validation import cross_val_predict\n", "from sklearn import metrics\n", "\n", "predictions = cross_val_predict(lm, X_rem, Y_rem, cv=6)\n", "\n", "r2_score = metrics.r2_score(Y_rem, predictions)\n", "print(r2_score)\n", "\n", "mean_squared_error = metrics.mean_squared_error(Y_rem, predictions)\n", "print(mean_squared_error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### * Imputing Missing Values" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.652390305044\n", "0.0188594742411\n" ] } ], "source": [ "from pandas import read_csv\n", "from sklearn.preprocessing import Imputer\n", "\n", "X_val = X.values\n", "Y_val = Y.values\n", "imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)\n", "X_imp = imputer.fit_transform(X_val)\n", "\n", "\n", "from sklearn import linear_model\n", "lm = linear_model.LinearRegression()\n", "\n", "from sklearn.cross_validation import cross_val_score, cross_val_predict\n", "from sklearn import metrics\n", "\n", "\n", "predictions = cross_val_predict(lm, X_val, Y_val, cv=6)\n", "\n", "r2_score = metrics.r2_score(Y_val, predictions)\n", "print(r2_score)\n", "\n", "mean_squared_error = metrics.mean_squared_error(Y_val, predictions)\n", "print(mean_squared_error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### * Using PCA to remove features" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.439494977767\n", "0.0304100552493\n" ] } ], "source": [ "from sklearn.preprocessing import Imputer\n", "from sklearn.cross_validation import cross_val_score, cross_val_predict\n", "from sklearn import metrics\n", "from sklearn import linear_model\n", "from sklearn.decomposition import KernelPCA\n", "\n", "X_val = X.values\n", "Y_val = Y.values\n", "imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)\n", "X_imp = imputer.fit_transform(X_val)\n", "\n", "\n", "#kpca = KernelPCA(n_components=50, kernel='linear')\n", "kpca = KernelPCA(n_components=50, kernel='poly', degree=3)\n", "X_KPCA = kpca.fit_transform(X_val)\n", "\n", "\n", "\n", "lm = linear_model.LinearRegression()\n", "\n", "\n", "predictions = cross_val_predict(lm, X_KPCA, Y_val, cv=6)\n", "\n", "r2_score = metrics.r2_score(Y_val, predictions)\n", "print(r2_score)\n", "\n", "mean_squared_error = metrics.mean_squared_error(Y_val, predictions)\n", "print(mean_squared_error)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
microsoft/dowhy
docs/source/example_notebooks/dowhy_ranking_methods.ipynb
1
32500
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Ranking of estimation methods for a given dataset " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We illustrate the comparison of various estimation methods for a given datasets by ranking them according to their performance against refutation tests accounting for both the observed unmodelled confounding error and unobserved confounding error. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Importing all the required libraries\n", "import sys\n", "import argparse\n", "import xgboost\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "import pdb\n", "import random\n", "from xgboost import XGBRegressor\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LassoCV\n", "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor\n", "from sklearn.linear_model import LogisticRegressionCV\n", "from sklearn.metrics import mean_absolute_error\n", "from dowhy import CausalModel\n", "from datetime import datetime\n", "from collections import namedtuple\n", "\n", "import statsmodels.api as sm\n", "from sklearn import linear_model\n", "\n", "import dowhy\n", "from dowhy.utils import dgp\n", "from dowhy.utils.dgps.linear_dgp import LinearDataGeneratingProcess\n", "from dowhy import CausalModel\n", "from datetime import datetime\n", "from collections import namedtuple\n", "from dowhy.causal_refuters.add_unobserved_common_cause import AddUnobservedCommonCause\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.lines as mlines\n", "import matplotlib.transforms as mtransforms\n", "\n", "# Config dict to set the logging level\n", "import logging.config\n", "DEFAULT_LOGGING = {\n", " 'version': 1,\n", " 'disable_existing_loggers': False,\n", " 'loggers': {\n", " '': {\n", " 'level': 'WARN',\n", " },\n", " }\n", "}\n", "\n", "logging.config.dictConfig(DEFAULT_LOGGING)\n", "# Disabling warnings output\n", "import warnings\n", "from sklearn.exceptions import DataConversionWarning, ConvergenceWarning\n", "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n", "warnings.filterwarnings(action='ignore', category=ConvergenceWarning)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def convert_singleton_to_float(arr):\n", " '''Helper function.'''\n", " array = []\n", "\n", " if len(arr) == 1 and type(arr[0]) != np.ndarray:\n", " return arr[0]\n", "\n", " for element in arr:\n", " while type(element) == np.ndarray or isinstance(element, list) :\n", " if len(element) > 1:\n", " raise ValueError(\"This script only accepts one value for the refute\")\n", " element = element[0]\n", " array.append(element)\n", "\n", " return array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def ensure_dir(file_path):\n", " directory = os.path.dirname(file_path)\n", " if not os.path.exists(directory):\n", " os.makedirs(directory)\n", "\n", "RESULTSFOLDER = \"results/\"\n", "ensure_dir(RESULTSFOLDER)\n", "# Create the estimator named tuple to wrap the name and properties\n", "Estimator = namedtuple('Estimator', ['name','params'])\n", "Refuter = namedtuple('Refuter', ['name','params'])\n", "\n", "class Experiment():\n", " '''\n", " Class to define the experiment setup to compare a list of estimators across a list of refuters for the given dataset. \n", " '''\n", " def __init__(self, **kwargs):\n", " self.experiment_name = kwargs['experiment_name']\n", " self.experiment_id = kwargs['experiment_id']\n", " self.num_experiments = kwargs['num_experiments']\n", " self.sample_sizes = kwargs['sample_sizes']\n", " self.dgps = kwargs['dgps']\n", " self.estimators = kwargs['estimators']\n", " self.refuters = kwargs['refuters']\n", " self.results = []\n", " self.simulate_unobserved_confounding = kwargs[\"simulate_unobserved_confounding\"]\n", "\n", " # Handle input errors in sample_sizes\n", " if isinstance(self.sample_sizes, list) == False:\n", " if type(self.sample_sizes) != int:\n", " raise ValueError('The input to \"sample_sizes\" should be an int or a list')\n", " else:\n", " self.sample_sizes = [self.sample_sizes]\n", "\n", " # Handle input errors in DGPs\n", " if isinstance(self.dgps, list) == False:\n", " if isinstance(self.dgps, DataGeneratingProcess) == False:\n", " raise ValueError('The input to \"dgps\" should be a list or a subclass of \"DataGeneratingProcess\"')\n", " else:\n", " self.dgps = [self.dgps]\n", "\n", " # Handle inputs errors in estimators\n", " if isinstance(self.estimators, list) == False:\n", " if isinstance(self.estimators, Estimator) == False:\n", " raise ValueError('The input to \"estimators\" should be a list or an Estimator namedtuple')\n", " else:\n", " self.estimators = [self.estimators]\n", "\n", " # Handle input errors in refuters\n", " if isinstance(self.refuters, list) == False:\n", " if isinstance(self.refuters, Refuter) == False:\n", " raise ValueError('The input to \"refuters\" should be a list of a Refuter namedtuple')\n", " else:\n", " self.refuters = [self.refuters]\n", "\n", " def experiment(self):\n", " print(\"\\n\\nRunning Experiment:\",self.experiment_name + '_' + str(self.experiment_id) )\n", "\n", " for exp in range(self.num_experiments):\n", " print(\"\\n\\nRunning Experiment Number:\",exp)\n", "\n", " for sample_size in self.sample_sizes:\n", "\n", " print(\"\\n\\nCurrent Sample Size:\",sample_size)\n", "\n", " for dgp in self.dgps:\n", " print(\"\\n\\nThe current DGP:\")\n", " print(dgp)\n", " estimates = []\n", " estimate_values = []\n", " estimated_effect = []\n", " new_effect = []\n", " p_value = []\n", " data = dgp.generate_data(sample_size)\n", " print(\"printing data shape\")\n", " print(data.values.shape)\n", " print(dgp.true_value)\n", " print(\"check\")\n", " if dgp.treatment_is_binary:\n", " data[dgp.treatment] = data[dgp.treatment].astype(bool)\n", " #k = len(dgp.confounder)-4\n", " #confounder_list = random.sample(dgp.confounder, k)\n", " confounder_list = ['w2','w3']\n", "\n", " \n", " s = set(confounder_list)\n", " unobserved_confounders = [x for x in dgp.confounder if x not in s]\n", " df_unobserved_confounders = pd.DataFrame(data = data[[c for c in data.columns if c in unobserved_confounders]])\n", "\n", " df_unobserved_confounders.to_csv(\"results/unobserved_confounders.csv\")\n", " print(\"printing length of confounder list:\", len(confounder_list))\n", " print(\"printing confounder list:\", confounder_list)\n", "\n", " \n", "\n", " print(\"data columns\")\n", " \n", " print(\"data columns\", data.columns)\n", " model = CausalModel(\n", " data = data,\n", " treatment = dgp.treatment,\n", " outcome = dgp.outcome,\n", " common_causes = confounder_list,\n", " effect_modifiers = dgp.effect_modifier\n", " )\n", " model.view_model()\n", " from IPython.display import Image, display\n", " display(Image(filename=\"causal_model.png\"))\n", "\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " \n", " print(\"identified_estimand:\", identified_estimand)\n", " #print(\"identified_estimand:\", identified_estimand)\n", " print(\"\\n\\nRunning the estimators:\\n\")\n", " for estimator in self.estimators:\n", " print(\"The current estimator:\", estimator)\n", " print(\"estimator.params\", estimator.params)\n", " estimate = model.estimate_effect(\n", " identified_estimand,\n", " method_name = estimator.name,\n", " method_params = estimator.params\n", " )\n", " print(\"printing estimate's type\")\n", " print(type(estimate))\n", " estimates.append(estimate)\n", " estimate_values.append(estimate.value)\n", " estimate_values = convert_singleton_to_float(estimate_values)\n", " print(\"estimate_values\", estimate_values)\n", " print(\"\\n\\nRunning the refuters:\\n\")\n", " for refuter in self.refuters:\n", " print(\"The current refuter:\", refuter)\n", " \n", " for estimate in estimates:\n", " if self.simulate_unobserved_confounding == True:\n", " print(\"********%%%%%%%%%$$$$$&&^**^^^^*^*^*\")\n", " if refuter.name == 'dummy_outcome_refuter':\n", " add_unobserved_confounder = AddUnobservedCommonCause(data, identified_estimand, estimate)\n", " print(\"add_unobserved_confounder\", add_unobserved_confounder)\n", " unobserved_confounder_values = add_unobserved_confounder.include_simulated_confounder(convergence_threshold = 0.11, c_star_max = 1500)\n", " refuter.params['unobserved_confounder_values'] = unobserved_confounder_values\n", " print('refuter.params', refuter.params)\n", " refute = model.refute_estimate(\n", " identified_estimand,\n", " estimate,\n", " method_name = refuter.name,\n", " **refuter.params,\n", " \n", " \n", "\n", " )\n", " print(\"printing refute's type\")\n", " print(type(refute))\n", " if(refuter.name == 'dummy_outcome_refuter'):\n", " refute = refute[0]\n", " if refute.refutation_result is not None:\n", " p_value.append(refute.refutation_result['p_value'])\n", " else:\n", " p_value.append(None) \n", "\n", " estimated_effect.append(refute.estimated_effect)\n", " #print(\"refute.estimate_effect()\", refute.estimate_effect())\n", " new_effect.append(refute.new_effect)\n", "\n", " estimated_effect = convert_singleton_to_float(estimated_effect)\n", " new_effect = convert_singleton_to_float(new_effect)\n", " p_value = convert_singleton_to_float(p_value)\n", " true_value = convert_singleton_to_float(dgp.true_value)\n", " \n", " print(\"estimated effect\", estimated_effect)\n", " print(\"new_effect\", new_effect)\n", " print(\"p_value\", p_value)\n", " print(\"true value\", true_value)\n", " self.results.append([exp, sample_size, dgp.NAME, *estimate_values, *estimated_effect, *new_effect, *p_value, true_value])\n", "\n", "\n", " print(\"\\n\\nCompleted all experiments. Saving the data...\")\n", "\n", " COLUMNS = ['EXPERIMENT', 'SAMPLE_SIZE', 'DGP']\n", " RESULT_CATEGORIES = ['ESTIMATED_EFFECT', 'NEW_EFFECT', 'P_VALUE']\n", " estimator_names = [estimator.name for estimator in self.estimators]\n", " refuter_names = [refuter.name for refuter in self.refuters]\n", "\n", " for estimator_name in estimator_names:\n", " COLUMNS += ['ORIGINAL_ESTIMATE'+ ':' + estimator_name]\n", "\n", " for result_category in RESULT_CATEGORIES:\n", " for refuter_name in refuter_names:\n", " for estimator_name in estimator_names:\n", " COLUMNS += [refuter_name + ':' + estimator_name + ':' + result_category]\n", "\n", " COLUMNS += ['TRUE_VALUE']\n", "\n", " csv_file = RESULTSFOLDER + self.experiment_name+ '_' + str(self.experiment_id) + '_' + str(datetime.utcnow().date()) + '_data.csv'\n", " onlyres_csv_file = RESULTSFOLDER + \"onlyres_\"+ self.experiment_name+ '_' + str(self.experiment_id) + '_' + str(datetime.utcnow()) + '_data.csv'\n", " self.results = pd.DataFrame(data=self.results,columns=COLUMNS)\n", " self.results.to_csv(csv_file.replace(\" \", \"\"), index=False)\n", "\n", " print(\"Data has been saved in \",csv_file)\n", "\n", " return csv_file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Defining the Data Generating Process \n", "ldgp = LinearDataGeneratingProcess(treatment=['t1'], outcome=['y'], confounder=['w1','w2', 'w3','w4','w5','w6'], effect_modifier=['x1','x2'], seed=None, treatment_is_binary=True)\n", "\n", "#Defining the sample size \n", "sample_size = 1000\n", "\n", "dgp_dict = {'ldgp':ldgp}\n", "dgp_list = []\n", "dgp_list.append( dgp_dict['ldgp'] )\n", "\n", "\n", "# Create a namedtuple to store the name of the estimator and the parameters passed\n", "estimator_list = [\"backdoor.linear_regression\",\n", " #\"backdoor.propensity_score_stratification\",\n", " \"backdoor.propensity_score_matching\",\n", " \"backdoor.propensity_score_weighting\",\n", " \"backdoor.econml.dml.DML\",\n", " \"backdoor.econml.dr.LinearDRLearner\",\n", " #\"backdoor.econml.metalearners.TLearner\",\n", " #\"backdoor.econml.metalearners.XLearner\",\n", " #\"backdoor.causalml.inference.meta.LRSRegressor\",\n", " #\"backdoor.causalml.inference.meta.XGBTRegressor\",\n", " #\"backdoor.causalml.inference.meta.MLPTRegressor\",\n", " #\"backdoor.causalml.inference.meta.BaseXRegressor\"\n", " ]\n", "method_params= [ None,\n", " #None,\n", " { \"init_params\":{} },\n", " { \"init_params\":{} },\n", " {\"init_params\":{'model_y':GradientBoostingRegressor(),\n", " 'model_t': GradientBoostingRegressor(),\n", " \"model_final\":LassoCV(fit_intercept=False),\n", " 'featurizer':PolynomialFeatures(degree=1, include_bias=True)},\n", " \"fit_params\":{}},\n", " {\"init_params\":{ 'model_propensity': LogisticRegressionCV(cv=3, solver='lbfgs', multi_class='auto'),\n", " },\n", " \"fit_params\":{}\n", " },\n", " '''{\"init_params\": {'models': GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(sample_size/100))\n", " },\n", " \"fit_params\":{}\n", " },\n", " {\"init_params\":{'models': GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(sample_size/100)),\n", " 'propensity_model': RandomForestClassifier(n_estimators=100, max_depth=6,\n", " min_samples_leaf=int(sample_size/100))\n", " },\n", " \"fit_params\":{}\n", " },\n", " {\"init_params\":{},},\n", " {\"init_params\":{\n", " 'learner':XGBRegressor()\n", " }\n", " }'''\n", " ]\n", "estimator_tuples = []\n", "refuter_tuples = []\n", "\n", "refuter_list = ['dummy_outcome_refuter']\n", "refuter_params = [{'num_simulations':5,'transformation_list': [('random_forest',{'n_estimators':100, 'max_depth':6})], 'true_causal_effect':(lambda x:0.5)}]\n", "\n", "\n", "# Iterate through the names and parameters to create a list of namedtuples\n", "for name, param in zip(estimator_list,method_params):\n", " estimator_tuples.append(Estimator._make([name, param]))\n", " \n", "for name, param in zip(refuter_list, refuter_params):\n", " refuter_tuples.append(Refuter._make([name, param]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_MAEs(res):\n", " true_value_column = res.columns[-1]\n", " estimate_columns=res.columns[3:-1]\n", " #print(estimate_columns)\n", " #print(type(estimate_columns))\n", " estimate_columns.append(pd.Index(res[\"TRUE_VALUE\"]))\n", " #print(estimate_columns)\n", " fig, ax = plt.subplots()\n", " MAE ={}\n", " for colname in estimate_columns:\n", " if colname not in ('ORIGINAL_ESTIMATE:backdoor.propensity_score_weighting',):\n", " #'ORIGINAL_ESTIMATE:backdoor.econml.metalearners.TLearner'):\n", " plt.plot(res[colname], res[\"TRUE_VALUE\"], marker='o', linestyle=\"None\", label=colname)\n", " \"Mean Absolute Error (MAE): {}\".format(mean_absolute_error(res[colname], res[\"TRUE_VALUE\"]))\n", " MAE[colname] = mean_absolute_error(res[colname], res[\"TRUE_VALUE\"])\n", " fig.suptitle('Calibration plot showing the accuracy of different causal estimators [P(T=1)=0.9]')\n", " ax.set_xlabel('Estimated effect')\n", " ax.set_ylabel('True causal effect')\n", " plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.20),\n", " fancybox=True, shadow=True, ncol=2)\n", " plt.show()\n", " print(\"Printing MAE of various estimates: \")\n", " MAE_values = {k: v for k, v in sorted(MAE.items(), key=lambda item: item[1], reverse = True)}\n", " for k,v in MAE_values.items():\n", " print(k, v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_estimators_and_refuters(refuter, estimator): \n", " x = list(res['EXPERIMENT'])\n", " y1 = list(res[refuter+':'+estimator+':ESTIMATED_EFFECT'])\n", " y2 = list(res[refuter+':'+estimator+':NEW_EFFECT'])\n", " #print(res['TRUE_VALUE'])\n", " y3 = list(res['TRUE_VALUE'])\n", " y4 = list(res[refuter+':'+estimator+':P_VALUE'])\n", " plt.scatter(x, y1, c =\"blue\", label = \"Estimated Effect\") \n", " plt.scatter(x, y2, c =\"red\", label = \"New Effect\")\n", " plt.scatter(x, y3, c =\"green\", label = \"True Value\")\n", " plt.scatter(x, y4, c =\"yellow\", label = \"P Value\")\n", " plt.xlabel(\"EXPERIMENT\") \n", " plt.ylabel(\"EFFECT\")\n", " legend = plt.legend(loc=4, fontsize='small', fancybox=True)\n", " plt.title(estimator) \n", " plt.show()\n", " plt.savefig(estimator+'.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_deviations(estimator_list, deviation_list):\n", " plt.scatter(estimator_list, deviation_list)\n", " plt.xticks(estimator_list, estimator_list, rotation='vertical')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Observed unmodelled confounding error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each estimator, we use dummy outcome refuter to check the observed unmodelled confounding error for each estimator. That is, we run the refutation test for each estimator only on the observed confounders and analyse what amount of confounding error is present unmodelled amongst the observed variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Define the properties of the experiment\n", "# The name of the experiment\n", "# The experiment ID\n", "# The number of experiments to be run with the SAME parameters\n", "# The size of the samples to be run\n", "# The list of DGPs to be run\n", "# The list of estimators\n", "observed_confounding_error = Experiment(\n", " experiment_name='Test',\n", " experiment_id='1',\n", " num_experiments=10, # 10\n", " sample_sizes=sample_size,\n", " dgps=dgp_list,\n", " estimators=estimator_tuples,\n", " refuters=refuter_tuples,\n", " simulate_unobserved_confounding = False \n", ")\n", "\n", "# Run the experiment\n", "res = pd.read_csv(observed_confounding_error.experiment())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#PLOT\n", "#This plot shows the Mean Absolute Error of the Orginal Estimate from the true value and of the New Effect from \n", "#the expected value for each estimator. \n", "plot_MAEs(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ranking based on Original Estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Original Estimate is calculated in the presence of the True Value (that is, the ground truth). However in many real life datasets, the ground truth may not be known. Hence, we want the ranking produced by our refutation tests to be in coherence with that obtained from the Original Estimates. According to the Original Estimate values, the ranking of the estimators should be as follows (the method with the least MAE should get the best rank):\n", "1. DMLCateEstimator \n", "2. LinearRegression \n", "3. LinearDRLearner \n", "4. Propensity Score Matching " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "estimator_list = [\"backdoor.linear_regression\",\n", " #\"backdoor.propensity_score_stratification\",\n", " \"backdoor.propensity_score_matching\",\n", " \"backdoor.econml.dml.DML\",\n", " \"backdoor.econml.dr.LinearDRLearner\",\n", " #\"backdoor.econml.metalearners.TLearner\",\n", " #\"backdoor.econml.metalearners.XLearner\",\n", " #\"backdoor.causalml.inference.meta.LRSRegressor\",\n", " #\"backdoor.causalml.inference.meta.XGBTRegressor\",\n", " #\"backdoor.causalml.inference.meta.MLPTRegressor\",\n", " #\"backdoor.causalml.inference.meta.BaseXRegressor\"\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#This plot shows the deviation of the original estimate, the new effect and the estimated effect from the true value \n", "refuter = 'dummy_outcome_refuter'\n", "deviation_list = []\n", "for estimator in estimator_list:\n", " plot_estimators_and_refuters(refuter, estimator)\n", " avg_deviation = ((res[refuter+':'+estimator+':NEW_EFFECT']).sum(axis=0))\n", " print(avg_deviation)\n", " deviation_list.append(avg_deviation)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_deviations(estimator_list, deviation_list)\n", "for i in range(len(estimator_list)):\n", " print(estimator_list[i] +\": \"+ str(deviation_list[i]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "{k: v for k, v in sorted(zip(estimator_list, deviation_list), key=lambda item: item[1], reverse = True)}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ranking based on New Effect (Refutatation results) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ranking based on absolute value of deviations is :\n", "1. Propensity Score Matching \n", "2. Linear DR Learner \n", "3. DML CATE Estimator \n", "4. Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, the observed unmodelled confounding error is not able to match the ranking based on the Original Estimate. It is not even able to tell that the clear winner amongst the methods according to the true value is DML CATE Estimator " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unobserved confounding error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each estimator, we now simulate unobserved confounders and check its effect using dummy outcome refuter to check the unobserved confounding error for each estimator. That is, we run the refutation test for each estimator not only on the observed confounder, but also on an unobserved confounder that we simulate using the AddUnobservedCommonCause class and analyse whether there is a strong confounder that is unobserved (missing) and needs to be accounted for. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "unobserved_confounding_error = Experiment(\n", " experiment_name='Test',\n", " experiment_id='2',\n", " num_experiments=10, # 10\n", " sample_sizes=sample_size,\n", " dgps=dgp_list,\n", " estimators=estimator_tuples,\n", " refuters=refuter_tuples,\n", " simulate_unobserved_confounding = True\n", ")\n", "\n", "# Run the experiment\n", "res = pd.read_csv(unobserved_confounding_error.experiment())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "##This plot shows the Mean Absolute Error of the Orginal Estimate from the true value and of the New Effect from \n", "#the expected value for each estimator.\n", "plot_MAEs(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ranking based on Original Estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Original Estimate is calculated in the presence of the True Value (that is, the ground truth). However in many real life datasets, the ground truth may not be known. Hence, we want the ranking produced by our refutation tests to be in coherence with that obtained from the Original Estimates. According to the Original Estimate values, the ranking of the estimators should be as follows (the method with the least MAE should get the best rank):\n", "1. DMLCateEstimator \n", "2. Propensity Score Matching \n", "3. LinearRegression \n", "4. LinearDRLearner " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#This plot shows the deviation of the original estimate, the new effect and the estimated effect from the true value\n", "refuter = 'dummy_outcome_refuter'\n", "deviation_list = []\n", "for estimator in estimator_list:\n", " plot_estimators_and_refuters(refuter, estimator)\n", " avg_deviation = ((res[refuter+':'+estimator+':NEW_EFFECT']).sum(axis=0))\n", " print(avg_deviation)\n", " deviation_list.append(avg_deviation)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_deviations(estimator_list, deviation_list)\n", "for i in range(len(estimator_list)):\n", " print(estimator_list[i] +\": \"+ str(deviation_list[i]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "{k: v for k, v in sorted(zip(estimator_list, deviation_list), key=lambda item: item[1], reverse = True)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ranking based on New Effect (Refutatation results) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ranking based on absolute value of deviations is :\n", "1. DML\n", "2. Linear DR Learner \n", "3. Propensity Score Matching\n", "4. Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### We can see that this ranking produces the same top-ranked estimator as the one based on Original Estimate. Thus ranking based on the unobserved confounding error solves the problem and gives us a close-to-correct ranking amongst methods." ] } ], "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": "3.8.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }
mit
y12studio/y12docker
pscibook/nbsrv/readme.ipynb
1
39090
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import datasrc as dc\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f=dc.read_bitcoin()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7fdcbcd3d190>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZHum5uakOAsDuSraYAyyhU3kH6h/B2w3sp7GxdQtwYddxrj0SXtf/S/wvMjZ9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\nGw152NZDnhQtkBj6WXc99NCFh6olxA576cvoRmz9Z7cP3ji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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdcbcf8cd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f[[\"Value\",\"120MA\",\"30MA\",\"7MA\"]].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
daverick/alella
google cloud/google cloud with python/cloud.google.compute.zones.ipynb
1
26949
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#imports\n", "import ipywidgets as widgets" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load getCredentialsFromFile.py\n", "\n", "\n", "def getCredentials():\n", " from oauth2client import file\n", " import httplib2\n", " import ipywidgets as widgets\n", " print(\"Getting the credentials from file...\")\n", " storage = file.Storage(\"oauth2.dat\")\n", " credentials=storage.get()\n", " if credentials is None or credentials.invalid:\n", " print( '❗')\n", " display(widgets.Valid(\n", " value=False,\n", " description='Credentials are ',\n", " disabled=False))\n", " display(widgets.HTML('go create a credential valid file here: <a target=\"_blank\" href=\"cloud.google.auth.ipynb.ipynb\">gcloud authorization notebook</a> and try again'))\n", " else:\n", " http_auth = credentials.authorize(httplib2.Http())\n", " print('✅ Ok')\n", " return credentials\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting the credentials from file...\n", "✅ Ok\n" ] } ], "source": [ "credentials=getCredentials()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#create the service\n", "from apiclient.discovery import build\n", "compute_service = build('compute', 'v1',credentials=credentials)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4260602fc491416286471fea72f275e0" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#set the projectId\n", "projectCodeWidget=widgets.Text(description=\"Your project code:\")\n", "valid=widgets.Valid(value=False,description='',disabled=False)\n", "display(widgets.Box([projectCodeWidget,valid]))\n", "def valueChange(sender):\n", " if projectCodeWidget.value!=\"\":\n", " valid.value=True\n", " valid.description='OK'\n", " else:\n", " valid.value=False\n", " valid.description=''\n", "\n", "projectCodeWidget.observe(valueChange, 'value')" ] }, { "cell_type": "code", "execution_count": 9, "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>creationTimestamp</th>\n", " <th>description</th>\n", " <th>id</th>\n", " <th>kind</th>\n", " <th>name</th>\n", " <th>region</th>\n", " <th>selfLink</th>\n", " <th>status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>asia-east1-a</td>\n", " <td>2220</td>\n", " <td>compute#zone</td>\n", " <td>asia-east1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>asia-east1-b</td>\n", " <td>2221</td>\n", " <td>compute#zone</td>\n", " <td>asia-east1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2014-07-15T10:44:08.663-07:00</td>\n", " <td>asia-east1-c</td>\n", " <td>2222</td>\n", " <td>compute#zone</td>\n", " <td>asia-east1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2016-08-15T17:05:00.316-07:00</td>\n", " <td>asia-northeast1-c</td>\n", " <td>2252</td>\n", " <td>compute#zone</td>\n", " <td>asia-northeast1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2016-08-15T17:05:00.313-07:00</td>\n", " <td>asia-northeast1-a</td>\n", " <td>2250</td>\n", " <td>compute#zone</td>\n", " <td>asia-northeast1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2016-08-15T17:05:00.315-07:00</td>\n", " <td>asia-northeast1-b</td>\n", " <td>2251</td>\n", " <td>compute#zone</td>\n", " <td>asia-northeast1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2017-02-07T04:53:31.630-08:00</td>\n", " <td>asia-southeast1-b</td>\n", " <td>2261</td>\n", " <td>compute#zone</td>\n", " <td>asia-southeast1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2017-02-07T04:53:31.627-08:00</td>\n", " <td>asia-southeast1-a</td>\n", " <td>2260</td>\n", " <td>compute#zone</td>\n", " <td>asia-southeast1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2017-04-18T06:18:01.025-07:00</td>\n", " <td>australia-southeast1-b</td>\n", " <td>2282</td>\n", " <td>compute#zone</td>\n", " <td>australia-southeast1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2017-04-04T07:53:44.445-07:00</td>\n", " <td>australia-southeast1-a</td>\n", " <td>2281</td>\n", " <td>compute#zone</td>\n", " <td>australia-southeast1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2017-04-04T07:53:44.445-07:00</td>\n", " <td>australia-southeast1-c</td>\n", " <td>2280</td>\n", " <td>compute#zone</td>\n", " <td>australia-southeast1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2014-09-02T15:52:35.238-07:00</td>\n", " <td>europe-west1-c</td>\n", " <td>2103</td>\n", " <td>compute#zone</td>\n", " <td>europe-west1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2014-12-15T13:42:35.748-08:00</td>\n", " <td>europe-west1-d</td>\n", " <td>2104</td>\n", " <td>compute#zone</td>\n", " <td>europe-west1-d</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>europe-west1-b</td>\n", " <td>2101</td>\n", " <td>compute#zone</td>\n", " <td>europe-west1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2017-03-14T04:48:48.304-07:00</td>\n", " <td>europe-west2-b</td>\n", " <td>2291</td>\n", " <td>compute#zone</td>\n", " <td>europe-west2-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2017-03-14T04:48:48.304-07:00</td>\n", " <td>europe-west2-c</td>\n", " <td>2292</td>\n", " <td>compute#zone</td>\n", " <td>europe-west2-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2017-03-14T04:48:48.302-07:00</td>\n", " <td>europe-west2-a</td>\n", " <td>2290</td>\n", " <td>compute#zone</td>\n", " <td>europe-west2-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2014-10-29T11:20:06.229-07:00</td>\n", " <td>us-central1-c</td>\n", " <td>2002</td>\n", " <td>compute#zone</td>\n", " <td>us-central1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>us-central1-a</td>\n", " <td>2000</td>\n", " <td>compute#zone</td>\n", " <td>us-central1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>us-central1-b</td>\n", " <td>2001</td>\n", " <td>compute#zone</td>\n", " <td>us-central1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2016-06-14T17:29:18.761-07:00</td>\n", " <td>us-central1-f</td>\n", " <td>2004</td>\n", " <td>compute#zone</td>\n", " <td>us-central1-f</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2015-09-03T15:15:02.330-07:00</td>\n", " <td>us-east1-d</td>\n", " <td>2234</td>\n", " <td>compute#zone</td>\n", " <td>us-east1-d</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2015-09-08T10:59:59.040-07:00</td>\n", " <td>us-east1-c</td>\n", " <td>2233</td>\n", " <td>compute#zone</td>\n", " <td>us-east1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2015-09-08T16:57:06.746-07:00</td>\n", " <td>us-east1-b</td>\n", " <td>2231</td>\n", " <td>compute#zone</td>\n", " <td>us-east1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2017-02-14T06:05:24.188-08:00</td>\n", " <td>us-east4-b</td>\n", " <td>2271</td>\n", " <td>compute#zone</td>\n", " <td>us-east4-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2017-02-14T06:05:24.188-08:00</td>\n", " <td>us-east4-c</td>\n", " <td>2272</td>\n", " <td>compute#zone</td>\n", " <td>us-east4-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-02-14T06:05:24.188-08:00</td>\n", " <td>us-east4-a</td>\n", " <td>2270</td>\n", " <td>compute#zone</td>\n", " <td>us-east4-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2017-03-14T04:48:48.314-07:00</td>\n", " <td>us-west1-c</td>\n", " <td>2212</td>\n", " <td>compute#zone</td>\n", " <td>us-west1-c</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2016-06-10T09:30:55.192-07:00</td>\n", " <td>us-west1-b</td>\n", " <td>2211</td>\n", " <td>compute#zone</td>\n", " <td>us-west1-b</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2016-06-10T09:30:55.189-07:00</td>\n", " <td>us-west1-a</td>\n", " <td>2210</td>\n", " <td>compute#zone</td>\n", " <td>us-west1-a</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>https://www.googleapis.com/compute/v1/projects...</td>\n", " <td>UP</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " creationTimestamp description id kind \\\n", "0 2016-06-14T17:29:18.761-07:00 asia-east1-a 2220 compute#zone \n", "1 2016-06-14T17:29:18.761-07:00 asia-east1-b 2221 compute#zone \n", "2 2014-07-15T10:44:08.663-07:00 asia-east1-c 2222 compute#zone \n", "3 2016-08-15T17:05:00.316-07:00 asia-northeast1-c 2252 compute#zone \n", "4 2016-08-15T17:05:00.313-07:00 asia-northeast1-a 2250 compute#zone \n", "5 2016-08-15T17:05:00.315-07:00 asia-northeast1-b 2251 compute#zone \n", "6 2017-02-07T04:53:31.630-08:00 asia-southeast1-b 2261 compute#zone \n", "7 2017-02-07T04:53:31.627-08:00 asia-southeast1-a 2260 compute#zone \n", "8 2017-04-18T06:18:01.025-07:00 australia-southeast1-b 2282 compute#zone \n", "9 2017-04-04T07:53:44.445-07:00 australia-southeast1-a 2281 compute#zone \n", "10 2017-04-04T07:53:44.445-07:00 australia-southeast1-c 2280 compute#zone \n", "11 2014-09-02T15:52:35.238-07:00 europe-west1-c 2103 compute#zone \n", "12 2014-12-15T13:42:35.748-08:00 europe-west1-d 2104 compute#zone \n", "13 2016-06-14T17:29:18.761-07:00 europe-west1-b 2101 compute#zone \n", "14 2017-03-14T04:48:48.304-07:00 europe-west2-b 2291 compute#zone \n", "15 2017-03-14T04:48:48.304-07:00 europe-west2-c 2292 compute#zone \n", "16 2017-03-14T04:48:48.302-07:00 europe-west2-a 2290 compute#zone \n", "17 2014-10-29T11:20:06.229-07:00 us-central1-c 2002 compute#zone \n", "18 2016-06-14T17:29:18.761-07:00 us-central1-a 2000 compute#zone \n", "19 2016-06-14T17:29:18.761-07:00 us-central1-b 2001 compute#zone \n", "20 2016-06-14T17:29:18.761-07:00 us-central1-f 2004 compute#zone \n", "21 2015-09-03T15:15:02.330-07:00 us-east1-d 2234 compute#zone \n", "22 2015-09-08T10:59:59.040-07:00 us-east1-c 2233 compute#zone \n", "23 2015-09-08T16:57:06.746-07:00 us-east1-b 2231 compute#zone \n", "24 2017-02-14T06:05:24.188-08:00 us-east4-b 2271 compute#zone \n", "25 2017-02-14T06:05:24.188-08:00 us-east4-c 2272 compute#zone \n", "26 2017-02-14T06:05:24.188-08:00 us-east4-a 2270 compute#zone \n", "27 2017-03-14T04:48:48.314-07:00 us-west1-c 2212 compute#zone \n", "28 2016-06-10T09:30:55.192-07:00 us-west1-b 2211 compute#zone \n", "29 2016-06-10T09:30:55.189-07:00 us-west1-a 2210 compute#zone \n", "\n", " name region \\\n", "0 asia-east1-a https://www.googleapis.com/compute/v1/projects... \n", "1 asia-east1-b https://www.googleapis.com/compute/v1/projects... \n", "2 asia-east1-c https://www.googleapis.com/compute/v1/projects... \n", "3 asia-northeast1-c https://www.googleapis.com/compute/v1/projects... \n", "4 asia-northeast1-a https://www.googleapis.com/compute/v1/projects... \n", "5 asia-northeast1-b https://www.googleapis.com/compute/v1/projects... \n", "6 asia-southeast1-b https://www.googleapis.com/compute/v1/projects... \n", "7 asia-southeast1-a https://www.googleapis.com/compute/v1/projects... \n", "8 australia-southeast1-b https://www.googleapis.com/compute/v1/projects... \n", "9 australia-southeast1-a https://www.googleapis.com/compute/v1/projects... \n", "10 australia-southeast1-c https://www.googleapis.com/compute/v1/projects... \n", "11 europe-west1-c https://www.googleapis.com/compute/v1/projects... \n", "12 europe-west1-d https://www.googleapis.com/compute/v1/projects... \n", "13 europe-west1-b https://www.googleapis.com/compute/v1/projects... \n", "14 europe-west2-b https://www.googleapis.com/compute/v1/projects... \n", "15 europe-west2-c https://www.googleapis.com/compute/v1/projects... \n", "16 europe-west2-a https://www.googleapis.com/compute/v1/projects... \n", "17 us-central1-c https://www.googleapis.com/compute/v1/projects... \n", "18 us-central1-a https://www.googleapis.com/compute/v1/projects... \n", "19 us-central1-b https://www.googleapis.com/compute/v1/projects... \n", "20 us-central1-f https://www.googleapis.com/compute/v1/projects... \n", "21 us-east1-d https://www.googleapis.com/compute/v1/projects... \n", "22 us-east1-c https://www.googleapis.com/compute/v1/projects... \n", "23 us-east1-b https://www.googleapis.com/compute/v1/projects... \n", "24 us-east4-b https://www.googleapis.com/compute/v1/projects... \n", "25 us-east4-c https://www.googleapis.com/compute/v1/projects... \n", "26 us-east4-a https://www.googleapis.com/compute/v1/projects... \n", "27 us-west1-c https://www.googleapis.com/compute/v1/projects... \n", "28 us-west1-b https://www.googleapis.com/compute/v1/projects... \n", "29 us-west1-a https://www.googleapis.com/compute/v1/projects... \n", "\n", " selfLink status \n", "0 https://www.googleapis.com/compute/v1/projects... UP \n", "1 https://www.googleapis.com/compute/v1/projects... UP \n", "2 https://www.googleapis.com/compute/v1/projects... UP \n", "3 https://www.googleapis.com/compute/v1/projects... UP \n", "4 https://www.googleapis.com/compute/v1/projects... UP \n", "5 https://www.googleapis.com/compute/v1/projects... UP \n", "6 https://www.googleapis.com/compute/v1/projects... UP \n", "7 https://www.googleapis.com/compute/v1/projects... UP \n", "8 https://www.googleapis.com/compute/v1/projects... UP \n", "9 https://www.googleapis.com/compute/v1/projects... UP \n", "10 https://www.googleapis.com/compute/v1/projects... UP \n", "11 https://www.googleapis.com/compute/v1/projects... UP \n", "12 https://www.googleapis.com/compute/v1/projects... UP \n", "13 https://www.googleapis.com/compute/v1/projects... UP \n", "14 https://www.googleapis.com/compute/v1/projects... UP \n", "15 https://www.googleapis.com/compute/v1/projects... UP \n", "16 https://www.googleapis.com/compute/v1/projects... UP \n", "17 https://www.googleapis.com/compute/v1/projects... UP \n", "18 https://www.googleapis.com/compute/v1/projects... UP \n", "19 https://www.googleapis.com/compute/v1/projects... UP \n", "20 https://www.googleapis.com/compute/v1/projects... UP \n", "21 https://www.googleapis.com/compute/v1/projects... UP \n", "22 https://www.googleapis.com/compute/v1/projects... UP \n", "23 https://www.googleapis.com/compute/v1/projects... UP \n", "24 https://www.googleapis.com/compute/v1/projects... UP \n", "25 https://www.googleapis.com/compute/v1/projects... UP \n", "26 https://www.googleapis.com/compute/v1/projects... UP \n", "27 https://www.googleapis.com/compute/v1/projects... UP \n", "28 https://www.googleapis.com/compute/v1/projects... UP \n", "29 https://www.googleapis.com/compute/v1/projects... UP " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zones=compute_service.zones().list(project=projectCodeWidget.value).execute()\n", "from pandas import DataFrame\n", "DataFrame.from_dict(zones['items'])" ] }, { "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
bjshaw/phys202-2015-work
assignments/assignment04/TheoryAndPracticeEx02.ipynb
1
169249
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Theory and Practice of Visualization Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Violations of graphical excellence and integrity" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Find a data-focused visualization on one of the following websites that is a *negative* example of the principles that Tufte describes in *The Visual Display of Quantitative Information*.\n", "\n", "* [CNN](http://www.cnn.com/)\n", "* [Fox News](http://www.foxnews.com/)\n", "* [Time](http://time.com/)\n", "\n", "Upload the image for the visualization to this directory and display the image inline in this notebook." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "bd4340d93d2efdf5c3864b5caca1f6ba", "grade": true, "grade_id": "theorypracticeex02a", "points": 2 } }, "outputs": [ { "data": { "image/jpeg": 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1Yq23LaksZqzzu1ETjEzs4Bh004et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\ntbp1t3bpbNBGbN8D/PJ9HOH6jfdZ9HH1qs2ytFcNoUJEBBzFvLdG47e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2JXgv8AE5rwW1426uIPDxDuQdNtHK0N1J0Gmp7T7UH6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\nTw7NUHvuTP7izPUfYe7INp5mLbWDsX607pabxedLkKZhEhqAm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EBAQEBAQEBAQEBAQE\nBAQEBAQEBAQEEFurYmzt2V219x4itk2R6+U6dgMjNe3y5Bo9mv6Lgg1TGfLl0UxtkWa21q75AQ4C\nzLYtM1HH93YllZ+ZBY0MMMMTIYWNiijAbHGwBrWtHAAAcAAgwc3t7AZ6qynnMZUytSOQTMr3YI7E\nbZAC0PDJWuaHBriNfWUH3iMJhcLSFHD0K2NpNcXtq04Y4Ig53FxDIw1up7+CD0yWMxuUpS0MlVhu\n0Zxyz1bEbZYnt9DmPBafpQV5Z+WvohZsixJtaFsgOvLFYtxR8Dr+7jmaz8yDdNs7P2ttekaW3sXW\nxlZx5nsrxhhefS93vPPrcSgmEGp7v6T9Od4SifcWBrXrIHL8Vo6Gct7gZoXRyEDu8XBBWfy5dGtl\nwbAwW4c1tpjd2ONl1iW+yUyMdHblZE7yJyWRkRNZykMHp7eKC+EBAQEBAQEBAQEBAQEBAQEBAQEB\nAQEBAQEBAQEBAQEGq9WP9lm8v+Q8l/wORBWNqzuSRnRDB4bL2MTHmMVNFekgeQHRRYuCRzvLPNG+\nRrA/yi9pDHkO0QS1nF5XavU7b+2YNxZjIYPetHKwW4792SeetNTgbK2xUnd95E4iTTRp0Hb6EEB0\nmw1LbVfqNuyTJZaw3a+ezetGS/M6vZZVqxvMlmJ7uSWdwJ1kfx1APcgwJdyYuTYP9cf+lfl36KP4\nq3FNzFcURYDPPFD8MB5S3+5cpbz696DYt67l3duDNdIX7byk2Fduutcs2xE4mMRS4+KdznQuPlyv\nhY97ovMBAfoUH7ua7V25uHC9PLu+b2LxVitazWY3BlMjDHflZ5rYoKkNuUMEYdJzOIYOblaQ3Qao\nPvZ26sZh+qeM2zgt6O3ft/cFK08RT5GPKz07tPSXXz2F8jY5Yi7Rsh7QdDw0QQ+3485vrC5m7NvP\nJ4jqZBPdbW2xBfFSGlLXkeKsD6XASxuaxpdI9p5tTxQZfWbbORyNbplezt+7VzVvN4bG5StRuSxV\nY55Ip3zT12RkNZO2QkMmb4g3h2ILrw2LZisXXx7LNm22s3kFm7K6xYfxJ1klfq5549pQVhTq5jqR\nvTdHnZ/J4jbG2rn4NQp4ey+k+a3HGyS1NPPF438rnhrWa6AevXUMGDcm6Mfs3qttbIZWe7ltm0LE\nuMzZcI7bqtvHyWKjnvj5T50RYR5g0JI17UELuiPduF6O0uqLN15Z+4oKmOyDqRsF2NfHZfC34Z1Q\ngNc3kl0MjtXk+Inig6DQUvufL57F/Mb5+EwMu4LT9nxMkqQ2K1UsjOTlJkL7L42EAgDQceKCU3d1\nT3vg9k7gzeS2fNgDQqA0blm7RtRutTzR14WuZXkkdo103OdRpo3TtIQaVujdO3tnYSHdWD6pP3Fn\n8fLXfksTNmILte/FJI2OyyKiw8sZDXl7PKaOXRBuua/Gd7dU8ltWLNX8Nt3bFGrPkRipzVs2L18u\nfE19hn3jY2Qx68rdNT29yDXaMFzZ/XjJS5PI2MtjsZsizcp2bkgktCtHfjkfFNLo0vc2Rry17vqk\nA66II7F5nBbi2lDuPK9Wjid55CubsVGDN1atKnNI3ngrOolwYWxjlbIJQXE66nVBl7m3/u3cfSrp\njnsJkHYzOZ7P4+nYnic9kLpSyzDKJmN5fMhM0XM6M+E6aetBt2V2HubGY2tUh3xfhw1i0bW6c5kr\noF1kTYg2OCg7yhDXZLNpz8W8o93XXRBrG2N2YvG9Xtvbc2ru69ujCZqvkGZVl62/JRQzVIhNE+va\nfzauOjg9rXkAaekINm6Gfveov/PXK/5OugfMx/sU3B/jMf8A6yrILOexj2OY9ocxwIc0jUEHgQQU\nHNezt6yYrov06wAzUe3mbhu5GpbzkssUJq4+lasul8qWxqxkjmtZGxx7CeHHRBPRbqwG1977Wbtn\nqA/dONz18YrL4i1loss9jrDHfD2YdDJLEGyt5XaeHiBwQeXTPaGS3JnOo0c+byWKwtfdmR8iHE2H\nUpJLL/L818s0f3hDWNj5WggcTrrw0D0uHL5XYfUnZufzF6zd2T58tbIxTur2LVObHvsVI7hh5PNa\nWuLZAeD9Bqg3boZtuHE9OsBcjv5C47KYnGzvhu2pbMMB+Fa7kqxyEthZ49OVvDQD0ILCQEBAQEBA\nQEBAQEGibx3Nm8d1Q6e4SnZ8rF5x+XGUr8kbvNFSl50Pjc0vZyycfARr36oHXPc2b2x0qz2dwdn4\nPK0mQGtY5I5eUvsxRu8ErXsOrXkcWoN7QEBAQEBAQaJ1y3Nm9sdL8xm8HZ+EylV9MQWOSOXlE12C\nF/gla9h1jkcOIQb2gICAgICAgICDVOlm9Zt7bCxO6JqraUuSbK51VjzI1nlTvh4OIaTr5evYg++p\n+7be0Ng5rclSsLlrG1/Mhgdryl7nNYHP00PIzm5nadwQaPJm+om081syXKboh3VQ3Vdjx1nHtpwV\nTH58LpBaqSQhr3RRFvi8zXwkd54BcCDRN47mzeO6odPcJTs+Vi84/LjKV+SN3mipS86HxuaXs5ZO\nPgI179UG9oCDRN47mzeO6odPcJTs+Vi84/LjKV+SN3mipS86HxuaXs5ZOPgI179UG9oCAgICAgIC\nAgICAgICAg0vq5vPM7M2mzcONggngq3ajcsLDXuDaM0oilezkczle0vbo46gd4KDacrk6+NxNzKT\nHWtTryWZHA8OSJheTr7Ag1/pZuPPbm2BhtwZ2GCvkcpCbLoKrXsibFI9xh0Ej5HamLlJOvag2tBr\n2zt50t0tzTqsEkH4JlreFn83l8ctJwa+RnKT4Hc3DXig2FAQEBAQEBBEbwy9nC7SzeYqtY+zjaFq\n5AyUExmSCF0jQ8NLSWkt46EIK/wGX+YDN7Wxe4Kcu1HNylKvfhpSV8jE7lsRNlEZlFiUBwD9NeUj\nVBtvTXfLd57ZGTkqHH5GtYmoZbHOcHmvcrO5JY+Ydo7HD1FBtSAgICDReo+9NyYjM7Y23tmvTkzm\n57FmOGfIeYa0MNODz5nuZEWPceXsAcEErj7e/RuyClkKdR23Bho5bOUr6tccx53LLDGx8rniHyvG\n3WP9vuQemzt50t0tzTqsEkH4JlreFn83l8ctJwa+RnKT4Hc3DXig2FAQEBAQEBAQEBAQEBAQEBAQ\nEBAQEGq9WP8AZZvL/kPJf8DkQVrU/wDeL5fP+Sr3+o40Gy79/wBuPSv/ABe4f+BQoNW2fYxdmTqt\n02uWRS3NuPM5ubH0ZmvDpKt6mxsc7ToGlha0n3kGJh9+7GxvTmpgzgYJOp1GizGt21JjzJbkyUMY\nga933RDonPb5hfzcvL9bVBsu44bsHU7otBfbEy9FDmGWmV2hkLZW4loeI2jg1gdryj0IPrqXXxmA\n6nYPfGeoNubYmxk+Dy1p8BtMpOMzbNeeRgD+VjnBzC/l4a8e1Bk7U3Lh9xdQKh2PjKT9p4+pNJlc\n/HSETZLUmjIa9WctZq5rS50nKCNOGqDUMhvTpduTbk1Xq1jK9Df9J88NmnWqWIbvmwvcIHUJW+bM\n4OZyFpEhbr28EH1uV+5sf0m6V5Tejpm3cNuHFXtwWpmvfJBWjFgNks6Bz+ZrHxtkJ483bxQXph8x\njM1jK+UxdhtrH22eZWsM15Xs105hqAdOCCpsLunC9MN6bxxe7JXYvEZ/JOz+Fy0sb3Vp3Woo22Yf\nNaHcskckfuHu4+hBGRss5faHWffZqy1MduXGTQ4hthj4pZqmNxksLLPI/QhkrnuLNQDog9Opv/ZG\nZ/yHhv7eqgvRBWDf+03J/wAyo/8AWr0Ex1s2zktzdLc/h8ZH52QlhjmqwDiZJKs8dlsY4t4vMPKO\nKDRMn1E2Lm8PUx2xMLStb4yMleL8JlxurqHM9vxL7odExrGws5u0jU9nBBJ5HO4/p31iz2b3E59P\nbe76NEx5gxvfXiu44Ph+HlewO8svjfzN14FBDtuVt+9aMqKLHswmT2PexdHISNfGLIddiEs0bXhr\n/La6fkB04lp01GiDw2lvnpzt7YtLAbhwkMe/cLUbjpdvux5kuWrNZnlROh0jd5wscrXB4dpx1QZ+\n84c5FtDpG3O1q9PMO3biJL1SpGIYYpJGWXmNrG6gFvNo7Tv1QSfXGXG1tybEvbmhM+xaty4/OB8T\npqzLBrgUJLDGgjkZJz+8CPUg13N9QNq3+r/TbI4v7vaVA5OjDmGwSRU5Ll6s1jIISWt5uIZ4mjl1\ndprwOgbf0M/e9Rf+euV/yddB+fMyQ3onuBzjoA/Hkk9gH4lWQM51/wBivxVmLaV/+sW45Y3R4rF4\n+GWeSSw8csfPo3lYwOILnOPZr2oNTyewLGy9m9M8jkaH4tBsyWb+sdVkZtckOUieLc4iHP5oryvD\nuAPZqAgnau79q7i3bt+h03x2PyFeOz8TuHMx0AIalWJhLY2zFkfLPLJoGAakaIITpt1DwG0c11Di\n3NY/DcXZ3VkZ6WRkjkMD5g5kc0Jka1wa9rWxuDT2h3DsOgZ238dktw7d6s7ugpTsZvCGaDBQSRvj\nmsVqWOdVryiJ4DgJnE8mo1P5EG1dC927eznTnBUMXcbYu4TGUKWVrhrmvgsR1xG6N4cBx5onILCQ\nEBAQEBAQEBAQEFYdQf8AbX0n/wAZnv8AVqB8zP8AsO3R/i6v/DYUETunblPcfzFVsXkJJPww7SM9\n2rG98YssZki1sMjo3Nd5fPI15APHlAPAkIMXHx47pr1Q3PisBEau2mbRO5H4nnkdCy3WsSROdE1z\nncgexniDe0oNOw1ropnNnVslui5kbO/MlV+KtbgFPNOnr3J2c7fh5YYTG1kBIawR+DRveEE1uDMZ\n3cvSnpJayctill8huTH0r1nQxWQ4xW6kso14tke0Fwd3E6oJDcvTvbuC6q7OwOAFjE4fd8GUh3JT\nrWJ2ttx4+KKxHzv5/MD3OcWveHcxaSNeJQS208Bitn9e7W3NuRHH4HI7YGUsY1j3ug+MivtriVjH\nFwaTG7Q6dqDWehPSXau6ultDJ7phflZJ5LceMY+aVraUEVqWPSu1jmtY90rXyF/vant0AADC3Hlc\nna+WTdmJydp927tjMtwMlyUl0kraOXrNjc9x953luaNfUg3W3gsd1E6u7lxG5Q67t3Z9bHw1sKXy\nNryW8hE6y6zMxpaHuYzRjdeAQavuHaBrS7/6UUJJX4a1gY9y7YpzPdMKc0U7mvhhc4lzI3WImlrd\nfDx07SgnN655m+unvTzHB3Od738Y7IRMHDyKzPjbreGg8D4NEHxjtl4XdfXDqTWzrHXMVUjwrnYs\nve2CaWak4MklaxzefymxuDQeHiJ7QEGF0s6bbd3LDuvG7jE2XwW2twX8NgMRammdBVrwubLqAHjn\ncfPDQXa8oaNO0oISLdt7bHSfN4KHJWalSrvaxtLH5FplmtVcb8QHOMbmB8rnshEgZyjm7OXjog8d\ny5XpFgYKOc6ZxX6u6sfdqvdHFSzDDeqmVrLMFh1mNscgMTi7WR2vDgdSg3fcm3MfuX5iHYfK88uI\nO0YbNui1742WHRZKdkbJiwtc6MGYu5NdCQNexB57C2ht527upnTqar8Tsum/DWKmFne+SGJ9qF1i\nRsfMeZrPNhY4N14aIPv5WdqbdpdLcLuGpQjhzOThnjv3W6h8rY7crWB3HTgGDuQbp1S3VUwm3W0X\nY78ayW4JPwnF4QkNFqaw0gtkcfciazUyO7ggrbpPtGxsjftPCb4l/Ec2/H8my8oXySVIa8YJuY+s\nJeLJItR4j4nx+gDQhcOz92Yjdu3Km4cO6R2Ou+Z5DpW8jz5UroXHl48OaM6epBpPUH/bX0n/AMZn\nv9WoLPQUBd2/0k+KyNLPvt9Qt/OsTm5YxsNuSeGR5c6OGLynywU2ws5WaOlGhGp9ADWcQ/Mb0x/Q\nhuRvzm3e/HYbtxsjmTyVoIzHK3zQecOkrxFhcDzcdddUFgbg2rg+mu89mZjacBxdHOZZuCzePikl\nMFn4yKR0Er43OcPMjkj1D+308EHztvaOC6m7n3jmt4QnLVMRmbGBwuMlklFerHQaxssrI2uaPMmk\ncS53b6EGlb3sXcb0j6sbEsWprsG1LWOGLmsvMsrKF+WvYrwukPF/leIDXjpoO4ILw2X04wu2p5cr\nrNd3NfhZHmMzYnmlksOB5j4XuLGMDvdaxoAHBBtiAgICAgICAgICAggd/bbbubZWbwDhq7JUpoIu\nwaSuYfKdx4eGTlKCos7vC3nPlgw/wztcxueGlt2EcQX2pZhTnGnvEkRScEEzvbOYjH7swHTm1uf+\nqO3qOF+Ot32XIsfNYbHIKlWrFYeQY/3b3v5CCQOHYUHnsPPUrG+M50+xm75tz7eu4U5KllY8gLl6\nlI6X4SxC29ES8OHmMkj1PM3tQajskRbF2j1M3pDkclZs7fz+ap1adm7NJVnlD44oZrMLjyyzOkkb\nzSHxFBl7iy+ExG0rG5MV1dOQ3vQr/GvpuzVSejanjHmTVm45jvK5H6OZG2NvN2aE94bBv3Obnz+7\nOlUO2sxPhIN0U8nYtPie4s8l1SvNzGJw5JJI43v8ovb4Xnm0QS+b2XlcayhRyG+ruM2PTjnlv5S1\nk/Ky1m7LJrHE+2+ONsdeOPmI5Hh3dpoNUET0r3dXf1Tym1MJuO5uba/4PHk69vISutSRWW2fIkZD\nZeA6WItcDrqRzcAe1BH9Jtq7n3z0voZnL7yzle7KbceMfSvSQ8ghsyxh9l2hfYf5jT77i3k5WgcD\nqGIzqTuXN9N+mt3L5WXBYrcM89bdW5qzmwvi+F8yOBvnEcsHxckXieNOXuQb/sXbu4sNu2w+jn7O\n4Ng38c2WvPkLv4hPFkWzcpbDM4ueYXQ6k8SOZBP9Tf8AZvuv/kbIf8FkQVts3fvU3G9K9ujGdOrV\n+OthqLKlz8QpckzG1o2sm8mN8lnlcNHcgZzd3AoI6luentjo3SyGJz0TMlvXcHl5XcEjW1o6V7JT\nufekdHOPu/h44XtHmd4DvdQez90be2rura0u1uoUm6os1lIMPl8Pby8WVJZc1bHaiY1znQmOUN15\ndG6HTRBLVaG4d09Yd/YOXcWSx+3sZHiZBVo2XwS+ZYqEtbDJ4jDGS17pBHyl7uXU8DqGBU35nNi4\nvqnjreQnzceyfhJcJcyUnnWD+J1hJDDPKQHSCOVwGp8RB9iCI3Fl8JiNpWNyYrq6chvehX+NfTdm\nqk9G1PGPMmrNxzHeVyP0cyNsbebs0J7wy97YKlunqd0ty7MplakW6YL9rlq354fhmsxTJW/Ccpb5\nBk1+9LQOfvQbVyZB3WqXaJy2RGHOx2NDRbmEgmN99c2w/m/zny/7t7/rQal0m29cw2I6j5/D3Mpk\nMvhc3uCvjsZLcnmr25oGAwvngJImnkfoDIfEUHvtrHbhz2z8Xu3Z2+b+b3rH8Jay2Js5AfAPfI9v\nxlSakQGVg1peGaMB8PD0oL6QEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQ\nEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBBXXVfCZ8Zbae88FQky1zalyd9nGQlgmlpXYDBZMIe5rXSM\nboWt70GudQs5mepe3HbJwm1s1RGXnrtyeTzFJ9CtVqxTMmlcHSuaZJPuwGtYDqg2N2Kyf/WIjy3w\nc/4WNovqm/5b/h/iDk2SeT5unJ5nIObl1104oMPMbXu5brhkjYqTjCZDZDsXLkPKcYPNmvyc0PmE\nchk8t3Nya66cUEXtfee9Nq7Kq7NtbQy93dWGrDGY6xVreZjLIhb5VWb43URRsLA0v8zRw48EHru/\nbe8ztfpdVyZnzecx+58VaztyCMyBgayczSv8tujYo3PDecgDs9KCb3risnY6x9NchXpzzUKDM6L1\nuON7oYDNUibF5sgBazzHAhvMeJ7EB2Kyf/WIjy3wc/4WNovqm/5b/h/iDk2SeT5unJ5nIObl1104\noK+6C78yu3OlNCvc23l8rXkmuyYefE1XXGPYbczXRSlh+5eJ2v8Af0bykHXt0DI3FsPdlb5dNw1L\nOPln3VuHJszV7GVGGxK2a1lYJnRgRc5eY4WDmI7ND3DVBtWTOa2N1QzW5mYW9mdubrq0xckxUBt2\nat6gx0LOeuz7x0ckJHiaDoRxQZXT/H5zN7+zvUHLYufD1rNODD4CjeaGXPhIJHzSzTRdsXmSu1aw\n8eHHuQaX0kwt2Pqnb23ZhIxvTb8TbjHuGh/4+sMsVj7v/kwkb2+zgg3nZWKydfrH1KyFinPDQvsw\nQo25I3thnMNSVsvlSEBr/LcQHcp4HtQOjWKyePk34b9OeoLm7snaqefG+Lzq8jIAyaPnA543cp0c\nOB0QaRW2Huu9tDeL6VCSHPY7f1zcm369thgba+GsRyRFjpQAWTM5gx/uk9+iDZ8rvjeu7oqGF2rg\nM1t27PZgfmMtlKnwsVOpG8PnbE+XmbPI8DkaI9e3XUIJBuKyf/WIky3wc/4Wdosqi/5b/h/iBk3y\neT5unJ5nIebl1104oGysVk6/WPqVkLFOeGhfZghRtyRvbDOYakrZfKkIDX+W4gO5TwPagj/l4fks\nVsalszLYbJY3K4Fs7bc9uq+KpIX2pHt+HsHwTDleD4UEj1YwO435La28NvUjlbu1Lk00+Ha9rH2K\ntuEwT+Tz+EysbxYD69OPAhrfUDKZbqVhYduYDamZx+VNmGxDnsvWkxkOMfC4E2GSOPPLIBqwMi11\n1PHRBuXRttit0+xeItYifC3MIz8Nt1JmPa10tbwvnhe4ASxTH7xr26g6oIDqxLfx3UfpzuKPE5HK\nY7EPzByH4XUmuyRizTbDFzMha7Tme7v9foQbBiOqdLJ5Otj2bb3JVfZeIxYuYi1Xrs1+tJK9oaxv\nrKDQ+mea3Ts/Zo2S/aOWm3bWntsbkG1R+HWpp53vZclvaiIM0eC/Ul2g00PBBpe0cTvvG47pXHX2\nzlfxHZlnONy0EtWSJj45dHnyppOWMiWCRzYnFwDn+FBZWSt5fqLvPasNbA5TFbf23fOYyd3L1XU+\nezBE9lWGCOQ88h55CXOA5QO9B84q9mem+6N01be38pl9v5/JSZvFZDD1XXXNmtsb8TXnii8cZEjN\nWOcNCD2oNV3Zs7eOS6R9SM9cw1hm49526lirg4mGxcjp1JYIq0cjYg4mQRsc9zR7o9eqDodAQEBA\nQEBAQEBAQEBBzbtuhZf1uj6cvhccTtrO5HdzW6fdths14nUWs7f3Vi286n0oNz6hsw+3ereL3jua\nmyfat7DSYS3dlg8+GnZjs/FQSTDSTlbI172B3Lw7yglen+4MfuDeuRubVxtWLZVSiyCPMRVBXdbv\nyS88jYJC2Nz4Y42gO0Ghd3oK5r1Kec2v1Z6aicRbvymfzGTxuJkD2SSwiWKzXkaSA3kkMQ0OunFB\nOX+p2wLm1TX27t+rY6i2IRBBth2M0sQXnDleJ43RtDY4Xauc5ztNB2oJvdEFiv1d6PQWRGLMVTOM\nnEDeSLnbQgDvLb9Vmvuj0IMLqRkNuYnrNhcpvuNv9UY8LLHibNqJ01ODLGzrI5/BzGvdXDQ0uCDC\n29vXD5L5i4r8bZKmIyW2fwvbtmaGSFl10F0zvMDXtaeUeMDUDUN1HAjUNk+Wf/Ypt/8AxmQ/1lZQ\naF003hPtro305dkqEVrY+QOSrbntSV32TW1symo8taeUROk5hIXMd3diCZ2P/VGfq9TudKWlm2JK\nVp+8TTZLHinSnlFIRMcGwicP5tfLHu6/pILO6m/7N91/8jZD/gsiCvunnXPpTiunG2KNvcEP4hR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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='tax-dollars.jpg')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Describe in detail the ways in which the visualization violates graphical *integrity* and *excellence*:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "51d112a58baebcf9db9e75eb596a408c", "grade": true, "grade_id": "theorypracticeex02b", "points": 8, "solution": true } }, "source": [ "The first violation is the lack of title on the actual visualization itself. Without a title, it is unclear what the graph is about and time is wasted trying to discern the subject. The images of the nickels and pennies representing the number of cents for each category are unnecessary additions that add no new information to the image. They are just wasting space and taking up too much room. Also, the central image is untitled, so it is unclear without context what all these categories are leading too. For the everything else category, the text describing it is out of the way and small, which makes it easily missed. The categories are also all over the place, and are not arranged in any sort of logical way, meaning that they again waste space. This entire visualization's purpose seems to be more about looking pretty than to actually represent the data in a concise way. " ] } ], "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
opengeostat/pygslib
sandbox/gam from gslib exe.ipynb
1
211032
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id !== undefined) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var element_id = msg.content.text.trim();\n", " Bokeh.index[element_id].model.document.clear();\n", " delete Bokeh.index[element_id];\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(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) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._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", " root._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", " root._bokeh_is_loading--;\n", " if (root._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", " };\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(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.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", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(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) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._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 root._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 root._bokeh_is_loading--;\n if (root._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 };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(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.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}(window));" }, "metadata": {}, 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\" />'\n", "</script>\n", "\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import gam\n", "import pygslib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# test with arbitrary gslib files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "parameters = {\n", " 'datafl' : '../pygslib/data/true.dat', # path to file, or none (to use '_xxx_.in') or numpy array (with columns [x,y])\n", " 'ivar' : [1,2], # variables column numbers to be used in ivtail and ivhead, # variables column numbers to be used in ivtail and ivhead,\n", " 'tmin' : -1.0e21, # trimming limits min and max (raws out of this range will be ignored)\n", " 'tmax' : 1.0e21,\n", " 'outfl' : None, # path to the output file or None (to use '_xxx_.out')\n", " 'igrid' : 1, # grid realization number\n", " 'nx' : 50, # number of rows, cols and levels\n", " 'ny' : 50,\n", " 'nz' : 1,\n", " 'xmn' : .5, # coordinates of the centroid of first/corner block\n", " 'ymn' : .5,\n", " 'zmn' : .5,\n", " 'xsiz' : 1., # grid node separation\n", " 'ysiz' : 1.,\n", " 'zsiz' : 1., \n", " 'nlag' : 10, # number of lags\n", " 'igdir' : [[1,0,0],\n", " [0,1,0]], # [[ixd1,iyd1,izd1],...] directions along the grid (unit offsets) (array with shape [ndir,3])\n", " 'standardize': 1, # standardize sill? (0=no, 1=yes)\n", " 'ivpar': [[1, 1, 4, None],\n", " [1, 1, 3, None],\n", " [2, 2, 1, None],\n", " [2, 2, 3, None],\n", " [1, 1, 9, 2.5]]} # tail, head, variogram type, and cut (with shape [nvarg,4]) \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Parameters for GAM\n", " *******************\n", "\n", "START OF PARAMETERS:\n", "../pygslib/data/true.dat -file with data\n", "2 1 2 - number of variables: column numbers\n", "-1e+21 1e+21 - trimming limits\n", "_xxx_.out -file for variogram output\n", "1 -realization number\n", "50 0.5 1.0 -nx,xmn,xsiz\n", "50 0.5 1.0 -ny,ymn,ysiz\n", "1 0.5 1.0 -nz,zmn,zsiz\n", "2 10 -number of direction and number of lags\n", "1 0 0\n", "0 1 0 -directions along the grid (unit offsets) (array with shape [ndir,3])\n", "1 -standardize sill? (0=no, 1=yes)\n", "5 -number of variograms\n", "1 1 4 None\n", "1 1 3 None\n", "2 2 1 None\n", "2 2 3 None\n", "1 1 9 2.5 -tail, head, variogram type, cut (array with shape [nvarg,4])\n", "\n", "\n", "cut[i] is only required if ivtype[i] == 9 or == 10\n", "\n", "\n", "\r\n", "GAM Version: 2.905\r\n", "\r\n", " data file = ../pygslib/data/true.dat \r\n", " number of variables = 2\r\n", " columns = 1 2\r\n", " trimming limits = -1.000000E+21 1.000000E+21\r\n", " output file = _xxx_.out \r\n", " grid number = 1\r\n", " nx,xmn,xsiz = 50 5.000000E-01 1.000000\r\n", " ny,ymn,ysiz = 50 5.000000E-01 1.000000\r\n", " nz,zmn,zsiz = 1 5.000000E-01 1.000000\r\n", " ndir,nlag = 2 10\r\n", " direction = 1 0 0\r\n", " direction = 0 1 0\r\n", " flag to standardize sills = 1\r\n", " number of variograms = 5\r\n", " tail,head,type = 1 1 4\r\n", " tail,head,type = 1 1 3\r\n", " tail,head,type = 2 2 1\r\n", " tail,head,type = 2 2 3\r\n", " tail,head,type = 1 1 9\r\n", " indicator threshold: 2.500000\r\n", "\r\n", "Variable number 1\r\n", " Number = 2500\r\n", " Average = 2.580196\r\n", " Variance = 26.531800\r\n", "Variable number 2\r\n", " Number = 2500\r\n", " Average = 2.320084\r\n", " Variance = 7.318668\r\n", "\r\n", "Variogram 1 Correlogram : tail=Primary head=Primary \r\n", "Variogram 2 Covariance : tail=Primary head=Primary \r\n", "Variogram 3 Semivariogram : tail=Secondary head=Secondary \r\n", "Variogram 4 Covariance : tail=Secondary head=Secondary \r\n", "Variogram 5 Indicator 1/2 Variogram: tail=Indicator 1 head=Indicator 1 \r\n", "\r\n", "GAM Version: 2.905 Finished\r\n", "\r\n", "Stop - Program terminated.\r\n", "\r\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vg, fig, ax = gam.gam(parameters)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>average separation</th>\n", " <th>var funct</th>\n", " <th>number of pairs</th>\n", " <th>mean on tail</th>\n", " <th>mean on head</th>\n", " <th>variance tail</th>\n", " <th>variance head</th>\n", " <th>tail</th>\n", " <th>head</th>\n", " <th>type</th>\n", " <th>cut</th>\n", " </tr>\n", " <tr>\n", " <th>Variogram</th>\n", " <th>Direction</th>\n", " <th>Lag</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", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"20\" valign=\"top\">0</th>\n", " <th rowspan=\"10\" valign=\"top\">0</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>0.49386</td>\n", " <td>2450</td>\n", " <td>2.53364</td>\n", " <td>2.53909</td>\n", " <td>24.77946</td>\n", " <td>26.68244</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>0.35267</td>\n", " <td>2400</td>\n", " <td>2.49753</td>\n", " <td>2.52300</td>\n", " <td>24.33429</td>\n", " <td>27.06660</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>0.21134</td>\n", " <td>2350</td>\n", " <td>2.46945</td>\n", " <td>2.50771</td>\n", " <td>23.99160</td>\n", " <td>27.10748</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>0.14590</td>\n", " <td>2300</td>\n", " <td>2.45293</td>\n", " <td>2.49312</td>\n", " <td>23.63398</td>\n", " <td>27.23370</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>0.14299</td>\n", " <td>2250</td>\n", " <td>2.46641</td>\n", " <td>2.49146</td>\n", " <td>24.02911</td>\n", " <td>27.46606</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>0.09852</td>\n", " <td>2200</td>\n", " <td>2.48491</td>\n", " <td>2.50075</td>\n", " <td>24.45203</td>\n", " <td>27.90143</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>0.06420</td>\n", " <td>2150</td>\n", " <td>2.50757</td>\n", " <td>2.52079</td>\n", " <td>24.82824</td>\n", " <td>28.44364</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>0.05674</td>\n", " <td>2100</td>\n", " <td>2.53510</td>\n", " <td>2.53821</td>\n", " <td>25.29958</td>\n", " <td>29.01886</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>0.03009</td>\n", " <td>2050</td>\n", " <td>2.55013</td>\n", " <td>2.56459</td>\n", " <td>25.46875</td>\n", " <td>29.58677</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>0.04508</td>\n", " <td>2000</td>\n", " <td>2.57868</td>\n", " <td>2.55968</td>\n", " <td>25.91320</td>\n", " <td>29.43568</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">1</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>0.49794</td>\n", " <td>2450</td>\n", " <td>2.61572</td>\n", " <td>2.51775</td>\n", " <td>26.99231</td>\n", " <td>25.34337</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>0.35596</td>\n", " <td>2400</td>\n", " <td>2.64921</td>\n", " <td>2.45918</td>\n", " <td>27.46367</td>\n", " <td>24.61412</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>0.26077</td>\n", " <td>2350</td>\n", " <td>2.68369</td>\n", " <td>2.37889</td>\n", " <td>27.93690</td>\n", " <td>20.20762</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>0.21491</td>\n", " <td>2300</td>\n", " <td>2.72356</td>\n", " <td>2.34000</td>\n", " <td>28.42551</td>\n", " <td>20.06138</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>0.18971</td>\n", " <td>2250</td>\n", " <td>2.75722</td>\n", " <td>2.31314</td>\n", " <td>28.91733</td>\n", " <td>20.05589</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>0.11553</td>\n", " <td>2200</td>\n", " <td>2.79176</td>\n", " <td>2.30304</td>\n", " <td>29.45732</td>\n", " <td>20.22440</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>0.06660</td>\n", " <td>2150</td>\n", " <td>2.78771</td>\n", " <td>2.27861</td>\n", " <td>28.73200</td>\n", " <td>19.80329</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>0.03007</td>\n", " <td>2100</td>\n", " <td>2.78614</td>\n", " <td>2.25039</td>\n", " <td>28.82400</td>\n", " <td>19.69888</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>0.00824</td>\n", " <td>2050</td>\n", " <td>2.79200</td>\n", " <td>2.17433</td>\n", " <td>29.15552</td>\n", " <td>17.10762</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>-0.01659</td>\n", " <td>2000</td>\n", " <td>2.81165</td>\n", " <td>2.15231</td>\n", " <td>29.65246</td>\n", " <td>16.47518</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">1</th>\n", " <th rowspan=\"10\" valign=\"top\">0</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>12.69870</td>\n", " <td>2450</td>\n", " <td>2.53364</td>\n", " <td>2.53909</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>9.05096</td>\n", " <td>2400</td>\n", " <td>2.49753</td>\n", " <td>2.52300</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>5.38966</td>\n", " <td>2350</td>\n", " <td>2.46945</td>\n", " <td>2.50771</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>3.70148</td>\n", " <td>2300</td>\n", " <td>2.45293</td>\n", " <td>2.49312</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>3.67338</td>\n", " <td>2250</td>\n", " <td>2.46641</td>\n", " <td>2.49146</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>2.57340</td>\n", " <td>2200</td>\n", " <td>2.48491</td>\n", " <td>2.50075</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>1.70601</td>\n", " <td>2150</td>\n", " <td>2.50757</td>\n", " <td>2.52079</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>1.53731</td>\n", " <td>2100</td>\n", " <td>2.53510</td>\n", " <td>2.53821</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>0.82612</td>\n", " <td>2050</td>\n", " <td>2.55013</td>\n", " <td>2.56459</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>1.24504</td>\n", " <td>2000</td>\n", " <td>2.57868</td>\n", " <td>2.55968</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <th>...</th>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">3</th>\n", " <th rowspan=\"10\" valign=\"top\">1</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>6.81336</td>\n", " <td>2450</td>\n", " <td>2.34399</td>\n", " <td>2.25918</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>5.98391</td>\n", " <td>2400</td>\n", " <td>2.36914</td>\n", " <td>2.20534</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>4.93632</td>\n", " <td>2350</td>\n", " <td>2.39641</td>\n", " <td>2.16029</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>3.78569</td>\n", " <td>2300</td>\n", " <td>2.42585</td>\n", " <td>2.13018</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>2.64749</td>\n", " <td>2250</td>\n", " <td>2.45196</td>\n", " <td>2.11109</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>1.73774</td>\n", " <td>2200</td>\n", " <td>2.47143</td>\n", " <td>2.10190</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>0.95898</td>\n", " <td>2150</td>\n", " <td>2.48291</td>\n", " <td>2.09609</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>0.33199</td>\n", " <td>2100</td>\n", " <td>2.48809</td>\n", " <td>2.09287</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>-0.11095</td>\n", " <td>2050</td>\n", " <td>2.48430</td>\n", " <td>2.09213</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>-0.39648</td>\n", " <td>2000</td>\n", " <td>2.46351</td>\n", " <td>2.09358</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"20\" valign=\"top\">4</th>\n", " <th rowspan=\"10\" valign=\"top\">0</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>0.07327</td>\n", " <td>2450</td>\n", " <td>0.25714</td>\n", " <td>0.25102</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>0.10146</td>\n", " <td>2400</td>\n", " <td>0.25458</td>\n", " <td>0.24500</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>0.11745</td>\n", " <td>2350</td>\n", " <td>0.25106</td>\n", " <td>0.24085</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>0.13370</td>\n", " <td>2300</td>\n", " <td>0.25043</td>\n", " <td>0.23870</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>0.14467</td>\n", " <td>2250</td>\n", " <td>0.25156</td>\n", " <td>0.23867</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>0.15568</td>\n", " <td>2200</td>\n", " <td>0.25364</td>\n", " <td>0.23864</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>0.16488</td>\n", " <td>2150</td>\n", " <td>0.25628</td>\n", " <td>0.24000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>0.16643</td>\n", " <td>2100</td>\n", " <td>0.25857</td>\n", " <td>0.24000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>0.16683</td>\n", " <td>2050</td>\n", " <td>0.26098</td>\n", " <td>0.24244</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>0.16950</td>\n", " <td>2000</td>\n", " <td>0.26350</td>\n", " <td>0.24150</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">1</th>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>0.08041</td>\n", " <td>2450</td>\n", " <td>0.26041</td>\n", " <td>0.25388</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.0</td>\n", " <td>0.10375</td>\n", " <td>2400</td>\n", " <td>0.26333</td>\n", " <td>0.24917</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.0</td>\n", " <td>0.12298</td>\n", " <td>2350</td>\n", " <td>0.26638</td>\n", " <td>0.24426</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4.0</td>\n", " <td>0.14109</td>\n", " <td>2300</td>\n", " <td>0.27043</td>\n", " <td>0.24043</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.0</td>\n", " <td>0.15644</td>\n", " <td>2250</td>\n", " <td>0.27244</td>\n", " <td>0.23689</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6.0</td>\n", " <td>0.16636</td>\n", " <td>2200</td>\n", " <td>0.27500</td>\n", " <td>0.23409</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7.0</td>\n", " <td>0.17326</td>\n", " <td>2150</td>\n", " <td>0.27535</td>\n", " <td>0.23209</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8.0</td>\n", " <td>0.17667</td>\n", " <td>2100</td>\n", " <td>0.27619</td>\n", " <td>0.22667</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9.0</td>\n", " <td>0.18098</td>\n", " <td>2050</td>\n", " <td>0.27561</td>\n", " <td>0.22390</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10.0</td>\n", " <td>0.18475</td>\n", " <td>2000</td>\n", " <td>0.27800</td>\n", " <td>0.22250</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " average separation var funct number of pairs \\\n", "Variogram Direction Lag \n", "0 0 1 1.0 0.49386 2450 \n", " 2 2.0 0.35267 2400 \n", " 3 3.0 0.21134 2350 \n", " 4 4.0 0.14590 2300 \n", " 5 5.0 0.14299 2250 \n", " 6 6.0 0.09852 2200 \n", " 7 7.0 0.06420 2150 \n", " 8 8.0 0.05674 2100 \n", " 9 9.0 0.03009 2050 \n", " 10 10.0 0.04508 2000 \n", " 1 1 1.0 0.49794 2450 \n", " 2 2.0 0.35596 2400 \n", " 3 3.0 0.26077 2350 \n", " 4 4.0 0.21491 2300 \n", " 5 5.0 0.18971 2250 \n", " 6 6.0 0.11553 2200 \n", " 7 7.0 0.06660 2150 \n", " 8 8.0 0.03007 2100 \n", " 9 9.0 0.00824 2050 \n", " 10 10.0 -0.01659 2000 \n", "1 0 1 1.0 12.69870 2450 \n", " 2 2.0 9.05096 2400 \n", " 3 3.0 5.38966 2350 \n", " 4 4.0 3.70148 2300 \n", " 5 5.0 3.67338 2250 \n", " 6 6.0 2.57340 2200 \n", " 7 7.0 1.70601 2150 \n", " 8 8.0 1.53731 2100 \n", " 9 9.0 0.82612 2050 \n", " 10 10.0 1.24504 2000 \n", "... ... ... ... \n", "3 1 1 1.0 6.81336 2450 \n", " 2 2.0 5.98391 2400 \n", " 3 3.0 4.93632 2350 \n", " 4 4.0 3.78569 2300 \n", " 5 5.0 2.64749 2250 \n", " 6 6.0 1.73774 2200 \n", " 7 7.0 0.95898 2150 \n", " 8 8.0 0.33199 2100 \n", " 9 9.0 -0.11095 2050 \n", " 10 10.0 -0.39648 2000 \n", "4 0 1 1.0 0.07327 2450 \n", " 2 2.0 0.10146 2400 \n", " 3 3.0 0.11745 2350 \n", " 4 4.0 0.13370 2300 \n", " 5 5.0 0.14467 2250 \n", " 6 6.0 0.15568 2200 \n", " 7 7.0 0.16488 2150 \n", " 8 8.0 0.16643 2100 \n", " 9 9.0 0.16683 2050 \n", " 10 10.0 0.16950 2000 \n", " 1 1 1.0 0.08041 2450 \n", " 2 2.0 0.10375 2400 \n", " 3 3.0 0.12298 2350 \n", " 4 4.0 0.14109 2300 \n", " 5 5.0 0.15644 2250 \n", " 6 6.0 0.16636 2200 \n", " 7 7.0 0.17326 2150 \n", " 8 8.0 0.17667 2100 \n", " 9 9.0 0.18098 2050 \n", " 10 10.0 0.18475 2000 \n", "\n", " mean on tail mean on head variance tail \\\n", "Variogram Direction Lag \n", "0 0 1 2.53364 2.53909 24.77946 \n", " 2 2.49753 2.52300 24.33429 \n", " 3 2.46945 2.50771 23.99160 \n", " 4 2.45293 2.49312 23.63398 \n", " 5 2.46641 2.49146 24.02911 \n", " 6 2.48491 2.50075 24.45203 \n", " 7 2.50757 2.52079 24.82824 \n", " 8 2.53510 2.53821 25.29958 \n", " 9 2.55013 2.56459 25.46875 \n", " 10 2.57868 2.55968 25.91320 \n", " 1 1 2.61572 2.51775 26.99231 \n", " 2 2.64921 2.45918 27.46367 \n", " 3 2.68369 2.37889 27.93690 \n", " 4 2.72356 2.34000 28.42551 \n", " 5 2.75722 2.31314 28.91733 \n", " 6 2.79176 2.30304 29.45732 \n", " 7 2.78771 2.27861 28.73200 \n", " 8 2.78614 2.25039 28.82400 \n", " 9 2.79200 2.17433 29.15552 \n", " 10 2.81165 2.15231 29.65246 \n", "1 0 1 2.53364 2.53909 NaN \n", " 2 2.49753 2.52300 NaN \n", " 3 2.46945 2.50771 NaN \n", " 4 2.45293 2.49312 NaN \n", " 5 2.46641 2.49146 NaN \n", " 6 2.48491 2.50075 NaN \n", " 7 2.50757 2.52079 NaN \n", " 8 2.53510 2.53821 NaN \n", " 9 2.55013 2.56459 NaN \n", " 10 2.57868 2.55968 NaN \n", "... ... ... ... \n", "3 1 1 2.34399 2.25918 NaN \n", " 2 2.36914 2.20534 NaN \n", " 3 2.39641 2.16029 NaN \n", " 4 2.42585 2.13018 NaN \n", " 5 2.45196 2.11109 NaN \n", " 6 2.47143 2.10190 NaN \n", " 7 2.48291 2.09609 NaN \n", " 8 2.48809 2.09287 NaN \n", " 9 2.48430 2.09213 NaN \n", " 10 2.46351 2.09358 NaN \n", "4 0 1 0.25714 0.25102 NaN \n", " 2 0.25458 0.24500 NaN \n", " 3 0.25106 0.24085 NaN \n", " 4 0.25043 0.23870 NaN \n", " 5 0.25156 0.23867 NaN \n", " 6 0.25364 0.23864 NaN \n", " 7 0.25628 0.24000 NaN \n", " 8 0.25857 0.24000 NaN \n", " 9 0.26098 0.24244 NaN \n", " 10 0.26350 0.24150 NaN \n", " 1 1 0.26041 0.25388 NaN \n", " 2 0.26333 0.24917 NaN \n", " 3 0.26638 0.24426 NaN \n", " 4 0.27043 0.24043 NaN \n", " 5 0.27244 0.23689 NaN \n", " 6 0.27500 0.23409 NaN \n", " 7 0.27535 0.23209 NaN \n", " 8 0.27619 0.22667 NaN \n", " 9 0.27561 0.22390 NaN \n", " 10 0.27800 0.22250 NaN \n", "\n", " variance head tail head type cut \n", "Variogram Direction Lag \n", "0 0 1 26.68244 1 1 4 None \n", " 2 27.06660 1 1 4 None \n", " 3 27.10748 1 1 4 None \n", " 4 27.23370 1 1 4 None \n", " 5 27.46606 1 1 4 None \n", " 6 27.90143 1 1 4 None \n", " 7 28.44364 1 1 4 None \n", " 8 29.01886 1 1 4 None \n", " 9 29.58677 1 1 4 None \n", " 10 29.43568 1 1 4 None \n", " 1 1 25.34337 1 1 4 None \n", " 2 24.61412 1 1 4 None \n", " 3 20.20762 1 1 4 None \n", " 4 20.06138 1 1 4 None \n", " 5 20.05589 1 1 4 None \n", " 6 20.22440 1 1 4 None \n", " 7 19.80329 1 1 4 None \n", " 8 19.69888 1 1 4 None \n", " 9 17.10762 1 1 4 None \n", " 10 16.47518 1 1 4 None \n", "1 0 1 NaN 1 1 3 None \n", " 2 NaN 1 1 3 None \n", " 3 NaN 1 1 3 None \n", " 4 NaN 1 1 3 None \n", " 5 NaN 1 1 3 None \n", " 6 NaN 1 1 3 None \n", " 7 NaN 1 1 3 None \n", " 8 NaN 1 1 3 None \n", " 9 NaN 1 1 3 None \n", " 10 NaN 1 1 3 None \n", "... ... ... ... ... ... \n", "3 1 1 NaN 2 2 3 None \n", " 2 NaN 2 2 3 None \n", " 3 NaN 2 2 3 None \n", " 4 NaN 2 2 3 None \n", " 5 NaN 2 2 3 None \n", " 6 NaN 2 2 3 None \n", " 7 NaN 2 2 3 None \n", " 8 NaN 2 2 3 None \n", " 9 NaN 2 2 3 None \n", " 10 NaN 2 2 3 None \n", "4 0 1 NaN 1 1 9 2.5 \n", " 2 NaN 1 1 9 2.5 \n", " 3 NaN 1 1 9 2.5 \n", " 4 NaN 1 1 9 2.5 \n", " 5 NaN 1 1 9 2.5 \n", " 6 NaN 1 1 9 2.5 \n", " 7 NaN 1 1 9 2.5 \n", " 8 NaN 1 1 9 2.5 \n", " 9 NaN 1 1 9 2.5 \n", " 10 NaN 1 1 9 2.5 \n", " 1 1 NaN 1 1 9 2.5 \n", " 2 NaN 1 1 9 2.5 \n", " 3 NaN 1 1 9 2.5 \n", " 4 NaN 1 1 9 2.5 \n", " 5 NaN 1 1 9 2.5 \n", " 6 NaN 1 1 9 2.5 \n", " 7 NaN 1 1 9 2.5 \n", " 8 NaN 1 1 9 2.5 \n", " 9 NaN 1 1 9 2.5 \n", " 10 NaN 1 1 9 2.5 \n", "\n", "[100 rows x 11 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# test with numpy array\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Primary', 'Secondary'], dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pygslib.gslib.read_gslib_file('../pygslib/data/true.dat')\n", "data.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "parameters = {\n", " 'datafl' : data[['Primary', \n", " 'Secondary']].values, # path to file, or none (to use '_xxx_.in') or numpy array (with columns [x,y])\n", " 'tmin' : -1.0e21, # trimming limits min and max (raws out of this range will be ignored)\n", " 'tmax' : 1.0e21,\n", " 'outfl' : None, # path to the output file or None (to use '_xxx_.out')\n", " 'igrid' : 1, # grid realization number\n", " 'nx' : 50, # number of rows, cols and levels\n", " 'ny' : 50,\n", " 'nz' : 1,\n", " 'xmn' : .5, # coordinates of the centroid of first/corner block\n", " 'ymn' : .5,\n", " 'zmn' : .5,\n", " 'xsiz' : 1., # grid node separation\n", " 'ysiz' : 1.,\n", " 'zsiz' : 1., \n", " 'nlag' : 10, # number of lags\n", " 'igdir' : [[1,0,0],\n", " [0,1,0]], # [[ixd1,iyd1,izd1],...] directions along the grid (unit offsets) (array with shape [ndir,3])\n", " 'standardize': 1, # standardize sill? (0=no, 1=yes)\n", " 'ivpar': [[1, 1, 1, None],\n", " [1, 1, 3, None],\n", " [2, 2, 1, None],\n", " [2, 2, 3, None],\n", " [1, 1, 9, 2.5]]} # tail, head, variogram type, and cut (with shape [nvarg,4]) " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Parameters for GAM\n", " *******************\n", "\n", "START OF PARAMETERS:\n", "_xxx_.in -file with data\n", "2 1 2 - number of variables: column numbers\n", "-1e+21 1e+21 - trimming limits\n", "_xxx_.out -file for variogram output\n", "1 -realization number\n", "50 0.5 1.0 -nx,xmn,xsiz\n", "50 0.5 1.0 -ny,ymn,ysiz\n", "1 0.5 1.0 -nz,zmn,zsiz\n", "2 10 -number of direction and number of lags\n", "1 0 0\n", "0 1 0 -directions along the grid (unit offsets) (array with shape [ndir,3])\n", "1 -standardize sill? (0=no, 1=yes)\n", "5 -number of variograms\n", "1 1 1 None\n", "1 1 3 None\n", "2 2 1 None\n", "2 2 3 None\n", "1 1 9 2.5 -tail, head, variogram type, cut (array with shape [nvarg,4])\n", "\n", "\n", "cut[i] is only required if ivtype[i] == 9 or == 10\n", "\n", "\n", "\r\n", "GAM Version: 2.905\r\n", "\r\n", " data file = _xxx_.in \r\n", " number of variables = 2\r\n", " columns = 1 2\r\n", " trimming limits = -1.000000E+21 1.000000E+21\r\n", " output file = _xxx_.out \r\n", " grid number = 1\r\n", " nx,xmn,xsiz = 50 5.000000E-01 1.000000\r\n", " ny,ymn,ysiz = 50 5.000000E-01 1.000000\r\n", " nz,zmn,zsiz = 1 5.000000E-01 1.000000\r\n", " ndir,nlag = 2 10\r\n", " direction = 1 0 0\r\n", " direction = 0 1 0\r\n", " flag to standardize sills = 1\r\n", " number of variograms = 5\r\n", " tail,head,type = 1 1 1\r\n", " tail,head,type = 1 1 3\r\n", " tail,head,type = 2 2 1\r\n", " tail,head,type = 2 2 3\r\n", " tail,head,type = 1 1 9\r\n", " indicator threshold: 2.500000\r\n", "\r\n", "Variable number 1\r\n", " Number = 2500\r\n", " Average = 2.580196\r\n", " Variance = 26.531800\r\n", "Variable number 2\r\n", " Number = 2500\r\n", " Average = 2.320084\r\n", " Variance = 7.318668\r\n", "\r\n", "Variogram 1 Semivariogram : tail=v1 head=v1 \r\n", "Variogram 2 Covariance : tail=v1 head=v1 \r\n", "Variogram 3 Semivariogram : tail=v2 head=v2 \r\n", "Variogram 4 Covariance : tail=v2 head=v2 \r\n", "Variogram 5 Indicator 1/2 Variogram: tail=Indicator 1 head=Indicator 1 \r\n", "\r\n", "GAM Version: 2.905 Finished\r\n", "\r\n", "Stop - Program terminated.\r\n", "\r\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vg = gam.gam(parameters)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "any(np.array([1,2,3,4,5])==40)" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
OSGeo-live/CesiumWidget
GSOC/notebooks/Introduction.ipynb
1
6303
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "This GSoC 2015 Idea will focus on the development of a set of digital notebooks to explore several open source solution for geospatial data analysis, with the aim of bridging together the several software libraries already installed on the live to perform complex *geo-data-science workflows*. \n", "\n", "The notebooks are developed in the [Jupyter notebook server](https://jupyter.org/) environments which is heavly based on the [IPython](http://ipython.org/) project, are written id different languages (bash, python, R) and are organized in a series of \"topic-oriented\" geospatial notebooks.\n", "\n", "For a complete description of each projects installed on the OSGeo-Live refer to the [OSGeo-Live documentation](http://live.osgeo.org/en/index.html). This work will focus on the usage of several scientific python libraries like [numpy](http://www.numpy.org/), [scipy](http://www.scipy.org/), [pandas](http://pandas.pydata.org/), [matplotlib](http://matplotlib.org/) on GFOSS (Geographic Free and Open Source Software) projects like [GRASS](https://grass.osgeo.org/), [GDAL](http://www.gdal.org/), [OSSIM](https://trac.osgeo.org/ossim/), [maoserver](http://mapserver.org/) and more specialized software like [R](https://www.r-project.org/) for geostatistic workflow and [postgis](http://postgis.net/) as geospatial relational database.\n", "\n", "\n", "The geospatial notebook here developed will make use of the sample dataset pre-installed on the OSGeo-Live:\n", "\n", "* [Natural Earth](../../files/GSOC/docs/naturalearth_overview.html)\n", "\n", "* [OSGeo North Carolina, USA Educational dataset](../../files/GSOC/docs/nc_dataset_overview.html)\n", "\n", "* [OpenStreetMap](../../files/GSOC/docs/osm_dataset_overview.html)\n", "\n", "* [NetCDF Data Set](../../files/GSOC/docs/netcdf_dataset_overview.html)\n", "\n", "The geospatial notebok here developed are composed of 6 different sections with the aim of discover some of the several geospatial libraries installed on the OSGeo-Live. The geospatial notebooks developed here will walk the user from simple usage of command line tools such GDAL, PROJ and OSSIM to basic sql query on how to access database information to more complex geoprocessing including raw data parsing, numerical processing and the use of complete GIS platfom like GRASS GIS.\n", "\n", "\n", "# Topic Layout\n", "\n", "**[Access to Geospatial data](Access to Geospatial data/Introduction to Access to Geospatial data.ipynb)**\n", "\n", " * [GDAL-OGR Quickstart - bash](Access to Geospatial data/GDAL-OGR Quickstart.ipynb)\n", "\n", " * [GDAL-OGR with Python](Access to Geospatial data/GDAL-OGR with Python.ipynb)\n", "\n", " * [OSSIM Quickstart](Access to Geospatial data/OSSIM Quickstart.ipynb)\n", "\n", "**[Numerical Cartography](Numerical Cartography/An intro to Numerical Cartography.ipynb)**\n", "\n", " * [An intro to numerical cartography](Numerical Cartography/An intro to Numerical Cartography.ipynb)\n", "\n", " * [Map Projections](Numerical Cartography/Map Projections.ipynb)\n", "\n", " * [Spatial and Coordinate Reference System](Numerical Cartography/Spatial and Coordinate Reference System.ipynb)\n", "\n", " * [The Geodesic Problem](Numerical Cartography/The Geodesic Problem.ipynb)\n", "\n", " * [Working with coordinates](Numerical Cartography/Working with coordinates.ipynb)\n", "\n", " * [geometric-transformations](Numerical Cartography/geometric-transformation.ipynb)\n", "\n", "**[Map Algebra](Map Algebra/Intoduction to Map Algebra.ipynb)**\n", "\n", " * [Processing raster data as numerical array](Map Algebra/Processing raster data as numerical array.ipynb)\n", "\n", " * [General statistics - filtering - masked-array](Map Algebra/General statistics - filtering - masked-array.ipynb)\n", "\n", "**[Geoprocessing with Vectors](Geoprocessing with Vectors/Introduction to Geoprocessing with Vector.ipynb)**\n", "\n", " * [Basic Vector operations](Geoprocessing with Vectors/Basic Vector operations.ipynb)\n", "\n", " * [Intro to SQL and Postgis](Geoprocessing with Vectors/Intro to SQL and Postgis.ipynb)\n", " \n", "**[Introduction to GRASS GIS](Introduction to GRASS GIS/Introduction to GRASS GIS.ipynb)**\n", "\n", " * [GRASS working environment](Introduction to GRASS GIS/GRASS working environment.ipynb)\n", "\n", " * [Raster operation](Introduction to GRASS GIS/Raster operation.ipynb)\n", "\n", " * [Vector operation](Introduction to GRASS GIS/Vector operation.ipynb)\n", "\n", " * [Image processing](Introduction to GRASS GIS/Image processing.ipynb)\n", " \n", "**[Access to web based resources](Access to web based resources/Introduction to Access to web based resources.ipynb)**\n", "\n", " * [use of OWSlib to access web based resourcesusing OGC web standards](Access to web based resources)\n", "\n", " * [Example access and processing of NETCDF dataset](Access to web based resources)\n", "\n", "**[Simple web-gis products](Simple web-gis products/Introduction to Simple web-gis products.ipynb)**\n", "\n", " * [Publish result of processing in HTML+JS js widgets (openlayers, leaflet, Cesium)](Simple web-gis products/)\n", "\n", "**[Intro to geostatistic](Intro to geostatistic/Intro to geostatistic.ipynb)**\n", "\n", " * [Combined use of R, GRASS and Python](Intro to geostatistic/)" ] }, { "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
JoseGuzman/myIPythonNotebooks
Optimization/Polynomial regression.ipynb
1
158838
{ "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<H1>Polynomial fit</H1>" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4FEVBVVWV0/VVVVVQFEXtRnKHvV/QXvX97ed169bhrrvuMkw8zM+My5cvY9Gi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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f57330a25d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# generate some data\n", "np.random.seed(2)\n", "xdata = np.random.normal(3.0, 1.0, 100)\n", "ydata = np.random.normal(50.0, 30.0, 100) / xdata\n", "\n", "\n", "plt.plot(xdata, ydata, 'ko',ms=2);" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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R0YZv4c3/YRxy9Y1SCcTHC48nnhBqFEVFgFoNZGcLj7Iy4fJb/SW40dHALbcI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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f573307d390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# lets fit to a polynomial function of degree 8\n", "# and plot all together\n", "f = np.poly1d( np.polyfit(xdata, ydata, 8) )\n", "x = np.linspace(np.min(xdata), np.max(xdata), 100)\n", "plt.plot(xdata, ydata, 'ko', ms=2)\n", "plt.plot(x,f(x), 'red');" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.62830691926415927" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute r**2\n", "from sklearn.metrics import r2_score\n", "r2_score(ydata, f(xdata))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The r2_score is not very good and the large degree of the polynomial suggest an overfitting. The r2_score alone\n", "cannot not say which fitting is the best." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pol. deg 1 -> 0.278738\n", "Pol. deg 2 -> 0.360594\n", "Pol. deg 3 -> 0.422788\n", "Pol. deg 4 -> 0.480043\n", "Pol. deg 5 -> 0.553222\n", "Pol. deg 6 -> 0.605666\n", "Pol. deg 7 -> 0.615384\n", "Pol. deg 8 -> 0.628307\n", "Pol. deg 9 -> 0.643824\n" ] } ], "source": [ "# find the best polynomial\n", "mypoly = dict()\n", "for n in range(1, 10):\n", " f = np.poly1d( np.polyfit(xdata, ydata, n) )\n", " mypoly[n] = r2_score(ydata, f(xdata))\n", " print 'Pol. deg %d -> %f' %(n, mypoly[n])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<H2>Trial and test method</H2>\n", "To avoid overfitting, we'll split the data in two - 80% of it will be used for \"training\" our model, and the other 20% for testing it." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(80, 20)\n" ] }, { "data": { "image/png": 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XsbGxokOHDqJXr15i/vz50nx2evbsKXr27Gn34xQhJD+UgYiInMZj\nD0klIiL3Y1EgIiIViwIREalYFIiISMWiQEREKhYFIiJSsSgQEZGKRYGIiFQsCkREpGJRICIi1f8D\nO0O2wN38u2kAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f572a7c4f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#we'll select 80% of the data to train\n", "xtrain = xdata[:80]\n", "ytrain = ydata[:80]\n", "\n", "xtest = xdata[80:]\n", "ytest = ydata[80:]\n", "print(len(xtrain), len(xtest))\n", "plt.plot(xtrain, ytrain, 'ro', ms=2)\n", "plt.xlim(0,7), plt.ylim(0,200)\n", "plt.title('Train');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DDyzGAUgH4hIRXdHsfe902p5Cbm4uvvjiC6vbLl68CADaCW0dOnSAlBJHjx6tNbeoqAgA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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f572a4c34d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xtest, ytest, 'bo', ms=2)\n", "plt.xlim(0,7), plt.ylim(0,200)\n", "plt.title('Test');" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = np.poly1d(np.polyfit(xtrain, ytrain, 8))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ODg4OWLRokV7fwpIlS1BaWopp06aJWTRxODoC2rWbNm2StiyEEGIOYldVsrKy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\nDXbr1i0AQvP7yJEjcejQIXzwwQdISUn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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5732f158d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xtrain, ytrain, 'ko', ms=2)\n", "plt.plot(x, f(x),'r')\n", "plt.title('Train');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BAweioKAAixYt0tq3ZMkSnD59GiNHjoSrqytGjhwJGxsbzJ07V6tvYcGCBSgq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\nbds2REREYMaMGejRowc+//x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16qOsrIzPnz9fMy1CaGgof/vtt3lpaampiya5DRs2cIVC\nIYuawnfffccZY1yhUFQ7E6VCoeCFhYWmLmq9XL58mcfHx/OmTZtyOzs7HhAQwF999VV+7do1UxdN\nclOnTpXVhHgXLlzgsbGx3MfHhzdo0IAHBQXx1157jd+4ccPURZNEeXk5X7p0KQ8LC+P29vbc19eX\nT548+YH/NhnnMn+UgBBCSK2Z7SOphBBCjI+SAiGEEA1KCoQQQjQoKRBCCNGgpEAIIUSDkgIhhBAN\nSgqEEEI0KCkQQgjRoKRACCFEg5ICIYQQjf8H5tgCbaHK0MIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f572a57abd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xtest, ytest, 'ko', ms=2)\n", "plt.plot(x, f(x),'r')\n", "plt.title('Test');" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.64270695146918921, 0.3001816861070421)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(ytrain, f(xtrain)), r2_score(ytest, f(xtest))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the r2_score value of the test value is telling us that this fit is not very good" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pol. deg 1 -> r2(train) = 0.2647, r2(test) = 0.4225\n", "Pol. deg 2 -> r2(train) = 0.3616, r2(test) = 0.2935\n", "Pol. deg 3 -> r2(train) = 0.4296, r2(test) = 0.2727\n", "Pol. deg 4 -> r2(train) = 0.4831, r2(test) = 0.3936\n", "Pol. deg 5 -> r2(train) = 0.5545, r2(test) = 0.5041\n", "Pol. deg 6 -> r2(train) = 0.6025, r2(test) = 0.6050\n", "Pol. deg 7 -> r2(train) = 0.6170, r2(test) = 0.5461\n", "Pol. deg 8 -> r2(train) = 0.6427, r2(test) = 0.3002\n", "Pol. deg 9 -> r2(train) = 0.6634, r2(test) = 0.2162\n", "Pol. deg 10 -> r2(train) = 0.6819, r2(test) = 0.2249\n", "Pol. deg 11 -> r2(train) = 0.6875, r2(test) = 0.2682\n", "Pol. deg 12 -> r2(train) = 0.6919, r2(test) = 0.0595\n", "Pol. deg 13 -> r2(train) = 0.7050, r2(test) = 0.2260\n", "Pol. deg 14 -> r2(train) = 0.7073, r2(test) = 0.2938\n" ] } ], "source": [ "# let's compute train and test for all polynomial\n", "# find the best polynomial\n", "r2_test, r2_train = list(), list()\n", "polydeg = range(1,15)\n", "for n in polydeg:\n", " f = np.poly1d( np.polyfit(xtrain, ytrain, n) )\n", " r2train = r2_score(ytrain, f(xtrain))\n", " r2_train.append(r2train)\n", " r2test = r2_score(ytest, f(xtest))\n", " r2_test.append(r2test)\n", " print 'Pol. deg %2d -> r2(train) = %2.4f, r2(test) = %2.4f' %(n,r2train, r2test)\n", " \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the r2_scores of the test value, we can resolve that a fitting with a polynomial degree of six is the best" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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l9L4l9ez5JeT8Qs4OWHnPPi8vDwDUbkgCAGvXrkVhYSEKCgoMKvaMMcyfPx8D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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5732f4a110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(polydeg, r2_train, color='gray')\n", "plt.bar(polydeg, r2_test, color='red', alpha=.4)\n", "plt.xlim(1, 15);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# the best is to fit with a polynomial of degree 6\n", "f = np.poly1d(np.polyfit(xdata,ydata,6))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RowfNnj2bHnnkERIEgfr160cVFRUm2/773/8mQRAoIiKC/vrXv9KTTz5JLi4uFBERQdev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\nCozMTGD4cGg1GiS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gPvvsM4wePdpYEJxFzXZbJVJyfo6am6XjBOw5v8aOkajJnvNrbrLf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\nSUVORK5CYII=\n", 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gpl-2.0
spm2164/foundations-homework
10/homework_10.ipynb
1
4453
{ "cells": [ { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def rain_warning(precip_chance, precip_type):\n", " warning=''\n", " if precip_chance >= 0.9:\n", " warning=\"Whoa, it's almost certainly \"+precip_type+\"ing today. Bring an umbrella or something.\"\n", " elif precip_chance >= 0.5:\n", " warning=\"Better than even chance of \"+precip_type+\"ing today. Might want to look out for that\"\n", " elif precip_chance >= 0.1:\n", " warning=\"Non-zero chance of \"+precip_type+\"ing today. Just sayin'.\"\n", " else:\n", " warning=\"Pretty much nothing going on today. Might see some \"+ precip_type+\", but don't count on it.\"\n", " return(warning)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def send_mail(message_text, subject):\n", " return requests.post(\n", " \"https://api.mailgun.net/v3/sandbox5375a212cf1648e59f6321df8fdb88da.mailgun.org/messages\",\n", " auth=(\"api\", \"key-983839b29e1a96ab85533d0097cf5ba5\"),\n", " data={\"from\": \"weather bot <[email protected]>\",\n", " \"to\": \"[email protected]\",\n", " \"subject\": subject,\n", " \"text\": message_text})" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = 'https://api.forecast.io/forecast/'\n", "apikey = '53343f8442d30d598f50f5910124610a'\n", "latlong = '40.8117150,-73.9578630'\n", "response = requests.get(url+apikey+'/'+latlong)\n", "forecast_complete = response.json()" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [], "source": [ "current_conditions=forecast_complete['currently']\n", "forecast_today=forecast_complete['daily']['data'][0]" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ "forecast_email=[\"Right now it is \", current_conditions['summary'].lower(), \" out and feels like \", str(current_conditions['temperature']),\n", " \" degrees. Today it will be \", forecast_today['summary'][:-1].lower()+\", with a high of \", \n", " str(forecast_today['temperatureMax']), \" and a low of \", str(forecast_today['temperatureMin']),\" degrees. \"]\n", "if 'precipType' in forecast_today:\n", " forecast_email.append(rain_warning(forecast_today['precipProbability'], forecast_today['precipType']))\n", "else:\n", " forecast_email.append(\"Literally no chance of precipitation today. \")" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ "today_raw = datetime.datetime.today()\n", "today = today_raw.strftime(\"%A %B %d %Y\").split(' ')\n", "msg_subject=('8AM Weather forecast for ', today[0]+', ', today[1], ' ', today[2]+', ', today[3])\n" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Response [200]>" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "send_mail(''.join(forecast_email), ''.join(msg_subject))" ] }, { "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }
artistic-2.0
sujitpal/polydlot
src/pytorch/10-cumsum-prediction.ipynb
1
37844
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cumulative Sum Prediction\n", "\n", "This is the fifth toy example from Jason Brownlee's [Long Short Term Memory Networks with Python](https://machinelearningmastery.com/lstms-with-python/). It demonstrates the solution to a sequence-to-sequence (aka seq2seq) prediction problem. Per section 10.2 of the book:\n", "\n", "> The problem is defined as a sequence of random values between 0 and 1. This sequence is taken as input for the problem with each number provided once per time step. A binary label (0 or 1) is associated with each input. The output values are initially all 0. Once the cumulative sum of the input values in the sequence exceeds a threshold, then the output value flips from 0 to 1. A threshold of one quarter (1/4) of the sequence length is used, so for a sequence of length 10, the threshold is 2.5.\n", "\n", "> We will frame the problem to make the best use of the Bidirectional LSTM architecture.\n", "The output sequence will be produced after the entire input sequence has been fed into the\n", "model. Technically, this means this is a sequence-to-sequence prediction problem that requires\n", "a many-to-many prediction model. It is also the case that the input and output sequences have\n", "the same number of time steps (length)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division, print_function\n", "from sklearn.metrics import accuracy_score, confusion_matrix\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import shutil\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DATA_DIR = \"../../data\"\n", "MODEL_FILE = os.path.join(DATA_DIR, \"torch-10-cumsum-predict-{:d}.model\")\n", "\n", "TRAIN_SIZE = 7500\n", "VAL_SIZE = 100\n", "TEST_SIZE = 500\n", "\n", "SEQ_LENGTH = 10\n", "EMBED_SIZE = 1\n", "\n", "BATCH_SIZE = 32\n", "NUM_EPOCHS = 10\n", "LEARNING_RATE = 1e-3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.04742344 0.93090249 0.47292869 0.34986938 0.27752548 0.22485943\n", " 0.05711298 0.38595953 0.78495727 0.13500855]\n", "[0 0 0 0 0 0 0 1 1 1]\n" ] } ], "source": [ "def generate_sequence(seq_len):\n", " xs = np.random.random(seq_len)\n", " ys = np.array([0 if x < 2.5 else 1 for x in np.cumsum(xs).tolist()])\n", " return xs, ys\n", "\n", "X, Y = generate_sequence(SEQ_LENGTH)\n", "print(X)\n", "print(Y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7500, 10, 1) (7500, 10) (100, 10, 1) (100, 10) (500, 10, 1) (500, 10)\n" ] } ], "source": [ "def generate_data(seq_len, num_seqs):\n", " xseq, yseq = [], []\n", " for i in range(num_seqs):\n", " X, Y = generate_sequence(seq_len)\n", " xseq.append(X)\n", " yseq.append(Y)\n", " return np.expand_dims(np.array(xseq), axis=2), np.array(yseq)\n", "\n", "Xtrain, Ytrain = generate_data(SEQ_LENGTH, TRAIN_SIZE)\n", "Xval, Yval = generate_data(SEQ_LENGTH, VAL_SIZE)\n", "Xtest, Ytest = generate_data(SEQ_LENGTH, TEST_SIZE)\n", "\n", "print(Xtrain.shape, Ytrain.shape, Xval.shape, Yval.shape, Xtest.shape, Ytest.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Network\n", "\n", "The sequence length for the input and output sequences are the same size. Our network follows the model built (using Keras) in the book. Unlike the typical encoder-decoder LSTM architecture that is used for most seq2seq problems, here we have a single LSTM followed by a FCN layer at each timestep of its output. Each FCN returns a binary 0/1 output, which is concatenated to produce the predicted result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CumSumPredictor (\n", " (enc_lstm): LSTM(1, 50, batch_first=True, bidirectional=True)\n", " (fcn): Linear (100 -> 2)\n", " (fcn_relu): ReLU ()\n", " (fcn_softmax): Softmax ()\n", ")\n", "--- size debugging ---\n", "torch.Size([32, 10, 2])\n" ] } ], "source": [ "class CumSumPredictor(nn.Module):\n", " \n", " def __init__(self, seq_len, input_dim, hidden_dim, output_dim):\n", " super(CumSumPredictor, self).__init__()\n", " self.seq_len = seq_len\n", " self.hidden_dim = hidden_dim\n", " self.output_dim = output_dim\n", " # network layers\n", " self.enc_lstm = nn.LSTM(input_dim, hidden_dim, 1, batch_first=True, \n", " bidirectional=True)\n", " self.fcn = nn.Linear(hidden_dim * 2, output_dim) # bidirectional input\n", " self.fcn_relu = nn.ReLU()\n", " self.fcn_softmax = nn.Softmax()\n", " \n", " def forward(self, x):\n", " if torch.cuda.is_available():\n", " h = (Variable(torch.randn(2, x.size(0), self.hidden_dim).cuda()),\n", " Variable(torch.randn(2, x.size(0), self.hidden_dim).cuda()))\n", " else:\n", " h = (Variable(torch.randn(2, x.size(0), self.hidden_dim)),\n", " Variable(torch.randn(2, x.size(0), self.hidden_dim)))\n", "\n", " x, h = self.enc_lstm(x, h) # encoder LSTM\n", " x_fcn = Variable(torch.zeros(x.size(0), self.seq_len, self.output_dim))\n", " for i in range(self.seq_len): # decoder LSTM -> fcn for each timestep\n", " x_fcn[:, i, :] = self.fcn_softmax(self.fcn_relu(self.fcn(x[:, i, :])))\n", " x = x_fcn \n", " return x\n", " \n", "model = CumSumPredictor(SEQ_LENGTH, EMBED_SIZE, 50, 2)\n", "if torch.cuda.is_available():\n", " model.cuda()\n", "print(model)\n", "\n", "# size debugging\n", "print(\"--- size debugging ---\")\n", "inp = Variable(torch.randn(BATCH_SIZE, SEQ_LENGTH, EMBED_SIZE))\n", "outp = model(inp)\n", "print(outp.size())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss_fn = nn.CrossEntropyLoss()\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Network" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10: loss=4.743, acc=0.254, val_loss=4.109, val_acc=0.323\n", "Epoch 2/10: loss=3.827, acc=0.464, val_loss=3.728, val_acc=0.510\n", "Epoch 3/10: loss=3.640, acc=0.569, val_loss=3.647, val_acc=0.521\n", "Epoch 4/10: loss=3.562, acc=0.627, val_loss=3.597, val_acc=0.594\n", "Epoch 5/10: loss=3.527, acc=0.651, val_loss=3.570, val_acc=0.635\n", "Epoch 6/10: loss=3.500, acc=0.676, val_loss=3.542, val_acc=0.667\n", "Epoch 7/10: loss=3.484, acc=0.684, val_loss=3.496, val_acc=0.635\n", "Epoch 8/10: loss=3.460, acc=0.706, val_loss=3.580, val_acc=0.552\n", "Epoch 9/10: loss=3.454, acc=0.710, val_loss=3.468, val_acc=0.708\n", "Epoch 10/10: loss=3.433, acc=0.731, val_loss=3.469, val_acc=0.719\n" ] } ], "source": [ "def compute_accuracy(pred_var, true_var):\n", " if torch.cuda.is_available():\n", " ypred = pred_var.cpu().data.numpy()\n", " ytrue = true_var.cpu().data.numpy()\n", " else:\n", " ypred = pred_var.data.numpy()\n", " ytrue = true_var.data.numpy()\n", " pred_nums, true_nums = [], []\n", " for i in range(pred_var.size(0)): # for each row of output\n", " pred_nums.append(int(\"\".join([str(x) for x in ypred[i].tolist()]), 2))\n", " true_nums.append(int(\"\".join([str(x) for x in ytrue[i].tolist()]), 2))\n", " return pred_nums, true_nums, accuracy_score(pred_nums, true_nums)\n", "\n", "\n", "history = []\n", "for epoch in range(NUM_EPOCHS):\n", " \n", " num_batches = Xtrain.shape[0] // BATCH_SIZE\n", " shuffled_indices = np.random.permutation(np.arange(Xtrain.shape[0]))\n", " train_loss, train_acc = 0., 0.\n", " \n", " for bid in range(num_batches):\n", " \n", " # extract one batch of data\n", " Xbatch_data = Xtrain[shuffled_indices[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]]\n", " Ybatch_data = Ytrain[shuffled_indices[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]]\n", " Xbatch = Variable(torch.from_numpy(Xbatch_data).float())\n", " Ybatch = Variable(torch.from_numpy(Ybatch_data).long())\n", " if torch.cuda.is_available():\n", " Xbatch = Xbatch.cuda()\n", " Ybatch = Ybatch.cuda()\n", " \n", " # initialize gradients\n", " optimizer.zero_grad()\n", "\n", " # forward\n", " loss = 0.\n", " Ybatch_ = model(Xbatch)\n", " for i in range(Ybatch.size(1)):\n", " loss += loss_fn(Ybatch_[:, i, :], Ybatch[:, i])\n", " \n", " # backward\n", " loss.backward()\n", "\n", " train_loss += loss.data[0]\n", " \n", " _, ybatch_ = Ybatch_.max(2)\n", " _, _, acc = compute_accuracy(ybatch_, Ybatch)\n", " train_acc += acc\n", " \n", " optimizer.step()\n", " \n", " # compute training loss and accuracy\n", " train_loss /= num_batches\n", " train_acc /= num_batches\n", " \n", " # compute validation loss and accuracy\n", " val_loss, val_acc = 0., 0.\n", " num_val_batches = Xval.shape[0] // BATCH_SIZE\n", " for bid in range(num_val_batches):\n", " # data\n", " Xbatch_data = Xval[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]\n", " Ybatch_data = Yval[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]\n", " Xbatch = Variable(torch.from_numpy(Xbatch_data).float())\n", " Ybatch = Variable(torch.from_numpy(Ybatch_data).long())\n", " if torch.cuda.is_available():\n", " Xbatch = Xbatch.cuda()\n", " Ybatch = Ybatch.cuda()\n", "\n", " loss = 0.\n", " Ybatch_ = model(Xbatch)\n", " for i in range(Ybatch.size(1)):\n", " loss += loss_fn(Ybatch_[:, i, :], Ybatch[:, i])\n", " val_loss += loss.data[0]\n", "\n", " _, ybatch_ = Ybatch_.max(2)\n", " _, _, acc = compute_accuracy(ybatch_, Ybatch)\n", " val_acc += acc\n", " \n", " val_loss /= num_val_batches\n", " val_acc /= num_val_batches\n", " \n", " torch.save(model.state_dict(), MODEL_FILE.format(epoch+1))\n", " print(\"Epoch {:2d}/{:d}: loss={:.3f}, acc={:.3f}, val_loss={:.3f}, val_acc={:.3f}\"\n", " .format((epoch+1), NUM_EPOCHS, train_loss, train_acc, val_loss, val_acc))\n", " \n", " history.append((train_loss, val_loss, train_acc, val_acc))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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xx3pdkTHGmBCI7IDauNG1nPbsgffeg7Fjva7IGGNMiERuQG3a5IYu378fli6F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\nRBwxwk0PP38+/OtfIXxiY4w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+5nU9EeA4YIqIbMbtSj5ZRF7wtqSIkAVkqWpFC30hLrBM\nzSYBm1Q1R1VLgNeAYz2uyVORGlCfAQNEJF1EWuMOJC72uKawJyKCOyawVlX/5HU9kUBV71TVVFVN\nw/2dLVXVFv2rNhiquhPYJiJH+VZNBL72sKRIsBUYJyKJvv+rE2nhHUsickZdVS0VkeuBJbieLvNV\n9SuPy4oExwEXA1+KyCrful+o6tse1mSi1w3Ai74fkRuByz2uJ6yp6qcishD4HNfjdiUtfMgjG+rI\nGGNMWIrUXXzGGGOinAWUMcaYsGQBZYwxJixZQBljjAlLFlDGGGPCkgWUMcaYsGQBZYwxJiz9f9gi\nwujKdt0aAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a37c5d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "losses = [x[0] for x in history]\n", "val_losses = [x[1] for x in history]\n", "accs = [x[2] for x in history]\n", "val_accs = [x[3] for x in history]\n", "\n", "plt.subplot(211)\n", "plt.title(\"Accuracy\")\n", "plt.plot(accs, color=\"r\", label=\"train\")\n", "plt.plot(val_accs, color=\"b\", label=\"valid\")\n", "plt.legend(loc=\"best\")\n", "\n", "plt.subplot(212)\n", "plt.title(\"Loss\")\n", "plt.plot(losses, color=\"r\", label=\"train\")\n", "plt.plot(val_losses, color=\"b\", label=\"valid\")\n", "plt.legend(loc=\"best\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluate Network" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "saved_model = CumSumPredictor(SEQ_LENGTH, EMBED_SIZE, 50, 2)\n", "saved_model.load_state_dict(torch.load(MODEL_FILE.format(NUM_EPOCHS)))\n", "if torch.cuda.is_available():\n", " saved_model.cuda()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 0.746\n" ] } ], "source": [ "ylabels, ypreds = [], []\n", "num_test_batches = Xtest.shape[0] // BATCH_SIZE\n", "for bid in range(num_test_batches):\n", " Xbatch_data = Xtest[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]\n", " Ybatch_data = Ytest[bid * BATCH_SIZE : (bid + 1) * BATCH_SIZE]\n", " Xbatch = Variable(torch.from_numpy(Xbatch_data).float())\n", " Ybatch = Variable(torch.from_numpy(Ybatch_data).long())\n", " if torch.cuda.is_available():\n", " Xbatch = Xbatch.cuda()\n", " Ybatch = Ybatch.cuda()\n", "\n", " Ybatch_ = saved_model(Xbatch)\n", " _, ybatch_ = Ybatch_.max(2)\n", "\n", " pred_nums, true_nums, _ = compute_accuracy(ybatch_, Ybatch)\n", " ylabels.extend(true_nums)\n", " ypreds.extend(pred_nums)\n", "\n", "print(\"Test accuracy: {:.3f}\".format(accuracy_score(ylabels, ypreds)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y=[0 0 0 0 0 0 1 1 1 1], yhat=[0 0 0 0 0 0 1 1 1 1], correct=True\n", "y=[0 0 0 0 0 1 1 1 1 1], yhat=[0 0 0 0 0 1 1 1 1 1], correct=True\n", "y=[0 0 0 0 0 0 1 1 1 1], yhat=[0 0 0 0 0 0 1 1 1 1], correct=True\n", "y=[0 0 0 0 0 0 0 1 1 1], yhat=[0 0 0 0 0 0 0 1 1 1], correct=True\n", "y=[0 0 0 0 0 0 1 1 1 1], yhat=[0 0 0 0 0 0 1 1 1 1], correct=True\n", "y=[0 0 0 0 0 0 1 1 1 1], yhat=[0 0 0 0 0 0 1 1 1 1], correct=True\n", "y=[0 0 0 0 1 1 1 1 1 1], yhat=[0 0 0 0 0 1 1 1 1 1], correct=False\n", "y=[0 0 0 0 1 1 1 1 1 1], yhat=[0 0 0 0 1 1 1 1 1 1], correct=True\n", "y=[0 0 0 0 1 1 1 1 1 1], yhat=[0 0 0 0 1 1 1 1 1 1], correct=True\n", "y=[0 0 0 1 1 1 1 1 1 1], yhat=[0 0 0 1 1 1 1 1 1 1], correct=True\n" ] } ], "source": [ "Xbatch_data = Xtest[0:10]\n", "Ybatch_data = Ytest[0:10]\n", "Xbatch = Variable(torch.from_numpy(Xbatch_data).float())\n", "Ybatch = Variable(torch.from_numpy(Ybatch_data).long())\n", "if torch.cuda.is_available():\n", " Xbatch = Xbatch.cuda()\n", " Ybatch = Ybatch.cuda()\n", "\n", "Ybatch_ = saved_model(Xbatch)\n", "_, ybatch_ = Ybatch_.max(2)\n", "\n", "if torch.cuda.is_available():\n", " ybatch__data = ybatch_.cpu().data.numpy()\n", "else:\n", " ybatch__data = ybatch_.data.numpy()\n", "\n", "for i in range(Ybatch_data.shape[0]):\n", " label = Ybatch_data[i]\n", " pred = ybatch__data[i]\n", " correct = \"True\" if np.array_equal(label, pred) else \"False\"\n", " print(\"y={:s}, yhat={:s}, correct={:s}\".format(str(label), str(pred), correct))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(NUM_EPOCHS):\n", " os.remove(MODEL_FILE.format(i + 1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
MohammedAttya/numerical-mooc
lessons/02_spacetime/02_02_nonlinearConvection.ipynb
1
5173
{ "metadata": { "name": "", "signature": "sha256:1191bafb5bbd17be710420034c791682fdfb3ad8002b1d28d2333fea541e2d0c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c) L.A. Barba, G.F. Forsyth, C. Cooper, 2014. Based on [CFDPython](https://github.com/barbagroup/CFDPython), (c) L. A. Barba, also under CC-BY license." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Non-linear Convection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're back! This is the second IPython Notebook of the series *Space and Time \u2014 Introduction of Finite-difference solutions of PDEs*, the second module of [\"Practical Numerical Methods with Python\"](http://openedx.seas.gwu.edu/courses/GW/MAE6286/2014_fall/about).\n", "\n", "In the previous notebook, *\"1-D Linear Convection\"*, we learned how to solve the linear convection equation using the finite-difference method. Now let's move on to the non-linear convection equation, using the same methods as before. The 1-D convection equation is:\n", "\n", "\\begin{equation}\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} = 0\\end{equation}\n", "\n", "Instead of a constant factor $c$ multiplying the second term, now we have the solution $u$ multiplying it. Thus, the second term of the equation is now *non-linear*. We're going to use the same discretization as in linear convection \u2014 forward difference in time and backward difference in space. Here is the discretized equation.\n", "\n", "\\begin{equation}\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + u_i^n \\frac{u_i^n-u_{i-1}^n}{\\Delta x} = 0\\end{equation}\n", "\n", "Solving for the only unknown term, $u_i^{n+1}$, yields:\n", "\n", "\\begin{equation}u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n)\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, the Python code starts by loading the necessary libraries. Then, we declare some variables that determine the discretization in space and time (you should experiment by changing these parameters to see what happens). Then, we create the initial condition $u_0$ by initializing the array for the solution using $u = 2\\ @\\ 0.5 \\leq x \\leq 1$ and $u = 1$ everywhere else in $(0,2)$ (i.e., a hat function)." ] }, { "cell_type": "code", "collapsed": true, "input": [ "import numpy #we're importing numpy \n", "import matplotlib.pyplot as plt #and our 2D plotting library, calling it plt\n", "%matplotlib inline\n", "\n", "nx = 41\n", "dx = 2./(nx-1)\n", "nt = 20 #nt is the number of timesteps we want to calculate\n", "dt = .025 #dt is the amount of time each timestep covers (delta t)\n", "\n", "u = numpy.ones(nx) #as before, we initialize u with every value equal to 1.\n", "u[.5/dx : 1/dx+1]=2 #then set u = 2 between 0.5 and 1 as per our I.C.s\n", "\n", "un = numpy.ones(nx) #initialize our placeholder array un, to hold the time-stepped solution" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code snippet below is *unfinished*. We have copied over the line from the linear convection case that executes the time-stepping update. Can you edit this code to execute the non-linear convection instead?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for n in range(nt): #iterate through time\n", " un = u.copy() ##copy the existing values of u into un\n", " for i in range(1,nx): ##now we'll iterate through the u array\n", " \n", " ###This is the line from Step 1, copied exactly. Edit it for our new equation.\n", " ###then uncomment it and run the cell to evaluate Step 2 \n", " \n", " ###u[i] = un[i]-c*dt/dx*(un[i]-un[i-1]) \n", "\n", " \n", "plt.plot(numpy.linspace(0,2,nx),u) ##Plot the results" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do you observe about the evolution of the hat function under the non-linear convection equation? What happens when you change the numerical parameters and run again?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "css_file = '../../styles/numericalmoocstyle.css'\n", "HTML(open(css_file, \"r\").read())" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
CedricVallee/pythonFinancialAnalyst
Multinomial.ipynb
1
12454
{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load packages\n", "import pandas as pd\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.naive_bayes import MultinomialNB" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ggroup Filename \\\n", "0 2030 1994_Q1_100885_UNION PACIFIC CORP_10-K_1994-03-29 \n", "1 2010 1994_Q1_101829_UNITED TECHNOLOGIES CORP -DE-_1... \n", "2 2010 1994_Q1_12927_BOEING CO_10-K_1994-03-17 \n", "3 2010 1994_Q1_15840_BUTLER MANUFACTURING CO_10-K_199... \n", "4 2010 1994_Q1_18230_CATERPILLAR INC_10-K_1994-03-02 \n", "5 2010 1994_Q1_217346_TEXTRON INC_10-K_1994-03-30 \n", "6 2010 1994_Q1_26172_CUMMINS ENGINE CO INC_10-K_1994-... \n", "7 2010 1994_Q1_277135_GRAINGER W W INC_10-K_1994-03-28 \n", "8 2010 1994_Q1_277509_FEDERAL SIGNAL CORP -DE-_10-K_1... \n", "9 2030 1994_Q1_278041_INTERNATIONAL SHIPHOLDING CORP_... \n", "10 2020 1994_Q1_27996_DELUXE CORP_10-K_1994-03-31 \n", "11 2020 1994_Q1_310431_CBI INDUSTRIES INC -DE-_10-K_19... \n", "12 2020 1994_Q1_315213_HALF ROBERT INTERNATIONAL INC -... \n", "13 2010 1994_Q1_33619_ESTERLINE TECHNOLOGIES CORP_10-K... \n", "14 2010 1994_Q1_40533_GENERAL DYNAMICS CORP_10-K_1994-... \n", "15 2010 1994_Q1_40545_GENERAL ELECTRIC CO_10-K_1994-03-11 \n", "16 2020 1994_Q1_45599_HARLAND JOHN H CO_10-K_1994-03-30 \n", "17 2010 1994_Q1_45876_HARSCO CORP_10-K_1994-03-29 \n", "18 2010 1994_Q1_48305_HONEYWELL INC_10-K_1994-03-08 \n", "19 2010 1994_Q1_48898_HUBBELL INC_10-K_1994-03-25 \n", "20 2010 1994_Q1_49826_ILLINOIS TOOL WORKS INC_10-K_199... \n", "21 2020 1994_Q1_52466_IONICS INC_10-K_1994-03-29 \n", "22 2010 1994_Q1_54381_KAMAN CORP_10-K_1994-03-11 \n", "23 2020 1994_Q1_55135_KELLY SERVICES INC_10-K_1994-03-14 \n", "24 2030 1994_Q1_56047_KIRBY CORP_10-K_1994-03-15 \n", "25 2010 1994_Q1_62996_MASCO CORP -DE-_10-K_1994-03-25 \n", "26 2030 1994_Q1_700674_AIR EXPRESS INTERNATIONAL CORP ... \n", "27 2030 1994_Q1_702165_NORFOLK SOUTHERN CORP_10-K_1994... \n", "28 2020 1994_Q1_714278_INFORMATION RESOURCES INC_10-K_... \n", "29 2010 1994_Q1_72331_NORDSON CORP_10-K_1994-01-28 \n", "... ... ... \n", "5277 2010 2014_Q4_1423221_Quanex Building Products CORP_... \n", "5278 2020 2014_Q4_1546640_ADT Corp_10-K_2014-11-12 \n", "5279 2020 2014_Q4_25212_COURIER Corp_10-K_2014-12-01 \n", "5280 2010 2014_Q4_26076_CUBIC CORP -DE-_10-K_2014-11-26 \n", "5281 2010 2014_Q4_32604_EMERSON ELECTRIC CO_10-K_2014-11-19 \n", "5282 2010 2014_Q4_33619_ESTERLINE TECHNOLOGIES CORP_10-K... \n", "5283 2010 2014_Q4_46619_HEICO CORP_10-K_2014-12-18 \n", "5284 2010 2014_Q4_50725_GRIFFON CORP_10-K_2014-11-13 \n", "5285 2010 2014_Q4_52988_JACOBS ENGINEERING GROUP INC -DE... \n", "5286 2020 2014_Q4_63296_MATTHEWS INTERNATIONAL CORP_10-K... \n", "5287 2010 2014_Q4_64472_GENCOR INDUSTRIES INC_10-K_2014-... \n", "5288 2010 2014_Q4_67887_MOOG INC._10-K_2014-11-10 \n", "5289 2010 2014_Q4_6955_ACTUANT CORP_10-K_2014-10-27 \n", "5290 2020 2014_Q4_717954_UNIFIRST CORP_10-K_2014-10-29 \n", "5291 2010 2014_Q4_72331_NORDSON CORP_10-K_2014-12-15 \n", "5292 2010 2014_Q4_737758_TORO CO_10-K_2014-12-22 \n", "5293 2010 2014_Q4_764401_INSTEEL INDUSTRIES INC_10-K_201... \n", "5294 2020 2014_Q4_771497_ABM INDUSTRIES INC -DE-_10-K_20... \n", "5295 2010 2014_Q4_775158_OSHKOSH CORP_10-K_2014-11-13 \n", "5296 2010 2014_Q4_801898_JOY GLOBAL INC_10-K_2014-12-19 \n", "5297 2010 2014_Q4_80420_POWELL INDUSTRIES INC_10-K_2014-... \n", "5298 2010 2014_Q4_808450_NAVISTAR INTERNATIONAL CORP_10-... \n", "5299 2020 2014_Q4_831641_TETRA TECH INC_10-K_2014-11-19 \n", "5300 2020 2014_Q4_833444_TYCO INTERNATIONAL LTD_10-K_201... \n", "5301 2010 2014_Q4_866706_ESCO TECHNOLOGIES INC_10-K_2014... \n", "5302 2010 2014_Q4_868857_AECOM TECHNOLOGY CORP_10-K_2014... \n", "5303 2010 2014_Q4_883902_NCI BUILDING SYSTEMS INC_10-K_2... \n", "5304 2020 2014_Q4_886206_FRANKLIN COVEY CO_10-K_2014-11-14 \n", "5305 2010 2014_Q4_906193_KEY TECHNOLOGY INC_10-K_2014-12-12 \n", "5306 2010 2014_Q4_923120_GREENBRIER COMPANIES INC_10-K_2... \n", "\n", " MDA \n", "0 Item Management s Discussion and Analysis of F... \n", "1 Item Management s Discussion and Analysis of R... \n", "2 Item Management s Discussion and Analysis of F... \n", "3 Item Management s Discussion and Analysis of F... \n", "4 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "6 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF R... \n", "7 Item Management s Discussion and Analysis of F... \n", "8 Item Management s Discussion and Analysis of F... \n", "9 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "10 Item Management s Discussion and Analysis of F... \n", "11 Item Management s Discussion and Analysis of F... \n", "12 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "13 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "14 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "15 Item Management s Discussion and Analysis of F... \n", "16 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "17 Item Management s Discussion of Financial Cond... \n", "18 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "19 Item Management s Discussion and Analysis of F... \n", "20 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "21 Item MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "22 Item Management s Discussion and Analysis of F... \n", "23 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "24 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "25 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "26 Item Management s Discussion and Analysis of F... \n", "27 Item Management s Discussion and Analysis of F... \n", "28 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "29 Item Management s Discussion and Analysis of F... \n", "... ... \n", "5277 Item Management s Discussion and Analysis of F... \n", "5278 Table of Contents Item Management s Discussio... \n", "5279 Item Management s Discussion and Analysis of F... \n", "5280 Item MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5281 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5282 Item Management s Discussion and Analysis of F... \n", "5283 Index Item MANAGEMENT S DISCUSSION AND ANALYS... \n", "5284 Item Management s Discussion and Analysis of F... \n", "5285 Table of Contents Item MANAGEMENT S DISCUSSIO... \n", "5286 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS cont... \n", "5287 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5288 Item Management s Discussion and Analysis of F... \n", "5289 Table of Contents Item Management s Discussio... \n", "5290 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF ... \n", "5291 Management s Discussion and Analysis of Finan... \n", "5292 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5293 Item Management s Discussion and Analysis of F... \n", "5294 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5295 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5296 Item Management s Discussion and Analysis of ... \n", "5297 Payments Due by Period Payments Due by Period... \n", "5298 Item Management s Discussion and Analysis of ... \n", "5299 Item Management s Discussion and Analysis of F... \n", "5300 Table of Contents Item Management s Discussio... \n", "5301 Filtration PTI Technologies Inc PTI VACCO Ind... \n", "5302 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5303 Item Management s Discussion and Analysis of F... \n", "5304 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5305 ITEM MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "5306 Item MANAGEMENT S DISCUSSION AND ANALYSIS OF F... \n", "\n", "[5307 rows x 3 columns]\n" ] } ], "source": [ "# Load MS Excel file\n", "dataset=pd.read_excel('matrix.xlsx')\n", "dataset=dataset[['ggroup','Filename','MDA']]\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Split the dataset in train/test ratio: 0.20\n", "train_set, test_set = train_test_split(dataset, test_size = 0.20)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create the classifier\n", "vectorizer = CountVectorizer(stop_words=\"english\")\n", "counts = vectorizer.fit_transform(train_set.MDA.values)\n", "classifier = MultinomialNB(fit_prior=\"False\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MultinomialNB(alpha=1.0, class_prior=None, fit_prior='False')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Train the classifier\n", "classifier.fit(counts, train_set.ggroup)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted 2010 2020 2030 All\n", "Actual \n", "2010 607 55 5 667\n", "2020 19 213 1 233\n", "2030 15 9 138 162\n", "All 641 277 144 1062\n" ] } ], "source": [ "# Test the classifier\n", "predictions = classifier.predict(vectorizer.transform(test_set.MDA.values)) \n", "test_set_pred = pd.Series(predictions, index=test_set.index)\n", "\n", "tab = pd.crosstab(test_set.ggroup, test_set_pred, rownames=['Actual'], colnames=['Predicted'], margins=True) # Print confusion matrix\n", "print(tab)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mklokocka/seminator
notebooks/bSCC.ipynb
1
413481
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import spot\n", "from spot.jupyter import display_inline\n", "from spot.seminator import seminator, ViaTGBA\n", "spot.setup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Effect of the Bottom-SCC optimisation on semi-deterministic automata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The orange states below form deterministic bottom SCCs. After processing by Seminator, they are both in the 1st (violet) and 2nd (green) component. Simplifications cannot merge these duplicates as one is accepting and one is not. In fact, we do not need the copy in the first component as there is no non-determinism and so there is nothing to wait for. We have to make every edge entering such SCC as a cut-edge." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style='vertical-align:text-top;display:inline-block;width:33%;'><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: input Pages: 1 -->\n", "<svg width=\"231pt\" height=\"360pt\"\n", " viewBox=\"0.00 0.00 230.84 360.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(0.9174311926605504 0.9174311926605504) rotate(0) translate(4 389)\">\n", "<title>input</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-389 248,-389 248,4 -4,4\"/>\n", "<text text-anchor=\"start\" x=\"105\" y=\"-370.8\" 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output\")\n", " display_inline(in_a, out_a, simp_a, per_row=3, show=\".vn\")\n", " \n", "example()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enabling the bottom-SCC optimization simplifies the output automata as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style='vertical-align:text-top;display:inline-block;width:33%;'><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: input Pages: 1 -->\n", "<svg width=\"231pt\" height=\"360pt\"\n", " viewBox=\"0.00 0.00 230.84 360.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(0.9174311926605504 0.9174311926605504) rotate(0) translate(4 389)\">\n", 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Removing the states 3 and 4 from the fist part of the cut-deterministic automaton would remove state $\\{3\\}$ and would merge the states $\\{1,3,4\\}$ and $\\{1,3\\}$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style='vertical-align:text-top;display:inline-block;width:33%;'><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: input Pages: 1 -->\n", "<svg width=\"231pt\" height=\"360pt\"\n", " viewBox=\"0.00 0.00 230.84 360.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(0.9174311926605504 0.9174311926605504) rotate(0) translate(4 389)\">\n", "<title>input</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" 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In fact, we can avoid all SCCs that are already good for semi-deterministic automata (_semi-deterministic SCC_). SCC $C$ is semi-deterministic if $C$ and all successors of $C$ are deterministic. This is ilustrated on the following example and states 1 and 5." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style='vertical-align:text-top;display:inline-block;'><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: input Pages: 1 -->\n", "<svg width=\"734pt\" height=\"358pt\"\n", " viewBox=\"0.00 0.00 734.00 358.09\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(0.7575757575757576 0.7575757575757576) rotate(0) translate(4 468)\">\n", "<title>input</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-468 963.5,-468 963.5,4 -4,4\"/>\n", "<text text-anchor=\"start\" x=\"462.75\" y=\"-449.8\" font-family=\"Lato\" font-size=\"14.00\">input</text>\n", "<text text-anchor=\"start\" x=\"432.75\" y=\"-435.8\" font-family=\"Lato\" font-size=\"14.00\">Inf(</text>\n", "<text text-anchor=\"start\" x=\"454.75\" y=\"-435.8\" font-family=\"Lato\" font-size=\"14.00\" fill=\"#1f78b4\">⓿</text>\n", "<text text-anchor=\"start\" x=\"470.75\" y=\"-435.8\" font-family=\"Lato\" font-size=\"14.00\">)&amp;Inf(</text>\n", "<text text-anchor=\"start\" x=\"506.75\" y=\"-435.8\" font-family=\"Lato\" font-size=\"14.00\" fill=\"#ff4da0\">❶</text>\n", "<text text-anchor=\"start\" x=\"522.75\" y=\"-435.8\" font-family=\"Lato\" font-size=\"14.00\">)</text>\n", "<text text-anchor=\"start\" x=\"435.75\" y=\"-421.8\" font-family=\"Lato\" font-size=\"14.00\">[gen. 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"metadata": {}, "output_type": "display_data" } ], "source": [ "def example2(**opts):\n", " in_a = spot.translate('G((((a & b) | (!a & !b)) & (GF!b U !c)) | (((!a & b) | (a & !b)) & (FGb R c)))')\n", " spot.highlight_nondet_states(in_a, 1)\n", " in_a.set_name(\"input\")\n", " options = { \"cut_det\": True, \"highlight\": True, \"jobs\": ViaTGBA, \"skip_levels\": True, \"pure\": True, **opts}\n", " out_a = seminator(in_a, **options, postprocess=False)\n", " out_a.set_name(\"output\")\n", " simp_a = seminator(in_a, **options, postprocess=True)\n", " simp_a.set_name(\"simplified output\")\n", " display_inline(in_a, out_a, simp_a, show=\".nhs\")\n", "\n", "example2()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style='vertical-align:text-top;display:inline-block;'><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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"text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "example2(bscc_avoid=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reusing the semi-deterministic components with TGBA acceptance\n", "In the previous example we have saved several states by not including the semi-deterministic components in the 1st part of the result. However, we still got 6 (and 5 after postprocessing) states out of the 3 deterministic states $1, 5$, and $6$. This can be tackled by reusing the semi-deterministic components as they are. This immediately leads to a TGBA on the output and we have to adress this in the parts which still rely on breakpoint construction. The edges that are accepting will now carry all the marks that are needed (as they do in the original automaton anyway). 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gpl-3.0
kunaltyagi/SDES
notes/python/p_norvig/logic/Cheryl.ipynb
1
15124
{ "metadata": { "name": "", "signature": "sha256:b9e2e6c3f7e82432833f1416dd0552c13753f683e1888682faf2910ef7d9f8ff" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "When is Cheryl's Birthday?\n", "===\n", "\n", "<div align=right>Peter Norvig, April 2015</div>\n", "\n", "This logic puzzle has been [making the rounds](https://www.google.com/webhp?#q=cheryl%27s+birthday):<i>\n", "\n", "\n", "1. Albert and Bernard just became friends with Cheryl, and they want to know when her birtxhday is. Cheryl gave them a list of 10 possible dates:\n", "<pre>\n", " May 15 May 16 May 19\n", " June 17 June 18\n", " July 14 July 16\n", " August 14 August 15 August 17\n", "</pre>\n", " \n", "2. Cheryl then tells Albert and Bernard separately the month and the day of the birthday respectively.\n", " \n", "3. **Albert**: I don't know when Cheryl's birthday is, but I know that Bernard does not know too.\n", " \n", "4. **Bernard**: At first I don't know when Cheryl's birthday is, but I know now.\n", " \n", "5. **Albert**: Then I also know when Cheryl's birthday is.\n", " \n", "6. So when is Cheryl's birthday?</i>\n", "\n", "Problem-Solving Tools\n", "---\n", "\n", "Cheryl's puzzle was designed to be solved with a pencil, the greatest problem-solving tool in the history of mathematics (although some prefer a pen, chalk, marker, or a [stick for drawing in the sand](http://www.hellenicaworld.com/Greece/Science/en/Archimedes.html)). But I will show how to solve it with another tool: **computer code**. I choose this tool for four reasons: \n", "- It is a more direct way to find the solution. All I have to do is faithfully describe the problem with code that says: *\"for each of the 10 possible dates, tell Albert the month and Bernard the day and check if statements 3 through 5 are true.\"* The intended [pencil and paper solution](https://scontent-iad.xx.fbcdn.net/hphotos-xpa1/v/t1.0-9/s720x720/11111802_983395601695416_3208022346737572922_n.jpg?oh=15fcb7edc4689dd9c71385b613446465&oe=55DD4488) requires not just understanding the *problem*, but also creatively discovering the steps of the *solution*&mdash;a harder task.\n", "- With tested, debugged code, you're less likely to make a mistake that leads you to a [wrong answer](http://www.theguardian.com/science/alexs-adventures-in-numberland/2015/apr/15/why-the-cheryl-birthday-problem-turned-into-the-maths-version-of-thatdress).\n", "- You'll learn how to solve problems that are similar, but can't be solved with pencil and paper because they have millions of possibilities rather than just 10.\n", "- Solving puzzles is fun; programming is fun; solving puzzles with programs is double fun.\n", "\n", "We will translate each of the 6 statements in the puzzle into Python code:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Cheryl gave them a list of 10 possible dates:\n", "---\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "DATES = ['May 15', 'May 16', 'May 19',\n", " 'June 17', 'June 18',\n", " 'July 14', 'July 16',\n", " 'August 14', 'August 15', 'August 17']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also define accessor functions for the month and day of a date:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def Month(date): return date.split()[0]\n", "\n", "def Day(date): return date.split()[1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "Month('May 15')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "'May'" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "Day('May 15')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "'15'" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Cheryl then tells Albert and Bernard separately the month and the day of the birthday respectively.\n", "---\n", "\n", "We can define the idea of **telling**, and while we're at it, the idea of **knowing** a birthdate:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def tell(part, possible_dates=DATES):\n", " \"Cheryl tells a part of her birthdate to someone; return a new list of possible dates that match the part.\"\n", " return [date for date in possible_dates if part in date]\n", "\n", "def know(possible_dates):\n", " \"A person knows the birthdate if they have exactly one possible date.\"\n", " return len(possible_dates) == 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we use a *list of dates* to represent someone's knowledge of the possible birthdates, and that someone *knows* the birthdate when they get down to only one possibility. For example: If Cheryl tells Albert that her birthday is in May, he would have a list of three possible birthdates:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tell('May')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "['May 15', 'May 16', 'May 19']" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And if she tells Bernard that her birthday is on the 15th, he would end up with two possibilities:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tell('15')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "['May 15', 'August 15']" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With two possibilities, Bernard does not know the birthdate:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "know(tell('15'))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "False" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Overall Strategy\n", "---\n", "\n", "When Cheryl tells Albert `'May'` then *he* knows there are three possibilities, but *we* (the puzzle solvers) don't, because we don't know what Cheryl said. So what can we do? We will consider *all* of the possible dates, one at a time. For example, first consider `'May 15'`. Cheryl tells Albert `'May'` and Bernard `'15'`, giving them the lists of possible birthdates shown above. We can then check whether statements 3 through 5 are true in this scenario. If they are, then `'May 15'` is a solution to the puzzle. Repeat the process for each of the possible dates. If all goes well, there should be exactly one solution. \n", "\n", "Here is the main function, `cheryls_birthday`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def cheryls_birthday(possible_dates=DATES):\n", " \"Return a list of the possible dates for which statements 3 to 5 are true.\"\n", " return filter(statements3to5, possible_dates)\n", "\n", "def statements3to5(date): return statement3(date) and statement4(date) and statement5(date)\n", "\n", "## TO DO: define statement3, statement4, statement5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ " (*Python note:* `filter(predicate, items)` returns a list of all items for which `predicate(item)` is true.)\n", " \n", " 3. Albert: I don't know when Cheryl's birthday is, but I know that Bernard does not know too.\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `statement3` takes as input a possible birthdate and returns true if Albert's statement is true for that birthdate. How do we go from Albert's English statement to a Python function? Let's paraphrase in a form that is closer to Python code:\n", "\n", "> **Albert**: After Cheryl told me the month of her birthdate, I didn't know her birthday. I don't know which day Cheryl told Bernard, but I know that for all of the possible dates, if Bernard is told that day, he wouldn't know the birthdate.\n", "\n", "That I can translate directly into code:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def statement3(date):\n", " \"Albert: I don't know when Cheryl's birthday is, but I know that Bernard does not know too.\"\n", " possible_dates = tell(Month(date))\n", " return (not know(possible_dates) \n", " and all(not know(tell(Day(d)))\n", " for d in possible_dates))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can try the function on a date:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "statement3('May 15')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "False" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, we can see all the dates that satisfy statement 3:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "filter(statement3, DATES)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "['July 14', 'July 16', 'August 14', 'August 15', 'August 17']" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Bernard: At first I don't know when Cheryl's birthday is, but I know now.\n", "---\n", "\n", "Again, a paraphrase:\n", "\n", "> **Bernard:** At first Cheryl told me the day, and I didn't know. Then I considered just the dates for which Albert's statement 3 is true, and now I know." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def statement4(date):\n", " \"Bernard: At first I don't know when Cheryl's birthday is, but I know now.\"\n", " at_first = tell(Day(date))\n", " return (not know(at_first)\n", " and know(filter(statement3, at_first)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see which dates satisfy both statement 3 and statement 4:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "filter(statement4, filter(statement3, DATES))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "['July 16', 'August 15', 'August 17']" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wait a minute&mdash;I thought that Bernard **knew**?! Why are there three possible dates remaining? Bernard does indeed know the birthdate,\n", "because he knows something we don't know: the day. We won't know the birthdate until after statement 5.\n", "\n", "5. Albert: Then I also know when Cheryl's birthday is.\n", "---\n", "\n", "Albert is saying that after hearing the month and Bernard's statement 4, he now knows Cheryl's birthday:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def statement5(date):\n", " \"Albert: Then I also know when Cheryl's birthday is.\"\n", " return know(filter(statement4, tell(Month(date))))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "6. So when is Cheryl's birthday?\n", "---\n", "\n", "Let's see:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "cheryls_birthday()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "['July 16']" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Success!** We have deduced that Cheryl's birthday is **July 16**. It is now `True` that we know Cheryl's birthday:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "know(cheryls_birthday())" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "True" ] } ], "prompt_number": 17 } ], "metadata": {} } ] }
gpl-3.0
minimaxir/sf-arrests-when-where
crime_data_sf.ipynb
1
107855
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing When and Where San Francisco Criminal Arrests Occur Using R and ggplot2\n", "\n", "by [Max Woolf](http://minimaxir.com)\n", "\n", "This notebook is the complement to my blog posts [Analyzing San Francisco Crime Data to Determine When Arrests Frequently Occur](http://minimaxir.com/2015/12/sf-arrests/) and [Mapping Where Arrests Frequently Occur in San Francisco Using Crime Data](http://minimaxir.com/2015/12/sf-arrest-maps/).\n", "\n", "*This notebook is licensed under the MIT License. If you use the code or data visualization designs contained within this notebook, it would be greatly appreciated if proper attribution is given back to this notebook and/or myself. Thanks! :)*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: ‘dplyr’\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "Registering fonts with R\n", "\n", "Attaching package: ‘scales’\n", "\n", "The following objects are masked from ‘package:readr’:\n", "\n", " col_factor, col_numeric\n", "\n", "Note: the specification for S3 class “AsIs” in package ‘RJSONIO’ seems equivalent to one from package ‘jsonlite’: not turning on duplicate class definitions for this class.\n" ] }, { "data": { "text/plain": [ "R version 3.2.2 (2015-08-14)\n", "Platform: x86_64-apple-darwin13.4.0 (64-bit)\n", "Running under: OS X 10.11.1 (El Capitan)\n", "\n", "locale:\n", "[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8\n", "\n", "attached base packages:\n", "[1] grid stats graphics grDevices utils datasets methods \n", "[8] base \n", "\n", "other attached packages:\n", "[1] ggmap_2.5.2 stringr_1.0.0 digest_0.6.8 RColorBrewer_1.1-2\n", "[5] scales_0.3.0 extrafont_0.17 ggplot2_1.0.1 dplyr_0.4.3 \n", "[9] readr_0.1.1 \n", "\n", "loaded via a namespace (and not attached):\n", " [1] Rcpp_0.12.1 plyr_1.8.3 base64enc_0.1-3 \n", " [4] tools_3.2.2 uuid_0.1-2 jsonlite_0.9.17 \n", " [7] evaluate_0.8 gtable_0.1.2 lattice_0.20-33 \n", "[10] png_0.1-7 IRdisplay_0.3 DBI_0.3.1 \n", "[13] mapproj_1.2-4 IRkernel_0.5 parallel_3.2.2 \n", "[16] proto_0.3-10 rzmq_0.7.7 Rttf2pt1_1.3.3 \n", "[19] repr_0.4 maps_3.0.0-2 RgoogleMaps_1.2.0.7\n", "[22] R6_2.1.1 jpeg_0.1-8 RJSONIO_1.3-0 \n", "[25] sp_1.2-0 reshape2_1.4.1 extrafontdb_1.0 \n", "[28] magrittr_1.5 MASS_7.3-43 assertthat_0.1 \n", "[31] colorspace_1.2-6 geosphere_1.4-3 stringi_0.5-5 \n", "[34] munsell_0.4.2 rjson_0.2.15 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "options(warn = -1)\n", "\n", "# IMPORTANT: This assumes that all packages in \"Rstart.R\" are installed,\n", "# and the fonts \"Source Sans Pro\" and \"Open Sans Condensed Bold\" are installed\n", "# via extrafont. If ggplot2 charts fail to render, you may need to change/remove the theme call.\n", "\n", "source(\"Rstart.R\")\n", "library(ggmap)\n", "\n", "options(repr.plot.mimetypes = 'image/png', repr.plot.width = 4, repr.plot.height = 3, repr.plot.res = 300)\n", "\n", "sessionInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Processing the Data\n", "\n", "We load the data using `readr` and `read_csv()` since it's faster. Since there is a lot of redundant data (e.g. address, coordinates), we only load the columns we need." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "|================================================================================| 100% 360 MB\n" ] } ], "source": [ "path <- \"~/Downloads/SFPD_Incidents_-_from_1_January_2003.csv\"\n", "\n", "df <- read_csv(path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>IncidntNum</th><th scope=col>Category</th><th scope=col>Descript</th><th scope=col>DayOfWeek</th><th scope=col>Date</th><th scope=col>Time</th><th scope=col>PdDistrict</th><th scope=col>Resolution</th><th scope=col>Address</th><th scope=col>X</th><th scope=col>Y</th><th scope=col>Location</th><th scope=col>PdId</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>150996567</td><td>BURGLARY</td><td>BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY</td><td>Sunday</td><td>11/15/2015</td><td>23:58</td><td>INGLESIDE</td><td>NONE</td><td>3200 Block of HARRISON ST</td><td>-122.4115</td><td>37.74605</td><td>(37.7460485796086, -122.411460219918)</td><td>1.509966e+13</td></tr>\n", "\t<tr><th scope=row>2</th><td>156283485</td><td>LARCENY/THEFT</td><td>PETTY THEFT OF PROPERTY</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>BAYVIEW</td><td>NONE</td><td>17TH ST / DEHARO ST</td><td>-122.4016</td><td>37.76484</td><td>(37.7648403636386, -122.401600659931)</td><td>1.562835e+13</td></tr>\n", "\t<tr><th scope=row>3</th><td>150997195</td><td>VANDALISM</td><td>MALICIOUS MISCHIEF, GRAFFITI</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>PARK</td><td>NONE</td><td>2500 Block of 15TH ST</td><td>-122.4378</td><td>37.76603</td><td>(37.7660311509137, -122.437848713459)</td><td>1.509972e+13</td></tr>\n", "\t<tr><th scope=row>4</th><td>150996501</td><td>VANDALISM</td><td>MALICIOUS MISCHIEF, VANDALISM OF VEHICLES</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>SOUTHERN</td><td>NONE</td><td>1ST ST / FOLSOM ST</td><td>-122.3945</td><td>37.7873</td><td>(37.7872982355244, -122.394484874311)</td><td>1.509965e+13</td></tr>\n", "\t<tr><th scope=row>5</th><td>150996501</td><td>SUSPICIOUS OCC</td><td>SUSPICIOUS OCCURRENCE</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>SOUTHERN</td><td>NONE</td><td>1ST ST / FOLSOM ST</td><td>-122.3945</td><td>37.7873</td><td>(37.7872982355244, -122.394484874311)</td><td>1.509965e+13</td></tr>\n", "\t<tr><th scope=row>6</th><td>156280936</td><td>LARCENY/THEFT</td><td>PETTY THEFT OF PROPERTY</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>NORTHERN</td><td>NONE</td><td>1800 Block of GEARY BL</td><td>-122.432</td><td>37.78425</td><td>(37.7842501079896, -122.432035315509)</td><td>1.562809e+13</td></tr>\n", "\t<tr><th scope=row>7</th><td>150998171</td><td>VEHICLE THEFT</td><td>STOLEN AUTOMOBILE</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>TARAVAL</td><td>NONE</td><td>2300 Block of 30TH AV</td><td>-122.4875</td><td>37.74345</td><td>(37.7434503392393, -122.487471191928)</td><td>1.509982e+13</td></tr>\n", "\t<tr><th scope=row>8</th><td>150996777</td><td>VANDALISM</td><td>MALICIOUS MISCHIEF, BREAKING WINDOWS</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>CENTRAL</td><td>ARREST, BOOKED</td><td>400 Block of STOCKTON ST</td><td>-122.407</td><td>37.78992</td><td>(37.789918101686, -122.406977563692)</td><td>1.509968e+13</td></tr>\n", "\t<tr><th scope=row>9</th><td>151001038</td><td>VEHICLE THEFT</td><td>STOLEN AUTOMOBILE</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>SOUTHERN</td><td>NONE</td><td>HOWARD ST / 9TH ST</td><td>-122.4132</td><td>37.77499</td><td>(37.7749926445385, -122.413163134276)</td><td>1.51001e+13</td></tr>\n", "\t<tr><th scope=row>10</th><td>150996498</td><td>ASSAULT</td><td>AGGRAVATED ASSAULT WITH BODILY FORCE</td><td>Sunday</td><td>11/15/2015</td><td>22:59</td><td>MISSION</td><td>NONE</td><td>3100 Block of 16TH ST</td><td>-122.4236</td><td>37.76487</td><td>(37.7648666651043, -122.423637302048)</td><td>1.509965e+13</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllll}\n", " & IncidntNum & Category & Descript & DayOfWeek & Date & Time & PdDistrict & Resolution & Address & X & Y & Location & PdId\\\\\n", "\\hline\n", "\t1 & 150996567 & BURGLARY & BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY & Sunday & 11/15/2015 & 23:58 & INGLESIDE & NONE & 3200 Block of HARRISON ST & -122.4115 & 37.74605 & (37.7460485796086, -122.411460219918) & 1.509966e+13\\\\\n", "\t2 & 156283485 & LARCENY/THEFT & PETTY THEFT OF PROPERTY & Sunday & 11/15/2015 & 23:30 & BAYVIEW & NONE & 17TH ST / DEHARO ST & -122.4016 & 37.76484 & (37.7648403636386, -122.401600659931) & 1.562835e+13\\\\\n", "\t3 & 150997195 & VANDALISM & MALICIOUS MISCHIEF, GRAFFITI & Sunday & 11/15/2015 & 23:30 & PARK & NONE & 2500 Block of 15TH ST & -122.4378 & 37.76603 & (37.7660311509137, -122.437848713459) & 1.509972e+13\\\\\n", "\t4 & 150996501 & VANDALISM & MALICIOUS MISCHIEF, VANDALISM OF VEHICLES & Sunday & 11/15/2015 & 23:15 & SOUTHERN & NONE & 1ST ST / FOLSOM ST & -122.3945 & 37.7873 & (37.7872982355244, -122.394484874311) & 1.509965e+13\\\\\n", "\t5 & 150996501 & SUSPICIOUS OCC & SUSPICIOUS OCCURRENCE & Sunday & 11/15/2015 & 23:15 & SOUTHERN & NONE & 1ST ST / FOLSOM ST & -122.3945 & 37.7873 & (37.7872982355244, -122.394484874311) & 1.509965e+13\\\\\n", "\t6 & 156280936 & LARCENY/THEFT & PETTY THEFT OF PROPERTY & Sunday & 11/15/2015 & 23:00 & NORTHERN & NONE & 1800 Block of GEARY BL & -122.432 & 37.78425 & (37.7842501079896, -122.432035315509) & 1.562809e+13\\\\\n", "\t7 & 150998171 & VEHICLE THEFT & STOLEN AUTOMOBILE & Sunday & 11/15/2015 & 23:00 & TARAVAL & NONE & 2300 Block of 30TH AV & -122.4875 & 37.74345 & (37.7434503392393, -122.487471191928) & 1.509982e+13\\\\\n", "\t8 & 150996777 & VANDALISM & MALICIOUS MISCHIEF, BREAKING WINDOWS & Sunday & 11/15/2015 & 23:00 & CENTRAL & ARREST, BOOKED & 400 Block of STOCKTON ST & -122.407 & 37.78992 & (37.789918101686, -122.406977563692) & 1.509968e+13\\\\\n", "\t9 & 151001038 & VEHICLE THEFT & STOLEN AUTOMOBILE & Sunday & 11/15/2015 & 23:00 & SOUTHERN & NONE & HOWARD ST / 9TH ST & -122.4132 & 37.77499 & (37.7749926445385, -122.413163134276) & 1.51001e+13\\\\\n", "\t10 & 150996498 & ASSAULT & AGGRAVATED ASSAULT WITH BODILY FORCE & Sunday & 11/15/2015 & 22:59 & MISSION & NONE & 3100 Block of 16TH ST & -122.4236 & 37.76487 & (37.7648666651043, -122.423637302048) & 1.509965e+13\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 13]\n", "\n", " IncidntNum Category Descript\n", " (int) (chr) (chr)\n", "1 150996567 BURGLARY BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY\n", "2 156283485 LARCENY/THEFT PETTY THEFT OF PROPERTY\n", "3 150997195 VANDALISM MALICIOUS MISCHIEF, GRAFFITI\n", "4 150996501 VANDALISM MALICIOUS MISCHIEF, VANDALISM OF VEHICLES\n", "5 150996501 SUSPICIOUS OCC SUSPICIOUS OCCURRENCE\n", "6 156280936 LARCENY/THEFT PETTY THEFT OF PROPERTY\n", "7 150998171 VEHICLE THEFT STOLEN AUTOMOBILE\n", "8 150996777 VANDALISM MALICIOUS MISCHIEF, BREAKING WINDOWS\n", "9 151001038 VEHICLE THEFT STOLEN AUTOMOBILE\n", "10 150996498 ASSAULT AGGRAVATED ASSAULT WITH BODILY FORCE\n", "Variables not shown: DayOfWeek (chr), Date (chr), Time (chr), PdDistrict (chr),\n", " Resolution (chr), Address (chr), X (dbl), Y (dbl), Location (chr), PdId (dbl)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'# of Rows in Dataframe: 1842050'" ], "text/latex": [ "'# of Rows in Dataframe: 1842050'" ], "text/markdown": [ "'# of Rows in Dataframe: 1842050'" ], "text/plain": [ "[1] \"# of Rows in Dataframe: 1842050\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'Dataframe Size: 180.9 Mb'" ], "text/latex": [ "'Dataframe Size: 180.9 Mb'" ], "text/markdown": [ "'Dataframe Size: 180.9 Mb'" ], "text/plain": [ "[1] \"Dataframe Size: 180.9 Mb\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df %>% head(10)\n", "sprintf(\"# of Rows in Dataframe: %s\", nrow(df))\n", "sprintf(\"Dataframe Size: %s\", format(object.size(df), units = \"MB\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>Descript</th><th scope=col>DayOfWeek</th><th scope=col>Date</th><th scope=col>Time</th><th scope=col>PdDistrict</th><th scope=col>Resolution</th><th scope=col>X</th><th scope=col>Y</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>BURGLARY</td><td>BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY</td><td>Sunday</td><td>11/15/2015</td><td>23:58</td><td>INGLESIDE</td><td>NONE</td><td>-122.4115</td><td>37.74605</td></tr>\n", "\t<tr><th scope=row>2</th><td>LARCENY/THEFT</td><td>PETTY THEFT OF PROPERTY</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>BAYVIEW</td><td>NONE</td><td>-122.4016</td><td>37.76484</td></tr>\n", "\t<tr><th scope=row>3</th><td>VANDALISM</td><td>MALICIOUS MISCHIEF, GRAFFITI</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>PARK</td><td>NONE</td><td>-122.4378</td><td>37.76603</td></tr>\n", "\t<tr><th scope=row>4</th><td>VANDALISM</td><td>MALICIOUS MISCHIEF, VANDALISM OF VEHICLES</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>SOUTHERN</td><td>NONE</td><td>-122.3945</td><td>37.7873</td></tr>\n", "\t<tr><th scope=row>5</th><td>SUSPICIOUS OCC</td><td>SUSPICIOUS OCCURRENCE</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>SOUTHERN</td><td>NONE</td><td>-122.3945</td><td>37.7873</td></tr>\n", "\t<tr><th scope=row>6</th><td>LARCENY/THEFT</td><td>PETTY THEFT OF PROPERTY</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>NORTHERN</td><td>NONE</td><td>-122.432</td><td>37.78425</td></tr>\n", "\t<tr><th scope=row>7</th><td>VEHICLE THEFT</td><td>STOLEN AUTOMOBILE</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>TARAVAL</td><td>NONE</td><td>-122.4875</td><td>37.74345</td></tr>\n", "\t<tr><th scope=row>8</th><td>VANDALISM</td><td>MALICIOUS MISCHIEF, BREAKING WINDOWS</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>CENTRAL</td><td>ARREST, BOOKED</td><td>-122.407</td><td>37.78992</td></tr>\n", "\t<tr><th scope=row>9</th><td>VEHICLE THEFT</td><td>STOLEN AUTOMOBILE</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>SOUTHERN</td><td>NONE</td><td>-122.4132</td><td>37.77499</td></tr>\n", "\t<tr><th scope=row>10</th><td>ASSAULT</td><td>AGGRAVATED ASSAULT WITH BODILY FORCE</td><td>Sunday</td><td>11/15/2015</td><td>22:59</td><td>MISSION</td><td>NONE</td><td>-122.4236</td><td>37.76487</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " & Category & Descript & DayOfWeek & Date & Time & PdDistrict & Resolution & X & Y\\\\\n", "\\hline\n", "\t1 & BURGLARY & BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY & Sunday & 11/15/2015 & 23:58 & INGLESIDE & NONE & -122.4115 & 37.74605\\\\\n", "\t2 & LARCENY/THEFT & PETTY THEFT OF PROPERTY & Sunday & 11/15/2015 & 23:30 & BAYVIEW & NONE & -122.4016 & 37.76484\\\\\n", "\t3 & VANDALISM & MALICIOUS MISCHIEF, GRAFFITI & Sunday & 11/15/2015 & 23:30 & PARK & NONE & -122.4378 & 37.76603\\\\\n", "\t4 & VANDALISM & MALICIOUS MISCHIEF, VANDALISM OF VEHICLES & Sunday & 11/15/2015 & 23:15 & SOUTHERN & NONE & -122.3945 & 37.7873\\\\\n", "\t5 & SUSPICIOUS OCC & SUSPICIOUS OCCURRENCE & Sunday & 11/15/2015 & 23:15 & SOUTHERN & NONE & -122.3945 & 37.7873\\\\\n", "\t6 & LARCENY/THEFT & PETTY THEFT OF PROPERTY & Sunday & 11/15/2015 & 23:00 & NORTHERN & NONE & -122.432 & 37.78425\\\\\n", "\t7 & VEHICLE THEFT & STOLEN AUTOMOBILE & Sunday & 11/15/2015 & 23:00 & TARAVAL & NONE & -122.4875 & 37.74345\\\\\n", "\t8 & VANDALISM & MALICIOUS MISCHIEF, BREAKING WINDOWS & Sunday & 11/15/2015 & 23:00 & CENTRAL & ARREST, BOOKED & -122.407 & 37.78992\\\\\n", "\t9 & VEHICLE THEFT & STOLEN AUTOMOBILE & Sunday & 11/15/2015 & 23:00 & SOUTHERN & NONE & -122.4132 & 37.77499\\\\\n", "\t10 & ASSAULT & AGGRAVATED ASSAULT WITH BODILY FORCE & Sunday & 11/15/2015 & 22:59 & MISSION & NONE & -122.4236 & 37.76487\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 9]\n", "\n", " Category Descript DayOfWeek\n", " (chr) (chr) (chr)\n", "1 BURGLARY BURGLARY OF APARTMENT HOUSE, UNLAWFUL ENTRY Sunday\n", "2 LARCENY/THEFT PETTY THEFT OF PROPERTY Sunday\n", "3 VANDALISM MALICIOUS MISCHIEF, GRAFFITI Sunday\n", "4 VANDALISM MALICIOUS MISCHIEF, VANDALISM OF VEHICLES Sunday\n", "5 SUSPICIOUS OCC SUSPICIOUS OCCURRENCE Sunday\n", "6 LARCENY/THEFT PETTY THEFT OF PROPERTY Sunday\n", "7 VEHICLE THEFT STOLEN AUTOMOBILE Sunday\n", "8 VANDALISM MALICIOUS MISCHIEF, BREAKING WINDOWS Sunday\n", "9 VEHICLE THEFT STOLEN AUTOMOBILE Sunday\n", "10 ASSAULT AGGRAVATED ASSAULT WITH BODILY FORCE Sunday\n", "Variables not shown: Date (chr), Time (chr), PdDistrict (chr), Resolution\n", " (chr), X (dbl), Y (dbl)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'# of Rows in Dataframe: 1842050'" ], "text/latex": [ "'# of Rows in Dataframe: 1842050'" ], "text/markdown": [ "'# of Rows in Dataframe: 1842050'" ], "text/plain": [ "[1] \"# of Rows in Dataframe: 1842050\"" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'Dataframe Size: 126.9 Mb'" ], "text/latex": [ "'Dataframe Size: 126.9 Mb'" ], "text/markdown": [ "'Dataframe Size: 126.9 Mb'" ], "text/plain": [ "[1] \"Dataframe Size: 126.9 Mb\"" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns = c(\"Category\", \"Descript\", \"DayOfWeek\", \"Date\", \"Time\", \"PdDistrict\", \"Resolution\", \"X\", \"Y\")\n", "\n", "# select() requires column indices, so use which() to find them\n", "df <- df %>% select(which(names(df) %in% columns))\n", "\n", "df %>% head(10)\n", "sprintf(\"# of Rows in Dataframe: %s\", nrow(df))\n", "sprintf(\"Dataframe Size: %s\", format(object.size(df), units = \"MB\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The All-Caps text is ugly: let's force the text in the appropriate columns into proper case. (see [this Stack Overflow question](http://stackoverflow.com/questions/15776732/how-to-convert-a-vector-of-strings-to-title-case))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>Descript</th><th scope=col>DayOfWeek</th><th scope=col>Date</th><th scope=col>Time</th><th scope=col>PdDistrict</th><th scope=col>Resolution</th><th scope=col>X</th><th scope=col>Y</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Burglary</td><td>Burglary Of Apartment House, Unlawful Entry</td><td>Sunday</td><td>11/15/2015</td><td>23:58</td><td>Ingleside</td><td>None</td><td>-122.4115</td><td>37.74605</td></tr>\n", "\t<tr><th scope=row>2</th><td>Larceny/Theft</td><td>Petty Theft Of Property</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>Bayview</td><td>None</td><td>-122.4016</td><td>37.76484</td></tr>\n", "\t<tr><th scope=row>3</th><td>Vandalism</td><td>Malicious Mischief, Graffiti</td><td>Sunday</td><td>11/15/2015</td><td>23:30</td><td>Park</td><td>None</td><td>-122.4378</td><td>37.76603</td></tr>\n", "\t<tr><th scope=row>4</th><td>Vandalism</td><td>Malicious Mischief, Vandalism Of Vehicles</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>Southern</td><td>None</td><td>-122.3945</td><td>37.7873</td></tr>\n", "\t<tr><th scope=row>5</th><td>Suspicious Occ</td><td>Suspicious Occurrence</td><td>Sunday</td><td>11/15/2015</td><td>23:15</td><td>Southern</td><td>None</td><td>-122.3945</td><td>37.7873</td></tr>\n", "\t<tr><th scope=row>6</th><td>Larceny/Theft</td><td>Petty Theft Of Property</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>Northern</td><td>None</td><td>-122.432</td><td>37.78425</td></tr>\n", "\t<tr><th scope=row>7</th><td>Vehicle Theft</td><td>Stolen Automobile</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>Taraval</td><td>None</td><td>-122.4875</td><td>37.74345</td></tr>\n", "\t<tr><th scope=row>8</th><td>Vandalism</td><td>Malicious Mischief, Breaking Windows</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>Central</td><td>Arrest, Booked</td><td>-122.407</td><td>37.78992</td></tr>\n", "\t<tr><th scope=row>9</th><td>Vehicle Theft</td><td>Stolen Automobile</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>Southern</td><td>None</td><td>-122.4132</td><td>37.77499</td></tr>\n", "\t<tr><th scope=row>10</th><td>Assault</td><td>Aggravated Assault With Bodily Force</td><td>Sunday</td><td>11/15/2015</td><td>22:59</td><td>Mission</td><td>None</td><td>-122.4236</td><td>37.76487</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " & Category & Descript & DayOfWeek & Date & Time & PdDistrict & Resolution & X & Y\\\\\n", "\\hline\n", "\t1 & Burglary & Burglary Of Apartment House, Unlawful Entry & Sunday & 11/15/2015 & 23:58 & Ingleside & None & -122.4115 & 37.74605\\\\\n", "\t2 & Larceny/Theft & Petty Theft Of Property & Sunday & 11/15/2015 & 23:30 & Bayview & None & -122.4016 & 37.76484\\\\\n", "\t3 & Vandalism & Malicious Mischief, Graffiti & Sunday & 11/15/2015 & 23:30 & Park & None & -122.4378 & 37.76603\\\\\n", "\t4 & Vandalism & Malicious Mischief, Vandalism Of Vehicles & Sunday & 11/15/2015 & 23:15 & Southern & None & -122.3945 & 37.7873\\\\\n", "\t5 & Suspicious Occ & Suspicious Occurrence & Sunday & 11/15/2015 & 23:15 & Southern & None & -122.3945 & 37.7873\\\\\n", "\t6 & Larceny/Theft & Petty Theft Of Property & Sunday & 11/15/2015 & 23:00 & Northern & None & -122.432 & 37.78425\\\\\n", "\t7 & Vehicle Theft & Stolen Automobile & Sunday & 11/15/2015 & 23:00 & Taraval & None & -122.4875 & 37.74345\\\\\n", "\t8 & Vandalism & Malicious Mischief, Breaking Windows & Sunday & 11/15/2015 & 23:00 & Central & Arrest, Booked & -122.407 & 37.78992\\\\\n", "\t9 & Vehicle Theft & Stolen Automobile & Sunday & 11/15/2015 & 23:00 & Southern & None & -122.4132 & 37.77499\\\\\n", "\t10 & Assault & Aggravated Assault With Bodily Force & Sunday & 11/15/2015 & 22:59 & Mission & None & -122.4236 & 37.76487\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 9]\n", "\n", " Category Descript DayOfWeek\n", " (chr) (chr) (chr)\n", "1 Burglary Burglary Of Apartment House, Unlawful Entry Sunday\n", "2 Larceny/Theft Petty Theft Of Property Sunday\n", "3 Vandalism Malicious Mischief, Graffiti Sunday\n", "4 Vandalism Malicious Mischief, Vandalism Of Vehicles Sunday\n", "5 Suspicious Occ Suspicious Occurrence Sunday\n", "6 Larceny/Theft Petty Theft Of Property Sunday\n", "7 Vehicle Theft Stolen Automobile Sunday\n", "8 Vandalism Malicious Mischief, Breaking Windows Sunday\n", "9 Vehicle Theft Stolen Automobile Sunday\n", "10 Assault Aggravated Assault With Bodily Force Sunday\n", "Variables not shown: Date (chr), Time (chr), PdDistrict (chr), Resolution\n", " (chr), X (dbl), Y (dbl)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proper_case <- function(x) {\n", " return (gsub(\"\\\\b([A-Z])([A-Z]+)\", \"\\\\U\\\\1\\\\L\\\\2\" , x, perl = TRUE))\n", "}\n", "\n", "df <- df %>% mutate(Category = proper_case(Category),\n", " Descript = proper_case(Descript),\n", " PdDistrict = proper_case(PdDistrict),\n", " Resolution = proper_case(Resolution))\n", "\n", "df %>% head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Filtering the Data\n", "\n", "Let's filter `df` by Arrests to aggregate some intersting statistics." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>Descript</th><th scope=col>DayOfWeek</th><th scope=col>Date</th><th scope=col>Time</th><th scope=col>PdDistrict</th><th scope=col>Resolution</th><th scope=col>X</th><th scope=col>Y</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Vandalism</td><td>Malicious Mischief, Breaking Windows</td><td>Sunday</td><td>11/15/2015</td><td>23:00</td><td>Central</td><td>Arrest, Booked</td><td>-122.407</td><td>37.78992</td></tr>\n", "\t<tr><th scope=row>2</th><td>Assault</td><td>Battery</td><td>Sunday</td><td>11/15/2015</td><td>22:53</td><td>Northern</td><td>Arrest, Booked</td><td>-122.4187</td><td>37.78501</td></tr>\n", "\t<tr><th scope=row>3</th><td>Assault</td><td>Child Abuse (Physical)</td><td>Sunday</td><td>11/15/2015</td><td>22:53</td><td>Northern</td><td>Arrest, Booked</td><td>-122.4187</td><td>37.78501</td></tr>\n", "\t<tr><th scope=row>4</th><td>Other Offenses</td><td>Drivers License, Suspended Or Revoked</td><td>Sunday</td><td>11/15/2015</td><td>22:35</td><td>Southern</td><td>Arrest, Booked</td><td>-122.412</td><td>37.7809</td></tr>\n", "\t<tr><th scope=row>5</th><td>Stolen Property</td><td>Stolen Property, Possession With Knowledge, Receiving</td><td>Sunday</td><td>11/15/2015</td><td>22:20</td><td>Central</td><td>Arrest, Booked</td><td>-122.4185</td><td>37.80615</td></tr>\n", "\t<tr><th scope=row>6</th><td>Other Offenses</td><td>Tampering With A Vehicle</td><td>Sunday</td><td>11/15/2015</td><td>22:20</td><td>Central</td><td>Arrest, Booked</td><td>-122.4185</td><td>37.80615</td></tr>\n", "\t<tr><th scope=row>7</th><td>Warrants</td><td>Enroute To Department Of Corrections</td><td>Sunday</td><td>11/15/2015</td><td>22:20</td><td>Central</td><td>Arrest, Booked</td><td>-122.4185</td><td>37.80615</td></tr>\n", "\t<tr><th scope=row>8</th><td>Secondary Codes</td><td>Domestic Violence</td><td>Sunday</td><td>11/15/2015</td><td>22:00</td><td>Northern</td><td>Arrest, Booked</td><td>-122.4385</td><td>37.79941</td></tr>\n", "\t<tr><th scope=row>9</th><td>Assault</td><td>Threats Against Life</td><td>Sunday</td><td>11/15/2015</td><td>22:00</td><td>Northern</td><td>Arrest, Booked</td><td>-122.4385</td><td>37.79941</td></tr>\n", "\t<tr><th scope=row>10</th><td>Larceny/Theft</td><td>Lost Property, Petty Theft</td><td>Sunday</td><td>11/15/2015</td><td>21:40</td><td>Central</td><td>Arrest, Booked</td><td>-122.4185</td><td>37.80615</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " & Category & Descript & DayOfWeek & Date & Time & PdDistrict & Resolution & X & Y\\\\\n", "\\hline\n", "\t1 & Vandalism & Malicious Mischief, Breaking Windows & Sunday & 11/15/2015 & 23:00 & Central & Arrest, Booked & -122.407 & 37.78992\\\\\n", "\t2 & Assault & Battery & Sunday & 11/15/2015 & 22:53 & Northern & Arrest, Booked & -122.4187 & 37.78501\\\\\n", "\t3 & Assault & Child Abuse (Physical) & Sunday & 11/15/2015 & 22:53 & Northern & Arrest, Booked & -122.4187 & 37.78501\\\\\n", "\t4 & Other Offenses & Drivers License, Suspended Or Revoked & Sunday & 11/15/2015 & 22:35 & Southern & Arrest, Booked & -122.412 & 37.7809\\\\\n", "\t5 & Stolen Property & Stolen Property, Possession With Knowledge, Receiving & Sunday & 11/15/2015 & 22:20 & Central & Arrest, Booked & -122.4185 & 37.80615\\\\\n", "\t6 & Other Offenses & Tampering With A Vehicle & Sunday & 11/15/2015 & 22:20 & Central & Arrest, Booked & -122.4185 & 37.80615\\\\\n", "\t7 & Warrants & Enroute To Department Of Corrections & Sunday & 11/15/2015 & 22:20 & Central & Arrest, Booked & -122.4185 & 37.80615\\\\\n", "\t8 & Secondary Codes & Domestic Violence & Sunday & 11/15/2015 & 22:00 & Northern & Arrest, Booked & -122.4385 & 37.79941\\\\\n", "\t9 & Assault & Threats Against Life & Sunday & 11/15/2015 & 22:00 & Northern & Arrest, Booked & -122.4385 & 37.79941\\\\\n", "\t10 & Larceny/Theft & Lost Property, Petty Theft & Sunday & 11/15/2015 & 21:40 & Central & Arrest, Booked & -122.4185 & 37.80615\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 9]\n", "\n", " Category Descript\n", " (chr) (chr)\n", "1 Vandalism Malicious Mischief, Breaking Windows\n", "2 Assault Battery\n", "3 Assault Child Abuse (Physical)\n", "4 Other Offenses Drivers License, Suspended Or Revoked\n", "5 Stolen Property Stolen Property, Possession With Knowledge, Receiving\n", "6 Other Offenses Tampering With A Vehicle\n", "7 Warrants Enroute To Department Of Corrections\n", "8 Secondary Codes Domestic Violence\n", "9 Assault Threats Against Life\n", "10 Larceny/Theft Lost Property, Petty Theft\n", "Variables not shown: DayOfWeek (chr), Date (chr), Time (chr), PdDistrict (chr),\n", " Resolution (chr), X (dbl), Y (dbl)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'# of Rows in Dataframe: 587499'" ], "text/latex": [ "'# of Rows in Dataframe: 587499'" ], "text/markdown": [ "'# of Rows in Dataframe: 587499'" ], "text/plain": [ "[1] \"# of Rows in Dataframe: 587499\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "'Dataframe Size: 40.7 Mb'" ], "text/latex": [ "'Dataframe Size: 40.7 Mb'" ], "text/markdown": [ "'Dataframe Size: 40.7 Mb'" ], "text/plain": [ "[1] \"Dataframe Size: 40.7 Mb\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# grepl() is the best way to do in-text search\n", "df_arrest <- df %>% filter(grepl(\"Arrest\", Resolution))\n", "\n", "df_arrest %>% head(10)\n", "sprintf(\"# of Rows in Dataframe: %s\", nrow(df_arrest))\n", "sprintf(\"Dataframe Size: %s\", format(object.size(df_arrest), units = \"MB\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Crime Over Time\n", "\n", "Create a chart of crimes over time." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Date</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2003-01-01</td><td>172</td></tr>\n", "\t<tr><th scope=row>2</th><td>2003-01-02</td><td>144</td></tr>\n", "\t<tr><th scope=row>3</th><td>2003-01-03</td><td>191</td></tr>\n", "\t<tr><th scope=row>4</th><td>2003-01-04</td><td>123</td></tr>\n", "\t<tr><th scope=row>5</th><td>2003-01-05</td><td>161</td></tr>\n", "\t<tr><th scope=row>6</th><td>2003-01-06</td><td>184</td></tr>\n", "\t<tr><th scope=row>7</th><td>2003-01-07</td><td>181</td></tr>\n", "\t<tr><th scope=row>8</th><td>2003-01-08</td><td>233</td></tr>\n", "\t<tr><th scope=row>9</th><td>2003-01-09</td><td>183</td></tr>\n", "\t<tr><th scope=row>10</th><td>2003-01-10</td><td>135</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Date & count\\\\\n", "\\hline\n", "\t1 & 2003-01-01 & 172\\\\\n", "\t2 & 2003-01-02 & 144\\\\\n", "\t3 & 2003-01-03 & 191\\\\\n", "\t4 & 2003-01-04 & 123\\\\\n", "\t5 & 2003-01-05 & 161\\\\\n", "\t6 & 2003-01-06 & 184\\\\\n", "\t7 & 2003-01-07 & 181\\\\\n", "\t8 & 2003-01-08 & 233\\\\\n", "\t9 & 2003-01-09 & 183\\\\\n", "\t10 & 2003-01-10 & 135\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 2]\n", "\n", " Date count\n", " (date) (int)\n", "1 2003-01-01 172\n", "2 2003-01-02 144\n", "3 2003-01-03 191\n", "4 2003-01-04 123\n", "5 2003-01-05 161\n", "6 2003-01-06 184\n", "7 2003-01-07 181\n", "8 2003-01-08 233\n", "9 2003-01-09 183\n", "10 2003-01-10 135" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_daily <- df_arrest %>%\n", " mutate(Date = as.Date(Date, \"%m/%d/%Y\")) %>%\n", " group_by(Date) %>% \n", " summarize(count = n()) %>%\n", " arrange(Date)\n", "\n", "df_arrest_daily %>% head(10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "geom_smooth: method=\"auto\" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = \"cs\"). Use 'method = x' to change the smoothing method.\n" ] } ], "source": [ "plot <- ggplot(df_arrest_daily, aes(x = Date, y = count)) +\n", " geom_line(color = \"#F2CA27\", size = 0.1) +\n", " geom_smooth(color = \"#1A1A1A\") +\n", " fte_theme() +\n", " scale_x_date(breaks = date_breaks(\"2 years\"), labels = date_format(\"%Y\")) +\n", " labs(x = \"Date of Arrest\", y = \"# of Police Arrests\", title = \"Daily Police Arrests in San Francisco from 2003 – 2015\")\n", "\n", "max_save(plot, \"sf-arrest-when-1\", \"SF OpenData\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-1.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Crime Time Heatmap\n", "\n", "Aggregate counts of arrests by Day-of-Week and Time to create heat map. Fortunately, the Day-Of-Week part is pre-derived, but Hour is slightly harder." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Friday</td><td>0</td><td>3670</td></tr>\n", "\t<tr><th scope=row>2</th><td>Friday</td><td>1</td><td>2627</td></tr>\n", "\t<tr><th scope=row>3</th><td>Friday</td><td>2</td><td>2277</td></tr>\n", "\t<tr><th scope=row>4</th><td>Friday</td><td>3</td><td>1399</td></tr>\n", "\t<tr><th scope=row>5</th><td>Friday</td><td>4</td><td>986</td></tr>\n", "\t<tr><th scope=row>6</th><td>Friday</td><td>5</td><td>879</td></tr>\n", "\t<tr><th scope=row>7</th><td>Friday</td><td>6</td><td>1294</td></tr>\n", "\t<tr><th scope=row>8</th><td>Friday</td><td>7</td><td>2283</td></tr>\n", "\t<tr><th scope=row>9</th><td>Friday</td><td>8</td><td>2873</td></tr>\n", "\t<tr><th scope=row>10</th><td>Friday</td><td>9</td><td>3227</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " & DayOfWeek & Hour & count\\\\\n", "\\hline\n", "\t1 & Friday & 0 & 3670\\\\\n", "\t2 & Friday & 1 & 2627\\\\\n", "\t3 & Friday & 2 & 2277\\\\\n", "\t4 & Friday & 3 & 1399\\\\\n", "\t5 & Friday & 4 & 986\\\\\n", "\t6 & Friday & 5 & 879\\\\\n", "\t7 & Friday & 6 & 1294\\\\\n", "\t8 & Friday & 7 & 2283\\\\\n", "\t9 & Friday & 8 & 2873\\\\\n", "\t10 & Friday & 9 & 3227\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 3]\n", "Groups: DayOfWeek [1]\n", "\n", " DayOfWeek Hour count\n", " (chr) (dbl) (int)\n", "1 Friday 0 3670\n", "2 Friday 1 2627\n", "3 Friday 2 2277\n", "4 Friday 3 1399\n", "5 Friday 4 986\n", "6 Friday 5 879\n", "7 Friday 6 1294\n", "8 Friday 7 2283\n", "9 Friday 8 2873\n", "10 Friday 9 3227" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Returns the numeric hour component of a string formatted \"HH:MM\", e.g. \"09:40\" input returns 9\n", "get_hour <- function(x) {\n", " return (as.numeric(strsplit(x,\":\")[[1]][1]))\n", "}\n", "\n", "df_arrest_time <- df_arrest %>%\n", " mutate(Hour = sapply(Time, get_hour)) %>%\n", " group_by(DayOfWeek, Hour) %>% \n", " summarize(count = n())\n", "\n", "df_arrest_time %>% head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reorder and format Factors." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Friday</td><td>12 AM</td><td>3670</td></tr>\n", "\t<tr><th scope=row>2</th><td>Friday</td><td>1 AM</td><td>2627</td></tr>\n", "\t<tr><th scope=row>3</th><td>Friday</td><td>2 AM</td><td>2277</td></tr>\n", "\t<tr><th scope=row>4</th><td>Friday</td><td>3 AM</td><td>1399</td></tr>\n", "\t<tr><th scope=row>5</th><td>Friday</td><td>4 AM</td><td>986</td></tr>\n", "\t<tr><th scope=row>6</th><td>Friday</td><td>5 AM</td><td>879</td></tr>\n", "\t<tr><th scope=row>7</th><td>Friday</td><td>6 AM</td><td>1294</td></tr>\n", "\t<tr><th scope=row>8</th><td>Friday</td><td>7 AM</td><td>2283</td></tr>\n", "\t<tr><th scope=row>9</th><td>Friday</td><td>8 AM</td><td>2873</td></tr>\n", "\t<tr><th scope=row>10</th><td>Friday</td><td>9 AM</td><td>3227</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " & DayOfWeek & Hour & count\\\\\n", "\\hline\n", "\t1 & Friday & 12 AM & 3670\\\\\n", "\t2 & Friday & 1 AM & 2627\\\\\n", "\t3 & Friday & 2 AM & 2277\\\\\n", "\t4 & Friday & 3 AM & 1399\\\\\n", "\t5 & Friday & 4 AM & 986\\\\\n", "\t6 & Friday & 5 AM & 879\\\\\n", "\t7 & Friday & 6 AM & 1294\\\\\n", "\t8 & Friday & 7 AM & 2283\\\\\n", "\t9 & Friday & 8 AM & 2873\\\\\n", "\t10 & Friday & 9 AM & 3227\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 3]\n", "Groups: DayOfWeek [1]\n", "\n", " DayOfWeek Hour count\n", " (fctr) (fctr) (int)\n", "1 Friday 12 AM 3670\n", "2 Friday 1 AM 2627\n", "3 Friday 2 AM 2277\n", "4 Friday 3 AM 1399\n", "5 Friday 4 AM 986\n", "6 Friday 5 AM 879\n", "7 Friday 6 AM 1294\n", "8 Friday 7 AM 2283\n", "9 Friday 8 AM 2873\n", "10 Friday 9 AM 3227" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dow_format <- c(\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\")\n", "hour_format <- c(paste(c(12,1:11),\"AM\"), paste(c(12,1:11),\"PM\"))\n", "\n", "df_arrest_time$DayOfWeek <- factor(df_arrest_time$DayOfWeek, level = rev(dow_format))\n", "df_arrest_time$Hour <- factor(df_arrest_time$Hour, level = 0:23, label = hour_format)\n", "\n", "df_arrest_time %>% head(10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot <- ggplot(df_arrest_time, aes(x = Hour, y = DayOfWeek, fill = count)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6), legend.title = element_blank(), legend.position=\"top\", legend.direction=\"horizontal\", legend.key.width=unit(2, \"cm\"), legend.key.height=unit(0.25, \"cm\"), legend.margin=unit(-0.5,\"cm\"), panel.margin=element_blank()) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"# of Police Arrests in San Francisco from 2003 – 2015, by Time of Arrest\") +\n", " scale_fill_gradient(low = \"white\", high = \"#27AE60\", labels = comma)\n", "\n", "max_save(plot, \"sf-arrest-when-2\", \"SF OpenData\", w=6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-2.png)\n", "\n", "Hmm, why is there a surge on Wednesday afternoon, and at 4-5PM on all days? Let's look at subgroups to verify there isn't a latent factor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Factor by Crime Category\n", "\n", "Certain types of crime may be more time dependent. (i.e. more traffic violations when people leave work)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Other Offenses</td><td>183156</td></tr>\n", "\t<tr><th scope=row>2</th><td>Drug/Narcotic</td><td>98400</td></tr>\n", "\t<tr><th scope=row>3</th><td>Warrants</td><td>81426</td></tr>\n", "\t<tr><th scope=row>4</th><td>Assault</td><td>56934</td></tr>\n", "\t<tr><th scope=row>5</th><td>Larceny/Theft</td><td>31369</td></tr>\n", "\t<tr><th scope=row>6</th><td>Prostitution</td><td>14429</td></tr>\n", "\t<tr><th scope=row>7</th><td>Weapon Laws</td><td>11674</td></tr>\n", "\t<tr><th scope=row>8</th><td>Burglary</td><td>10449</td></tr>\n", "\t<tr><th scope=row>9</th><td>Trespass</td><td>10308</td></tr>\n", "\t<tr><th scope=row>10</th><td>Non-Criminal</td><td>10046</td></tr>\n", "\t<tr><th scope=row>11</th><td>Vandalism</td><td>9280</td></tr>\n", "\t<tr><th scope=row>12</th><td>Robbery</td><td>8168</td></tr>\n", "\t<tr><th scope=row>13</th><td>Stolen Property</td><td>8042</td></tr>\n", "\t<tr><th scope=row>14</th><td>Drunkenness</td><td>7202</td></tr>\n", "\t<tr><th scope=row>15</th><td>Secondary Codes</td><td>6960</td></tr>\n", "\t<tr><th scope=row>16</th><td>Disorderly Conduct</td><td>5769</td></tr>\n", "\t<tr><th scope=row>17</th><td>Fraud</td><td>4849</td></tr>\n", "\t<tr><th scope=row>18</th><td>Driving Under The Influence</td><td>4549</td></tr>\n", "\t<tr><th scope=row>19</th><td>Vehicle Theft</td><td>4376</td></tr>\n", "\t<tr><th scope=row>20</th><td>Forgery/Counterfeiting</td><td>4210</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Category & count\\\\\n", "\\hline\n", "\t1 & Other Offenses & 183156\\\\\n", "\t2 & Drug/Narcotic & 98400\\\\\n", "\t3 & Warrants & 81426\\\\\n", "\t4 & Assault & 56934\\\\\n", "\t5 & Larceny/Theft & 31369\\\\\n", "\t6 & Prostitution & 14429\\\\\n", "\t7 & Weapon Laws & 11674\\\\\n", "\t8 & Burglary & 10449\\\\\n", "\t9 & Trespass & 10308\\\\\n", "\t10 & Non-Criminal & 10046\\\\\n", "\t11 & Vandalism & 9280\\\\\n", "\t12 & Robbery & 8168\\\\\n", "\t13 & Stolen Property & 8042\\\\\n", "\t14 & Drunkenness & 7202\\\\\n", "\t15 & Secondary Codes & 6960\\\\\n", "\t16 & Disorderly Conduct & 5769\\\\\n", "\t17 & Fraud & 4849\\\\\n", "\t18 & Driving Under The Influence & 4549\\\\\n", "\t19 & Vehicle Theft & 4376\\\\\n", "\t20 & Forgery/Counterfeiting & 4210\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [20 x 2]\n", "\n", " Category count\n", " (chr) (int)\n", "1 Other Offenses 183156\n", "2 Drug/Narcotic 98400\n", "3 Warrants 81426\n", "4 Assault 56934\n", "5 Larceny/Theft 31369\n", "6 Prostitution 14429\n", "7 Weapon Laws 11674\n", "8 Burglary 10449\n", "9 Trespass 10308\n", "10 Non-Criminal 10046\n", "11 Vandalism 9280\n", "12 Robbery 8168\n", "13 Stolen Property 8042\n", "14 Drunkenness 7202\n", "15 Secondary Codes 6960\n", "16 Disorderly Conduct 5769\n", "17 Fraud 4849\n", "18 Driving Under The Influence 4549\n", "19 Vehicle Theft 4376\n", "20 Forgery/Counterfeiting 4210" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_top_crimes <- df_arrest %>%\n", " group_by(Category) %>% \n", " summarize(count = n()) %>%\n", " arrange(desc(count))\n", "\n", "df_top_crimes %>% head(20)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Assault</td><td>Friday</td><td>12 AM</td><td>408</td></tr>\n", "\t<tr><th scope=row>2</th><td>Assault</td><td>Friday</td><td>1 AM</td><td>341</td></tr>\n", "\t<tr><th scope=row>3</th><td>Assault</td><td>Friday</td><td>2 AM</td><td>326</td></tr>\n", "\t<tr><th scope=row>4</th><td>Assault</td><td>Friday</td><td>3 AM</td><td>149</td></tr>\n", "\t<tr><th scope=row>5</th><td>Assault</td><td>Friday</td><td>4 AM</td><td>105</td></tr>\n", "\t<tr><th scope=row>6</th><td>Assault</td><td>Friday</td><td>5 AM</td><td>88</td></tr>\n", "\t<tr><th scope=row>7</th><td>Assault</td><td>Friday</td><td>6 AM</td><td>113</td></tr>\n", "\t<tr><th scope=row>8</th><td>Assault</td><td>Friday</td><td>7 AM</td><td>193</td></tr>\n", "\t<tr><th scope=row>9</th><td>Assault</td><td>Friday</td><td>8 AM</td><td>238</td></tr>\n", "\t<tr><th scope=row>10</th><td>Assault</td><td>Friday</td><td>9 AM</td><td>254</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & Category & DayOfWeek & Hour & count\\\\\n", "\\hline\n", "\t1 & Assault & Friday & 12 AM & 408\\\\\n", "\t2 & Assault & Friday & 1 AM & 341\\\\\n", "\t3 & Assault & Friday & 2 AM & 326\\\\\n", "\t4 & Assault & Friday & 3 AM & 149\\\\\n", "\t5 & Assault & Friday & 4 AM & 105\\\\\n", "\t6 & Assault & Friday & 5 AM & 88\\\\\n", "\t7 & Assault & Friday & 6 AM & 113\\\\\n", "\t8 & Assault & Friday & 7 AM & 193\\\\\n", "\t9 & Assault & Friday & 8 AM & 238\\\\\n", "\t10 & Assault & Friday & 9 AM & 254\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 4]\n", "Groups: Category, DayOfWeek [1]\n", "\n", " Category DayOfWeek Hour count\n", " (chr) (fctr) (fctr) (int)\n", "1 Assault Friday 12 AM 408\n", "2 Assault Friday 1 AM 341\n", "3 Assault Friday 2 AM 326\n", "4 Assault Friday 3 AM 149\n", "5 Assault Friday 4 AM 105\n", "6 Assault Friday 5 AM 88\n", "7 Assault Friday 6 AM 113\n", "8 Assault Friday 7 AM 193\n", "9 Assault Friday 8 AM 238\n", "10 Assault Friday 9 AM 254" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_time_crime <- df_arrest %>%\n", " filter(Category %in% df_top_crimes$Category[2:19]) %>%\n", " mutate(Hour = sapply(Time, get_hour)) %>%\n", " group_by(Category, DayOfWeek, Hour) %>% \n", " summarize(count = n())\n", "\n", "df_arrest_time_crime$DayOfWeek <- factor(df_arrest_time_crime$DayOfWeek, level = rev(dow_format))\n", "df_arrest_time_crime$Hour <- factor(df_arrest_time_crime$Hour, level = 0:23, label = hour_format)\n", "\n", "df_arrest_time_crime %>% head(10)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = count)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"# of Police Arrests in San Francisco from 2003 – 2015, by Category and Time of Arrest\") +\n", " scale_fill_gradient(low = \"white\", high = \"#2980B9\") +\n", " facet_wrap(~ Category, nrow = 6)\n", "\n", "max_save(plot, \"sf-arrest-when-3\", \"SF OpenData\", w = 6, h = 8, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-3.png)\n", "\n", "Good, but the gradients aren't helpful because they are not normalized. We need to normalize the range on each facet. (unfortunately, this makes the value of the gradient unhelpful)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Category</th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th><th scope=col>norm</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Assault</td><td>Friday</td><td>12 AM</td><td>408</td><td>0.007166192</td></tr>\n", "\t<tr><th scope=row>2</th><td>Assault</td><td>Friday</td><td>1 AM</td><td>341</td><td>0.005989391</td></tr>\n", "\t<tr><th scope=row>3</th><td>Assault</td><td>Friday</td><td>2 AM</td><td>326</td><td>0.005725928</td></tr>\n", "\t<tr><th scope=row>4</th><td>Assault</td><td>Friday</td><td>3 AM</td><td>149</td><td>0.002617065</td></tr>\n", "\t<tr><th scope=row>5</th><td>Assault</td><td>Friday</td><td>4 AM</td><td>105</td><td>0.001844241</td></tr>\n", "\t<tr><th scope=row>6</th><td>Assault</td><td>Friday</td><td>5 AM</td><td>88</td><td>0.001545649</td></tr>\n", "\t<tr><th scope=row>7</th><td>Assault</td><td>Friday</td><td>6 AM</td><td>113</td><td>0.001984754</td></tr>\n", "\t<tr><th scope=row>8</th><td>Assault</td><td>Friday</td><td>7 AM</td><td>193</td><td>0.00338989</td></tr>\n", "\t<tr><th scope=row>9</th><td>Assault</td><td>Friday</td><td>8 AM</td><td>238</td><td>0.004180279</td></tr>\n", "\t<tr><th scope=row>10</th><td>Assault</td><td>Friday</td><td>9 AM</td><td>254</td><td>0.004461306</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Category & DayOfWeek & Hour & count & norm\\\\\n", "\\hline\n", "\t1 & Assault & Friday & 12 AM & 408 & 0.007166192\\\\\n", "\t2 & Assault & Friday & 1 AM & 341 & 0.005989391\\\\\n", "\t3 & Assault & Friday & 2 AM & 326 & 0.005725928\\\\\n", "\t4 & Assault & Friday & 3 AM & 149 & 0.002617065\\\\\n", "\t5 & Assault & Friday & 4 AM & 105 & 0.001844241\\\\\n", "\t6 & Assault & Friday & 5 AM & 88 & 0.001545649\\\\\n", "\t7 & Assault & Friday & 6 AM & 113 & 0.001984754\\\\\n", "\t8 & Assault & Friday & 7 AM & 193 & 0.00338989\\\\\n", "\t9 & Assault & Friday & 8 AM & 238 & 0.004180279\\\\\n", "\t10 & Assault & Friday & 9 AM & 254 & 0.004461306\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 5]\n", "Groups: Category [1]\n", "\n", " Category DayOfWeek Hour count norm\n", " (chr) (fctr) (fctr) (int) (dbl)\n", "1 Assault Friday 12 AM 408 0.007166192\n", "2 Assault Friday 1 AM 341 0.005989391\n", "3 Assault Friday 2 AM 326 0.005725928\n", "4 Assault Friday 3 AM 149 0.002617065\n", "5 Assault Friday 4 AM 105 0.001844241\n", "6 Assault Friday 5 AM 88 0.001545649\n", "7 Assault Friday 6 AM 113 0.001984754\n", "8 Assault Friday 7 AM 193 0.003389890\n", "9 Assault Friday 8 AM 238 0.004180279\n", "10 Assault Friday 9 AM 254 0.004461306" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_time_crime <- df_arrest_time_crime %>%\n", " group_by(Category) %>%\n", " mutate(norm = count/sum(count))\n", "\n", "df_arrest_time_crime %>% head(10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = norm)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"Police Arrests in San Francisco from 2003 – 2015 by Time of Arrest, Normalized by Type of Crime\") +\n", " scale_fill_gradient(low = \"white\", high = \"#2980B9\") +\n", " facet_wrap(~ Category, nrow = 6)\n", "\n", "max_save(plot, \"sf-arrest-when-4\", \"SF OpenData\", w = 6, h = 8, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-4.png)\n", "\n", "Much more helpful." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Factor by Police District\n", "\n", "Same as above, but with a different facet." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>PdDistrict</th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th><th scope=col>norm</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Bayview</td><td>Friday</td><td>12 AM</td><td>347</td><td>0.005714474</td></tr>\n", "\t<tr><th scope=row>2</th><td>Bayview</td><td>Friday</td><td>1 AM</td><td>195</td><td>0.003211304</td></tr>\n", "\t<tr><th scope=row>3</th><td>Bayview</td><td>Friday</td><td>2 AM</td><td>151</td><td>0.002486702</td></tr>\n", "\t<tr><th scope=row>4</th><td>Bayview</td><td>Friday</td><td>3 AM</td><td>90</td><td>0.00148214</td></tr>\n", "\t<tr><th scope=row>5</th><td>Bayview</td><td>Friday</td><td>4 AM</td><td>101</td><td>0.001663291</td></tr>\n", "\t<tr><th scope=row>6</th><td>Bayview</td><td>Friday</td><td>5 AM</td><td>81</td><td>0.001333926</td></tr>\n", "\t<tr><th scope=row>7</th><td>Bayview</td><td>Friday</td><td>6 AM</td><td>100</td><td>0.001646822</td></tr>\n", "\t<tr><th scope=row>8</th><td>Bayview</td><td>Friday</td><td>7 AM</td><td>226</td><td>0.003721819</td></tr>\n", "\t<tr><th scope=row>9</th><td>Bayview</td><td>Friday</td><td>8 AM</td><td>313</td><td>0.005154554</td></tr>\n", "\t<tr><th scope=row>10</th><td>Bayview</td><td>Friday</td><td>9 AM</td><td>397</td><td>0.006537885</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & PdDistrict & DayOfWeek & Hour & count & norm\\\\\n", "\\hline\n", "\t1 & Bayview & Friday & 12 AM & 347 & 0.005714474\\\\\n", "\t2 & Bayview & Friday & 1 AM & 195 & 0.003211304\\\\\n", "\t3 & Bayview & Friday & 2 AM & 151 & 0.002486702\\\\\n", "\t4 & Bayview & Friday & 3 AM & 90 & 0.00148214\\\\\n", "\t5 & Bayview & Friday & 4 AM & 101 & 0.001663291\\\\\n", "\t6 & Bayview & Friday & 5 AM & 81 & 0.001333926\\\\\n", "\t7 & Bayview & Friday & 6 AM & 100 & 0.001646822\\\\\n", "\t8 & Bayview & Friday & 7 AM & 226 & 0.003721819\\\\\n", "\t9 & Bayview & Friday & 8 AM & 313 & 0.005154554\\\\\n", "\t10 & Bayview & Friday & 9 AM & 397 & 0.006537885\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 5]\n", "Groups: PdDistrict [1]\n", "\n", " PdDistrict DayOfWeek Hour count norm\n", " (chr) (fctr) (fctr) (int) (dbl)\n", "1 Bayview Friday 12 AM 347 0.005714474\n", "2 Bayview Friday 1 AM 195 0.003211304\n", "3 Bayview Friday 2 AM 151 0.002486702\n", "4 Bayview Friday 3 AM 90 0.001482140\n", "5 Bayview Friday 4 AM 101 0.001663291\n", "6 Bayview Friday 5 AM 81 0.001333926\n", "7 Bayview Friday 6 AM 100 0.001646822\n", "8 Bayview Friday 7 AM 226 0.003721819\n", "9 Bayview Friday 8 AM 313 0.005154554\n", "10 Bayview Friday 9 AM 397 0.006537885" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_time_district <- df_arrest %>%\n", " mutate(Hour = sapply(Time, get_hour)) %>%\n", " group_by(PdDistrict, DayOfWeek, Hour) %>% \n", " summarize(count = n()) %>%\n", " group_by(PdDistrict) %>%\n", " mutate(norm = count/sum(count))\n", "\n", "df_arrest_time_district$DayOfWeek <- factor(df_arrest_time_district$DayOfWeek, level = rev(dow_format))\n", "df_arrest_time_district$Hour <- factor(df_arrest_time_district$Hour, level = 0:23, label = hour_format)\n", "\n", "df_arrest_time_district %>% head(10)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot <- ggplot(df_arrest_time_district, aes(x = Hour, y = DayOfWeek, fill = norm)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"Police Arrests in San Francisco from 2003 – 2015 by Time of Arrest, Normalized by Station\") +\n", " scale_fill_gradient(low = \"white\", high = \"#8E44AD\") +\n", " facet_wrap(~ PdDistrict, nrow = 5)\n", "\n", "max_save(plot, \"sf-arrest-when-5\", \"SF OpenData\", w = 6, h = 8, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-5.png)\n", "\n", "Not helpful either. Meh." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Factor by Month\n", "\n", "If crime is tied to activities, the period at which activies end may impact." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Month</th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th><th scope=col>norm</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>April</td><td>Friday</td><td>12 AM</td><td>292</td><td>0.005988884</td></tr>\n", "\t<tr><th scope=row>2</th><td>April</td><td>Friday</td><td>1 AM</td><td>187</td><td>0.003835347</td></tr>\n", "\t<tr><th scope=row>3</th><td>April</td><td>Friday</td><td>2 AM</td><td>209</td><td>0.004286564</td></tr>\n", "\t<tr><th scope=row>4</th><td>April</td><td>Friday</td><td>3 AM</td><td>98</td><td>0.002009968</td></tr>\n", "\t<tr><th scope=row>5</th><td>April</td><td>Friday</td><td>4 AM</td><td>103</td><td>0.002112517</td></tr>\n", "\t<tr><th scope=row>6</th><td>April</td><td>Friday</td><td>5 AM</td><td>53</td><td>0.001087023</td></tr>\n", "\t<tr><th scope=row>7</th><td>April</td><td>Friday</td><td>6 AM</td><td>107</td><td>0.002194557</td></tr>\n", "\t<tr><th scope=row>8</th><td>April</td><td>Friday</td><td>7 AM</td><td>190</td><td>0.003896876</td></tr>\n", "\t<tr><th scope=row>9</th><td>April</td><td>Friday</td><td>8 AM</td><td>216</td><td>0.004430133</td></tr>\n", "\t<tr><th scope=row>10</th><td>April</td><td>Friday</td><td>9 AM</td><td>284</td><td>0.005824805</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Month & DayOfWeek & Hour & count & norm\\\\\n", "\\hline\n", "\t1 & April & Friday & 12 AM & 292 & 0.005988884\\\\\n", "\t2 & April & Friday & 1 AM & 187 & 0.003835347\\\\\n", "\t3 & April & Friday & 2 AM & 209 & 0.004286564\\\\\n", "\t4 & April & Friday & 3 AM & 98 & 0.002009968\\\\\n", "\t5 & April & Friday & 4 AM & 103 & 0.002112517\\\\\n", "\t6 & April & Friday & 5 AM & 53 & 0.001087023\\\\\n", "\t7 & April & Friday & 6 AM & 107 & 0.002194557\\\\\n", "\t8 & April & Friday & 7 AM & 190 & 0.003896876\\\\\n", "\t9 & April & Friday & 8 AM & 216 & 0.004430133\\\\\n", "\t10 & April & Friday & 9 AM & 284 & 0.005824805\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 5]\n", "Groups: Month [1]\n", "\n", " Month DayOfWeek Hour count norm\n", " (chr) (fctr) (fctr) (int) (dbl)\n", "1 April Friday 12 AM 292 0.005988884\n", "2 April Friday 1 AM 187 0.003835347\n", "3 April Friday 2 AM 209 0.004286564\n", "4 April Friday 3 AM 98 0.002009968\n", "5 April Friday 4 AM 103 0.002112517\n", "6 April Friday 5 AM 53 0.001087023\n", "7 April Friday 6 AM 107 0.002194557\n", "8 April Friday 7 AM 190 0.003896876\n", "9 April Friday 8 AM 216 0.004430133\n", "10 April Friday 9 AM 284 0.005824805" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_time_month <- df_arrest %>%\n", " mutate(Month = format(as.Date(Date, \"%m/%d/%Y\"), \"%B\"), Hour = sapply(Time, get_hour)) %>%\n", " group_by(Month, DayOfWeek, Hour) %>% \n", " summarize(count = n()) %>%\n", " group_by(Month) %>%\n", " mutate(norm = count/sum(count))\n", "\n", "df_arrest_time_month$DayOfWeek <- factor(df_arrest_time_month$DayOfWeek, level = rev(dow_format))\n", "df_arrest_time_month$Hour <- factor(df_arrest_time_month$Hour, level = 0:23, label = hour_format)\n", "\n", "df_arrest_time_month %>% head(10)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set order of month facets by chronological order instead of alphabetical\n", "df_arrest_time_month$Month <- factor(df_arrest_time_month$Month,\n", " level = c(\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"))\n", "\n", "plot <- ggplot(df_arrest_time_month, aes(x = Hour, y = DayOfWeek, fill = norm)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"Police Arrests in San Francisco from 2003 – 2015 by Time of Arrest, Normalized by Month\") +\n", " scale_fill_gradient(low = \"white\", high = \"#E74C3C\") +\n", " facet_wrap(~ Month, nrow = 4)\n", "\n", "max_save(plot, \"sf-arrest-when-6\", \"SF OpenData\", w = 6, h = 6, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-6.png)\n", "\n", "That is not helpful either!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Factor By Year\n", "\n", "Perhaps things changed overtime?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Year</th><th scope=col>DayOfWeek</th><th scope=col>Hour</th><th scope=col>count</th><th scope=col>norm</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2003</td><td>Friday</td><td>12 AM</td><td>295</td><td>0.005575084</td></tr>\n", "\t<tr><th scope=row>2</th><td>2003</td><td>Friday</td><td>1 AM</td><td>195</td><td>0.003685225</td></tr>\n", "\t<tr><th scope=row>3</th><td>2003</td><td>Friday</td><td>2 AM</td><td>181</td><td>0.003420645</td></tr>\n", "\t<tr><th scope=row>4</th><td>2003</td><td>Friday</td><td>3 AM</td><td>155</td><td>0.002929281</td></tr>\n", "\t<tr><th scope=row>5</th><td>2003</td><td>Friday</td><td>4 AM</td><td>61</td><td>0.001152814</td></tr>\n", "\t<tr><th scope=row>6</th><td>2003</td><td>Friday</td><td>5 AM</td><td>95</td><td>0.001795366</td></tr>\n", "\t<tr><th scope=row>7</th><td>2003</td><td>Friday</td><td>6 AM</td><td>152</td><td>0.002872586</td></tr>\n", "\t<tr><th scope=row>8</th><td>2003</td><td>Friday</td><td>7 AM</td><td>240</td><td>0.004535662</td></tr>\n", "\t<tr><th scope=row>9</th><td>2003</td><td>Friday</td><td>8 AM</td><td>296</td><td>0.005593983</td></tr>\n", "\t<tr><th scope=row>10</th><td>2003</td><td>Friday</td><td>9 AM</td><td>395</td><td>0.007464943</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Year & DayOfWeek & Hour & count & norm\\\\\n", "\\hline\n", "\t1 & 2003 & Friday & 12 AM & 295 & 0.005575084\\\\\n", "\t2 & 2003 & Friday & 1 AM & 195 & 0.003685225\\\\\n", "\t3 & 2003 & Friday & 2 AM & 181 & 0.003420645\\\\\n", "\t4 & 2003 & Friday & 3 AM & 155 & 0.002929281\\\\\n", "\t5 & 2003 & Friday & 4 AM & 61 & 0.001152814\\\\\n", "\t6 & 2003 & Friday & 5 AM & 95 & 0.001795366\\\\\n", "\t7 & 2003 & Friday & 6 AM & 152 & 0.002872586\\\\\n", "\t8 & 2003 & Friday & 7 AM & 240 & 0.004535662\\\\\n", "\t9 & 2003 & Friday & 8 AM & 296 & 0.005593983\\\\\n", "\t10 & 2003 & Friday & 9 AM & 395 & 0.007464943\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 5]\n", "Groups: Year [1]\n", "\n", " Year DayOfWeek Hour count norm\n", " (chr) (fctr) (fctr) (int) (dbl)\n", "1 2003 Friday 12 AM 295 0.005575084\n", "2 2003 Friday 1 AM 195 0.003685225\n", "3 2003 Friday 2 AM 181 0.003420645\n", "4 2003 Friday 3 AM 155 0.002929281\n", "5 2003 Friday 4 AM 61 0.001152814\n", "6 2003 Friday 5 AM 95 0.001795366\n", "7 2003 Friday 6 AM 152 0.002872586\n", "8 2003 Friday 7 AM 240 0.004535662\n", "9 2003 Friday 8 AM 296 0.005593983\n", "10 2003 Friday 9 AM 395 0.007464943" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_time_year <- df_arrest %>%\n", " mutate(Year = format(as.Date(Date, \"%m/%d/%Y\"), \"%Y\"), Hour = sapply(Time, get_hour)) %>%\n", " group_by(Year, DayOfWeek, Hour) %>% \n", " summarize(count = n()) %>%\n", " group_by(Year) %>%\n", " mutate(norm = count/sum(count))\n", "\n", "df_arrest_time_year$DayOfWeek <- factor(df_arrest_time_year$DayOfWeek, level = rev(dow_format))\n", "df_arrest_time_year$Hour <- factor(df_arrest_time_year$Hour, level = 0:23, label = hour_format)\n", "\n", "df_arrest_time_year %>% head(10)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot <- ggplot(df_arrest_time_year, aes(x = Hour, y = DayOfWeek, fill = norm)) +\n", " geom_tile() +\n", " fte_theme() +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Day of Week of Arrest\", title = \"Police Arrests in San Francisco from 2003 – 2015 by Time of Arrest, Normalized by Year\") +\n", " scale_fill_gradient(low = \"white\", high = \"#E67E22\") +\n", " facet_wrap(~ Year, nrow = 6)\n", "\n", "max_save(plot, \"sf-arrest-when-7\", \"SF OpenData\", w = 6, h = 6, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sf-arrest-when-7.png)\n", "\n", "Ack, not really." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot with ggmap\n", "\n", "Let's try working with maps. *(Ed. Note: Due to their size, the maps will not be embedded directly into the notebook, but they will be available in the repository.}*\n", "\n", "We can use the CSV output of the [Bounding Box Tool](http://boundingbox.klokantech.com) to easily choose explicit bounds." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Map from URL : http://tile.stamen.com/toner-lite/13/1308/3165.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1309/3165.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1310/3165.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1311/3165.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1308/3166.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1309/3166.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1310/3166.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1311/3166.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1308/3167.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1309/3167.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1310/3167.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1311/3167.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1308/3168.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1309/3168.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1310/3168.png\n", "Map from URL : http://tile.stamen.com/toner-lite/13/1311/3168.png\n" ] } ], "source": [ "bbox = c(-122.516441,37.702072,-122.37276,37.811818)\n", "\n", "# credit to /u/all_genes_considered for map setting suggestion\n", "map <- get_map(location = bbox, source = \"stamen\", maptype = \"toner-lite\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test map download." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<strong>pdf:</strong> 2" ], "text/latex": [ "\\textbf{pdf:} 2" ], "text/markdown": [ "**pdf:** 2" ], "text/plain": [ "pdf \n", " 2 " ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "png(\"sf-arrest-where-0.png\", w=900, h=900, res=300)\n", "ggmap(map)\n", "dev.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"white space\" issue noted in the bootstrap article is still present due to the fixed ratio of the ggmap. You will need to tweak chart dimensions accordingly." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot <- ggmap(map) +\n", " geom_point(data = df_arrest, aes(x=X, y=Y), color = \"#27AE60\", size = 0.5, alpha = 0.01) +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " theme(plot.margin = unit(c(0.3, 0.3, -0.25, 0), \"cm\")) +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015\")\n", "\n", "max_save(plot, \"sf-arrest-where-1\", \"SF OpenData\", w = 3.8, h = 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can facet the map by the Type of Crime using `facet_wrap`. (contrary to notes in the documentation, setting the ggplot as the `base_layer` is apparently not necessary, and imposes a performance penalty)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot <- ggmap(map) +\n", " geom_point(data = df_arrest %>% filter(Category %in% df_top_crimes$Category[2:19]), aes(x=X, y=Y, color=Category), size=0.75, alpha=0.05) +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, by Type of Crime\") +\n", " facet_wrap(~ Category, nrow = 3)\n", "\n", "max_save(plot, \"sf-arrest-where-2\", \"SF OpenData\", w = 14.2, h = 8, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's normalize the above plot for each facter, with Hex aggregation." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Do not show hex if sum is below threshold\n", "sum_thresh <- function(x, threshold = 10^-3) {\n", " if (sum(x) < threshold) {return (NA)}\n", " else {return (sum(x))}\n", "}\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data = df_arrest %>% filter(Category %in% df_top_crimes$Category[2:19]) %>% group_by(Category) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#2980B9\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Type of Crime\") +\n", " facet_wrap(~ Category, nrow = 3)\n", "\n", "max_save(plot, \"sf-arrest-where-3\", \"SF OpenData\", w = 14.2, h = 8, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facet by police districts." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot <- ggmap(map) +\n", " stat_summary_hex(data = df_arrest %>% group_by(PdDistrict) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#8E44AD\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Police District\") +\n", " facet_wrap(~ PdDistrict, nrow = 2)\n", "\n", "max_save(plot, \"sf-arrest-where-4\", \"SF OpenData\", w = 13, h = 6, tall = T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facet by months. (The raw month must be appended to the original `df_arrest` data frame now)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_arrest <- df_arrest %>% mutate(Month=format(as.Date(Date, \"%m/%d/%Y\"), \"%B\"))\n", "df_arrest$Month <- factor(df_arrest$Month,\n", " level = c(\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"))\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data = df_arrest %>% group_by(Month) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#E74C3C\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Month\") +\n", " facet_wrap(~ Month, nrow=2)\n", "\n", "max_save(plot, \"sf-arrest-where-5\", \"SF OpenData\", w=13, h=5, tall=T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facet by year." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_arrest <- df_arrest %>% mutate(Year=format(as.Date(Date, \"%m/%d/%Y\"), \"%Y\"))\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data=df_arrest %>% group_by(Year) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#E67E22\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Year\") +\n", " facet_wrap(~ Year, nrow=2)\n", "\n", "max_save(plot, \"sf-arrest-where-6\", \"SF OpenData\", w=10.5, h=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facet by hour of day." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_arrest <- df_arrest %>% mutate(Hour = sapply(Time, get_hour))\n", "df_arrest$Hour <- factor(df_arrest$Hour, level = 0:23, label = hour_format)\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data=df_arrest %>% group_by(Hour) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#1ABC9C\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Hour\") +\n", " facet_wrap(~ Hour, nrow=4)\n", "\n", "max_save(plot, \"sf-arrest-where-7\", \"SF OpenData\", w=10.5, h=8, tall=T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facet by Day of Week" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_arrest$DayOfWeek <- factor(df_arrest$DayOfWeek, level = dow_format)\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data=df_arrest %>% group_by(DayOfWeek) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#16A085\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by Day of Week\") +\n", " facet_wrap(~ DayOfWeek, nrow=2)\n", "\n", "max_save(plot, \"sf-arrest-where-8\", \"SF OpenData\", w=10.5, h=6, tall=T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus: Does Social Security payments lead to arrests?\n", "\n", "Followup analysis to [/u/NowProveIt's comment on Reddit](https://www.reddit.com/r/sanfrancisco/comments/3vfgg2/analyzing_san_francisco_crime_data_to_determine/cxn29wd) suggesting that SSI payments lead to higher activity on Wednesday. Here's a code fragment to create a data frame of Wednesdays and their month-wise ordinals." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Date</th><th scope=col>weekday</th><th scope=col>month</th><th scope=col>year</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2003-01-01</td><td>Wednesday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>2</th><td>2003-01-02</td><td>Thursday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>3</th><td>2003-01-03</td><td>Friday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>4</th><td>2003-01-04</td><td>Saturday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>5</th><td>2003-01-05</td><td>Sunday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>6</th><td>2003-01-06</td><td>Monday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>7</th><td>2003-01-07</td><td>Tuesday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>8</th><td>2003-01-08</td><td>Wednesday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>9</th><td>2003-01-09</td><td>Thursday</td><td>January</td><td>2003</td></tr>\n", "\t<tr><th scope=row>10</th><td>2003-01-10</td><td>Friday</td><td>January</td><td>2003</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & Date & weekday & month & year\\\\\n", "\\hline\n", "\t1 & 2003-01-01 & Wednesday & January & 2003\\\\\n", "\t2 & 2003-01-02 & Thursday & January & 2003\\\\\n", "\t3 & 2003-01-03 & Friday & January & 2003\\\\\n", "\t4 & 2003-01-04 & Saturday & January & 2003\\\\\n", "\t5 & 2003-01-05 & Sunday & January & 2003\\\\\n", "\t6 & 2003-01-06 & Monday & January & 2003\\\\\n", "\t7 & 2003-01-07 & Tuesday & January & 2003\\\\\n", "\t8 & 2003-01-08 & Wednesday & January & 2003\\\\\n", "\t9 & 2003-01-09 & Thursday & January & 2003\\\\\n", "\t10 & 2003-01-10 & Friday & January & 2003\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 4]\n", "\n", " Date weekday month year\n", " (date) (chr) (chr) (chr)\n", "1 2003-01-01 Wednesday January 2003\n", "2 2003-01-02 Thursday January 2003\n", "3 2003-01-03 Friday January 2003\n", "4 2003-01-04 Saturday January 2003\n", "5 2003-01-05 Sunday January 2003\n", "6 2003-01-06 Monday January 2003\n", "7 2003-01-07 Tuesday January 2003\n", "8 2003-01-08 Wednesday January 2003\n", "9 2003-01-09 Thursday January 2003\n", "10 2003-01-10 Friday January 2003" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date <- \"2003-01-01\"\n", "end_date <- \"2015-11-15\"\n", "\n", "# Create a vector of all days between start and end date\n", "days <- seq(as.Date(start_date), as.Date(end_date), \"days\")\n", "\n", "df_dates <- tbl_df(data.frame(Date = days)) %>%\n", " mutate(weekday = format(Date, \"%A\"),\n", " month = format(Date, \"%B\"),\n", " year = format(Date, \"%Y\"))\n", "\n", "df_dates %>% head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use window function shennanigans to get the ordinal ranks." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Date</th><th scope=col>weekday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>rank</th><th scope=col>ordinal</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>01/01/2003</td><td>Wednesday</td><td>January</td><td>2003</td><td>First</td><td>First Wednesday</td></tr>\n", "\t<tr><th scope=row>2</th><td>01/08/2003</td><td>Wednesday</td><td>January</td><td>2003</td><td>Second</td><td>Second Wednesday</td></tr>\n", "\t<tr><th scope=row>3</th><td>01/15/2003</td><td>Wednesday</td><td>January</td><td>2003</td><td>Third</td><td>Third Wednesday</td></tr>\n", "\t<tr><th scope=row>4</th><td>01/22/2003</td><td>Wednesday</td><td>January</td><td>2003</td><td>Fourth</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>5</th><td>02/05/2003</td><td>Wednesday</td><td>February</td><td>2003</td><td>First</td><td>First Wednesday</td></tr>\n", "\t<tr><th scope=row>6</th><td>02/12/2003</td><td>Wednesday</td><td>February</td><td>2003</td><td>Second</td><td>Second Wednesday</td></tr>\n", "\t<tr><th scope=row>7</th><td>02/19/2003</td><td>Wednesday</td><td>February</td><td>2003</td><td>Third</td><td>Third Wednesday</td></tr>\n", "\t<tr><th scope=row>8</th><td>02/26/2003</td><td>Wednesday</td><td>February</td><td>2003</td><td>Fourth</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>9</th><td>03/05/2003</td><td>Wednesday</td><td>March</td><td>2003</td><td>First</td><td>First Wednesday</td></tr>\n", "\t<tr><th scope=row>10</th><td>03/12/2003</td><td>Wednesday</td><td>March</td><td>2003</td><td>Second</td><td>Second Wednesday</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Date & weekday & month & year & rank & ordinal\\\\\n", "\\hline\n", "\t1 & 01/01/2003 & Wednesday & January & 2003 & First & First Wednesday\\\\\n", "\t2 & 01/08/2003 & Wednesday & January & 2003 & Second & Second Wednesday\\\\\n", "\t3 & 01/15/2003 & Wednesday & January & 2003 & Third & Third Wednesday\\\\\n", "\t4 & 01/22/2003 & Wednesday & January & 2003 & Fourth & Fourth Wednesday\\\\\n", "\t5 & 02/05/2003 & Wednesday & February & 2003 & First & First Wednesday\\\\\n", "\t6 & 02/12/2003 & Wednesday & February & 2003 & Second & Second Wednesday\\\\\n", "\t7 & 02/19/2003 & Wednesday & February & 2003 & Third & Third Wednesday\\\\\n", "\t8 & 02/26/2003 & Wednesday & February & 2003 & Fourth & Fourth Wednesday\\\\\n", "\t9 & 03/05/2003 & Wednesday & March & 2003 & First & First Wednesday\\\\\n", "\t10 & 03/12/2003 & Wednesday & March & 2003 & Second & Second Wednesday\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 6]\n", "Groups: year, month [3]\n", "\n", " Date weekday month year rank ordinal\n", " (chr) (chr) (chr) (chr) (fctr) (chr)\n", "1 01/01/2003 Wednesday January 2003 First First Wednesday\n", "2 01/08/2003 Wednesday January 2003 Second Second Wednesday\n", "3 01/15/2003 Wednesday January 2003 Third Third Wednesday\n", "4 01/22/2003 Wednesday January 2003 Fourth Fourth Wednesday\n", "5 02/05/2003 Wednesday February 2003 First First Wednesday\n", "6 02/12/2003 Wednesday February 2003 Second Second Wednesday\n", "7 02/19/2003 Wednesday February 2003 Third Third Wednesday\n", "8 02/26/2003 Wednesday February 2003 Fourth Fourth Wednesday\n", "9 03/05/2003 Wednesday March 2003 First First Wednesday\n", "10 03/12/2003 Wednesday March 2003 Second Second Wednesday" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Text values to replace numeric ordinals\n", "ordinals <- c(\"First\", \"Second\", \"Third\", \"Fourth\")\n", "\n", "df_dates <- df_dates %>%\n", " filter(weekday == \"Wednesday\") %>%\n", " group_by(year, month) %>%\n", " mutate(rank = rank(Date)) %>%\n", " filter(rank <= 4) %>% # removes the rare 5th Wednesday\n", " mutate(Date = format(Date, format = \"%m/%d/%Y\"), # needs to be proper format for merging\n", " rank = factor(rank, levels = 1:4, labels = ordinals),\n", " ordinal = paste(rank, weekday))\n", "\n", "df_dates %>% head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Combine with the arrest data frame." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Joining by: \"Date\"\n" ] }, { "data": { "text/html": [ "'NA values present from Merge: FALSE'" ], "text/latex": [ "'NA values present from Merge: FALSE'" ], "text/markdown": [ "'NA values present from Merge: FALSE'" ], "text/plain": [ "[1] \"NA values present from Merge: FALSE\"" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Date</th><th scope=col>Time</th><th scope=col>X</th><th scope=col>Y</th><th scope=col>ordinal</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>11/26/2003</td><td>11:15</td><td>-122.4106</td><td>37.7825</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>2</th><td>09/03/2003</td><td>22:17</td><td>-122.3891</td><td>37.71968</td><td>First Wednesday</td></tr>\n", "\t<tr><th scope=row>3</th><td>09/14/2011</td><td>23:24</td><td>-122.4108</td><td>37.78321</td><td>Second Wednesday</td></tr>\n", "\t<tr><th scope=row>4</th><td>11/10/2004</td><td>16:00</td><td>-122.4162</td><td>37.76363</td><td>Second Wednesday</td></tr>\n", "\t<tr><th scope=row>5</th><td>06/06/2007</td><td>20:09</td><td>-122.4164</td><td>37.7816</td><td>First Wednesday</td></tr>\n", "\t<tr><th scope=row>6</th><td>12/10/2008</td><td>15:52</td><td>-122.411</td><td>37.78414</td><td>Second Wednesday</td></tr>\n", "\t<tr><th scope=row>7</th><td>02/22/2006</td><td>10:55</td><td>-122.4085</td><td>37.77376</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>8</th><td>10/23/2013</td><td>23:30</td><td>-122.4296</td><td>37.76775</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>9</th><td>03/28/2007</td><td>10:25</td><td>-122.4466</td><td>37.78225</td><td>Fourth Wednesday</td></tr>\n", "\t<tr><th scope=row>10</th><td>08/02/2006</td><td>17:52</td><td>-122.4124</td><td>37.783</td><td>First Wednesday</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Date & Time & X & Y & ordinal\\\\\n", "\\hline\n", "\t1 & 11/26/2003 & 11:15 & -122.4106 & 37.7825 & Fourth Wednesday\\\\\n", "\t2 & 09/03/2003 & 22:17 & -122.3891 & 37.71968 & First Wednesday\\\\\n", "\t3 & 09/14/2011 & 23:24 & -122.4108 & 37.78321 & Second Wednesday\\\\\n", "\t4 & 11/10/2004 & 16:00 & -122.4162 & 37.76363 & Second Wednesday\\\\\n", "\t5 & 06/06/2007 & 20:09 & -122.4164 & 37.7816 & First Wednesday\\\\\n", "\t6 & 12/10/2008 & 15:52 & -122.411 & 37.78414 & Second Wednesday\\\\\n", "\t7 & 02/22/2006 & 10:55 & -122.4085 & 37.77376 & Fourth Wednesday\\\\\n", "\t8 & 10/23/2013 & 23:30 & -122.4296 & 37.76775 & Fourth Wednesday\\\\\n", "\t9 & 03/28/2007 & 10:25 & -122.4466 & 37.78225 & Fourth Wednesday\\\\\n", "\t10 & 08/02/2006 & 17:52 & -122.4124 & 37.783 & First Wednesday\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 5]\n", "\n", " Date Time X Y ordinal\n", " (chr) (chr) (dbl) (dbl) (chr)\n", "1 11/26/2003 11:15 -122.4106 37.78250 Fourth Wednesday\n", "2 09/03/2003 22:17 -122.3891 37.71968 First Wednesday\n", "3 09/14/2011 23:24 -122.4108 37.78321 Second Wednesday\n", "4 11/10/2004 16:00 -122.4162 37.76363 Second Wednesday\n", "5 06/06/2007 20:09 -122.4164 37.78160 First Wednesday\n", "6 12/10/2008 15:52 -122.4110 37.78414 Second Wednesday\n", "7 02/22/2006 10:55 -122.4085 37.77376 Fourth Wednesday\n", "8 10/23/2013 23:30 -122.4296 37.76775 Fourth Wednesday\n", "9 03/28/2007 10:25 -122.4466 37.78225 Fourth Wednesday\n", "10 08/02/2006 17:52 -122.4124 37.78300 First Wednesday" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrest_wed <- df_arrest %>%\n", " filter(DayOfWeek == \"Wednesday\") %>%\n", " inner_join(df_dates) %>%\n", " select(Date, Time, X, Y, ordinal)\n", "\n", "sprintf(\"NA values present from Merge: %s\", sum(is.na(df_arrest_wed %>% select(ordinal))) > 0)\n", "set.seed(42)\n", "df_arrest_wed %>% sample_n(10)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>ordinal</th><th scope=col>Hour</th><th scope=col>count</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>First Wednesday</td><td>0</td><td>783</td></tr>\n", "\t<tr><th scope=row>2</th><td>First Wednesday</td><td>1</td><td>581</td></tr>\n", "\t<tr><th scope=row>3</th><td>First Wednesday</td><td>2</td><td>460</td></tr>\n", "\t<tr><th scope=row>4</th><td>First Wednesday</td><td>3</td><td>363</td></tr>\n", "\t<tr><th scope=row>5</th><td>First Wednesday</td><td>4</td><td>247</td></tr>\n", "\t<tr><th scope=row>6</th><td>First Wednesday</td><td>5</td><td>282</td></tr>\n", "\t<tr><th scope=row>7</th><td>First Wednesday</td><td>6</td><td>356</td></tr>\n", "\t<tr><th scope=row>8</th><td>First Wednesday</td><td>7</td><td>621</td></tr>\n", "\t<tr><th scope=row>9</th><td>First Wednesday</td><td>8</td><td>795</td></tr>\n", "\t<tr><th scope=row>10</th><td>First Wednesday</td><td>9</td><td>831</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " & ordinal & Hour & count\\\\\n", "\\hline\n", "\t1 & First Wednesday & 0 & 783\\\\\n", "\t2 & First Wednesday & 1 & 581\\\\\n", "\t3 & First Wednesday & 2 & 460\\\\\n", "\t4 & First Wednesday & 3 & 363\\\\\n", "\t5 & First Wednesday & 4 & 247\\\\\n", "\t6 & First Wednesday & 5 & 282\\\\\n", "\t7 & First Wednesday & 6 & 356\\\\\n", "\t8 & First Wednesday & 7 & 621\\\\\n", "\t9 & First Wednesday & 8 & 795\\\\\n", "\t10 & First Wednesday & 9 & 831\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [10 x 3]\n", "Groups: ordinal [1]\n", "\n", " ordinal Hour count\n", " (chr) (dbl) (int)\n", "1 First Wednesday 0 783\n", "2 First Wednesday 1 581\n", "3 First Wednesday 2 460\n", "4 First Wednesday 3 363\n", "5 First Wednesday 4 247\n", "6 First Wednesday 5 282\n", "7 First Wednesday 6 356\n", "8 First Wednesday 7 621\n", "9 First Wednesday 8 795\n", "10 First Wednesday 9 831" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_arrests_ord <- df_arrest_wed %>%\n", " mutate(Hour = sapply(Time, get_hour)) %>%\n", " group_by(ordinal, Hour) %>%\n", " summarize(count = n())\n", "\n", "df_arrests_ord %>% head(10)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_arrests_ord$ordinal <- factor(df_arrests_ord$ordinal, levels = c(\"First Wednesday\", \"Second Wednesday\", \"Third Wednesday\", \"Fourth Wednesday\"))\n", "\n", "plot <- ggplot(df_arrests_ord, aes(x = Hour, y = count, color = ordinal)) +\n", " geom_line() +\n", " fte_theme() +\n", " scale_x_continuous(breaks = c(0,4,8,12,16,20), labels = c(\"12 AM\", \"4 AM\", \"8 AM\", \"12 PM\", \"4 PM\", \"8 PM\")) +\n", " scale_y_continuous(labels = comma) +\n", " theme(legend.title = element_blank(), legend.position=\"top\", legend.direction=\"horizontal\", legend.key.height=unit(0.25, \"cm\"), legend.margin=unit(-0.5,\"cm\")) +\n", " labs(x = \"Hour of Arrest (Local Time)\", y = \"Total # of Arrests by Hour\", title = \"# of Police Arrests in San Francisco from 2003 – 2015 on Wednesdays, by Hour\")\n", "\n", "max_save(plot, \"ssi-crime-1\", \"SF OpenData\", w = 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](ssi-crime-1.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a normallized map of the Wednesdays, because why not?" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_arrest_wed$ordinal <- factor(df_arrest_wed$ordinal, levels = c(\"First Wednesday\", \"Second Wednesday\", \"Third Wednesday\", \"Fourth Wednesday\"))\n", "\n", "plot <- ggmap(map) +\n", " stat_summary_hex(data=df_arrest_wed %>% group_by(ordinal) %>% mutate(w=1/n()), aes(x=X, y=Y, z=w), fun=sum_thresh, alpha = 0.8, color=\"#CCCCCC\") +\n", " fte_theme() +\n", " theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) +\n", " scale_fill_gradient(low = \"#DDDDDD\", high = \"#E74C3C\") +\n", " labs(title = \"Locations of Police Arrests Made in San Francisco from 2003 – 2015, Normalized by # Wednesday\") +\n", " facet_wrap(~ ordinal, nrow=2)\n", "\n", "max_save(plot, \"ssi-crime-2\", \"SF OpenData\", w = 5.5, h = 6, tall=T)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# The MIT License (MIT)\n", "\n", "Copyright (c) 2015 Max Woolf\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy\n", "of this software and associated documentation files (the \"Software\"), to deal\n", "in the Software without restriction, including without limitation the rights\n", "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n", "copies of the Software, and to permit persons to whom the Software is\n", "furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all\n", "copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n", "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n", "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n", "SOFTWARE." ] } ], "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 }
mit
ipython/ipywidgets
docs/source/examples/Controller.ipynb
1
1424
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Game Controllers in the Jupyter Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The controller widget automatically detects connected gamepad and joysticks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from ipywidgets import Controller" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Controller()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the case where multiple controllers are connected, you may address the controllers by index" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Controller(index=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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
sebastien-forestier/CogSci2017
notebook/analysis_vocal.ipynb
1
158623
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cPickle\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# PARAMS\n", "log_dir = \"/home/sforesti/avakas/scratch/sforestier001/logs/CogSci2017/2017-01-17_19-32-17-EXPLO-0.5\"\n", "\n", "\n", "filename = log_dir + '/results/vocal.pickle'\n", "with open(filename, 'r') as f:\n", " data_vocal = cPickle.load(f)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1bb843e0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "n_trials = 500\n", "n_iter = 80000\n", "iter_ds = 100\n", "trial_list = range(1,n_trials + 1) \n", "config_name = \"RMB\"\n", "\n", "x = [iter_ds*i for i in range(n_iter/iter_ds)]\n", "\n", "for trial in [166]:\n", " #human_sounds = data_vocal[config_name][trial][\"human_sounds\"] \n", " human_sounds = [\"yeo\", \"iuo\", \"eou\", \"eyu\", \"uye\", \"oey\"] # Sort for better color rendering \n", " for hs in human_sounds:\n", " plt.plot(x, [data_vocal[config_name][trial][\"errors\"][i][hs] for i in range(n_iter/iter_ds)], label=hs, lw=3)\n", " \n", "plt.ylim([0, 1.]) \n", "plt.xlim([0, n_iter]) \n", "plt.plot((0, n_iter), (0.4, 0.4), 'k--', lw=2)\n", "\n", "legend = plt.legend(frameon=True, fontsize=16, ncol=2)\n", "plt.xlabel(\"Time\", fontsize=18)\n", "plt.ylabel(\"Vocal Error\", fontsize=18)\n", "\n", "#plt.xticks([0, 20000, 40000, 60000, 80000], [\"$0$\", r\"$2\\times10^4$\", r\"$4\\times10^4$\", r\"$6\\times10^4$\", r\"$8\\times10^4$\"], fontsize = 16)\n", "plt.xticks([0, 20000, 40000, 60000, 80000], [\"$0$\", \"$20000$\", \"$40000$\", \"$60000$\", \"$80000$\"], fontsize = 16)\n", "plt.yticks(fontsize = 16)\n", "\n", "frame = legend.get_frame()\n", "frame.set_facecolor('1.')\n", "frame.set_edgecolor('0.')\n", "\n", "plt.savefig('../figs/fig_vocal_errors_example.pdf', format='pdf', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19\n", "30\n", "47\n", "64\n", "73\n", "82\n", "98\n", "105\n", "109\n", "119\n", "121\n", "127\n", "137\n", "139\n", "157\n", "166\n", "179\n", "202\n", "227\n", "243\n", "244\n", "255\n", "275\n" ] }, { "ename": "KeyError", "evalue": "'human_sounds'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-48-17660a905377>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtrial\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtrial_list\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhuman_sounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_vocal\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconfig_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"human_sounds\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata_vocal\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconfig_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"errors\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhuman_sounds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.4\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mdata_vocal\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconfig_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"errors\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhuman_sounds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.4\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mdata_vocal\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconfig_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"errors\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhuman_sounds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmin_hs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mmin_error\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'human_sounds'" ] } ], "source": [ "for trial in trial_list:\n", " human_sounds = data_vocal[config_name][trial][\"human_sounds\"] \n", " if data_vocal[config_name][trial][\"errors\"][-1][human_sounds[0]] > 0.4 or data_vocal[config_name][trial][\"errors\"][-1][human_sounds[1]] > 0.4 or data_vocal[config_name][trial][\"errors\"][-1][human_sounds[0]] > 0.4:\n", " min_hs = 0\n", " min_error = 1\n", " for i in range(6):\n", " if data_vocal[config_name][trial][\"errors\"][-1][human_sounds[i]] < min_error:\n", " min_error = data_vocal[config_name][trial][\"errors\"][-1][human_sounds[0]]\n", " min_hs = i\n", " if min_hs < 3:\n", " print trial" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cToWjipc//QnWdpu/wxNCCDFN+ZwQ6HQ61q5dyyuvvDIW8UwL9989l6RYHY21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\nwVX8XqiRKD0vhIjScYzA2OqWvUJnzpzRsWPHyrY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1bc00da690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ee_list_s = []\n", "ee_list_ns = []\n", "\n", "for trial in trial_list:\n", " if data_vocal[config_name][trial].has_key(\"human_sounds\"):\n", " human_sounds = data_vocal[config_name][trial][\"human_sounds\"] \n", " for hs in human_sounds[:3]:\n", " ee_list_s += [[data_vocal[config_name][trial][\"errors\"][i][hs] for i in range(n_iter/iter_ds)]]\n", "\n", " for hs in human_sounds[3:]:\n", " ee_list_ns += [[data_vocal[config_name][trial][\"errors\"][i][hs] for i in range(n_iter/iter_ds)]]\n", " \n", " \n", "x = [iter_ds*i for i in range(n_iter/iter_ds)]\n", "plt.errorbar(x, np.mean(ee_list_s, axis=0)[:n_iter/iter_ds], np.std(ee_list_s, axis=0)[:n_iter/iter_ds], label=\"toy names\", lw=2, errorevery=50)\n", "plt.errorbar(x, np.mean(ee_list_ns, axis=0)[:n_iter/iter_ds], np.std(ee_list_ns, axis=0)[:n_iter/iter_ds], label=\"distractors\", lw=2, errorevery=50)\n", "\n", "plt.xlabel(\"Time\", fontsize=20)\n", "plt.ylabel(\"Vocal Error\", fontsize=20)\n", "plt.ylim([0, 0.7]) \n", "plt.xlim([0, n_iter]) \n", "plt.plot((0, n_iter), (0.4, 0.4), 'k--', lw=2)\n", "legend=plt.legend(frameon=True, fontsize=20)\n", "frame = legend.get_frame()\n", "frame.set_facecolor('1.')\n", "frame.set_edgecolor('0.')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "583\n", "563\n", "Avg diff 0.0605169649579\n", "Std 0.0527086938597 0.0570622849779\n" ] }, { "data": { "image/png": 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cBuEwtp4W3C6wunyYNm9qmqg+ebuuB81iRTP7MZQVlEYiFsNqtxe6\nWUIIIWa5aQcQNTU1k07dfPjhh6e8RigUIhgMjjpWVFSEaZr09fVNGECEw+EJn5vIyltK6eqKTOs1\n47FYNEoXuSld5B7zXCJhEIslME2Ix5KEw0MkYg6SRhHJxZUwFGUw2EFPsANLNIQyQ5hKx1QWLFoS\nm92OZrWhNA0MA4vNgmZ3pYo1tRkubFQKC0PE0sMXiYF+LOYQZixKeoOMdKBjopx+lNU5s+0RQggx\nJ0z70+m+++5j9+7dbNu2bdxVJ3OZSeHz+cYc6+vrQylFUVHRuK9paGiYsj5iPJo2fv1BR8f4m34t\nXjy2bfk4f3CwnJ7uKB/56PgzVBr+5f8BzPQaFWDRu7BaFJ9++H8f9/xfvvATbBaw2GzoFgsGikTC\nZONffn7c8080/JJkEuIJk3jCRJmgWxSf+uLo8585kPp65tUfEUuYJGNxDGVFaaDpFm6/73/K6f1m\nzFR/yvlyvpy/8M6faHh1rrT/ep1vmua4z+fbNa0DcbXLVvv9/jG7dobDYYBxMwyBQGDcoONalJVN\nb9OvfJxfUVE84flrv/BXJIZiWBxWIr1h+to6iIW7Jzxfj7YRNyAOKEDTbSQnyQ5Eu3ogMUBSpYo8\ntWSURHLiWSPRQYVhQAIniaEkChOVnHin1f6eXuzeImw2Hbvdgm7RsFonrvcoRP/L+df//FxfN1vb\nf73On+r1s739cv7sPn+mKHOaocquXbvysg7E3XffzWuvvZZ93NTURF1dHYcOHRpzbkNDA62trdnH\nb7/9NqdPn2bbtm1s3rx5yvt1doanbM9sY5omiYRBMmmgkjEUJsnBflS8n2SkG13FGUpYGQiHiQ/E\nSA7149T7MRMJkoYikYSBuMI0dZJJA4tm4nQonA4dl2UIZXORtBUDJi/8LERQpeo3Pru6G++S8lQW\nxKKwWhS6lh7NSCdIzEScWLgXYzCEaaT20zAtTpS7BN23BJvHj65rqHTBqa4rLBYNi0VD1zX0zFdN\nYaaf13UNpXIrwi20sjLvnPw7db1JP+VG+il30le5uV4BxrQzEJl1IL7xjW+wevXqMc8/+eST4wYB\nV6qpqeHYsWNs2rQJSM3m2LZtW/b5QCBAc3MzmzdvHjN0UV9fT2trK9u3b59u8+cMpRRWq57+LT49\nvdKdyc7cDIADyJSimqYJQxGMaB9mchBl96J0O2gKMxZFWVwohwcwIRkHqzP7YW155b9n72vXkng9\nYzMHSo1YNMRiw+ZYAixJRRTxKCrUCkPdJIJdxCNeBh1lGPai1HLgJpiYaEpDaWCz6mi6GhUsKKXQ\nNDUcZOgqHWgodC31VUsfnwtBhhBCzHcFWwfiscce49ChQxw7doyWlhZWrFiRDSYglZEYr+6hvr6e\no0eP0trayqFDh9i2bdu0CyvnI6UUOLzojnEiT+cVdSVXLMWtawakRzOS093SWymwuTEX3QaxCHqk\nA2usC83owjT84FkCvhswdRtG0iRpmMRjCWIxI73ulolhmCggmYo0UCg0PfXHomujshkwnLEwDBNN\nU1itGhaLnspyWFOZjkxAIoQQYmZMewhjvKzDqAsqxenTp6+pUfkmKa/J/bf/6/tcSt4IwGdvusAN\nVWuv7YLJOES70KJdqQ3K3CUozxJM7w1gcUz5csMwSSZTwzeJhIGRTAUZpmmmAhbMTK0pScNEU6lg\nw2rRsVi1bIZC1xWmmZqWq1tSQyaZrEYm4LCOOD9XkkbNjfRTbqSfcid9lZtZO4QB174OhJhdLLoB\n6Z04k/nYklO3gncZhmdJaqv04GXo74FIG3iXg/eGSTck0zSFpumTFmFmmKaJkTRJJA3isSSD0Tgm\nJiZgJE00PVUMahoGmtJS17ZkajJ0FKBb0sc1lR0qyXyvaxpaOuMhwydCCDGsIOtAiNnFog8nofIS\nQGQoDVyLMJylmAO96H0XUf29qXoJZzGmvzK1oNa13EIpdEsqs5BZbXQ8pmmmMxup7EY8niQ2lMgW\nhmICCnSlUHr6upqGblEo1KiC0IFogp6efnQ9HVxkg4zhzIYlHZQIIcR8Ne0AYqodMKezo6aYHSwj\nPuiSiRnYEEwplKsEw+GHaBcq0oMW7kRFOsHuxXSXgXfZjC6aldrPRKHrADpO59gMSCabkUwPl6QW\nCEumhk8MUJjpgEMRDg1ipIdRMoGCpitsNj279ohKH7NYdSy6Smc5Rmc0JLMhhJirJv2JndlvIhwO\nEwwGCYVCVFdXj5rCuWvXLiKRCF6vF7/fj9/v55vf/ObMtlrklXVEBqI3DLG4ic06Ax9omg6eJZie\nJSSHwtDfjtbfjQpfhvAl8JWnAokCbT6WzWakH0+0ondxsQuLJbMnipn9k0wmiQ0lMc14atgEUv9J\n12GgUgGFpmWCBkZs1pYKNKyZ4ZT0eZqm0LOPU38k2BBCzAaTBhBf//rXsz+sNmzYQFVV1Zjah6ef\nfprGxkaOHDnC0aNHUUpJADHHWEcEC2cGb+P9Y2EWl1pYvsTC8sUWvO4Z+EC3e8HuxTCSqYLL3gBa\ntAci7ZiuEnCWps6Z5TIf6ik6znHW8soMn2QDjUSqXsMwwUwaGJniUNPMDosowGLVRwUZGQpQ6dkn\nVqs+Zupr6vz0uelvrnwshBDXasqccVVVFQcOHKCiomLc571eb3athr179/Liiy/mvZFiZq24sYg/\ndiVT25EDJhrt3Qbt3TH+0BzD51bcsNjCzRUW/J7pz1qYVDorYbgWYYQvofV8iApfRtk84FmM6V0G\nNu+s28V0OkYPnwATZDaGMxnpmSdJg4SRWsHLNIdnnphKYRqpUtFM/cV4010nbA8MZzTSC3plAqFs\nhmTE40xAItkPIcRIk07jXL16NT//+c+zKz1OND0zM6QRCoW4++67ZRrnHDMY7OWDn/+EcyfP0Klu\nostyy4Tn2qywqFhn2SKdimUWXI48ZydMA2IRVOgSmjJQTn9qUzF3GaarbFZkJYqLXfT2RgvdjOHV\nShMG8YSBYRipqa5margE0sMoRuqfuFIqfQCM9FRYAD09vKJrakQGRI0JRpQCXU8ViNpseurccWau\nZIIMmXKXG+mn3Elf5WbWTOMcuUz0M888w8mTJ7P7VgBUV1dz8OBBPB4PPp/vum3iIfJHs1gpu/U2\nNKuNuy/8K0OBdwhY76TFehcXbR8jyXDBYSwOlzqSXOpI8mZzjLJijapbbNxQpufnt1Olgd2HWeYj\nGYtAtBut/xIq0omye8DuA4cP01kKjqLh3PwCNHK10qlX1xhfJrNhGMNLpyfiqWEV0zQxDROFSg2z\nmKnAA5NRRaCZrEWGpkDTNeIxg1BoIH3ucG1HZpaKZDOEmNsmDSCu3MDq8OHDQCqQOHz4MIcPHx6z\noVa+N70SM09lfvhb7cQ++ffY3qpj9el6VsdeJdFvo7XsS7xb8u9o64FEYvRrO3sNfvPGIC6H4obF\nOssXW1hSqmeLDK+JzQM2T+rDKxaG/g60aC9Ks6Ds7tRUUEcROPzpYKIwxZdz2cjhCYslt/4bFWyk\nsx+mmQBSs1XSMQZGwiTSP5TNboys6dBUKsORGhYZHh5JxRTDBaapabLpoZXUwdRzI9quMpkTmTYr\nxHU1aQAx0W8Ijz/++LjBw2SvEbOXZkn/NTABTSd+507QNKynXsBCjBs766kw32Hws/8n4aSbts4k\ngbYEHd3JTEac6KDJ2ZYEZ1tSEYbLoahYZuGm5RaKfdf426ZSqcyD3ZcKJuLRdDDxAcriRNmcqWDC\newM4i0G3XVN/iMlpWmq6Kkxel1JU5ER1pVYLHZnZMIzUMubZIRIzvZeLCWjpwCBzkSuGVoDs0IuW\njiRGFopmgwo1OjjJBCjZ77VUQDL8fapoVYIQIXI3aQARDAb57ne/i9/vH/Ocz+fj8OHDo4Ys+vr6\nxmzTLWa/TAZi5OBT/CM7MC0ubH9KZZ30rtM4fvkt1L378d3oYtWNVgYGDd4+E6PlcoL4FZmJ6KDJ\ne+fivHcuTpFXo2KZhRK/Rolfw2m/hkxBeu8NbDelZnAkBqC/C63/fbT+Lky7B3zLwb0ELBNUK4rr\nIjUtVkuHGTpMvNs8MDycMvJ700zNWkkkDZIxY3hqbLqGI/VtZngFRn78GyYoM1UwSibLAWialq3n\nyAQxpmmmMyNkMyOZAlM9M6U2mw1Jb+4m02rFAjdlEeVE/zgy/+DGOyZFlHOLaZp0vfMGna0X8S+v\nHPWc5b0fYXvjn7OPk8vuYuhzT49a9MkwTLp6DS52JLjUkSTUbzBZKUxZscaNy60sKtJwu7T8rDmR\nGILBXrT+DpTNiXIWpTb5chSlhjfsvrwNccyWIsrZbjb008hAZORUWiNppGtLU3UeSSPz84vs+cpU\n2ayH0kgPu2hYLKNrPjLTaq/MfGSm32ra8PfZ66S/V0pRVualpzuSvobKBicSmIwlRZS5mTVFlJs2\nbRo3AzGevr4+Xn311WtulLi+lFKo9MZTV0rc9iVMzYL9tf8bAP3yG9ga/5FY9beyQYSmKRaX6iwu\n1fnomlRA0dad5MNAgovtCZJXLG7Z2WvQ2TuUfeywK4p92vAfv47XNc0foBY7eJZiuMpgoBsV6UUz\nO8BiTxVfWuzpmoniVG2FdYpfh8W8MLKuQp/mTODxgo5k0mRwMAmmiYlCZTd4GxGsZIpAxrRjxPoc\n6UBC1zWGBpOEQgNj1vsYlQ0ZMQQzJijJBCtaaifb4ccjA5nxvwpxLaYMIA4ePDitC061W6eYnTRN\nJ5kku0X2SMlb/4J4tBvrOz8AwHL+F6hoB0Of2gvOknGupbihzMINZRYGBg0udSbpDRr0BJN0B8dm\nJwaHTC53JrncObwPh0WHYp9GkU9Pf9Uo8mpY9Cl+6Gk6uBdjuheTNJIQ709t6NXfgwqnshNYnamp\noY6iVIbC7pvT60yImTFm/Y5pGpn9yH41Uhu9ZZZNNwyTWMxgcCgBpkJhYGQCbpXZp8XENNP1HJm6\nj3QCJFXHoY0ITNLFpunXTxYkTBVcjMyijApSJnpOZbIrZItdRxbHSsAy/0waQEy170W+XiMKz2rV\nUEaMrvY+3H4/TueIH0RA/Pa/hoFurGd/CoDe8Q6OV/4DsU9/G2PRxEGj06GxskKD9Dpkg0MGFy4l\nuNyZJDJg0B81x2QoABLJTKZi+EmlwOfWKPZrlKQzFcW+SYZANH24+BIgPgBDfahoEM3oAM2KsrnB\n6gCHP7XOhKtUZnOIvBiZ/ZhMcbGDVAXH5EZOuR3+PlWQmp12mzDT2ZHhwtRMnYhpquwwDQqUqTBV\npuhEQSqnMtxclV0gNb2w2HCgAmTXC5kqUBlJSwcXE82+yS5mpoZ3x9VHrLQqZpdJayDmCxkzm5pf\nD3O28bd0tYeIOlegW3WKvDrWkdMxTRNL8wtY3zqESudoTc1K7O7dJFduuar7GqZJpN+kN5SkN2Sk\n/gQNBmO5/7X0uFJDICXpgKLYp+GcaoEr00xnJ/pQ8QgaRnqdCW9quMPux/SVj7vt+GwY258LpJ9y\nMxv7aWTWhHSgktm51kx/PxyoQCb4yHycjCxwzXw1MqM92eeG75cpZs3EW9mN5zSwWIZTQCXFLoKh\ngdS5Y+pNxq9ByQRC2XMywzza6F+SuOI6c3m453rVQEgAIYDUX7i2994hduEPhIZsdMYXMxQz8bo0\nXM4rxmYvvY79d/+AikWyx+K3fYn4nX+Tlx01TdNkYMikLxNQhAz6QklC/bn/VXVm6ir8GiU+nSKf\nhtupJp6mlxhKTQ2N92dnpShXcaoQ01maGvbQ7WBzU1zimXU/8Gej2fjBOBst9H4aOftmuObEyO4b\nkzmmAK/PRSiU6qtMEJKpOdE0LRuAjJx1k5HdJVeNzp6MNFGgMF4wMfKYJb0J3sialUwWJXN8vPtc\neb3J2jAdEkDkkQQQUysr89LR3kei5U2Sbc0M+W+jrVcnMmBitym8Lm3U4lAqfBH7b55E6zuXPWYU\n30Lsrv8NY/HtM9LGeCIVVPSEUjUVvSGDYDhTTT81pcDtVHhcGl63hselsl89rhH1FaYB/Z2ooSCa\nEQerE2V1pIIjix3f0uUEh2yp2R0zuAX5XLfQPxhzJf2Uu4n6atx6k3TmBFL/pE2Gh4AyGZErC7IM\nE1RmKAcTIz20k36Y/WqO+j61Jw1aaqhHaVq2AHZUJiRH6VGeMQW0w/HOyM3yhh9nak80TXHLLWXT\n6NWrJwGEAIanRxnhDhIXTmCaSZS/nK7eJB09SZJJ8Ho0XI4R/xjiA9ia/hFLy2+z1zFRJNZ8mfj6\nf5+a7TDDkkmTYCQ17NETMugNJukNGySTU7/2Sk6HwpsOKrzuVNFmmd/AYg6g4gOQHECLRXD7fQzE\nNEybC+Xwp2Z2OEtk3YkryAdjbqSfcjcb+2rUbJ2kQdLI1KaYJE0TDIVSqVVKRg/dDNeoZCbzZAIE\nc2TQkhnyUZngYpxgZERAoZRiQ/VN12VRtLwHEJFIBI9n5j84pkMCiKllAgjTSJI4/3uSnR+gL12D\n0lMLRrX3JAmGDZRSOGwKt0ulfmPP1EW8/QNUMpa9nuEqI/bJJzAWr7vu78UwTcL9ZiqYCBn0BA1C\n/QYDg9P/q54p3FxUrLG4VKfUp7HIZxILtqFi6doJmzs1xGH3gtWFaXWlZnlYXQs6qJiNP+xnI+mn\n3C3Uvho5zDPyWOrr6MdG0uD2jyzHZpv57GjeA4jt27dz6NChfF7ymkkAMbWRC7QYfRdJXHgD00yi\nL1oJQCJpEgwn6QuZRAdTU82cDi0bSKj+Tmy/r0W/fCJ7TVNpxD+ynURVzayY2ZBImkSiBpF+k3DU\nINJvEI6mjvVHzSun7k/K5VCU+DWKveC1RvGoEF7rIE6njmaxgs2VGt7QdLA4MN1LwLVoQU0XXag/\n7KdL+il30ldTSyYNqtYuvS4BxKR32L1797QuFg6HaWxsvKYGicLTipajhTtIXj6JmRhCWexYdEVp\nkYUSv8ngkElXb2rFya4eKPbr2N1lDP3Zf0I//wtsb/wLKhZGmQa2t55H73iHoY1/d12GNCZj0RVF\nXp2iceqLkoZJ/4CZCir6DYIRg+6+VJ3FeKKDJtHBJK3tAHYgNeaoaeB2mHhsCdz2OKXuAYqc3Tgc\nXXh8TnSLlg4oysDmTc3y0O3DFVRCCDFHTLmU9bQvqGQp67noyiVijXAHifOvY5oGeknlmPNN02Rg\n0KStK0Go38Q1KhvRju3fnkbvah6+nucG4h/dTrLiU3Pqt/ChmElPMEl7d5LuPoP+gdRQyHhrV0zF\nohm47UnsliROO9jtFpx2KPVBSakTh9eDsnlSm4HZvbMia3Mt5LfF3Eg/5U76amqzJgMBcOLEiZxr\nGhvLCXwAACAASURBVEKhEF/+8pevuVGi8JSnDOUtw2h/DzO5DHXFeghKKVxOxQ2LLVjS9RHRQbBZ\nFQ57GdbPfRf3ycNYT9cDoEUuYf+372D4byT28f+AsezOQrytabPbFMvKLCwrG/6n4nHbab0cpSdo\n0BdOBRWRqEn/gEEsPvG1EoZGcEADrDAmpo1j1bvxOTvxucHnVviKXBSVOPEW+9GdXtllVAgxq0wa\nQNTU1EyrINLn87Fnz55rbpQoPKUUenEFZqgNI3h53CwEgMOuUbFUUVqUKloMRkxCkVTFcXTVDopK\n1uB87R9RidTiL1rwPI5ffItExaeIf/xvUrUBc4ymKYp8OkW+sZmUeNzMrrAZ6jfo6k0yMGQSHTSn\nLOKMJzW6Ixrd2eU1htJ/+rBZTJz2VH/b7RbsDitur52iUi/+Yg9OlxWrde5kdoQQc19eiygDgQCt\nra1s2LAhX5fMCxnCmNp4u9yZhkGy9Q8kLjejL7kNlcOMgqRhMjCYWp+hN5Sa0uS1hPGf+xHW936Y\nDSQATLuP2D2PkSyvnlPpep/XQSg8OK3XZBbHGoql/gwOmQzFU6tw9gST9IWNMVuiT5fNqigtdeB0\n23G57bjcNtxuGy6XFbfbhs2uX9cV9STdnBvpp9xJX01tVg1hTCYSiYx63NzczP79+zl27Ng1NUrM\nDkrT0EpWoIU7MfpaszMyJqNrCo9Lx+3U8HtNOnqShPu99K/4a2zL/xLve/8Vz4X/kZrPPBTC/psn\nMbzLia//9yRv+vN5W0yolMLlULgc4z9vmiaDMZNwJJW5CEVSf/oiBgMDuc0QicVNLrcNAAPjPq/r\nCteIgMLlzny1UVzsxOG0oOtzJ5ATQhTWtAOIQCDA7t27aW5uHvf5ioqKa26UmD2UexGabynJy6cw\nY1GUzZXb65TC40qtGdEbShIMm/z/7d1rcBzXefD5/+nuueEyA1ICdSFB+iaThC5J/EaMSb7ZlNcW\nSXtTScSsQO6nRBRlp2q3IteKrtoqr2DLVlK7JSiR/E0WqZTKH1KAYjlJ1RsR9OvE+5YJRLZiO5YI\nSr7J4oC68QLMDIC5dZ+zH05PA4P7kAQwIJ9f1XAw3T0zjUPMzDPPec45JTJ8sON/J795P50//hrx\n0vsAOIXzJIb+H4Lf/CuV3/s/7QiFG4xSilRCkUrAppvquyK0MVQq9RmMcsWQn7Qzc04UNaWSWXJG\nziAwFPJlCvnygsd4nu0iiSdcEgmPWMzBi7nEYy7JlEeqJUZLKkaqxV6SSW/drRMghLg2Gu7CeOSR\nRxgeHmb37t1ks1nABg3ZbJbz58/zve99TyaSWofm68Ko0VPj+G//ED01hnfL9it+Dt83jBfszJaV\nyUmSb5+idfS7xKuXSZgCLj4m1kr1zv8N/2Ofg2Tmip9rJV1JF8ZKM+HkWflJTalsmCpqpoo+U1MB\nUyXNVNnB19c+u6AUJFMxWlpiJFMeyWSMZMKjtS3Orbe1EwRaAo0lSFp++aStlraaXRgNBxC7du3i\npZdeYsuWLYyMjNDf38/jjz8OQG9vL7//+7/PfffdtyIne6UkgFjaYgEEgP/OawTnX8PZ2IVKXN1C\nLcWSXc9iYtJQLpZwf/kyXvYHxM0UCVMgoQt4ToD+0B/gf/xPFl0ufC00YwCxHNWqYXLKZ2qqwtRk\nhampKsWSIl9yyBc9Kr6DWWLp6StVCzRSqRgtrTHa2xO0tcXtdXucWNzFc222YzWm4G0m8qG4fNJW\nS2vqGohMJsOWLVsA6O7u5vXXX4/2felLX+LP//zPmy6AEFfP3bgNnXuXYGwU79adV/VYqaTD5qSD\nMYaqH6O09X9l7Nd3U3x1gErxIgVnEwqD9/ZbxH7z13iZW3G3/8+YD33qhp4a+mrFYoqOTIyOTAxo\nrd9pDMYvUymXqE5NUS4HVKuaIDBUA5eK9ij5HlNVj2LFoVhWTFUUleryPuyNgeJUleJUlcuXFj/W\ncRSe5+DFHFpb49EIk3h8+pJIerS02DqOVCqG50nthhCrreEAor29nYmJCcbHx9myZQuZTIY33niD\nHTt2MD4+vmBthFjfVDKN27EZM3EBU8qjkumrf0yliMcgHnNJ39NNsOP/Ij/yAybP/BvVsXepqiRV\nlYRCGefV/0biJ/+Eu/W/wLb/akeFSEr82lEKFUuSiCVJtHUQdUIaA0EFdBXjl1D+JCrwAQ1odGAo\nlaEYxCn5cUq+Q6niUCh7lAOPiSnDVFlR8Zf/f6W1oVIJqFQCpiYXmVhjhljcJZXySIVZjlRLjFTK\ni7IetW2JVR6JIsT1rOEA4tChQ/zu7/4uSimeeeYZHn74Yf7sz/6M3bt3Mzw83FAR5fHjx9m6dSvj\n4+OAnXdiIYVCgf7+fjKZDG+//TaAzDmxypyN21Dj7xJcPod7WzfqGg+9dONJNvz2Z9jw258heO8s\n5f/8R0q/GKJkUhRVB8UgDm+9Bm+9hnPTR4l/+F7i234HJ7bA0AZx9ZQKsz4JO0sm1I0IUUDKaFJB\nGeOXQft29eOgSFtSMTllCzaDQFMqQ6kCk5UYE34LhZLLREkxWVL4gcLXCj+Ys8LyslQrAdVKQD63\ncIFo7ddJJD2SCY9E0gsDDZvhSGcStLUloqzGjdaVIkSjriiAyOfzvP766+zZs4e2tjb279/PwICd\ncfDrX//6sh6nr6+Pe+65h3379kW3BwcH2b9//7zHP/vss3UBw8GDB3nxxRd54IEHGv0VxBVSiTbc\nmz+MKY7Z1To7P7Zi3+bcW3fScutOkv/TONUz/0L1Z/9EuZCnqlJUVAulS7+gdOlXVH88gHvbDtj2\n+7i37iQeW2C5W7FylANeCuWl6jY77UlM3NaKOEAL0GIMG6tTUM6B0ShjwGgIKjimilEexvHwTZwK\nMSZKMUpVRdWHSqCoBi5lX1GqOEyV7JokxfLclQoXYgyUij6l4tKTbsTjbv2IlLD7pDYMtlYcmgiD\nEelGETeaazaRVDabpaOjg/b25RXY7dq1ix/+8IfR7eHhYZ577jmef/75eY/ft28fDz/8cBQwPPLI\nIyilePrpp5d8LimiXNpSRZQ1RmuC90bwz/8nTvpWnNabVuHswOiAIPtjqmcH8d/8HhqHCaeTSWcj\nBvvGHWy4A3X7b+Ft20WqJU7MW5lAYr0WUa62htvJGNBVCMLukqCC0r5Nc+gATIAyGoUO0xQKlMIY\nqJg4xSBF0XdtjUbFoVhRFKsuxYpLsQylsqZSvaaLD9dxPYdEwo2yG7G4i+soHNchmfRoa4tHQUh0\nSXjccks7+bz8PS2HFFEuramLKAFGR0cpFArs3DldTHf27Fn27NmzrPuPjIzM+ZaYyWQYHh5e8D7P\nP/98VLwJNmD5wz/8wwbPXFwt5Ti4m+5AT15CX3oLlWhb1gyVV/+8Lt62e/G23Yv+vT+n+sZ3cd/8\nLu25EQLi+CpB6dIYpbE3CX55itztn4Tb7ia+cTOJuIPr2DW8HMlONC+l7HofbhwVt0Wesz/uo9vG\ngAlsYKF9XF2l3S/Rrou2RsPYGg1H+/Y/XjkoN0ag4pQDj5LvUqpAsawoVh3KVYf8lGKyBFMl29XS\nqMDXTPl62XUbM7meQyLuRtmOWnAxc1umI0mqJYbnOrieE10LsVYaDiAGBwejb//PP/98NG31+Pg4\nBw8e5Bvf+MaSq3jmcjkymfox/rXMxcTExLzzSMwMHs6cOYNSiiNHjjR6+uIaUF4Cb9PH8Ys5ggu/\nxr11+zWvh1iMs2ELid0PEv/kn6Pff5PKT/8B/83/TtIUMPodykEbxV9lqfz6X/Bbb6Vw813ozp2Y\njg/juB6eaxfJ8jyF69gZGsU6oxQoDxwPSNQNPp0ZdARREaiPCcoov0xClUh4hoynIaVRJrAPSRiU\noNAoKr5LxSSoBDHK2qMaOJR9W7MxWVKUqopy1aFUgXIV9BWs0BqdZy34mGos+HBdRbw24ZdnL647\n42fPwfPcaFRLbXv9Zf790hUoltJwAPHUU09x7NgxhoaGuPvuu6PtPT09GGP48pe/zLe//e1FHyOf\nz5PL5eq2dXR0YIxhfHx8wYmoCoUCL7/8MoODgzzxxBONnrq4hpzMbbi3bEdnf4IeP4+7YfVnIFVK\n4d66g9SB/xu99/P4P/9XKj/5B5KTF20wgaJcOI9feB3/NwmCWDt+5z1UMneQb9+Kad+Mcm2xXMwF\nx1V4LjgOxD0lgcX1YGYRKEtkNaINGowmpqt41RKtQQV02QYaOgizG2GYYQxgMCh841CqulQCj3IQ\np2o8AqMIjGKyaAOOiq+o+A4VHypVFV4vv4ZjtiAwdnjsld19Ua6romAkFnOJxW1wYi8qyoJMByrT\nwYvrOcRi4f2iaxcv/FkKVK8PDQcQxhiOHj3K0aNH5+y7++67+epXv7rkY6TTc4cAjo+Po5Sio6Nj\nwfu1t7fT09NDT08PBw8e5PDhw4uO3BAry+n8GG6pQPDu6+hYEqdt7aagdto3Ef8vh4n91v34bw3j\n//L/w3/r30lWC0DBfkqUgdE3CEY9yqoN32kl2HQXlY13U87cQaVtC1p5dhRBmJloSSgSCZupkG9k\nNwjl2Ivj1RWGLhp46ADHBLSYgJRftYvG6SmU1jarkbFdKgobeIRPBK5Ha3sr44WAiu9S1o7NfPgO\n5TDIKPu2piM3oSj7EATgB4YgYMnpy69GEBiCwA6nhca7ZRbjuopYzMX1bDDiOArHsTUkqZSd1TSe\n8GhJxexEYzGXVEuMTGYFf2HRsCuaB2Kh0Q/PPvvssoZxZjIZ8vl83bZCwRbwLZZ9mFmgefjwYXp7\ne5cVQHR2Xt3MiTeKK2knveGTFFsV1fd+juckcdOb1viDtgU6Pwe7Poeplim99SOKPz9N8RenCXLv\nAeDi02LGIRiHd8/Du4MYFNprQ23+HdRtd1HJfJxS+z2UKopiGeIxh1RSkYg7aG1It8vQ0eWQdlqY\n0T74ZXRQJR0L+0AC3xaSKqI6D2U0NlypFY7a7gWDS+AmKVcdqoGiGthshx+A74MfYLdrJxwma4/z\nfagGhqpvp5ev+jYgqfr24oeXlWSDkytbfrYWfHh12Q2nfpvnRuu4xOLzbLuOMyO+fxV9aQ1qOIA4\nduwYR44c4fjx43zyk58knU6Tz+c5efIk+Xx+WXMzdHd3z8lC5HK5BYswh4eHefDBB3n11VejAMMY\ng1JqwZqJmWQUxtKWOwpjPqbt41TjBfRvfoFquWDni3Dcpe+4Gjo/ger8BKk9/wf60lsE539K8N5Z\ngndHMLnz0WEKg+sX4O3/AW//DxJAvK2TxOY/oLj5UxSSH+byuItS0NaawPcrJBMOyfj6f8NZKTJa\nZTlc0u2t0+3khpeFhKNRMAEm8KFaAV3BMQEJo8NgIwDXRO/uduRKLSCoDwyUcmyfXRiY2CG5CYxy\n8XWY7dBQDRx8HcM3LoFxCTQEeFRNjEDX5vBQdrt28H1NtRpQ9TXVSoBfDW9X7QynVyMKPkpXFoAs\nxnFs12WtO2ZO10zYdRN147gO8bgbLTIXi0/Xk9SCE9dVq/qlKgg0t2+++on+lqPhAGLPnj08/fTT\nPPbYY/T399ftO3r0KA899NCyHqenp4dTp05F80CcPn2aQ4cORfuz2SwjIyPs37+fu+66i0OHDtUF\nCkNDQxw4cKDpFu66EalkO7GP7iVo2YB//mcEH/wC9+aPoLz4Wp9aRCmFe/NHcG/+CPyW3aYLHxCc\n+w/88z8lOP8zTP7d+vtMXCD55j+QfPMfaPfaqd7yCSo33YNz2z1c4naKJS9Mv2JHeThhUWbY/RHz\npNtDXGPOdISh5nn3ni9vULctrO8grN2IfjYBRgeooAyVIsqAY7StDzKalNI4TlhkWiuYVo59jXtx\nIBzmBLbuxPFAuWFXkBv+HAfHRePgG5eqrwi0QuOgjUOgFeWKoVjWlEqaSkWTL1Qol7WdlXSqGnan\nrBytDVqbqw5yZnPCYMOddW0Dlrnb63727FDgun1hoOO6Tv0+T+G5q1fQflXzQIyMjJDNZkmn09x1\n113LngOi5sSJE3R1dXHu3DkymUxdt8jAwACDg4OcOHECsMNET58+jVKKsbExlFI8+uijy3oeyUAs\n7WoyEDXGGIILv8R/+0dgtJ1u2o1dozNcebrwAcH5/yR4/w38N/8VUxyb9zgDVN00U527qGzopnrT\nDqqtH8Io1/ZJh5lm11UkYgrPs++p8ZjCu4EKMyUDsTzrrp10ANjRLaY6hQoqoMOuljA7ojDhqBZl\nj1UQdb84bjiKZsa1owAnvD3PB2CYHWlrTzGer+Br116MY69ndtUEUA0Ifza2Syew3TW168DXVP0A\n39f2UtUEgb7iYtZm88T/e6A5V+OcaXR0lGw2S1dXV90wy2YjAcTSrkUAURNcfIvquVchqOBu2o5y\nV/4P+VozQZXg3TP4b/53/HOvYvLvLX58LAW3fQJz22+h01sppbYwoTopVSCodWEr8FyFG470UMpm\nLGxgwXXR/zrTuvtgXCPXfTvVZhudcTHat9Oe68CObjFBlA1RtayI40TH1+pAWtuSTE2UpkfCRN0v\naka2Y2aXzIztM4VFsigHYi3gxmwmxIkRBA6+ttkRP7CvX1+rsHg17ELRYXdNYCiXfTsSpliNApJq\nxXbVVP0AHax+VLJaAcQVPcPw8DBf+cpXyGaz0batW7fy+OOP88lPfvKanZxYn9ybPwxGU/3NKwQX\nf2mnvHbWVxCh3Bjelt/G2/LbAOj8+wTv/Izg/H9i3n0N/9Lb9cdXi3DuNOrcaRygDWiLtxBs3IHZ\n8BHMlnuZaLuLUhDDD6BSMRhjoip6J+wC8VyIeeq6DSrEDaiWZZhR3LHYX/Vi3TCqPUkQTo8+HZjY\n4bVG++HPvh39ojUYO6mYzYbUum1MFJDYWU3tRHWO4+A4Hp4TI6FUeJIzgpD56lOiTIpDfXeNCyoG\njovBITAzLlrNvda1wAT0zNuBrrvWgZ6zrf5ar+p7RsMZiJGREQ4ePEhXVxfd3d1kMhlyuRxnzpzh\n/PnzfOc731lyIqnVJhmIpV3LDASE3Rkf/Bx/9KeAxu38ePMUVl6lDRtauDSatd0d2R/jZ3+Myb2z\njHsqVMdmnI0fRm35HZxb78ZPbWIyaGGqDKWyoVq1QUXtC1htPgrXna6zcNR0wNHMrvtv1teItNPy\nrUhbBZUw8NCYoIIKqmGGI7DBxcyaEaWgNpV6+NGpwlqSuiAlDDpsB46y73214MKNhY8TZk4W4niz\nLi44HoZwbhPlTmdRosDFIcCl++4tzdmF8ad/+qd8/vOfn3fRq5MnT/Lcc88tOZHUapMAYmnXOoAA\nG0ToD96kOvqftiZiwxZUYv0PqZ1vPn6dexc/+2P0xV+iL59DX/z1gjUUs6lUBmfTDtybP4LZ8GH8\n9IeYim9mshpnqqTtCpU67AVx7Bcjpey03LEYdr2FsHgzFlO4TRJYyAfj8kg7Ld+6aKuZXTa12U11\n2G0TVFG1rMjs45QKAxCbPbF1JNjbKJRyMGHgodxYGDyoGd02ltaGHfv/pDm7MPL5/IIrZh44cICn\nnnrqqk9KXB+UUjibthMDqqM/xX/vTZy2m3EytzfVCI1rwcncRjzzv0S3jTGYiQvoy78hGP0p/q9P\no8eyUd/tTKaYI3j7FYK3X4m2pZRD68YPoTbtRHfuRG+8gyCzjWrgUQ2g6hsqFUO5YtDGRA+rHBtQ\neC54nh0JEo8rWQNEiNUyT5cNzO22Weqb+5zRM3q6VsRUS3ahOe2D76MMUTASaI2ulqEZA4j29nbe\neOONebspzp492/BIDHF9U0rh3rIDJ7MZ/90zBB/8nKA4jtt5ByrRutant2KUUqj2TTjtm/C27SKx\n9/MYv4y+fI7gvRGCt3+IHjuHnrgA1Xm+URmNvvRruPRrOPvfUICHItbSgWq92bbppjvQbbfDzXeg\nY22Uy1CY1JQqhmoVylWN0fYLSjzMTMQ8wnHusrCYEOuGClOM2FFtKtZSt3tmsKGrFcw8X1RWwhVN\nJPUnf/In7N27l+7ubsBmJUZHRxkaGlpwOW5xY1PJdrwP/R7uxq34b/8I//2zuDd9BKd141qf2qpR\nXgJ30x24m+6Ae/4YCDMV46MEF36BvvQbe7n8FnpslPkmTzZTY5ipMfSFX9TtcTZsJXnbnbTe/FGc\nmz6MufXDBPEOimXDpXFNsaSpYJicAtR0LUXtfSkRUzhuuE0R7ZMgQwixkCueSKq3t5fTp09H29Pp\nNE8//XS0OqcQsymlUJnb8e74A3j7VfSYHcmgWjbcsBMuKaVQG7pwZi1GZipTNqh4/w2C998keP8N\nTO5dFkp86rFz6LFzzJybT6UyJG+7i67b7oKOLnS6i3LiViq+y0TRROPiq1VDqRQ+bvjf4NTmCkKF\ngcTMwELhhSPlHEVU5FmryxBC3BgWLaL8vd/7PV555ZWFdkcTSdVGZDQrKaJc2koUUS5GF8fxf2PT\n+CreirOhC5VYH7OKzldEuRpM4GOmLqPHzxO8+xp6/Dz68ts2G6GXOUOfclCZ23HCoMXp2IJu20I5\ndRtBciPauFFQ4UdDyeyiTUFgV400hEtX1945wgADwroXBzwX0m1JporluiBDFiWba10UBjYJaaul\n+dUKd/7+blJtK19OsGgAsWPHDg4fPsyxY8fW9ZTREkAsbbUDCLDfsv2Lb6HfG8GUCzgbt+G03rSq\n53Al1iqAWIjxywTvv4l+/0305bcILr2FvvQbqDa4yLPjodK34mRuDy+bcTputwFH5nZw4xgTTqzj\n2yl/Aw3FsqFUtsNPa4szBYEhlYozMVmJJtGyWY1wum+XcDre6VEktQyHc4MFGfKhuHzSVktrqgAC\n7It5//79HD58eF1OFCUBxNLWIoCo0cVx/Ld/ZLMRrTfjZDY39eyVzRZAzMcYjR7LEmT/ww4rHcui\nx7KYiQ+u/EET7TitG1Et4SX82WndiGq9CdXWidO+CRVLEQSGdCbFxUtTBIENMIplG1xUq8YuR62n\nMxm1SQNral0ntfVEoi4Ux0605YSjTa4H8qG4fNJWS1vNAGLJd+nvfOc7tLW1MTg4yGOPPUY+n6en\np4dDhw419fTVYv1wUh3EPvpfqWZ/ir7wC4KpcfvNt2XDupvBslko5eBu3Ia7cVvddlMtocdHo4BC\n586jx89jcucxU0vMW1EuoMsFuPz2ooepVAaVvh02bUOpJLFkO/FkhkwqYwOOWz8KiUw4NbChVNGU\nyjZRUavL0NpEM3ZqY6L5eewTTGcpvLpMhi0OjcdurAyGEGtl0QzEkSNH5oyqyGaz9Pf3MzAwQEdH\nB4cOHZqzUmazkQzE0tYyA1FjjMHkzuOP/hSdfx8c1367bb+1qeaNWA8ZiCthKlPo3Lvo3DuY3Hl0\n7p3oYvLvzTuHxZVSbTfbLEa8FZVoR2VuC+sytuJs3IqT6gAg0HaOi1oNhh8YJorhHBhVQ7UCgTHT\nNRlhvUVdYOHa4k5VWzIhvDRLBkO+VS+ftNXSmqYLYynDw8O8/PLLDA4Octddd3H48GHuu+++a3l+\n18RafzCuB80QQNQYv4LOvUvw3gi68AEYH5XssGnyVBo132p9q+h6DSAWY4zGFPOYqcuYycv2euoy\nevKSvT15ET1xAVP4wE5uc7USbdN1GOlbUakMJNpssJFsj65puQmNhx8YKr6hXDbkJgylskabcDkE\nM6N7RE3P5FlbzMx17SJntbVHVnsNEvlQXD5pq6U1VRfGYjo67LeEXC7H0NBQtNz22bNnr8nJiRuT\n8uK4N23D2bgVU/iA4P2ztj//wmVULInTfguqrVPS1KtIKQfV0gEtHXDzRxY8zujABhaXz5EyOSbH\nxjClvL0UczbDcfGXEFQXf8LyBPqDn6M/+PkSJ+ZEhZ/x9G0kkmkyyXZMvA0dbydIbKSavJkg0YEh\nRmAgCFdUrFShUrWzeRa1qSv2VGHGom74ajjCxJ3VbSJ/h+JGtWgAcerUKfbt21e3bWJigv7+fvr7\n++tW49yyZUvUnSHEtaCUQqVvwUnfgqlM2hEbF36JHh9FFXO2r71lg50XXjQF5bi2mLKtk7YNLVTn\nydSYwMfk38OUJzCVCRtYjI/aYs/xLPryOfCX+S3TaEzuHYLcOyw0kNULLyq1wXadhOen2jpRLR3o\nWDu+107FaaXqtFDULVRVksDE0NqOOqmGiycZZqyX5NRnMWpdJwrCdZLC4k/CbpQm6TIR4lpZ9jwQ\np06dor+/n6GhIcD2V6fTaQ4cOMDhw4dlHoh1rpm6MBZj/Ar+eyME747YYYrKsYtRtW9atXkkbsQu\njCtxpe1kjLFZjNw7mLGs7SYpFTClPFQm7M9lG3iYyYsrcOahWBKVzKBSGUyyA5Id6ORG/PgGqrEM\nvpum7KapOm0EbhuB20Jg3FoiI+wrqXWZTI8kmVmD4TiQbk8wOVmOFmdUhDUcrmQ3ZpMujKU1TRdG\nLpfjoYceqgsawM5GeejQoQUX1RJipSgvTmzLb+PduhM9cTGcofEd/PfewGnJoFpuuqFntrweKKVQ\nrTfZOUFuv3vRY41fRufetUWfhfcxpQkbXJQLdtTI5CXMxAXM5OXGi0CrJUy1hCm8P31u2NUIZue8\nNA4+Sfy4zYqR7EAn0phYGybeRtnpwHda0F4L2k2hvVZ8N4nxUgR+O8WyY5d9Dp9ERV0ndjirq8KV\nVqMMhz12esrx6boOIVbLkjUQQ0NDGGPo6uqKuihkwSyx1pSXwO3YjNuxGVPM4b//JvrCL9AXf4WK\npWyxZevNqHjL0g8m1i3lJXBv+hDc9KFFjzPat+uITFxAFy7Y64kLmGIOKpNhd4q9pmyDkEYCDgdN\nnCnilSmovDtn/+x3TAMExNB4aOWC46Fj7ahEGzrWSsVpJ1BJdKwNP3kzgZem6CbRbhJiKfCSEEva\nay8FXiIMKuaONlEzsiDRlOMOtXBlVmHpjC6Y2rVMUS4WsGQAsW/fPr7whS80dReFuLGpVIbY3tbP\n3wAAIABJREFUh3ZhNt+Nf+ltOyPj1Bgm/wFOPGUzEok2SLTLN7QblHI8VFsntHXi3rr08cYYG1gU\nc5hSzl4Xc1ExaP226X3LDTrs6qpVoFqLJiAYhzA7P3udWo1DRbWi8TAoDA5GKQwuGpcADxNvwXit\n6Fgrxg2zHamb0YkMOtZqZxJ14hg3ifGSmFiLDULcBDhuFEQsdMazu2CmAw41I9Coz57YuTlkYbbr\n1ZIBxDPPPLMa5yHEVVOxFLFbd2Bu2Y6ZGiO4+Cv05Sw6/z5Gj6K8pJ13IJ6CWMr+3ETzS4jmoZQK\nh422AZuXdR9jtA0wpsZttqOUq8toRJdSAVMJt5cKNlDxy4s+toMmaWbVKM2uXlugNMAGHDboCIij\nlUsQdcLYfbhxTLwV7bVCLIXxWjDxNkysFRNvI3DtPu21omMpjNtKEEth3BTaTdk14wkLTGecW/0M\no/OMbHHmLtg2fQkLVKOF2yQAaTaLBhDf/e53V+s8hLhmbB+6nWLZdH0CU8wRjL+DvvQWpjSGKY5h\njAYnZjMU8TaIp1BewgYW4ZuhEI1QykGlOiDVsWSXykwbNrRw+cIYpjxpazcqU5igCn4FUxyzAXA5\nDDTKk3bkSnmy7jbVhQsLa+EDaFz8+Rd09cNLA2YGJiaWglrNh9eCjrWEdR5tBF4b2mtDey0Ebgod\nZkd8N4lxk+DEwIlh3BjG8TB2zExUhFobvOIoRbFSZWoqiIKSWqbD88LjwoxIfddNeHGmu3LEtbFo\nANHV1bXYbiGannLcKJhg812YoGr7uifH0GNvo/PvoycvYvIVUK49PmYzFcRbUYk2yVKIFae8hA1g\nWzde0f2N9m3gUZ6YDjJKBUzuHXRxHIp5jF/EVEv2uFIBUy5gKlNQmWKhZeIXPecZgQnVgr00cs5g\ngw8cNK7tjlEORsUwXgLtpmwA4qYwTgLtthDEU7jGtYGHG0e7SbQbp+IkMG7Cdsd49tp49mflJTFu\nHNwkuC6Oml68rTYaZna3zMxj7O9a+2f69sxMyY1KFhoQNxTlxqJvie7NH7Z93aU8ulSwgUThQtin\nPY7Jv49xXJykLW5TqQ5ULLnWv4IQcyjHg2QalUw3fF9jDPilKJiYznJMRIEGtaGz5ULd9loXzZVM\nc24TBpooMwK1qAIqyzx3ICCOrxJoHAwuUWijnChDYotVbXASxNrQbivaS4GboBpvg1gbxolhnBi4\nMXDj4Hgzfo5jXJspsdtidr/yMK6H43o4XgzHdW1AMTsTMqsoNao3CUfTLHZMMwcoEkCIG5pSClIZ\n3FQGd4NdHM5oDZUJ9NQ4+uKv0OPn0cVxzNgoTiqNbzrRRewIDzfe1C9wIZailLJdd7EUtN7U8P2N\n0dPZj2oJqkVMZQpTnYLyZDi6xV5H3S61kS7Voq3/8MvhdQn0QlOCzXPugEcFz8wTcSyUVJnRVaPD\n4MJmQhRauVFhqlYemrAAI3o2prttUDMKWT17H8cDJw5uDB0FHvEwOzJ9G3f6GjcOXtxmT2Zvj/Yn\nUF7MdpPNM7pmZhGrDq54dYqGSQAhxCzKcSCZxk2mcTdutXMNFHPhLJjnqY6/Q5CfBMdDOR5Oy0Zb\nQ+HGbLeH1FCIG4hSzoyC06tntG/rP8KAwvgVOyeHX6YtpSiM5WzAUS1CpYipFm2wUpkKt9lrUylC\ndQpTmd4/O1Pi2PEtM56c+X9e7rkH03Uh9iGc6YtS9bdnjKYBNXc7Tl0WxeCg3YQdReMmo8BEzww6\nnBjaSaD27W385K+ABBBCLEF5Cdz2TbjtmzBBlXS7opw9j86/G9ZQXMLky4CDcm0KVSVabXGmF7ej\nPaTrQ4hlUY4HcW/eOVxSG1ooXeEssMYYCCr1gUVl0o6YKRfCoKRkg5egbAtTZwcyfhnj146pQFCB\noBrtU0ZP14XMOYGFTqyBX2JGcqauiHVG4AEQi/U18KBXTgIIIRqg3BhuazvuTR7uTdtsd0e5gC5P\n2rknCu9CtWzXdyi+gzGBzVS4MTvBVSw5nS6OJdd8ZVEhbhRKqbArIAF0rMhzmMCfDjLCmUzxS2GW\npDi9rVrC+HNvRz9XSzDndv1Im7oi1jUiAYQQV0E5znQNRcftwJ0AdrRHeRJTKqBzo+jCRSjbYk2j\nfRtUKIXykuEQ0qSdn8JLyqgPIdYp5XrgeqjE7KnArp4xejpLUi2FAUlxRpASZkZW8UuJBBBCrADl\nxqLlr92Ndji00QGUJwimxjCF9zCTl+1oj8nLaF0Nh5E69jqWsincWGo6sJBiTSFuWEo509nLRRh/\nmUNYrgEJIIRYJcpxIZXBS2WiiYZqQ+h0MY8pfIAujtkx+6UcOl/AGN8GFK6HcuN2xj/HC7tCWlCx\nxJJvKEIIsRIkgBBiDdWG0LmxFKRvibbXshW6mENPfIApXLTD3nTFrhA5NWaDD8fF8eIQb7HdIV7C\nBhde3BajCSHECpF3GCGaUC1b4aYyuBu31u0zgW9rKSY+wExdRufes2swBJdt0WatK8TxUMm0zVC4\n8TCwSEhXiBDimljTAOL48eNs3bqV8fFxAHp6epY8HuC1117j7rvv5ujRoyt+jkI0G+V6uK0boHVD\ntM34ZTssrZRHT16ys2sW7YyauvABYKJ5K2yhVzwMLGJhtiIGsYRkLYQQy7Zm7xZ9fX3cc8897Nu3\nL7o9ODjI/v375z2+t7eXr33ta9HtgwcPopTioYceWpXzFaKZRWsptHREGQtjjB1iWinZDMXkRczk\nJTtlcbWILubAaDsMXTk2a0E4MsT17BLPsbCA07W1F7WLZDGEEGsWQAwMDHDs2LHo9t69e3nuuefm\nDSAKhQLpdP0c74cPH+app56SAEKIBSilohk1SW8CPhbtM0HVBhGV4vS6BlNjdg6LSrjIksEGHMaE\nSxk600szhwuPUbsoN5yJs8UuROYl1uR3FkKsnjUJIEZGRuZ8g8lkMgwPD897/Pj4OCdOnODw4cNs\n2bIl2p7P51f0PIW4XqlwQSAbXNwyZ7/RGnTV1loUx2dMDzwF1TKYIJx9LxyPrrUNNLSPcVyU41Eu\npgkmKjb4cGN2FIkXj54bNyZdJkKsY2vy6s3lcmQymbpt7e3tAExMTNDWVj+neldXFy+99FJd8PCD\nH/yAPXv2rPzJCnEDskWYCdy2BLTdvOixtcCBagk9NYbOv4uZuASObxdG0mU7qZb253aZKNcGFm4M\n5cXC5Zjjdls8JTN1CtHE1iSAyOfz5HK5um0dHR0YYxgfH58TQADs3Lmz7v6vvPIKL7300oqfqxBi\ncUqpKKPgJtujGoxMZzuVCwWbzQjKmKpdAMmUCphSHsoFW/gZTumryxN2XQF0WPBpMxl48Wg6cJRr\nMxpOmNWIpcKpiYUQq21NAojZ9QxguymUUnR0LD1H+Re/+EVeeOEFNm/evBKnJ4S4hmw2ozaDXgdk\nbptzjNEB+GV0ecpO+V0cx+Q/wFQm7EqK2tghqkaDMeH6QwacmA00auuMON50XYYT1mkod3oCLlkp\nVYhrZk0CiEwmM6d+oVAoAMybfZipr6+Phx9+mB07diz7+To72xs/yRuQtNPySVstz7VoJ7uKol3x\n0OgAE/iYoEJQzBOMnScoXEJXJjH+JAQ++Dpa4FCBDSRqXSGOi/LidgZPN26HtNYyHY479/YqjTbZ\nsGHuypNiftJWizP+6n2sr0kA0d3dPScLkcvllqxpGBwcZO/evezevRuwxZjd3d1LPt+FC4UrP9kb\nRGdnu7TTMklbLc/KtJMDxO3Fa4PO26GzlsEo2UXM/CoEZQgDDVOesEszV4uY8hRmagLjj9v6DGOw\n+QwDqHCI6oynC6cRt1kMbzqrUctshPvqRqTURqUsM/jYsKGFsStcovpGI221NONXuGXr0sddC2tW\nAt3T08OpU6eieSBOnz7NoUOHov3ZbJaRkZFoWOfQ0FAUZBQKBcbHx/mXf/mXZQUQQojrm3JciLey\n3HyBHUFSthkNv2QzHJUippzHVEuooIrxy2Hmoxwu0Vy2AYcOwmLQWtDhzB8s1AUW3rxdKihFECui\ni2U7mZfjglLhMY4UkYqmtmYBxKOPPsqJEyc4deoU586dY9u2bVEwATA8PBxNLFUoFDhy5AhKKb7y\nla9Exxw4cGAtTl0Isc4pLxxS2sB9akNb0T4EPtoPsxuVqbBI1GZA0FU71NWv2AxIEN424VBXo4Fa\n5gMqpaQd7uq4zI1DVDj/Rlg4SnitnOnCUseb3jZjGnMczxaayqRfYoUoY4xZ+rD1TdLNS5O0/PJJ\nWy2PtNO0aKir9iGoogMf/BLogI0dKS59cDlcz6QCJkBpbQOOoAJ+FYKKLSINAoyugDG210VXZ3TD\ngA04VNiFwvQEXzOKS5VyIZYMtzvTI1tqQUoD3S+rTbowlmb8Crf81h7c+Mqv0iuzuAghxAqbOdSV\nWIqZY0Hine14avG5NmaLsiF+Be1XIKiC8THVsg02KpN28q+i7ZKx26am5+HQPiYMNuwU5rNP2JnO\nfMwKLqbn8KjdVuF+tz5bMjMwCbdJl8z1RQIIIYRYZ2oTfeElWO7AVBPMyICU8pjyZFjbUbQFp9pH\nGRN2w9hJwExQtSNbtI+pFMPMh7Z1IDOyHgaNLXCdoba2Sl0yI+yScWZlPWZ0zaiw/mM6+FDUMitB\nvIwpluq24cabPnNyvZIAQgghbgB2FVYPYkncZOPDa6P6DR2ACcJZRgN0OIeHqZZs4GEM6KoNPmZu\n14ENRoxvg5nA1ohQ8bELr2j7POE/06NjplVKKfyJMlFUotSsoEHNmHBMTQcgYYbEHuvM3Ve7jmpO\nal1BM4+feUz97Rs1cJEAQgghxJJUNDqkPudxtVNzzRuYGI32w4LUMLDAr9KSiVO6lLNdNoTDd8sF\nO1omqEKgMUEp7NLRNoAxOoxDtO36UYSZFDMrSJmRQVHhP2GmY+n4YDoQsZOV1TIqMwKQWgAzM7NS\nF+iE91ezsi8zgxaUPbcmCVwkgBBCCLFmGglMEp3teF5jhblG67rApHYxRmOCsJakFoDU9gdVe58g\nAOzkZZggLFwNMyiEgY0OQNeCIDvhmX0+bbMtYUEsGIyB8J/p69p5YrDBBjMChCVbr647RynHPh+r\ns06UBBBCCCGuW6pWY+HG6revwnNH2ZWZl9o8IjqsJQmDG1Mpgq6E3TzhCJygGgYvM+8LYOzP6DDI\nCad4NwZlApxVWuVWAgghhBBiBUTZlXnyKSsZwCgvtvRB14CMqRFCCCFEwySAEEIIIUTDJIAQQggh\nRMMkgBBCCCFEwySAEEIIIUTDJIAQQgghRMMkgBBCCCFEwySAEEIIIUTDJIAQQgghRMMkgBBCCCFE\nwySAEEIIIUTDJIAQQgghRMMkgBBCCCFEwySAEEIIIUTDJIAQQgghRMMkgBBCCCFEwySAEEIIIUTD\nJIAQQgghRMMkgBBCCCFEwySAEEIIIUTDJIAQQgghRMMkgBBCCCFEwySAEEIIIUTDJIAQQgghRMO8\ntXzy48ePs3XrVsbHxwHo6elZ8j6Dg4O89tprHDt2bKVPTwghhBALWLMMRF9fH1u3bmXfvn309PRw\n7tw5BgcHFzx+eHiY48eP09/fT6FQWMUzFUIIIcRsaxZADAwMsG/fvuj23r176e/vX/D43bt3c/To\nUbq7u1fj9IQQQgixiDUJIEZGRlBK1W3LZDIMDw+vxekIIYQQokFrEkDkcjkymUzdtvb2dgAmJibW\n4pSEEEII0YA1CSDy+Ty5XK5uW0dHB8aYqKBSCCGEEM1rTQKIdDo9Z9v4+DhKKTo6OtbgjIQQQgjR\niDUZxpnJZMjn83XbaiMr2trarvnzdXa2X/PHvB5JOy2ftNXySDstj7TT8klbNY81CSC6u7vnZCFy\nuRx79uxZkee7cEGGfS6ls7Nd2mmZpK2WR9ppeaSdlk/aanlWK8has2GcPT09nDp1Krp9+vRpDh06\nFN3OZrOLzgshhBBCiLWzZjNRPvroo5w4cYJTp05x7tw5tm3bVjcvxPDwMIODg+zfvx+wQz+HhoY4\ndeoUuVyOrVu3smfPHnbu3LlWv4IQQghxw1LGGLPWJ7HSJOW1NEkNLp+01fJIOy2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eAAAB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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1b43571d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ta = 500\n", "tb = 100\n", "var=\"std\"\n", "\n", "import numpy as np\n", "import seaborn as sns\n", "import brewer2mpl\n", "bmap = brewer2mpl.get_map('Dark2', 'qualitative', 4)\n", "colors = bmap.mpl_colors\n", "\n", "def time_limit(error_evolution):\n", " tl = len(error_evolution)-1\n", " while tl > 0 and error_evolution[tl] < 0.4:\n", " tl -= 1\n", " return tl\n", " \n", "def synchro(ee_list):\n", " l = []\n", " for ee in ee_list:\n", " tl_ee = time_limit(ee)\n", " if tl_ee < n_iter/iter_ds-ta and tl_ee > tb:\n", " l += [ee[tl_ee-tb:tl_ee+ta]]\n", " return l\n", "\n", " \n", "ee_s_synchro = synchro(ee_list_s)\n", "ee_ns_synchro = synchro(ee_list_ns)\n", " \n", "print len(ee_s_synchro)\n", "print len(ee_ns_synchro)\n", "\n", "\n", "x = [iter_ds*(i - tb) for i in range(ta+tb)]\n", "\n", "\n", "if False:\n", " (_, caps, _) = plt.errorbar(x, np.mean(ee_s_synchro, axis=0), np.std(ee_s_synchro, axis=0), label=\"Toy Names\", lw=3, color=colors[1], errorevery=50)\n", " for cap in caps:\n", " cap.set_markeredgewidth(2)\n", " (_, caps, _) = plt.errorbar(x, np.mean(ee_ns_synchro, axis=0), np.std(ee_ns_synchro, axis=0), label=\"Distractors\", lw=3, color=colors[2], errorevery=50)\n", " for cap in caps:\n", " cap.set_markeredgewidth(2)\n", "else:\n", " y = np.mean(ee_s_synchro, axis=0)\n", " error = np.std(ee_s_synchro, axis=0)\n", " plt.plot(x, y, label=\"Toy Names\", lw=3, color=colors[1])\n", " plt.fill_between(x, y-error, y+error, color=colors[1], alpha=0.2)\n", " plt.plot(x, y+error, color=colors[1], alpha=0.25)\n", " plt.plot(x, y-error, color=colors[1], alpha=0.25)\n", "\n", " y = np.mean(ee_ns_synchro, axis=0)\n", " error = np.std(ee_ns_synchro, axis=0)\n", " plt.plot(x, y, label=\"Distractors\", lw=3, color=colors[2])\n", " plt.fill_between(x, y-error, y+error, color=colors[2], alpha=0.2)\n", " plt.plot(x, y+error, color=colors[2], alpha=0.25)\n", " plt.plot(x, y-error, color=colors[2], alpha=0.25)\n", "\n", "print \"Avg diff\", abs(np.mean(ee_s_synchro, axis=0)[-1] - np.mean(ee_ns_synchro, axis=0)[-1])\n", "print \"Std\", np.std(ee_s_synchro, axis=0)[-1], np.std(ee_ns_synchro, axis=0)[-1]\n", "\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "plt.xlabel(\"Time after onset\", fontsize=18)\n", "plt.ylabel(\"Vocal Error\", fontsize=18)\n", "plt.ylim([0, 0.7]) \n", "plt.xlim([-iter_ds*tb, iter_ds*ta]) \n", "plt.plot((-iter_ds*tb, iter_ds*ta), (0.4, 0.4), 'k--', lw=2)\n", "legend = plt.legend(frameon=True, fontsize=16)\n", "\n", "plt.xticks(fontsize = 16)\n", "plt.yticks(fontsize = 16)\n", "\n", "frame = legend.get_frame()\n", "frame.set_facecolor('1.')\n", "frame.set_edgecolor('0.')\n", "\n", "plt.savefig('../figs/fig_vocal_errors.pdf', format='pdf', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data points: 1482 per condition\n" ] }, { "data": { "image/png": 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zRD+KZRnkF2Rgea6vL0T3fTtu3xoIKb5YQHPjExIaSvYkLLj+NvE9m2qK2FKx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\nOSbCeOQ3mPn6OugP/hKizzFoGxFGkwa3fHkRvnTrImRlZ8j7druC2PX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1bb90f7c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "from scipy import stats\n", "import numpy as np\n", "\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "sns.distplot([ee[-1] for ee in ee_list_s], bins=np.arange(0., 0.6, 0.025), color=colors[1], hist_kws={'edgecolor':'gray'}, kde_kws={\"lw\":3}, label=\"Toy Names\")\n", "sns.distplot([ee[-1] for ee in ee_list_ns], bins=np.arange(0., 0.6, 0.025), color=colors[2], hist_kws={'edgecolor':'gray'}, kde_kws={\"lw\":3}, label=\"Distractors\")\n", "legend = plt.legend(frameon=True, fontsize=16)\n", "plt.xlabel(\"Vocal error\", fontsize=18)\n", "plt.ylabel(\"Density\", fontsize=18)\n", "plt.plot((0.4, 0.4), (0, 7), 'k--', lw=2)\n", "\n", "plt.xticks(fontsize = 16)\n", "plt.yticks(fontsize = 16)\n", "\n", "frame = legend.get_frame()\n", "frame.set_facecolor('1.')\n", "frame.set_edgecolor('0.')\n", "\n", "print \"Data points:\", len(ee_list_s), \"per condition\"\n", "\n", "plt.savefig('../figs/fig_vocal_errors_distribution.pdf', format='pdf', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.38703353668e-73\n" ] } ], "source": [ "# STATS\n", "import scipy\n", "\n", "data1 = [ee[-1] for ee in ee_list_s]\n", "data2 = [ee[-1] for ee in ee_list_ns]\n", "\n", "z, p = scipy.stats.mannwhitneyu(data1, data2)\n", "p_value = p * 2\n", "print p_value" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
DigitalSlideArchive/HistomicsTK
docs/examples/polygon_merger_using_rtree.ipynb
1
24953
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Merging polygons (general purpose)\n", "\n", "**Overview:**\n", "\n", "This notebook describes how to merge annotations that are generated in piecewise when annotating a large structure, or that arise in an annotation study when one user adds annotations to another user's work as corrections. In these cases there is a collection of annotations that overlap and need to be merged without any regular or predictable interfaces.\n", "\n", "The example presented below addresses this case using an R-tree algorithm that identifies merging candidates without exhuastive search. While this approach can also merge annotations generated by tiled analysis it is slower than the alternative.\n", "\n", "This extends on some of the work described in Amgad et al, 2019:\n", "\n", "_Mohamed Amgad, Habiba Elfandy, Hagar Hussein, ..., Jonathan Beezley, Deepak R Chittajallu, David Manthey, David A Gutman, Lee A D Cooper, Structured crowdsourcing enables convolutional segmentation of histology images, Bioinformatics, 2019, btz083_\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Here is a sample result:**\n", "\n", "![polygon_merger](https://user-images.githubusercontent.com/22067552/80076675-84178800-851a-11ea-8f5d-552bca8402ed.png)\n" ] }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "**Implementation summary**\n", "\n", "This algorithm merges annotations in coordinate space, which means it can merge very large structures without encountering memory issues. The algorithm works as follows:\n", "\n", "- Identify contours that that have the same label (e.g. tumor)\n", "\n", "- Add bounding boxes from these contours to an [R-tree](https://en.wikipedia.org/wiki/R-tree). The R-tree implementation used here is modified from [here](https://code.google.com/archive/p/pyrtree/) and uses k-means clustering to balance the tree.\n", "\n", "- Starting from the bottom of the tree, merge all contours from leafs that belong to the same nodes.\n", "\n", "- Move one level up the hierarchy, each time incorporating merged contours from nodes that share a common parent. This is repeated until there is one merged contour at the root node. The contours are first dilated slightly to make sure any small gaps are filled in the merged result, then are eroded by the same factor after merging.\n", "\n", "- Save the coordinates from each merged polygon in a new pandas DataFrame.\n", "\n", "This process ensures that the number of comparisons is ``<< n^2``. This is very important since algorithm complexity plays a key role as whole slide images may contain tens of thousands of annotated structures.\n", "\n", "**Where to look?**\n", "\n", "```\n", "|_ histomicstk/\n", " |_annotations_and_masks/\n", " |_polygon_merger_v2.py\n", " |_tests/\n", " |_ test_polygon_merger.py\n", "```" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 1, "outputs": [], "source": [ "import os\n", "import sys\n", "CWD = os.getcwd()\n", "sys.path.append(os.path.join(CWD, '..', '..', 'histomicstk', 'annotations_and_masks'))\n", "import girder_client\n", "from histomicstk.annotations_and_masks.polygon_merger_v2 import Polygon_merger_v2\n", "from histomicstk.annotations_and_masks.masks_to_annotations_handler import (\n", " get_annotation_documents_from_contours, _discard_nonenclosed_background_group)\n", "from histomicstk.annotations_and_masks.annotation_and_mask_utils import parse_slide_annotations_into_tables" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "## 1. Connect girder client and set parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Connect girder client and set parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n", "SOURCE_SLIDE_ID = '5d5d6910bd4404c6b1f3d893'\n", "POST_SLIDE_ID = '5d586d76bd4404c6b1f286ae'\n", "\n", "gc = girder_client.GirderClient(apiUrl=APIURL)\n", "# gc.authenticate(interactive=True)\n", "gc.authenticate(apiKey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')\n", "\n", "# get and parse slide annotations into dataframe\n", "slide_annotations = gc.get('/annotation/item/' + SOURCE_SLIDE_ID)\n", "_, contours_df = parse_slide_annotations_into_tables(slide_annotations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Polygon merger\n", "\n", "The ``Polygon_merger_v2()`` is the top level function for performing the merging." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Init Polygon_merger object.\n", "\n", " Arguments:\n", " -----------\n", " contours_df : pandas DataFrame\n", " The following columns are needed.\n", "\n", " group : str\n", " annotation group (ground truth label).\n", " ymin : int\n", " minimun y coordinate\n", " ymax : int\n", " maximum y coordinate\n", " xmin : int\n", " minimum x coordinate\n", " xmax : int\n", " maximum x coordinate\n", " coords_x : str\n", " vertix x coordinates comma-separated values\n", " coords_y\n", " vertix y coordinated comma-separated values\n", " merge_thresh : int\n", " how close do the polygons need to be (in pixels) to be merged\n", " verbose : int\n", " 0 - Do not print to screen\n", " 1 - Print only key messages\n", " 2 - Print everything to screen\n", " monitorPrefix : str\n", " text to prepend to printed statements\n", "\n", " \n" ] } ], "source": [ "print(Polygon_merger_v2.__doc__)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Init Polygon_merger object.\n", "\n", " Arguments:\n", " -----------\n", " contours_df : pandas DataFrame\n", " The following columns are needed.\n", "\n", " group : str\n", " annotation group (ground truth label).\n", " ymin : int\n", " minimun y coordinate\n", " ymax : int\n", " maximum y coordinate\n", " xmin : int\n", " minimum x coordinate\n", " xmax : int\n", " maximum x coordinate\n", " coords_x : str\n", " vertix x coordinates comma-separated values\n", " coords_y\n", " vertix y coordinated comma-separated values\n", " merge_thresh : int\n", " how close do the polygons need to be (in pixels) to be merged\n", " verbose : int\n", " 0 - Do not print to screen\n", " 1 - Print only key messages\n", " 2 - Print everything to screen\n", " monitorPrefix : str\n", " text to prepend to printed statements\n", "\n", " \n" ] } ], "source": [ "print(Polygon_merger_v2.__init__.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Required arguments for initialization\n", "\n", "The only required argument is a dataframe of contours merge." ] }, { "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>annidx</th>\n", " <th>elementidx</th>\n", " <th>type</th>\n", " <th>group</th>\n", " <th>color</th>\n", " <th>xmin</th>\n", " <th>xmax</th>\n", " <th>ymin</th>\n", " <th>ymax</th>\n", " <th>bbox_area</th>\n", " <th>coords_x</th>\n", " <th>coords_y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>44457</td>\n", " <td>44703</td>\n", " <td>44191</td>\n", " <td>44260</td>\n", " <td>16974</td>\n", " <td>44614,44613,44608,44607,44602,44601,44596,4459...</td>\n", " <td>44191,44192,44192,44193,44193,44194,44194,4419...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>44350</td>\n", " <td>44682</td>\n", " <td>43750</td>\n", " <td>44154</td>\n", " <td>134128</td>\n", " <td>44350,44350,44353,44354,44359,44360,44364,4436...</td>\n", " <td>43750,44154,44151,44151,44146,44146,44142,4414...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>polyline</td>\n", " <td>roi</td>\n", " <td>rgb(200,0,150)</td>\n", " <td>44350</td>\n", " <td>44860</td>\n", " <td>43750</td>\n", " <td>44260</td>\n", " <td>260100</td>\n", " <td>44350,44350,44860,44860,44350</td>\n", " <td>43750,44260,44260,43750,43750</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>polyline</td>\n", " <td>mostly_lymphocytic_infiltrate</td>\n", " <td>rgb(0,0,255)</td>\n", " <td>44856</td>\n", " <td>44860</td>\n", " <td>43999</td>\n", " <td>44034</td>\n", " <td>140</td>\n", " <td>44860,44858,44858,44857,44857,44856,44856,4485...</td>\n", " <td>43999,44001,44002,44003,44006,44007,44018,4401...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>polyline</td>\n", " <td>mostly_lymphocytic_infiltrate</td>\n", " <td>rgb(0,0,255)</td>\n", " <td>44788</td>\n", " <td>44860</td>\n", " <td>43912</td>\n", " <td>43997</td>\n", " <td>6120</td>\n", " <td>44823,44822,44819,44818,44817,44813,44812,4480...</td>\n", " <td>43912,43913,43913,43914,43914,43918,43918,4392...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " annidx elementidx type group color \\\n", "0 0 0 polyline mostly_tumor rgb(255,0,0) \n", "1 0 1 polyline mostly_tumor rgb(255,0,0) \n", "2 1 0 polyline roi rgb(200,0,150) \n", "3 2 0 polyline mostly_lymphocytic_infiltrate rgb(0,0,255) \n", "4 2 1 polyline mostly_lymphocytic_infiltrate rgb(0,0,255) \n", "\n", " xmin xmax ymin ymax bbox_area \\\n", "0 44457 44703 44191 44260 16974 \n", "1 44350 44682 43750 44154 134128 \n", "2 44350 44860 43750 44260 260100 \n", "3 44856 44860 43999 44034 140 \n", "4 44788 44860 43912 43997 6120 \n", "\n", " coords_x \\\n", "0 44614,44613,44608,44607,44602,44601,44596,4459... \n", "1 44350,44350,44353,44354,44359,44360,44364,4436... \n", "2 44350,44350,44860,44860,44350 \n", "3 44860,44858,44858,44857,44857,44856,44856,4485... \n", "4 44823,44822,44819,44818,44817,44813,44812,4480... \n", "\n", " coords_y \n", "0 44191,44192,44192,44193,44193,44194,44194,4419... \n", "1 43750,44154,44151,44151,44146,44146,44142,4414... \n", "2 43750,44260,44260,43750,43750 \n", "3 43999,44001,44002,44003,44006,44007,44018,4401... \n", "4 43912,43913,43913,43914,43914,43918,43918,4392... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contours_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Initialize and run the merger" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ": mostly_lymphocytic_infiltrate: set_contours_slice\n", ": mostly_lymphocytic_infiltrate: create_rtree\n", ": mostly_lymphocytic_infiltrate: set_tree_dict\n", ": mostly_lymphocytic_infiltrate: set_hierarchy\n", ": mostly_lymphocytic_infiltrate: get_merged_multipolygon\n", ": mostly_lymphocytic_infiltrate: _add_merged_multipolygon_contours\n", ": mostly_tumor: set_contours_slice\n", ": mostly_tumor: create_rtree\n", ": mostly_tumor: set_tree_dict\n", ": mostly_tumor: set_hierarchy\n", ": mostly_tumor: get_merged_multipolygon\n", ": mostly_tumor: _add_merged_multipolygon_contours\n", ": mostly_stroma: set_contours_slice\n", ": mostly_stroma: create_rtree\n", ": mostly_stroma: set_tree_dict\n", ": mostly_stroma: set_hierarchy\n", ": mostly_stroma: get_merged_multipolygon\n", ": mostly_stroma: _add_merged_multipolygon_contours\n" ] } ], "source": [ "# init & run polygon merger\n", "pm = Polygon_merger_v2(contours_df, verbose=1)\n", "pm.unique_groups.remove(\"roi\")\n", "pm.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE:**\n", "\n", "The following steps are only \"aesthetic\", and just ensure the contours look nice when posted to Digital Slide Archive for viewing with GeoJS. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# add colors (aesthetic)\n", "for group in pm.unique_groups:\n", " cs = contours_df.loc[contours_df.loc[:, \"group\"] == group, \"color\"]\n", " pm.new_contours.loc[\n", " pm.new_contours.loc[:, \"group\"] == group, \"color\"] = cs.iloc[0]\n", "\n", "# get rid of nonenclosed stroma (aesthetic)\n", "pm.new_contours = _discard_nonenclosed_background_group(\n", " pm.new_contours, background_group=\"mostly_stroma\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### This is the result" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>annidx</th>\n", " <th>elementidx</th>\n", " <th>type</th>\n", " <th>group</th>\n", " <th>color</th>\n", " <th>xmin</th>\n", " <th>xmax</th>\n", " <th>ymin</th>\n", " <th>ymax</th>\n", " <th>bbox_area</th>\n", " <th>coords_x</th>\n", " <th>coords_y</th>\n", " <th>has_holes</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>polyline</td>\n", " <td>mostly_lymphocytic_infiltrate</td>\n", " <td>rgb(0,0,255)</td>\n", " <td>44670.0</td>\n", " <td>45472.0</td>\n", " <td>43750.0</td>\n", " <td>44200.0</td>\n", " <td>360900.0</td>\n", " <td>44670,44670,44670,44670,44670,44670,44670,4467...</td>\n", " <td>43901,43901,43907,43907,43908,43908,43909,4391...</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>46181.0</td>\n", " <td>46396.0</td>\n", " <td>43750.0</td>\n", " <td>43917.0</td>\n", " <td>35905.0</td>\n", " <td>46181,46181,46181,46181,46181,46181,46181,4618...</td>\n", " <td>43777,43777,43777,43778,43778,43778,43778,4377...</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>46312.0</td>\n", " <td>46396.0</td>\n", " <td>44167.0</td>\n", " <td>44350.0</td>\n", " <td>15372.0</td>\n", " <td>46312,46312,46312,46312,46312,46312,46315,4631...</td>\n", " <td>44252,44252,44252,44253,44253,44253,44256,4425...</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>44907.0</td>\n", " <td>46396.0</td>\n", " <td>44609.0</td>\n", " <td>46308.0</td>\n", " <td>2529811.0</td>\n", " <td>44907,44907,44907,44907,44907,44907,44907,4490...</td>\n", " <td>46230,46230,46230,46234,46234,46234,46234,4623...</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>polyline</td>\n", " <td>mostly_tumor</td>\n", " <td>rgb(255,0,0)</td>\n", " <td>45822.0</td>\n", " <td>46086.0</td>\n", " <td>43824.0</td>\n", " <td>43953.0</td>\n", " <td>34056.0</td>\n", " <td>45822,45822,45822,45822,45822,45822,45822,4582...</td>\n", " <td>43914,43914,43915,43915,43915,43915,43915,4391...</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " annidx elementidx type group color \\\n", "0 NaN NaN polyline mostly_lymphocytic_infiltrate rgb(0,0,255) \n", "1 NaN NaN polyline mostly_tumor rgb(255,0,0) \n", "2 NaN NaN polyline mostly_tumor rgb(255,0,0) \n", "3 NaN NaN polyline mostly_tumor rgb(255,0,0) \n", "4 NaN NaN polyline mostly_tumor rgb(255,0,0) \n", "\n", " xmin xmax ymin ymax bbox_area \\\n", "0 44670.0 45472.0 43750.0 44200.0 360900.0 \n", "1 46181.0 46396.0 43750.0 43917.0 35905.0 \n", "2 46312.0 46396.0 44167.0 44350.0 15372.0 \n", "3 44907.0 46396.0 44609.0 46308.0 2529811.0 \n", "4 45822.0 46086.0 43824.0 43953.0 34056.0 \n", "\n", " coords_x \\\n", "0 44670,44670,44670,44670,44670,44670,44670,4467... \n", "1 46181,46181,46181,46181,46181,46181,46181,4618... \n", "2 46312,46312,46312,46312,46312,46312,46315,4631... \n", "3 44907,44907,44907,44907,44907,44907,44907,4490... \n", "4 45822,45822,45822,45822,45822,45822,45822,4582... \n", "\n", " coords_y has_holes \n", "0 43901,43901,43907,43907,43908,43908,43909,4391... 0.0 \n", "1 43777,43777,43777,43778,43778,43778,43778,4377... 0.0 \n", "2 44252,44252,44252,44253,44253,44253,44256,4425... 0.0 \n", "3 46230,46230,46230,46234,46234,46234,46234,4623... 0.0 \n", "4 43914,43914,43915,43915,43915,43915,43915,4391... 0.0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.new_contours.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Visualize results on HistomicsTK" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# deleting existing annotations in target slide (if any)\n", "existing_annotations = gc.get('/annotation/item/' + POST_SLIDE_ID)\n", "for ann in existing_annotations:\n", " gc.delete('/annotation/%s' % ann['_id'])\n", "\n", "# get list of annotation documents\n", "annotation_docs = get_annotation_documents_from_contours(\n", " pm.new_contours.copy(), separate_docs_by_group=True,\n", " docnamePrefix='test',\n", " verbose=False, monitorPrefix=POST_SLIDE_ID + \": annotation docs\")\n", "\n", "# post annotations to slide -- make sure it posts without errors\n", "for annotation_doc in annotation_docs:\n", " resp = gc.post(\n", " \"/annotation?itemId=\" + POST_SLIDE_ID, json=annotation_doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can go to HistomicsUI and confirm that the posted annotations make sense\n", "and correspond to tissue boundaries and expected labels." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
lionell/laboratories
decision_theory/lab2.ipynb
3
7994
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import rankdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clusterized ranking" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Image](https://harzing.com/img/i/inclrank.jpeg)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "M = np.array([\n", " [5, 3, 1, 2, 8, 4, 6, 7],\n", " [5, 4, 3, 1, 8, 2, 6, 7],\n", " [1, 7, 5, 4, 8, 2, 3, 6],\n", " [6, 4, 2.5, 2.5, 8, 1, 7, 5],\n", " [8, 2, 4, 6, 3, 5, 1, 7],\n", " [5, 6, 4, 3, 2, 1, 7, 8],\n", " [6, 1, 2, 3, 5, 4, 8, 7],\n", " [5, 1, 3, 2, 7, 4, 6, 8],\n", " [6, 1, 3, 2, 5, 4, 7, 8],\n", " [5, 3, 2, 1, 8, 4, 6, 7],\n", " [7, 1, 3, 2, 6, 4, 5, 8],\n", " [1, 6, 5, 3, 8, 4, 2, 7]\n", "])\n", "n, m = M.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how we find **average** ranking." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 5. , 3.5, 2. , 1. , 7. , 3.5, 6. , 8. ])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_rank = rankdata(np.average(M, axis=0))\n", "average_rank" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this way we can get **median** ranking." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 5. , 2.5, 2.5, 1. , 8. , 4. , 6. , 7. ])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "median_rank = rankdata(np.median(M, axis=0))\n", "median_rank" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we need to compute **kernel of disagreement**." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[4, 7]]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adj = np.zeros((m, m), dtype=np.bool)\n", "kernel = []\n", "for i in range(m):\n", " for j in range(i + 1, m):\n", " if (average_rank[i] - average_rank[j])*(median_rank[i] - median_rank[j]) < 0:\n", " kernel.append([i, j])\n", " adj[i][j] = adj[j][i] = True\n", "kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a graph of the disagreement, we can easily find a full component via Depth First Search." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def dfs(i, used):\n", " if i in used:\n", " return []\n", " used.add(i)\n", " \n", " res = [i]\n", " for j in range(m):\n", " if adj[i][j]:\n", " res += dfs(j, used)\n", " return res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last thing to do, is to iterate in the correct order, and don't forget to print a whole cluster when needed." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 2, 1, 5, 0, 6, 4, 7]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "order = sorted(range(m), key=lambda i: (average_rank[i], median_rank[i]))\n", "order" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[3], [2], [1], [5], [0], [6], [4, 7]]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = []\n", "used = set()\n", "for i in order:\n", " cluster = dfs(i, used)\n", " if len(cluster) > 0:\n", " result.append(cluster)\n", "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Kemeny distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Image](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9tc8lC8WFpftjfLNbRNz1YXqf-PWSIiil1loBcY7kWieR68YpKQ)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "rankings = np.array([\n", " [[1], [2, 3], [4], [5], [6, 7]],\n", " [[1, 3], [4], [2], [5], [7], [6]],\n", " [[1], [4], [2], [3], [6], [5], [7]],\n", " [[1], [2, 4], [3], [5], [7], [6]],\n", " [[2], [3], [4], [5], [1], [6], [7]],\n", " [[1], [3], [2], [5], [6], [7], [4]],\n", " [[1], [5], [3], [4], [2], [6], [7]]\n", "])\n", "n = rankings.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to be able to build relation matrix out of the ranking." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def build(x):\n", " n = sum(map(lambda r: len(r), x)) # Total amount of objects\n", " m = np.zeros((n, n), dtype=np.bool)\n", " for r in x:\n", " for i in r:\n", " for j in range(n):\n", " if not m[j][i - 1] or j + 1 in r:\n", " m[i - 1][j] = True\n", " return m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can calculate Kemedy distances between each two rankings." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 5., 8., 5., 10., 8., 10.],\n", " [ 5., 0., 9., 6., 13., 11., 9.],\n", " [ 8., 9., 0., 5., 14., 14., 12.],\n", " [ 5., 6., 5., 0., 13., 13., 13.],\n", " [ 10., 13., 14., 13., 0., 16., 18.],\n", " [ 8., 11., 14., 13., 16., 0., 10.],\n", " [ 10., 9., 12., 13., 18., 10., 0.]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist = np.zeros((n, n))\n", "for i in range(n):\n", " for j in range(n):\n", " dist[i][j] = np.sum(build(rankings[i]) ^ build(rankings[j]))\n", "dist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's find **Kemeny median** for the ranks." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1], [2, 3], [4], [5], [6, 7]]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "median = np.argmin(np.sum(dist, axis=1))\n", "rankings[median]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
liyigerry/msm_test
examples/quadwell.ipynb
2
19157
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from msmbuilder.example_datasets import QuadWell, quadwell_eigs\n", "from msmbuilder.cluster import NDGrid\n", "from msmbuilder.msm import MarkovStateModel\n", "from sklearn.pipeline import Pipeline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving \"/home/gerry/msmbuilder_data/quadwell/version-1_random-state-0.pkl\"... (<type 'list'>)\n", "This dataset consists of 100 trajectories simulated with Brownian dynamics\n", "on the reduced potential function\n", "\n", "V = 4(x^8 + 0.8 exp(-80 x^2) + 0.2 exp(-80 (x-0.5)^2) + 0.5 exp(-40 (x+0.5)^2)).\n", "\n", "The simulations are governed by the stochastic differential equation\n", "\n", "dx_t/dt = -\\nabla V(x) + \\sqrt{2D} * R(t),\n", "\n", "where R(t) is a standard normal white-noise process, and D=1e3. The timsetep\n", "is 1e-3. Each trajectory is 10^3 steps long, and starts from a random\n", "initial point sampled from the uniform distribution on [-1, 1].\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log\n", " app.launch_new_instance()\n" ] } ], "source": [ "dataset = QuadWell(random_state=0).get()\n", "true_eigenvalues = quadwell_eigs(200)[0]\n", "true_timescales = -1 / np.log(true_eigenvalues[1:])\n", "print(QuadWell.description())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def msm_timescales(trajectories, n_states):\n", " pipeline = Pipeline([\n", " ('grid', NDGrid(min=-1.2, max=1.2)),\n", " ('msm', MarkovStateModel(n_timescales=4, reversible_type='transpose', verbose=False))\n", " ])\n", " pipeline.set_params(grid__n_bins_per_feature=n_states)\n", " pipeline.fit(trajectories)\n", " return pipeline.named_steps['msm'].timescales_\n", "\n", "n_states = [5, 10, 50, 100]\n", "ts = np.array([msm_timescales(dataset.trajectories, n) for n in n_states])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aj581C4YPh+Li2Me5pBqFQx3ZIpJzhg+HCROgvLzuYxPIxoaWpHdmK1GRiDRY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\nCVZ7CDoU9rombLuGxP0LwX6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f83e2fb0690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, c in enumerate(['b', 'r', 'm']):\n", " plt.plot(n_states, ts[:, i], c=c, marker='x')\n", " plt.axhline(true_timescales[i], ls='--', c=c, lw=2)\n", "\n", "plt.xlabel('Number of states')\n", "plt.ylabel('Timescale (steps)')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
thom056/ada-parliament-ML
04-VotingProfile/Clustering/4-Votation-Analysis.ipynb
1
13514
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sklearn\n", "import sklearn.ensemble\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import cross_val_score, train_test_split, cross_val_predict, learning_curve\n", "import sklearn.metrics\n", "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "# There's a lot of columns in the DF. \n", "# Therefore, we add this option so that we can see more columns\n", "pd.options.display.max_columns = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loads the NLP results" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entries in the DataFrame (3470, 17)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>armée</th>\n", " <th>asile / immigration</th>\n", " <th>assurances</th>\n", " <th>budget</th>\n", " <th>dunno</th>\n", " <th>entreprise/ finance</th>\n", " <th>environnement</th>\n", " <th>famille / enfants</th>\n", " <th>imposition</th>\n", " <th>politique internationale</th>\n", " <th>retraite</th>\n", " <th>text</th>\n", " <th>text_eng</th>\n", " <th>positive</th>\n", " <th>negative</th>\n", " <th>neutral</th>\n", " <th>compound</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.006995</td>\n", " <td>0.930066</td>\n", " <td>0.006993</td>\n", " <td>0.006993</td>\n", " <td>0.006993</td>\n", " <td>0.006994</td>\n", " <td>0.006993</td>\n", " <td>0.006993</td>\n", " 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<td>0.015152</td>\n", " <td>0.015152</td>\n", " <td>0.015152</td>\n", " <td>0.015154</td>\n", " <td>0.015152</td>\n", " <td>Une zone exempte d'armes nucléaires au coeur ...</td>\n", " <td>A nuclear weapon free zone in the heart of Eu...</td>\n", " <td>0.264</td>\n", " <td>0.176</td>\n", " <td>0.560</td>\n", " <td>0.2732</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " armée asile / immigration assurances budget dunno \\\n", "0 0.006995 0.930066 0.006993 0.006993 0.006993 \n", "1 0.018182 0.018185 0.018183 0.271557 0.564795 \n", "2 0.015152 0.389858 0.015152 0.232520 0.256405 \n", "\n", " entreprise/ finance environnement famille / enfants imposition \\\n", "0 0.006994 0.006993 0.006993 0.006993 \n", "1 0.018183 0.018182 0.018183 0.018183 \n", "2 0.015152 0.015152 0.015152 0.015152 \n", "\n", " politique internationale retraite \\\n", "0 0.006993 0.006993 \n", "1 0.018183 0.018184 \n", "2 0.015154 0.015152 \n", "\n", " text \\\n", "0 Arrêté fédéral concernant la contribution de l... \n", "1 Renforcement du Traité sur la non-proliférati... \n", "2 Une zone exempte d'armes nucléaires au coeur ... \n", "\n", " text_eng positive negative \\\n", "0 Federal decree on Switzerland's contribution i... 0.075 0.000 \n", "1 Strengthening of the Treaty on the non-prolif... 0.227 0.206 \n", "2 A nuclear weapon free zone in the heart of Eu... 0.264 0.176 \n", "\n", " neutral compound \n", "0 0.925 0.4404 \n", "1 0.567 0.0772 \n", "2 0.560 0.2732 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = '../../datas/nlp_results/'\n", "voting_df = pd.read_csv(path+'voting_with_topics_unique_sentiment.csv')\n", "print('Entries in the DataFrame',voting_df.shape)\n", "\n", "#Dropping the useless column\n", "#voting_df = voting_df.drop('Unnamed: 0',1)\n", "\n", "#Putting numerical values into the columns that should have numerical values\n", "\n", "#num_cols = ['BillTitle', 'BusinessTitle','text','text_eng','FirstName','LastName']\n", "#voting = voting_df.drop(num_cols,axis=1).apply(pd.to_numeric)\n", "#voting['text'] = voting_df.text\n", "#Inserting the full name at the second position\n", "#voting.insert(1,'Name', voting_df['FirstName'] + ' ' + voting_df['LastName'])\n", "\n", "voting_df.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Separates each deputee" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def split_df(df, field):\n", " \"\"\"\n", " Splits the input df along a certain field into multiple dictionaries which links each unique\n", " entry of the field to the entries in the dataframe\n", " \"\"\"\n", " # Retrieve first all the unique Name entries\n", " unique_field = df[field].unique()\n", " print('Number of unique entries in',field,':',len(unique_field))\n", " #Create a dictionary of DataFrames which stores all the info relative to a single deputee\n", " df_dict = {elem : pd.DataFrame for elem in unique_field}\n", "\n", " for key in df_dict.keys():\n", " df_dict[key] = df.loc[df[field] == key]\n", " \n", " return df_dict\n", "\n", "voting_dict = split_df(voting, 'Name')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "depute_dict = {}\n", "for deputee in voting_dict :\n", " df_deputee = voting_dict[deputee]\n", " df = df_deputee.groupby('Decision')['Decision'].count()\n", " #df.plot(kind='bar',title=deputee)\n", " plt.show()\n", " depute_dict[deputee] = df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stats on each deputee (yes/no/abstention) regarding to the topics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "max_ = [0]*7\n", "frequency = [0]*7\n", "winner_max = ['']*7\n", "winner_freq = ['']*7\n", "Decisions = ['Yes','No','Abstention','','Away','Excused','President']\n", "for deputee_frame in depute_dict :\n", " for i in range(1,8) :\n", " if i in depute_dict[deputee_frame].index :\n", " frequency_old = frequency[i-1]\n", " max_old = max_[i-1]\n", " max_[i-1] = max(max_[i-1],depute_dict[deputee_frame][i])\n", " frequency[i-1] = max(frequency[i-1],depute_dict[deputee_frame][i]/depute_dict[deputee_frame].sum())\n", " if frequency_old != frequency[i-1]:\n", " winner_freq[i-1] = deputee_frame\n", " if max_old != max_[i-1]:\n", " winner_max[i-1] = deputee_frame\n", "for i in range(7) : \n", " if max_[i]!=0 :\n", " print(\"for {0} :\".format(Decisions[i]) )\n", " print(\"{0} as the highest frequency:{1}\".format(winner_freq[i],frequency[i],i+1))\n", " print(\"{0} as the highest value:{1}\".format(winner_max[i],max_[i],i+1))\n", " print()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataset_tmp = []\n", "path = '../../datas/scrap/Voting'\n", "allFiles = glob.glob(os.path.join(path, 'Session*.csv'))\n", "\n", "for file_ in allFiles:\n", " print(file_)\n", " data_tmp = pd.read_csv(file_)\n", " print(data_tmp.shape)\n", " dataset_tmp += [data_tmp] \n", "data_frame = pd.concat(dataset_tmp)\n", "print(data_frame.shape)\n", "data_tmp.drop('Unnamed: 0',1,inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "columns = ['BillTitle','BusinessTitle','Canton','Decision','FirstName','LastName','ParlGroupName','VoteEnd']\n", "treated_data =data_frame[columns]\n", "treated_data['text'] = treated_data['BillTitle'] + ' ' + treated_data['BusinessTitle']\n", "treated_data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#columns = ['Decision','Name','ParlGroupCode','positive','negative','neutral','compound','text']\n", "columns= ['text','text_eng','positive','negative','neutral','compound']\n", "vote = voting_df.drop(columns,1)\n", "to_merge = voting_df[['text']]\n", "to_merge['subject']= vote.idxmax(axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "to_merge.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_for_viz = pd.merge(treated_data,to_merge)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_for_viz['VoteEnd'] = [x[0:7] for x in data_for_viz.VoteEnd]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_for_viz.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_for_viz[['VoteEnd']].to_json('viz_data_vote_month.json')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_for_viz.sort_values('VoteEnd', ascending = ['True'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 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-2.0
rkwrd/rkwrd.github.io
.ipynb_checkpoints/HETU-checkpoint.ipynb
1
3170
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "def create_hetus(ddmmyyyy, gender):\n", " # generator function to yield valid HETU numbers\n", " # ppkkvvyzzzq\n", " # pp - day\n", " # kk - month\n", " # vv - year\n", " # y - century (+ for 1800, - for 1900, A for 2000)\n", " # zzz - individual number (even for females, odd for males, values of 002-899)\n", " # q - checkdigit, calculated as t = (ppkkvvzzz mod 31) as q = t~[0-9, A-Y]\n", " \n", " checksum = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \\\n", " \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"H\", \"J\", \"K\", \"L\", \"M\", \"N\", \"P\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\", \"Y\"]\n", " hetus = []\n", " index = 0\n", " if gender == \"m\":\n", " index += 1 \n", " y = 0\n", " ddmmyy = str(ddmmyyyy)[0:4] + str(ddmmyyyy)[6:]\n", " \n", " if int(ddmmyyyy[4:6]) == 18:\n", " y = \"+\"\n", " elif int(ddmmyyyy[4:6]) == 19:\n", " y = \"-\"\n", " elif int(ddmmyyyy[4:6]) >= 20:\n", " y = \"A\"\n", "\n", " while index < 900:\n", " index += 2\n", " zzz = str(\"%03d\" % index)\n", " tmp = int(str(ddmmyy) + str(zzz))\n", " q = checksum[tmp % 31]\n", " het = str(ddmmyy) + str(y) + str(zzz) + str(q)\n", " hetus.append(het)\n", " \n", " return hetus\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "create_hetus(\"30061988\", \"male\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "['300688-002Y',\n", " '300688-0041',\n", " '300688-0063',\n", " '300688-0085',\n", " '300688-0107',\n", " '300688-0129',\n", " '300688-014B',\n", " '300688-016D',\n", " '300688-018F',\n", " '300688-020J',\n", " '300688-022L',\n", " '300688-024N',\n", " '300688-026R',\n", " '300688-028T',\n", " '300688-030V']" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "def zzz_estimator():\n", " # bY = total births for that year\n", " # bD = bY / 365\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
liganega/Gongsu-DataSci
ref_materials/exams/2018/midterm_2018.ipynb
1
13045
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2018년 2학기 공업수학 중간고사 시험지\n", "```\n", "\n", "```\n", "**이름**:\n", "```\n", "```\n", "\n", "**학번**:\n", "```\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제1. \n", "다음 코드의 출력된 결과를 답하여라.\n", "`a = [1, 2, [[3, 4], 5, 6, 7], (8, 9)]`\n", "\n", "1) 다음 코드의 출력된 결과를 답하여라.\n", "\n", "(1) `a[0:2]` \n", "```\n", "\n", "```\n", "(2) `a[-1]`\n", "```\n", "\n", "```\n", "2) `3`을 출력하는 코드를 작성하라.\n", "```\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", ".\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제2.\n", "\n", "삼각형의 밑변의 길이와 높이를 입력받아 삼각형의 넓이를 구하는 코드를 아래와 같이 작성하였는데, 오류가 났다. 어디에서 오류가 발생하였는지를 표시한 후, 그 이유를 간단히 설명하고, 오류가 없도록 코드를 수정하여라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "```\n", "a = input(\"삼각형의 밑변의 길이를 입력하세요: \")\n", "b = input(\"삼각형의 높이의 길이를 입력하세요: \")\n", "\n", "c = ab/2\n", "print(\"삼각형의 넓이는\", c, \"입니다.\")\n", "```\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제3.\n", "\n", "아래의 제시된 `100`을 입력한 값으로 나누는 코드를 완성하여라. 이때, `0`을 입력한 경우에는 `'0이 아닌 숫자를 입력하세요.'`, 숫자가 아닌 값을 입력한 경우에는 `'숫자를 입력하세요.'`를 출력하도록 만들어라.\n", "\n", "(참고 : `ZeroDivisionError`, `ValueError`) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "```\n", "input_number = input(\"A number to divide 100: \")\n", "\n", "(1)_______________\n", " number = (2) _______________\n", " print(\"100을 입력한 값으로 나눈 결과는\", 100/number, \"입니다.\")\n", " \n", "(3)_______________\n", " print(\"0이 아닌 숫자를 입력하세요.\")\n", " \n", "(4)________________\n", " print(\"숫자를 입력하세요.\")\n", "\n", "```\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "(1)\n", "\n", "\n", "(2)\n", "\n", "\n", "(3)\n", "\n", "\n", "(4)\n", "\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 4.\n", "\n", "`'Seoul_pop.csv'` 파일에 아래와 같은 자료가 저장되어 있다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "```\n", "### 1990년부터 5년 간격으로 측정된 서울시 인구수\n", "# 연도, 인구수\n", "\n", "1990 10,603,250\n", "1995 10,217,177\n", "2000 9,853,972\n", "2005 9,762,546\n", "2010 9,631,482\n", "```\n", "---\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위 파일을 읽어서 서울의 인구 변화 추이를 선그래프로 나타내고자 코드를 작성하였다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "```\n", "\n", "import matplotlib.pyplot as plt\n", "import csv\n", "\n", "year_list = []\n", "seoul_pop_list = []\n", "\n", "with open('Seoul_pop.csv') as f:\n", " reader = csv.reader(f)\n", " for line in reader:\n", " if line[0] == '#' or len(line) == 0 :\n", " continue\n", " else:\n", " year_list.append(line[0])\n", " seoul_pop_list.append(line[1])\n", " \n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "plt.plot(year_list, seoul_pop_list, color = 'green', marker = 'o', linestyle = '-')\n", "plt.title('Seoul Population')\n", "plt.show()\n", "\n", "```\n", "\n", "---\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "위의 코드 중 어디에서 오류가 발생하는지를 표시한 후, 오류가 난 이유를 간단히 설명하고, 오류가 없도록 코드를 수정하여라.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 5.\n", "\n", "아래는 0부터 20사이의 홀수들을 항목으로 갖는 리스트를 조건제시법으로 생성하는 방법이다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "[x for x in range(20) if x%2 == 1]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1부터 20 사이의 숫자들 중 3의 배수이거나 3으로 끝나는 숫자들의 제곱을 항목으로 갖는 리스트를 조건제시법으로 생성하는 코드를 작성하여라. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 6.\n", "\n", "아래와 같은 문장이 있다.\n", "\n", "`python = \"Python is a general-purpose programming language.\"`\n", "\n", "위 문장을 아래와 같이 만드는 코드를 작성하라.\n", "\n", "`python_list = ['PYTHON', 'IS', 'A', 'GENERAL-PURPOSE', 'PROGRAMMING', 'LANGUAGE']`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 7.\n", "\n", "문제 6에서 생성한 `python_list`에 들어 있는 문자열 중에서 길이가 8 이상인 것들만 리스트 형태로 출력되도록 만들어라 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 8.\n", "\n", "`record_list.txt` 파일은 여덟 명의 수영 선수의 50m 기록을 담고 있다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "player1 21.09\n", "player2 20.32\n", "player3 21.81\n", "player4 22.97\n", "player5 23.29\n", "player6 22.09\n", "player7 21.20\n", "player8 22.16\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(1) 아래 코드가 하는 일을 설명하고, 출력된 결과를 답하여라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "```\n", "record_f = open('scores_list.txt', 'r')\n", "record = record_f.read().split('\\n')\n", "\n", "record_dict = {}\n", "\n", "for line in record:\n", " (name, score) = line.split() \n", " record_dict[score] = name\n", " \n", "record_f.close()\n", "record_list = record_dict.keys()\n", "print(sorted(record_list))\n", "\n", "```\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 9.\n", "\n", "`a`와 `b`를 입력받아 `a`를 `b`로 나누었을 때의 몫과 나머지를 튜플로 리턴하는 함수 `quotient_remainder()`를 구현하라. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 문제 10.\n", "\n", "기상청에서 날씨 정보를 확인하는 프로그램을 작성하고자 한다.\n", "먼저 기상청 정보를 담고 있는 아래 사이트의 소스 코드를 읽어 온다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "```\n", "import urllib.request\n", "\n", "page = urllib.request.urlopen(\"http://www.weather.go.kr/weather/forecast/mid-term-rss3.jsp?stnId-108\")\n", "text = page.read().decode(\"utf8\")\n", "\n", "```\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "읽어 온 소스코드 내용의 앞 부분을 확인하면 다음과 같다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "```\n", "'<?xml version=\"1.0\" encoding=\"utf-8\" ?>\\r\\n<rss version=\"2.0\">\\r\\n<channel>\\r\\n<title>기상청 육상 중기예보</title>\\r\\n<link>http://www.kma.go.kr/weather/forecast/mid-term_01.jsp</link>\\r\\n<description>기상청 날씨 웹서비스</description>\\r\\n<language>ko</language>\\r\\n<generator>기상청</generator>\\r\\n<pubDate>2018년 09월 12일 (수)요일 06:00</pubDate>\\r\\n <item>\\r\\n<author>기상청</author>\\r\\n<category>육상중기예보</category>\\r\\n<title>전국 육상 중기예보 - 2018년 09월 12일 (수)요일 06:00 발표</title>\\r\\n<link>http://www.kma.go.kr/weather/forecast/mid-term_01.jsp</link>\\r\\n<guid>http://www.kma.go.kr/weather/forecast/mid-term_01.jsp</guid>\\r\\n<description>\\r\\n\\t<header>\\r\\n\\t\\t<title>전국 육상중기예보</title>\\r\\n\\t\\t<tm>201809120600</tm>\\r\\n\\t\\t<wf><![CDATA[기압골의 영향으로 15일은 전국(제주도 제외)에 비가 오겠고, 그 밖의 날은 고기압의 영향으로 대체로 맑겠습니다.<br />기온은 평년(최저기온: 11~20℃, 최고기온: 23~27℃)과 비슷하거나 조금 높겠습니다.<br />강수량은 평년(3~9mm)보다 조금 많겠습니다.]]></wf>\\r\\n\\t</header>\\r\\n\\t<body>\\r\\n\\t\\t\\t\\t\\r\\n\\r\\n\\t\\t<location wl_ver=\"3\">\\r\\n\\t\\t\\t\\t<province>서울ㆍ인천ㆍ경기도</province>\\r\\n\\t\\t\\t\\t<city>서울</city>\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t<data>\\r\\n\\t\\t\\t\\t\\t<mode>A02</mode>\\r\\n\\t\\t\\t\\t\\t<tmEf>2018-09-15 00:00</t'\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "아래의 코드를 완성하여 최고 기온을 출력하여라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "```\n", "where_s = \n", "\n", "where_e = \n", "\n", "text_weather = \n", "\n", "print(\"오늘의 최고 기온은\", text_weather, \"℃입니다.\")\n", "\n", "```\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", ".\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
PBrockmann/ipython_ferretmagic
notebooks/ferretmagic_06_InteractWidget.ipynb
2
189316
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>\n", "Patrick BROCKMANN - LSCE (Climate and Environment Sciences Laboratory)<br>\n", "<img align=\"left\" width=\"40%\" src=\"http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png\" \\><br><br>\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Updated: 2019/11/13" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load the ferret extension" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext ferretmagic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### \"Classic\" use with cell magic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre style=\"background-color:#ECF6CE; border-radius: 4px 4px 4px 4px; font-size: smaller\"> currently SET data sets:\n", " 1> /opt/ferret_dsets/data/monthly_navy_winds.cdf (default)\n", " \n", " name title I J K L\n", " UWND ZONAL WIND 1:144 1:73 ... 1:132\n", " M/S on grid GDN1 with -99.9 for missing data\n", " X=18.8E:18.8E(378.8) Y=91.2S:91.2N \n", " VWND MERIDIONAL WIND 1:144 1:73 ... 1:132\n", " M/S on grid GDN1 with -99.9 for missing data\n", " X=18.8E:18.8E(378.8) Y=91.2S:91.2N \n", " \n", " time range: 16-JAN-1982 20:00 to 17-DEC-1992 03:30\n", " \n", "</pre>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div class=\"myoutput\"><img 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\"/></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%ferret -s 600,400\n", "set text/font=arial\n", "use monthly_navy_winds.cdf\n", "show data/full\n", "plot uwnd[i=@ave,j=@ave,l=@sbx:12]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Explore interactive widgets" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2c03d56429c4175aa6f63affcd11d3d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='var', options=('uwnd', 'vwnd'), value='uwnd'), IntSlider(value=5, …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ipywidgets import interact\n", "\n", "@interact(var=['uwnd','vwnd'], smooth=(1, 20), vrange=(0.5,5,0.5))\n", "def plot(var='uwnd', smooth=5, vrange=1) :\n", " %ferret_run -s 600,400 'ppl color 6, 70, 70, 70; plot/grat=(dash,color=6)/vlim=-%(vrange)s:%(vrange)s %(var)s[i=@ave,j=@ave], %(var)s[i=@ave,j=@ave,l=@sbx:%(smooth)s]' % locals()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Another example with a map" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"myoutput\"><img 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\"/></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The line of code to make interactive\n", "%ferret_run -q -s 600,400 'cancel mode logo; \\\n", " ppl color 6, 70, 70, 70; \\\n", " shade/grat=(dash,color=6) %(var)s[l=%(lstep)s] ; \\\n", " go land' % {'var':'uwnd','lstep':'3'}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fe16f0ce27604438988bb39d3ef64d48", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='var', options=('uwnd', 'vwnd'), value='uwnd'), IntSlider(value=1, …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "from ipywidgets import interact\n", "\n", "play = widgets.Play(\n", " value=1,\n", " min=1,\n", " max=10,\n", " step=1,\n", " description=\"Press play\",\n", " disabled=False\n", ")\n", "slider = widgets.IntSlider(\n", " min=1,\n", " max=10\n", ")\n", "widgets.jslink((play, 'value'), (slider, 'value'))\n", "a=widgets.HBox([play, slider])\n", "\n", "@interact(var=['uwnd','vwnd'], lstep=slider, lstep1=play)\n", "def plot(var='uwnd', lstep=1, lstep1=1) :\n", " %ferret_run -q -s 600,400 'cancel mode logo; \\\n", " ppl color 6, 70, 70, 70; \\\n", " shade/grat=(dash,color=6)/lev=(-inf)(-10,10,2)(inf)/pal=mpl_Div_PRGn.spk %(var)s[l=%(lstep)s] ; \\\n", " go land' % locals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More informations on ipython widgets from\n", "* https://github.com/ipython/ipywidgets" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "widgets": { "state": { "869a99cd261e4ce2b147c15e7e2bffd4": { "views": [ { "cell_index": 7 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
mjlong/openmc
docs/source/pythonapi/examples/mgxs-part-i.ipynb
1
119819
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n", "\n", "* **General equations** for scalar-flux averaged multi-group cross sections\n", "* Creation of multi-group cross sections for an **infinite homogeneous medium**\n", "* Use of **tally arithmetic** to manipulate multi-group cross sections\n", "\n", "**Note:** This Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to Multi-Group Cross Sections (MGXS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. An example of U-235's continuous-energy fission cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Widi+P/wQePBAuhyEEOJoVEwTQogTkeJhQUv6jI0Fzp2zfwZCCJELFdOEEEII\nIYRYiYppJxIRESF3BIvwlhfgLzPllRZveQHrM8v19A1v55i3vEQ+Bw8ehEqlQkJCgtxRiBlUTDuR\nKVOmyB3BIrzlBfjLTHmlpbS8YgpeazJbOnTEnoW30s6xObzlrWyys7MxdepUtGjRAtWqVYObmxv8\n/f3x0ksvYd26dSgqKnJYlosXL0KlUiEmJsZkO4Emclc8mmfaiYSFhckdwSK85QX4y0x5paXEvOZ+\nLjsisz2LaSWeY1N4y1uZzJs3DwkJCWCMoUuXLnjhhRfg7e2NGzdu4NChQxg/fjxWrVqFn376ySF5\nNEWysWK5c+fOyM7ORq1atRySh1iPimlCCHEiUg3HsKRfmpCVONqCBQsQHx+PBg0aIC0tDR07dtRr\n8/XXX+O///2vwzJpZiY2NkOxh4cHvQqeEzTMgxBCnIRUvy2m30ITJbtw4QISEhLg6uqKPXv2GCyk\nAeDFF1/Enj17dJZt3rwZ3bp1Q7Vq1aBWq9GyZUssWrQIjx8/1ts+ICAAgYGBKCgowIwZM9CgQQO4\nu7ujSZMmWLJkiU7b+Ph4NGrUCACwceNGqFQq7dfGjRsBGB8z3b17d6hUKpSWlmLhwoVo0qQJ3N3d\n0aBBA8TFxekNVTE3nETT35PKysqwcuVKdOzYEd7e3vDy8kLHjh2xatUqvQ8A1u5j3bp1CAkJgZ+f\nHzw8PODv74/evXtj8+bNBvtRKiqmbcTTGxB37NghdwSL8JYX4C8z5ZWW0vKKuSNsbWa57jYr7Ryb\nw1veymDDhg0oKSnB4MGD8cwzz5hs6+rqqv3/mTNnIjo6GmfPnsWoUaMwdepUMMbw7rvvIiwsDMXF\nxTrbCoKA4uJihIWFYdu2bQgPD8eECRPw6NEjzJo1C/Hx8dq2PXr0wBtvvAEAaNOmDeLj47Vfbdu2\n1evXkOjoaKxYsQKhoaGYNGkSPDw88P777+PVV1812N7U2GtD60aMGIEpU6YgJycHEyZMwGuvvYac\nnBxMnjwZI0aMsHkfM2fOxPjx43H79m0MHz4cb731Fl588UXcuHEDX375pdF+xHLkGxDBiFWOHz/O\nALDjx4/LHUW0YcOGyR3BIrzlZYy/zJRXWkrL+9JLjA0YYLqNNZkbN2YsLk5cW4Cxo0ct3oVRSjvH\n5jg6L48/q+ytR48eTBAElpSUJHqbzMxMJggCCwwMZLdv39YuLykpYeHh4UwQBLZgwQKdbRo2bMgE\nQWDh4eFFg54qAAAgAElEQVSssLBQu/zWrVusevXqrFq1aqy4uFi7/OLFi0wQBBYTE2MwQ0ZGBhME\ngSUkJOgsDw0NZYIgsA4dOrB79+5pl+fn57PGjRszFxcXdv36de3yCxcumNxPaGgoU6lUOsuSk5OZ\nIAisU6dOrKCgQGcf7du3Z4IgsOTkZJv24evry+rVq8cePXqk1z4nJ8dgPxWJvbYd8T1Ad6adCG+/\nNuEtL8BfZsorLd7yAvxlprzEnBs3bgAA6tWrJ3qb9evXAwDee+89nQcAXVxcsGzZMqhUKiQlJelt\nJwgCli9fDjc3N+0yPz8/RERE4MGDB/jjjz+0y5mNv85ZunQpqlevrv2zWq3Gyy+/jLKyMmRlZdnU\n97p16wAAixYtgoeHh84+NENWDB2/JVQqFVxdXQ0O/6hZs6ZNfTsaPYBICCHEZvQAYuUzcSLw11+O\n25+/P7BqleP2Z8qJEycgCAJ69Oiht65p06bw9/fHxYsX8eDBA/j4+GjXVa9eHYGBgXrb1K9fHwBw\n7949u+QTBAEdOnTQW675wGDrfk6cOAEXFxeEhobqrQsNDYVKpcKJEyds2sfLL7+M5cuXo3nz5hg2\nbBief/55PPvss6hWrZpN/cqBimlCCHESUhWxgkDFdGWklMLWVnXq1EF2djauXr0qepvc3FwAQO3a\ntY32efXqVeTm5uoU08YKwSpVysut0tJS0RnM8fb2lmw/ubm5qFmzJlxcXAzuo1atWsjJybFpH//v\n//0/NGrUCOvXr8eiRYuwaNEiVKlSBeHh4Vi2bJnBDyVKRcM8CCHEiUgx8wbN5kGUrFu3bgCAb7/9\nVvQ2mqL4+vXrBtdrlvNwF1UzjKKkpMTg+vv37+stq1atGu7evWuwKC8pKUFOTo7Ohwhr9qFSqfDG\nG2/g5MmTuHnzJr788ktERkZi586d6NOnj94DnkpGxbQTMfeWJaXhLS/AX2bKKy3e8gLWZ7bkbrM9\ni2/ezjFveSuDmJgYVK1aFV9++SXOnDljsq1mWrl27dqBMYaDBw/qtTl37hyuXr2KwMBAnYLSUpq7\nvva8W22Ir68vAODKlSt6654cx63Rrl07lJaW4vvvv9dbd+jQIZSVlaFdu3Y27aMiPz8/REZGYvPm\nzejRowfOnj2L06dPmz4wBaFi2onw9uYt3vIC/GWmvNLiLS9gXWY5h3nwdo55y1sZNGzYEPHx8Sgq\nKkJ4eDiOHz9usN3evXvRp08fAMDYsWMBAPPnz9cZzlBaWoq3334bjDGMGzfOplyaAvTy5cs29WOO\nt7c3goODkZmZqfNhorS0FG+++SYKCwv1ttEc/6xZs/Do0SPt8oKCArzzzjsAoHP8lu6jqKgIhw8f\n1ttvcXEx7t69C0EQ4O7ubuUROx6NmXYi0dHRckewCG95Af4yU15pKS2vmCLW2sxyDfVQ2jk2h7e8\nlcWsWbNQUlKChIQEdOzYEV26dEH79u3h5eWFmzdv4tChQzh37pz2hS4hISGIi4vD0qVL0aJFCwwZ\nMgRqtRp79+7F6dOn0a1bN8yYMcOmTF5eXnj22Wdx6NAhjBo1Co0bN4aLiwsGDBiAli1bmtzW0plA\nZs6ciTFjxuC5557DkCFD4O7ujoyMDJSWlqJ169Y4deqUTvvo6Gjs3LkTW7ZsQfPmzTFgwAAIgoAd\nO3bg4sWLGD58uN61bMk+CgoK0K1bNzRu3Bjt2rVDw4YNUVhYiG+++QbZ2dno378/mjVrZtExyomK\naUIIIQ5FDyASOcyePRtDhw7FypUrkZGRgQ0bNqCwsBC1atVCmzZtMGvWLIwcOVLbfvHixWjbti1W\nrFiBTZs2obi4GI0bN8aCBQvw1ltvaR/20zD3whJD6z/77DNMnz4de/fu1c7A0aBBA5PFtLG+TK0b\nPXo0ysrK8P7772PTpk2oUaMGBgwYgAULFmDw4MEGt0lJSUFoaCjWrVuH1atXQxAEBAcHY8aMGZg4\ncaJN+/Dy8sKSJUuQkZGBH374ATt37oSPjw+CgoLwySefaO+M80Jgtk506KSysrLQvn17HD9+XGfc\nECGEKNVLLwFVqwLbt9u332bNyvt+/33zbQUBOHIECAmxbwZiGP2sIpWV2GvbEd8DNGbaiWRmZsod\nwSK85QX4y0x5pcVbXsD6zHI9gMjbOeYtLyHEPCqmncjSpUvljmAR3vIC/GWmvNJSWl4xBa8jMtvz\n96FKO8fm8JaXEGIeFdNOJDU1Ve4IFuEtL8BfZsorLSXmNXdX2NrMcj2AqMRzbApveQkh5lEx7UTU\narXcESzCW16Av8yUV1q85QWszyzX0ze8nWPe8hJCzKNimhBCiE3oDYiEEGdGxTQhhDgJ3uZuKiwE\nHjyQOwUhhJhGxbQTsXWCeUfjLS/AX2bKKy0l5jV3F9kRmcUW9bNnA+Hhptso8RybwlteQoh5VEw7\nkQYNGsgdwSK85QX4y0x5pcVbXkBZmQsKgLw8022UlFcM3vISQsyjNyDaKDY2FtWrV0d0dLTiXxM7\ndepUuSNYhLe8AH+ZKa+0lJjX3F1hazNLNYREqrxy4S0vIbxKSUlBSkoK7t+/L/m+qJi2UWJiIr1V\nihBCCCFEQTQ3OTVvQJQSDfMghBAnItXMG7z1Swgh9kLFtBPJzs6WO4JFeMsL8JeZ8kqLt7yA9Znl\nmimEt3PMW15CiHlUTDuRuLg4uSNYhLe8AH+ZKa+0eMsLWJdZzrvHvJ1j3vISQsyjYtqJrFixQu4I\nFuEtL8BfZsorLaXlFXP3WGmZzaG8hBC5UTHtRHibkom3vAB/mSmvtJSY19xdZCVmNuXwYb7y8nZ+\nK4OtW7di6tSp6NatG3x8fKBSqTBq1CiT25SWlmLt2rV4/vnn4evrC7VajaCgIAwfPhxnz561KkdR\nURHWrVuH/v37w9/fHx4eHvDy8kLjxo0RFRWF5ORkFBUVWdU3kRfN5kEIIcSh7Dm+esQIQOGzkhKZ\nzZ8/Hz///DO8vb1Rr149ZGdnQzDxqTIvLw8DBgxARkYG2rZti5iYGLi7u+Pq1avIzMzE2bNn0aRJ\nE4sy/Pbbbxg0aBD++OMP1KpVC7169ULDhg0hCAIuXbqEgwcPIi0tDUuWLMHPP/9s6yETB6NiuoLh\nw4fj4MGDKCgoQJ06dfD2229jwoQJcscihBDFk2ueaULMSUxMRP369REUFITvv/8ePXr0MNn+1Vdf\nRUZGBj799FODNUBJSYlF+7927RpeeOEF3LhxA3FxcUhISICbm5tOG8YYdu7ciWXLllnUN1EGGuZR\nwdy5c3H16lU8ePAAn3/+OaZNm4YLFy7IHctulixZIncEi/CWF+AvM+WVltLyiilMlZbZPL7y8nd+\n+de9e3cEBQUBKC9aTcnKykJqaiqGDx9u9GZalSqW3Yf897//jRs3bmDMmDFYvHixXiENAIIgYODA\ngcjIyNBZfvDgQahUKiQkJOB///sf+vTpA19fX6hUKly+fBkAUFhYiEWLFqFly5bw9PREtWrV8Pzz\nz2Pz5s16+6nYnyEBAQEIDAzUWbZhwwaoVCps3LgRX331Fbp06QIvLy/UqFEDQ4cOxblz5yw6H5UR\n3ZmuIDg4WPv/Li4u8PHxgbe3t4yJ7KugoEDuCBbhLS/AX2bKKy3e8gLWZ5Zvnmm+zjGP14Qz+eKL\nLwCUv/AjNzcXu3btwpUrV1CzZk306tVLW5SLVVBQgJSUFAiCgNmzZ5tt7+LiYnD5kSNHsHDhQjz/\n/POYMGECbt26BVdXVxQVFSEsLAyZmZlo3rw5pkyZgvz8fKSlpSE6OhonTpzA4sWL9fozNczF2Lpt\n27Zh7969GDRoEHr27IkTJ07gyy+/REZGBo4cOYKmTZuaPb7KiorpJ7z88svYtm0bACA1NRW1atWS\nOZH9GPskqlS85QX4y0x5paW0vGIKXmszWzIcw75tlXWOzVHaNUF0/fTTTwCAS5cuISgoCHfv3tWu\nEwQBEydOxEcffQSVStwv9o8dO4bi4mI0aNBA746vJb755huDw04WLlyIzMxM9O/fH9u3b9fmmjNn\nDjp16oSlS5eif//+eO6556zet8auXbvw1VdfoV+/ftplH330EWJjYzFp0iQcOHDA5n3wiorpJyQn\nJ6OsrAzp6emIiYnByZMn6elrQggxgd5SWAl16ADcuOH4/dauDRw75vj9/u3WrVsAgOnTpyMyMhLz\n589HvXr1cOTIEbz++utYuXIl/Pz8MHfuXFH93fj7HNatW9fg+k8++UTbBigv2EePHq1XeLdt29bg\nsJN169ZBpVLhgw8+0Cnwn3rqKcyePRsTJkzAunXr7FJM9+rVS6eQBoApU6bgo48+wnfffYfLly87\nbb1ExbQBKpUKAwcORFJSEtLT0zFlyhS5IxFCiM14fJiPCnWZ3LgB/PWX3CkcrqysDED5sM/Nmzdr\nhzy88MIL2Lp1Kzp06IBly5bh3//+N6pWrYqDBw/i4MGDOn0EBgbilVdeEbW/Tz/9FKdOndJZ1q1b\nN71iulOnTnrbPnz4EOfPn0f9+vXRuHFjvfW9evUCAJw4cUJUFnNCQ0P1lqlUKnTt2hXnz5936puP\n3BbTeXl5mDdvHk6ePIkTJ07gzp07mDt3rsFPi3l5eXjvvfeQlpaGu3fvolmzZnjnnXcQFRVlch8l\nJSXw8vKS6hAcLicnh6thK7zlBfjLTHmlpcS85opTR2S27zCPHADKOsemKPGaMKh2befa79+qV68O\nAOjfv7/e2OE2bdqgYcOGuHjxIrKzs9GyZUt8//33mDdvnk677t27a4vp2n8fz7Vr1wzur2KhGxMT\ng40bNxpsV9vAecnNzTW6ruJyTTtbPf300w7ZD4+4nc0jJycHa9asQXFxMSIjIwEYHzQ/aNAgbNq0\nCfHx8di3bx86duyI6OhopKSkaNvcvHkTW7duRX5+PkpKSrBlyxYcPXoUvXv3dsjxOMLYsWPljmAR\n3vIC/GWmvNJSYl5zxakjMtv3DrnyzrEpSrwmDDp2DLh61fFfMg7xAIBmzZoB+KeofpKvry8YY3j0\n6BGA8lnAysrKdL6+++47bfuOHTuiatWquHLlCv7880+T+zY104ih+qZatWoAoDNMpKLr16/rtAOg\nHQpibHq/+/fvG81w8+ZNg8s1+6+4H2fDbTEdEBCAe/fuISMjA4sWLTLabs+ePThw4ABWrVqFCRMm\nIDQ0FKtXr0bv3r0xY8YM7a90gPKB9P7+/njqqaewYsUKpKenw9/f3xGH4xDx8fFyR7AIb3kB/jJT\nXmkpLa+YIRPWZpbqAUTz4u3ZmeSUdk0QXS+88AIA4JdfftFb9/jxY5w9exaCICAgIEBUfx4eHhgx\nYgQYY5g/f749o8Lb2xtBQUG4evWqwenpNNPstWvXTrvM19cXALTT6lV07tw5PHjwwOj+nhzOApS/\nKTIzMxOCIKBt27aWHkKlwW0xXZGpT3Pbt2+Ht7c3hg4dqrM8JiYG165dw9GjRwGU//ri0KFDuH//\nPu7evYtDhw6ha9eukuZ2tIrfUDzgLS/AX2bKKy2l5RVTxFqTWb5p8QBAWefYHKVdE0TX4MGDUbdu\nXWzevFk7s4dGfHw8Hj58iB49euCpp54S3eeCBQtQu3ZtbNy4ETNnzkRhYaFem7KyMpOFrDFjx44F\nY0zv5mBOTg7+85//QBAEnd+GBAcHw8fHBzt37sTt27e1yx89eoRp06aZ3Nd3332H3bt36yxbsWIF\nzp8/jx49eqB+/foW568sKkUxbcqvv/6K4OBgvWlsWrZsCQA4ffq0Tf3369cPEREROl8hISHYsWOH\nTrv9+/cjIiJCb/vJkycjKSlJZ1lWVhYiIiKQk5Ojs3zu3Ll6E/5fvnwZERERyM7O1lm+fPlyzJgx\nQ2dZQUEBIiIikJmZqbM8JSUFMTExetmioqLoOOg46Dgq0XH88kuMXoFqj+O4dCkCjx6JOw4gApcu\niTuO3bsjkJdXef8+HHkczmzHjh0YM2aM9qUpQPm8zZplFf/O1Go1NmzYAEEQ0K1bN4wYMQJvv/02\nunbtiiVLluDpp5/Gp59+atH+69atiwMHDqBp06b473//i/r162P48OGYOXMm4uLiMHr0aAQEBGDH\njh0ICAhAw4YNRfetybZz5060bt0acXFxmDJlCpo3b47Lly8jLi4OXbp00bavUqUK3nzzTeTm5qJt\n27aYMmUKXn/9dbRs2RL5+fmoW7eu0RuUERERiIyMRFRUFP7973+jX79+mD59OmrWrImVK1dadE7s\n6YcfftB+f6SkpGhrscDAQLRp0waxsbHSh2CVwO3bt5kgCCwhIUFvXZMmTVjfvn31ll+7do0JgsAW\nL15s1T6PHz/OALDjx49btT0hhDjaiy8yNniw/ft95hnG3nhDXFuAse++E9d20iTGWrc23x9jjO3f\nz9gXX4jr15nQzyrG4uPjmSAITKVS6XwJgsAEQWCBgYF625w6dYoNGTKE+fn5MVdXV9awYUM2adIk\ndv36datzPH78mCUlJbHw8HBWt25d5ubmxtRqNQsKCmJDhw5lycnJrKioSGebjIwMo/WNRmFhIVu4\ncCFr0aIF8/DwYD4+Pqxbt24sNTXV6DZLly5lQUFB2mObOXMmKygoYAEBAXrnY/369UwQBLZx40a2\ne/duFhISwjw9PZmvry8bMmQIO3v2rNXnxBZir21HfA9U+jvT5B9P3sFQOt7yAvxlprzSUlpeMcMm\nrMls6TAPsWOmxfVbnjc1FfjoI8tyyEFp14Qz0DwkWFpaqvOleWDw/Pnzetu0atUKaWlpuHXrFh4/\nfoyLFy/i448/Njpzhhiurq4YO3YsvvrqK/z1118oLCxEfn4+zp07hy1btmDEiBGoWrWqzjbdu3dH\nWVkZ5syZY7RfNzc3zJo1C7/88gsKCgqQm5uLQ4cOmZyxbMaMGTh37pz22BYvXgwPDw9cuHDB4PnQ\n6NevH44cOYK8vDzcvXsXaWlpBqflczaVvpiuWbMm7ty5o7dc81ajmjVrOjqSbLKysuSOYBHe8gL8\nZaa80lJaXjFFrCMyiy2mxbVT1jk2R2nXBCHEdpW+mG7VqhXOnDmjMzAf+OdJ3RYtWsgRSxYff/yx\n3BEswltegL/MlFdaSsxr7m6vEjObxlde/s4vIcScSl9MR0ZGIi8vD1u3btVZvmHDBvj7+6Nz5842\n9R8bG4uIiAidOasJIYQYZ99hHv+05fENj4QonSAIRt/joWSahxEd8QAit29ABIC9e/ciPz8fDx8+\nBFA+M4emaA4PD4eHhwf69OmD3r17Y+LEiXjw4AGCgoKQkpKC/fv3Izk52eYLJDExkaY6IoRwQ6qC\nU755pqXrkxACvPLKK6Jfj64k0dHRiI6ORlZWFtq3by/pvrgupidNmoRLly4BKP/klJaWhrS0NAiC\ngAsXLmjfEb9t2za8++67mDNnDu7evYvg4GCkpqZi2LBhcsYnhJBKQaoHEKXOQQgh9sD1MI8LFy5o\nn8at+GRuaWmptpAGAE9PTyQmJuLatWsoLCzEiRMnnLKQNjRPqZLxlhfgLzPllZYS85orOK3JLO9d\n4fK8vAzzUOI1QQixDdfFNLHMlClT5I5gEd7yAvxlprzS4i0v4JjM9i16+TrHPF4ThBDTqJh2ImFh\nYXJHsAhveQH+MlNeaSkxr7lC1prM8g7zCJOgT+ko8ZoghNiGimlCCCEOJefDin8/ZkMIIXZDxTQh\nhDiRyvqQntjjCgiQNAYhxAlxPZuHEsTGxqJ69eraKViUbMeOHRg4cKDcMUTjLS/AX2bKKy3e8gKO\nyWzJ3WbzRfIOAPycY7muiTNnzjh8n4RIydw1nZKSgpSUFNy/f1/yLFRM24ineaZTUlK4+sHOW16A\nv8yUV1q85QWszyzV0A3zbVPAUzHt6GvC29sbADBy5EiH7ZMQR9Jc40+ieaaJJDZv3ix3BIvwlhfg\nLzPllZbS8oqZPs6azPI+gCg+rxIeUnT0NdGkSRP88ccf2pebEVKZeHt7o0mTJnLHoGKaEEKchSAA\nZWX279fSItW+wzyIOUooNgipzOgBREIIcRK8vNikIiny/vgjMGCA/fslhDgnKqYJIcRJSFVMK+F1\n4mKOTbP+zz+B9HT7ZyCEOCcqpp1ITEyM3BEswltegL/MlFdaSssrpuB0RGb7FtMxEvQpHaVdE2Lw\nlpnySou3vI5AxbQT4e3NW7zlBfjLTHmlpbS8Yu4gOyKz2MJX3B3vf/LyML5aadeEGLxlprzS4i2v\nI1Ax7USUPg/2k3jLC/CXmfJKi7e8gLIyiyu6y/PyUEgDyjq/YvGWmfJKi7e8jkDFNCGEEJvJ+Ypw\nsf3yMhSEEMIXmhrPRjy9AZEQQqQg1QOIvNxtJoQojyPfgEh3pm2UmJiI9PR0LgrpzMxMuSNYhLe8\nAH+ZKa+0eMsLWJdZqnmmxbX7Jy8PxbezXBNyorzS4iVvdHQ00tPTkZiYKPm+qJh2IkuXLpU7gkV4\nywvwl5nySou3vIBjMtt3uMU/eXkY5kHXhPQor7R4y+sIVEw7kdTUVLkjWIS3vAB/mSmvtHjLC1iX\nWao7wuL6te0cT5oElJTY1IVFnOWakBPllRZveR2Bimknolar5Y5gEd7yAvxlprzS4i0v4JjM9r1D\nbFveVauA4mI7RRGBrgnpUV5p8ZbXEaiYJoQQ4lBKGG5BCCH2QsU0IYQQh5KrmKYinhAiBSqmnciM\nGTPkjmAR3vIC/GWmvNLiLS9gfWb5ClW+zrEzXRNyobzS4i2vI1Ax7UQaNGggdwSL8JYX4C8z5ZUW\nb3kB6zJLNc+0OA2syiAXZ7km5ER5pcVbXkcQGKNffFkjKysL7du3x/Hjx9GuXTu54xBCiFkREeVF\n586d9u23dWugWzdgxQrzbQUB+OILQMzU/FOnAocOAadOme6PMWDCBODnn4GjR423LSoC3NzK9z9i\nRPl2ggAUFAAeHsDNm8DTT5vPRQjhhyPqNbozTQghxKHkvoVj7C527dqOzUEIqRyomCaEEGITJQyx\nsCSD3MU8IaRyoWLaiWRnZ8sdwSK85QX4y0x5pcVbXsAxmS0pZiu2zc0FCgufbJFtsK09LF4MzJ5t\n3z7pmpAe5ZUWb3kdgYppJxIXFyd3BIvwlhfgLzPllRZveQHHZLak6K14x7lXL2DBgidbiM9rabF9\n/Djw44+WbWMOXRPSo7zS4i2vI1Ax7URWiHk6SEF4ywvwl5nySou3vIBjMlt7Z/rhQ0N3pv/Jq4Th\nJubQNSE9yist3vI6AhXTToS36Wx4ywvwl5nySou3vID1ma0tkG3fj/RT+dmTM10TcqG80uItryNQ\nMU0IIcQmjipOze3HXJGuWf9kOx7uaBNClIuKaUIIIQ4l9s60o4pcmt2DEGILKqadyJIlS+SOYBHe\n8gL8Zaa80uItL+CYzPYtXv/Ja4/iu1Wrf/5fiiKbrgnpUV5p8ZbXEarIHYB3sbGxqF69OqKjoxEt\n5pVeMiooKJA7gkV4ywvwl5nySou3vIBjMtu3SP0nr9hhHqb88ouNccyga0J6lFdavORNSUlBSkoK\n7t+/L/m+6HXiVqLXiRNCeNO/P6BS2f914m3bAl26AB9/bL6tIADr1gExMebbTpsGZGT8U+A2awaE\nhwMffKDbH2PAa68BJ06Ynsru0SNArQZSUspfZ/7k68Q1d7Y1PxWHDgUePAC+/tp8VkKIMtHrxAkh\nhHDBUbN5EEKI0lAxTQghTkIps1ZIVUyL7Zdm8yCE2BMV004kJydH7ggW4S0vwF9myist3vIC1me2\npCC1djYPw/vIMbPe/H4deafcma4JuVBeafGW1xGomHYiY8eOlTuCRXjLC/CXmfJKi7e8gPWZ5Xtp\ni3TnWIoi25muCblQXmnxltcRqJh2IvHx8XJHsAhveQH+MlNeaSktr5ji0JrM8g6TiNf+nxTzV9v7\n2JR2TYjBW2bKKy3e8joCFdNOhLdZR3jLC/CXmfJKS2l5xRSGjshsy11s/WMQn9eaO832vjuttGtC\nDN4yU15p8ZbXEaiY/ltRURFiYmLQoEEDVKtWDSEhIfjhhx/kjkUIIYpnScGpmcrOEfvSOHwYKC21\nvA96MJEQIgYV038rKSlBo0aNcOTIEeTm5mLixImIiIjAo0eP5I5mF2fOAAcOyJ2CEFJZiS08bSmm\nTe3D1LquXYFr16zblhBCzKFi+m9qtRqzZ89GvXr1AACjR49GWVkZzp07J3My+6hXD3j//SS8/DJw\n9arcacRJSkqSO4LFeMtMeaXFW17A+szyjVcWn9eWO+KZmUBQkO6yPn0ASyc2cKZrQi6UV1q85XUE\nKqaNyM7OxqNHjxD05L+enPL2BoKCsvDvfwMTJgDJyXInMi8rK0vuCBbjLTPllRZveQHrMlt6Z9e+\n45Btzysmz+3bwPnzusu+/hrw87Ns385yTciJ8kqLt7yOQMW0AQUFBRg1ahRmz54NtVotdxy7+fjj\nj9G8ObBrF/DHH+Wv883NlTuVcR+LeTexwvCWmfJKi7e8gPWZLbkzbd9iWrpzLDZnWRkg9pksZ7om\n5EJ5pcVbXkegYvoJxcXFGDp0KFq0aIFZs2bJHUcSVaoACQnA+PFAZCSwf7/ciQghPLOkQLa0mH6y\nraltzfVrTREv5q47Y8CJE5b3TQipHLgtpvPy8hAXF4ewsDD4+flBpVIhISHBaNvY2Fj4+/vDw8MD\nbdu2xebNm/XalZWVYdSoUXB1dXWKMUHPPVd+l/rrr8vvUtNLjQgh1rC0mJablC+Y+eUXy9oTQvjH\nbTGdk5ODNWvWoLi4GJGRkQAAwci/0oMGDcKmTZsQHx+Pffv2oWPHjoiOjkZKSopOu9deew03b95E\namoqVCpuT41FPD2BDz4AJk0Chg8Htm+XOxEhhDfyjpm2LoOlfYrN3KqV/XMQQpSN24oxICAA9+7d\nQ0ZGBhYtWmS03Z49e3DgwAGsWrUKEyZMQGhoKFavXo3evXtjxowZKCsrAwBcunQJSUlJ+PHHH1Gr\nVnldjv8AACAASURBVC14e3vD29sbhw8fdtQhSS4iIsLouo4dgd27gR9/BMaOBe7fd2AwI0zlVSre\nMlNeafGWF7A+sxTDPJ5sa7hgFp9XiiIeAEaMEN/Wma4JuVBeafGW1xG4LaYrYib+hdy+fTu8vb0x\ndOhQneUxMTG4du0ajh49CgBo2LAhysrKkJ+fj4cPH2q/nnvuOUmzO9KUKVNMrndzAxYtKh9LPWgQ\n8M03DgpmhLm8SsRbZsorLd7yAtZllnLM9JP0t7Uurz23SUsT34+zXBNyorzS4i2vI1SKYtqUX3/9\nFcHBwXrDNlq2bAkAOH36tE399+vXDxERETpfISEh2LFjh067/fv3G/w0N3nyZL3x2VlZWYiIiEDO\nE4OY586diyVLlugsu3z5MiIiIpCdna2zfPny5ZgxY4bOsq5duyIiIgKZmZk6y1NSUhATE6P9c5cu\n5WOpJ02KQr9+O5CfL89xhIWFGTyOgoICUcehERUV5bC/j2bNmon++1DCcYSFhdl8XTnyOMLCwiT7\n/pDiOMLCwgweByDd97mp4zh50vxxhIWFWXxdnT0bgUePxB1HUVEEbtwQdxzp6REoKNA9jt9/f/Lv\no/wcf/NNFO7dE3ddrVs3GRXnp2ZMM91XBIAcneXnzhn/+wD0jwMw/fehuSaU8O+V2OsqLCxMEf9e\niT0OzTmW+98rscehySv3v1dij0OT98nj0JDzOFJSUrS1WGBgINq0aYPY2Fi9fuyOVQK3b99mgiCw\nhIQEvXVNmjRhffv21Vt+7do1JggCW7x4sVX7PH78OAPAjh8/btX2vPjmG8Z69GDs8GG5kxBCbFFa\nylhEBGP9+9u/786dGRs3TlxbDw/GEhPFtZ0+nbHg4H/+/MwzjMXG6rbR/BR7/XXG2rUz3A/A2KVL\njN2/X/7/X3zxz3YAY/n5//x/xZ+Kgwcz9uKL5f//5Ze66yq2FwTd/gghyuGIeq3S35kmtnnhBWDb\nNiApCZg5E3j8WO5EhBBrlJYCLi7SzaYh3zAP+Wky3b4tbw5CiDwqfTFds2ZN3LlzR2/53bt3teud\nxZO/GhGrevXyYrpLFyA8HPjpJzsHM8LavHLiLTPllZaS8mqKaXOsyeyoMdOGPwiIy2vthwhLsj7z\njPk2SromxOItM+WVFm95HaHSF9OtWrXCmTNntLN2aPzy92SgLVq0kCOWLJ6cCtBSAwYAmzcDK1YA\ns2ZJf5fa1rxy4C0z5ZWWkvKKLaatyezIl7boS9H2K7YvsYW1pe0KCsr/27698bZKuibE4i0z5ZUW\nb3kdodIX05GRkcjLy8PWrVt1lm/YsAH+/v7o3LmzTf3HxsYiIiKCi4vL0ItqLFWzJrBxY/lUeuHh\nwMmTdghmhD3yOhpvmSmvtJSUV2wxbU1mS+762n+YyWaHDP3Q5DY1K5gmR1aW8TZKuibE4i0z5ZUW\nL3k1DyM64gHEKpLvQUJ79+7VTmUHlM/MoSmaw8PD4eHhgT59+qB3796YOHEiHjx4gKCgIKSkpGD/\n/v1ITk42+qIXsRITE9GuXTubj4U3gwYBXbsC06YBzZsD77wDVK0qdypCiDFii2lrSflWQXtta8u+\nNP+/a5fj9k8IsV50dDSio6ORlZWF9qZ+XWQHXBfTkyZNwqVLlwCUv/0wLS0NaWlpEAQBFy5cQIMG\nDQAA27Ztw7vvvos5c+bg7t27CA4ORmpqKoYNGyZnfO499RSQklL+FR4OLFsGONGoGUK4oimmpXr7\noFQvbTH1Zw0x/Wnm3jDU3tT29riTXlAAqNW290MIUSaui+kLFy6Iaufp6YnExEQkJiZKnMj5CEL5\n27969ACmTgU6dQLeekvaO2CEEMtJeWfaUQ8gmttOrpk+xBTjSpyFhBBiH5V+zDT5h6EJ0O2lTp3y\nt4DVqgX07w/8+aftfUqZVyq8Zaa80lJS3opT45kq7KzJLO+Y6RhRhWrF/Uo1PaAYSromxOItM+WV\nFm95HYGKaSdS8a1FUhAEYOxYYOVKIDa2/L9PTKJiEanzSoG3zJRXWkrKqymmXVxMf19am1mqMdMV\n2xougsNMrLN+v7ZsY8iKFcDQocq6JsTiLTPllRZveR2BimknEh0d7ZD9BAQAO3eW/8COjAT+HtZu\nMUfltSfeMlNeaSkpb8ViuqTEeDtrMjtqmAdgaNtoyYZQVCzQS0vNt6+Y48kPLMuXA1u3KuuaEIu3\nzJRXWrzldQQqpokkVCpgyhTggw+A118H1q+nMYOEyKliMS2mMLSEI4tpaxmamcOSbSx9Xv3Jd4X9\n8Ydl2xNC+EHFtI14mmdaDo0bA199Bdy6Vf4rzmvX5E5EiHPSFNNVqpi+M20NS8dMWzubh7Flxmbp\nMNZO7HJj+7Ol7c2b4vsjhFjPkfNMUzFto8TERKSnp3Pxa4/MzExZ9uviAsycCSQklI+p3rhR3A9T\nufLagrfMlFdaSsor9s60tZltKZBt24/leeV8ANHfXznXhFhKuo7FoLzS4iVvdHQ00tPTHTKTGxXT\nTmTp0qWy7r958/K71DduAFFR5f81Re681uAtM+WVlpLyir0zbU1m+78i3Ph+9C0VPZuH1MNLxPRf\nWrrU7L99SqOk61gMyist3vI6AhXTTiQ1NVXuCKhSpfwu9ezZwKhR5dPpGaOEvJbiLTPllZaS8oq9\nM21NZinHTJtvazxvfr74/dib8dypqFPHkUlsp6TrWAzKKy3e8joCFdNORK2gV3C1bAns3g38/DMw\nejRw965+GyXlFYu3zJRXWkrKW1pa/mHWXDFtTWapxkyLowZj+hkuXAC8vGzv3ZKs4s5D+fktLLRt\n6lBHUtJ1LAbllRZveR2BimkiG1dX4D//ASZPLn848euv5U5ESOUl5QOIgOPGTIvNUFSkv87SIt5Y\nVnsM02jeHNi82fZ+CCHyo2KayK5zZ2DXLmDPHmDSJCAvT+5EhFQ+SpkaD7Ct8Da0rVRjoY31a2yY\nhiU5Ll8Gzp0z3UbOByUJIeJRMe1EZsyYIXcEo9Rq4MMPgcGDgYgI4LvvlJ3XGN4yU15pKSmv2Je2\nWJNZ3nmm/8lrbfFpr6nxxCnPW1ICzJkDPHpk7/7tT0nXsRiUV1q85XUEKqadSIMGDeSOYFavXuVv\nT9yxAzhypAEePJA7kWV4OMcVUV5pKSlvxWEepu5MW5NZ3jHT/+Q1VxQ78mUxxvele355GH6qpOtY\nDMorLd7yOgIV005k6tSpckcQxdsb+OgjYPHiqRg4EPj2W7kTicfLOdagvNJSUl6xd6atzeyIO9OG\ni/apinm7qrgPFcq5JsRS0nUsBuWVFm95HYGKaRvRGxCl060bkJ4ObNsGTJ0q7zRXhPBO7J1pa8j1\ninBLMjgin9zngBDyD0e+AbGK5Huo5BITE9GuXTu5Y1RaXl7Axx8D33wD9O8PLFgAhITInYoQ/ijl\nAUQpHlYU2x8Vu4Q4j+joaERHRyMrKwvt27eXdF90Z9qJZGdnyx3BIhXz9u5dfof644/LC2p7FwP2\nwvM55gHltV5xcfl0lOaGeViTWapiWtywiX/yKmn2C+NZDJ/fixeVW+wr6ToWg/JKi7e8jkDFtBOJ\ni4uTO4JFnsxbvTrw2Wfl01JFRABnz8oUzATez7HSUV7rFRWVF9PmhnlYk9mRDyDqbxunXVZxnalM\n9ii6L1823a/xYzR8fgMDDfepBEq6jsWgvNLiLa8jUDHtRFasWCF3BIsYyisIwNixwMqVwIwZwOLF\n5XfclKIynGMlo7zW0xTT5u5MW5NZEMS/zc/SQtZ8gWw475PFrKki3priPihIf5m4ae6Mn99LlyzP\n4QhKuo7FoLzS4i2vI1Ax7UR4m87GVN6GDYHt24H69YF+/YAzZxwYzITKdI6ViPJaT+ydaWunxpPi\npS3iNLDruGqxrH8VuPHzu3QpUFBgbb/SUdJ1LAbllRZveR2BimnCLUEAXn4Z2LSp/C71Z5/JnYgQ\n5RJ7Z9oa8s4zrUyHDwMdO1q2ze7dgKenNHkIIdKhYppwr06d8pe8/PknMGwYcOWK3IkIUZ6KxbSc\nD/Da/wHE8v7EtHVkEf/XX8CxY+LaXrsmbRZCiLSomHYiS5YskTuCRSzJW6UKEB8PzJsHvP56+Utf\nrP81rPUq8zlWAsprPbHDPKzJbEmRauvDf/r7WmJwnT0eMpSm+NY/v8nJun8eN06K/VpPSdexGJRX\nWrzldQQqpp1IgRIH45lgTd5mzYBdu8qLhsGDgdu3JQhmgjOcYzlRXus9fixumIc1mW15qNB2BQb7\nM/dqcfnon98vv9T987p1QEKCg+KIoKTrWAzKKy3e8joCFdNOJEFJ/zqLYG1elar87nRCAhAVBXz1\nlZ2DmeAs51gulNd6Fe9Mmyqmpc5s/4cVxeW1Zqy2NEW3ft6jR/VbxcdLsW/rKOk6FoPySou3vI5A\nxTSptFq1Ki+k//c/YPjw8jGMhDiroiLAze3/s3fm4VGVZ///TEgCCWFfBIIoIFRQqQRc+6pdEBBw\nFBRp3MFd1KZLqCuLSgtobVS0WkCtFQc3QFSwuLRWXvtaSfRX2UQtomxKAIEQAlnO748nh8xMzsyc\nM5kzZ57M/bmuuWZy5syZ73nyzMk399zPfUNWVuIXINrFMNQ/u/FGpiOZW6fVPMKPkw4LIgVBcA8x\n00KzJjcX7r8f7rkHrr0WnnvOa0WC4A1mZDo7Wz32gro6lWbiRo51+H7RXpcM8ywGXRDSBzHTaUR5\nebnXEhyRSL0nnKByqT/7DK6/Hg4cSNihQ0jnMU4Gojd+7JppNzXbrboRvH9syi07IMZ/PLdJnTlh\nl1Sax3YQve6im95kIGY6jZg0aZLXEhyRaL2ZmXDffap8nt8Pf/tbQg8PyBi7jeiNH7tm2k3N8aR5\nxDbf1noTsQCxKeY78nukzpywSyrNYzuIXnfRTW8yEDOdRkxPpRUtNnBL77BhsGwZvPUWXHVVYit+\nyBi7i+iNH7tm2qnmujr75rSuzpmZtlelY7qrEefEL0KcnugDuk4qzWM7iF530U1vMhAznUYUFBR4\nLcERbupt3RoefBB+8Qu49FJYvDgxx5UxdhfRGz92zbRTzbW19vOga2vVAsh4I9PWxtZar9W+5vsm\nPtXECc7nhHmN+uyzRGuxRyrNYzuIXnfRTW8yEDMtpDUFBariR2mpilLv3u21IkFwB7cWIJpmOtH7\nQmMjG8nYOjW8do/blKh0Ik34q6/C55+rOvqCIKQeYqaFtKdlS5g5E26+GcaPh+XLvVYkCInHbTNt\nx3jW1qq1C251TEyUgV29Gr79tmnHSKSZrqyE7dsTdzxBEBKLmOkmUlRUhN/vJxAIeC0lJgsWLPBa\ngiOSrfe001SU+p13VMWPffucH0PG2F1Eb/zU1CjTG8tMO9XsNDLtxEyH72dtrBdYPh8t3zqWQR8x\nAp591pbEOHA2vpddpu4ffljde1EjPJXmsR1Er7voojcQCOD3+ykqKnL9vcRMN5GSkhKWLVtGYWGh\n11JiUlZW5rUER3ihNycH/vAHuPxyuPBCZaydIGPsLqK3afh8sc20U81ummmI3mBFPS5rcppHcnE2\nvs8/H/rzSSclUIpNUm0ex0L0uosuegsLC1m2bBklJSWuv5eY6TTiscce81qCI7zUe/bZquLHK6/A\nbbfZr0stY+wuorfpxDLTTjUnMzJt/by13kRU4XCnNF7T5sSGDU16eVyk4jyOhuh1F930JgMx04IQ\ngbw8ePxxGDMGzj8fPvzQa0WCED+mMfR6AWJWliqRZ5donQ3tNmsJ39+J0Y7XlLsZ/d60yb1jC4Lg\nHDHTghCD4cNVhPrRR+Hee73JWRSEROGlma6pUQt+a2vt7W+vzrS7pEbXxFD69PFagSAIwYiZFgQb\ndOgAf/2r+iM2Zow3X7UKQlMwI6xeR6azs+2baYheZzreyLTd7eb7bdli7/iCIKQnYqbTCL/f77UE\nR6SaXp9PLUycPx/uvBN+9zuorg7dJ9U0x0L0uksq6o1lpp1qdtNM28uZbtCbqG6FhhH63kcfnZjj\nKhIzJxLfmTEyqTiPoyF63UU3vclAzHQaccstt3gtwRGpqrdnT5X20bs3jBoFn3zS8Fyqao6E6HWX\nVNSblRXdTDvVnMzIdDjK8N7SyPymNqk3J2KRivM4GqLXXXTTmwzETAfxpz/9iYKCArKzs5kxY4bX\nchLO8OHDvZbgiFTW6/NBYSEsXAizZsH996t80FTWbIXodZdU1JuREd14OtXs1Ey3bGl/3YE9g2xf\nr900D59P3dwx6ImbE8mKTqfiPI6G6HUX3fQmAzHTQfTo0YN7772XCy+8EF8yv0MTtKVrVwgE4Nhj\nJZdaSE+SHZluXGfa3ai0Dn8Kqqpg82avVQhC+pLptYBU4oILLgDg1VdfxdDnO0PBY8xc6h//GG65\nBX7yE7j1VhUBFISUYOhQFqzdAT3Vj0/t4shjS7p1Uz21beC2mY5GtMu0F23I3T6mFTt3wtKlqmur\n/NkSBG+QP/dpxNKlS72W4Ajd9PbsCVddtZTsbLjgAvjqK68VxUa3MRa9cbJ1K52rtsLWrY0eh9+W\nbt0KO3bYPrT3CxCX2i6hF4/ZTLxBTeyc6NpVGWo3SZl5bBPR6y666U0GYqbTiEAg4LUER+imF2DR\nogA33QR//CPcfDMsWJDa0SLdxlj0xofRvoN60KUL5OdT3iof8q1vgZwcFZm2iRel8b79Vi2iVJ8t\n98a4KSkekT/3idd7110JP2QIqTKP7SJ63UU3vclAzHQa8cILL3gtwRG66YUGzccdB6+9Brt2wcUX\nq6BfKqLbGIve+Kj883PqwZtvwpYtTDp3iyqebHF7obLSdooHeBOZPu44ePZZ86cXHP/DGi0P2/zZ\nvX+CU2NOOCFV5rFdRK+76KY3GWhrpisqKpgyZQrDhw+nS5cuZGRkRKzAUVFRQVFREfn5+eTk5DB4\n8OCYk0EWIApNpUULmDJFVfq45hp4+unUjlILzZdDh9w7drCZjjW/nRhvE6tLcWWlOpbTz1OimrwI\ngiAEo62ZLi8vZ968eVRXVzN27FggsgEeN24czz77LNOnT+fNN9/klFNOobCwsNFXFbW1tVRVVVFT\nU0N1dTVVVVXU1dW5fi5C82bAAHjjDSgvh/HjYft2rxUJ6UZVlXvHrq5WtaszMiDW5dKpmbaXM20d\nSW5KPMQsjdeU43gRj/n22+S/pyAIGlfzOPbYY9mzZw8Au3btYv78+Zb7LV++nLfffptAIMCECRMA\nOOecc9i8eTPFxcVMmDCBjPqyC/fddx/33nvvkdfOnDmTZ555hiuvvNLlsxGaOy1aQHExrFkDV12l\nItX101EQXMdNM11To8x0ixaxzXI8ZtpOaTyT4H2dVPpwIwL9y18m/pix6NZNoumC4AXaRqaDiVbG\nbsmSJbRp04bx48eHbJ84cSLbtm3jww8/PLJt+vTp1NXVhdyak5GeOHGi1xIcoZteiK35xBNVlHrd\nOrjiCti9O0nCIqDbGIve+KhykObhVHN1NWRmNpjpaNTWqn3tRm3DzbT5ODRdY6JmaRvuzokDBxJ/\nzFSZx3YRve6im95k0CzMdDTWrFnDgAEDjkSfTU466SQA1q5d26Tjjxo1Cr/fH3I744wzGpWOWbly\npWU/+8mTJ7NgwYKQbWVlZfj9fsrLy0O2T5s2jdmzZ4ds+/rrr/H7/WwI6xby6KOPUlxcHLLtnHPO\nwe/3s2rVqpDtgUDA8sMxYcIET89j+PDhludRWVmZsudRUFAQ8/eRlQUzZsD111fygx/4efBB785j\n+PDhTZ5Xyfx9DB8+3LXPhxvnYXYKS+bn3Oo8qg6qGhITp08HQk1lyHls387wfftYGQjYnlfr15cx\nb56fmprykDQPq/PYtu1rnnzSz7599s5j+XI/Bw6E/j6++CKAYQT/PtQYv/nmBPbuDS/ZZT2v5s2b\nDDSch2Go3wf4gdDfx+efTwNCzwO+pq7OD4R3aXoUKA7bVll/XPM8zO5xAayN9QQal89bWX+McELP\nA6CwMPHzavjw4Sl93Q0/D/Nz5/X1yu55mHq9vl7ZPY/gDoipdt0N1F+7/H4/vXv35uSTT6aoqKjR\ncRKO0QzYuXOn4fP5jBkzZjR6rl+/fsZ5553XaPu2bdsMn89nzJo1K673LC0tNQCjtLQ0rtcLgmEY\nRmWlYRQVGcaNNxrG/v1eqxGaK6v+vNbY1W2gYaxdaxiGYYwZE2HH0lKVfuzgurZ0qWHMn28Yl11m\nGN9/H33fV181jD//2TDOP9/esW+80TAGD274eehQw7j+esPIyDCMJ55Q7weG8c03hnHLLYYxaFDD\nvhs3qucMQ91/9ZVhfPGFevzyy6HPffttw2MwjI4dDWPOHMO46CLDGDGiYXvwzeez3p4Kt5077Y2v\nIKQDyfBrzT4yLQipTE6Oqkk9fjz4/bBypdeKhObIzi4DWTpzLQwcCNhbLGgXM80jK0s9jkaiqnmY\nOEnb8PmcVfGIVR4vlQs+/eQnXisQhPSi2ZvpTp06sWvXrkbbd9cnq3bq1CnZkgShET/9KSxbBitW\nwLXXwvffe61IaE5UVkJubsPPOTlw8GBijm0uQHTDTMfqYhit1J0To62raY7EmjXwzjteqxCE9KHZ\nm+lBgwaxfv36RiXuPv30UwBOPPFEL2R5QnhOUqqjm15omua8PBWlnjgRxo1LTpRatzEWvfFx8GCo\nmc7NVQbbCqeK44lMOzGosfe1pzjRiw/jj+wnZ04MGwZPPpmYY6XKPLaL6HUX3fQmg2ZvpseOHUtF\nRQUvv/xyyPZnnnmG/Px8TjvttCYdv6ioCL/fr0V7zTlz5ngtwRG66YXEaP7Rj1T3xNdfVy3JKyoS\nICwCuo2x6I2PAwcam+lIkWmnis0605mZKkodDXfqTM+x3NfKhEc6ntVr3YtIJ29O3Hhj6M/hv/P/\n/tfecVJlHttF9LqLLnrNxYjJWICobZ1pgBUrVnDgwAH2798PqMocpmkePXo0OTk5jBw5knPPPZeb\nbrqJffv20bdvXwKBACtXrmThwoVN7nRYUlJCQUFBk88lGSxatMhrCY7QTS8kTnPr1vDII/DuuyqX\n+v774cwzE3LoEHQbY9EbH/v3Q9u2DT9Hi0w7Vew0zaNlS+e5zuGPzZJ56jiLbB0vNcrigfMRThy5\nuaHj0LevvXFJlXlsF9HrLrroLSwspLCwkLKyMoYMGeLqe2ltpm+++WY2b94MqO6HL730Ei+99BI+\nn49NmzbRq1cvABYvXsxdd93F1KlT2b17NwMGDGDRokVccsklXspPOrnBoSkN0E0vJF7zT38KQ4bA\nrbeqHMg773S+gCsauo2x6I2PvXtDzXROTmQz7VSxmwsQ7UWmEzPGdqPWTSc15oST80qVeWwX0esu\nuulNBlqneWzatOlIc5Xa2tqQx6aRBmjdujUlJSVs27aNqqoqPv7447Qz0oK+tGsHf/kLHHMMjB6t\nGr4IghP27bMZmW7VSlX8aNXK9rHNNI9kVvOwbt4SvQNitGoehqEW7QX/bNV9UUeGDm28bedOd7ti\nCkK6oXVkWhDSBZ8PrrxSRap//Wvo319FqXNyvFYm6ICVmbbMmR44EBw2sqqpcW8BYrToqdOIcaz9\nBw1q+nukIqWl8M03KnUM4Lvv4KijYMsWb3UJQnNC68i04IzwzkOpjm56wX3NPXvCCy+o1I/Ro+GT\nT5p2PN3GWPTGR8cd62h92glHvtaIlubhVLObCxCtaFwar9jS9EYz7ImtJuKU5M+JXr1g2jT12Gz5\n/sAD9l+fKvPYLqLXXXTTmwzETKcRwakvOqCbXkie5gsvhBdfhHvvhccei79Ml25jLHrjI6u2Ct+6\ndUe+24+2ANGp5njSPOxGfMNTM6wXINrXG6kudXIj0N7MifDf9yuvqPvPP4/92lSZx3YRve6im95k\nIGY6jbj11lu9luAI3fRCcjV37gwvv6yM9OjRoTmfdtFtjEVvYohWGs+p5mnTVIq1GznT4Wba2gTf\nauufScOw/0+nu6XxvJkTTz0V+rOZ5tG/f+zXpuo8joTodRfd9CYDyZluIkVFRbRv3/5ICRZBSCYZ\nGarSx7hx8JvfwHHHwd13q/JjgmASbkJzcxObM9uihT0zbeZXO8mZtrOv1QJEJ/s1h9xoQRBCCQQC\nBAIBvk9CS2GJTDeRkpISli1bJkZa8JT8fAgE4OSTVZT644+9ViSkMtFypuPBrDXtRmk8qzrTwc+D\nvYhzcJQ7Ua3GdeTNNxtvmzkz+ToEwW0KCwtZtmwZJSUlrr+XmOk0YsOGDV5LcIRuesF7zRddpEz1\nnDkwY0Zsc+O1XqeIXudYmcFoaR5ONXfpAgMGuNcB0cpAh5bG22C7aUuk/cI7BUbbt+l4OycmTWq8\n7e674cMPI78mFeaxE0Svu+imNxmImU4jpkyZ4rUER+imF1JDc5cu8PzzKhdy9Ojolc5SQa8TRK9z\nqqoal42OtACxvBwKC51pPv10+wsQq6shO9u+UY2U5hEaYZ5iOxfa3C/8mK+/Hvk1ic+d9n5OWDFu\nXOTnUmEeO0H0uotuepOBmOk0Yu7cuV5LcIRueiF1NPt8UFiomr1Mnw6zZzeUxAomVfTaRfQ6Z98+\nyMsL3RYpzeOzJet44JMNcXUGsmOmDx9W+9nFykw3bswy1/YCRKfR5miNXuLH+znhlLlz59Kundcq\n7JMKnzsniF79ETOdRuhWzkY3vZB6mrt3VyX0jjoKxoyBjRtDn081vbEQvc7Ztw9qu3ZXZTe6dwci\nR6azaqsYxudxtcdzEplO1AJEZaJ72Y5Mp0b+s/dzIhaDB6v7w4fVmPXq1Yt9+7zV5IRU+Nw5QfTq\nj5hpQWjm+Hxw9dXw5z/DlClQUhJ/XWpBP/buBV+P7uoriiAzbZUzHU9Kg2lQkxWZDsb8tsVNM50a\nBjy5mM2gevaEt97yVosg6IDt0nilpaX44rjSDhgwgBzpeSwInnP00bBkCTz5JFxwAcydC8cc6Zdd\nggAAIABJREFU47UqwW3CW4lD5DSPpuQH21mAGByZrqtTpR2jYS8yre5jmd546ky7V2taD3buhP37\nvVYhCKmP7cj0KaecwtChQx3dTjnlFNavX++mfsEBs2fP9lqCI3TTC6mv2edTlQv++Ee46SYYP362\nVpG3VB/fcFJBr5WZbtHCOofeMMCpYtNwOolMR3r/cOrqIlf/MA05NMzhaGX0IFWizN7PiWh06KDu\ng6t+BM/jHTuSLCgOUuFz5wTRqz+Omrbcfffd9OnTx9a+dXV1XHvttXGJEtyhMpGFZZOAbnpBH83H\nHQevvQYjRlQyfjw88gj06OG1qtjoMr4mqaDXykxD5KhrvIqd5ExnZiozHSvlIzx6Hd4NUZnpyoSn\nebhbGs/7ORENs7/FkiXq/qOP4O9/b9DcvXuq/FMSmVT43DlB9OqPIzM9ZswYTj31VFv71tTUpIWZ\n1qkD4owZM7yW4Ajd9IJemlu0gLffnsHatTBxIlxyiYpGpfJX2zqNL6SG3n37oFs3+/vHq9iumTYj\n07FSQkAZ7mipIMpEz3Ctmoc7eD8nrNi2LfRn01T/5S+wY0dqao5EKnzunCB63SGZHRBtm+nFixfT\nv39/+wfOzGTx4sX07ds3LmG6UFJSQkFBgdcyBCFuTjgBli+Hhx+GCy+ERx8FWazdfNi3D8uyZlbG\ncsDvLlcPRo5UIWQbPLUL6AlDq+HEKuCx+ie6dYPVq0P2PXxYHTY7O7bxhsZpHuH/6AXnTNvB6cJb\nd0rj6YUOaR2CYIUZ5CwrK2PIkCGuvpdtM33hhRc6Png8rxEEIfm0aAG/+pUqn3fDDSpKffXVqR2l\nFuyxd691mocVmRV71IOdO20fvzPAVshG3TBLqG3frspBBPH4Lmh5HPxxD7RdifWqnSATHmmRojkv\n6+qCc6dj49QYf/cdvPees9ekA/ffr7omCoKgcJTmIWjM0KGUb91KZye9fD2mvLZWK72gn+Zwvf2B\n5UDFB7B7MrRvDy3iKaBpEZVMBOXl5XTu3Dnhx3WLVNC7axd0yj0Ia/8LffqoUh5Y/6N0uHM+G3fD\noHz7c7h8F3TuBNU1cOAAtK/crtxtXR1s3Rqyr2m8O4Ct1OFoFT/MnOmMjHLq6mKPcTxpHps3O9vf\nHuXUj0TK0bp1pGdCNUdrPZ4KpMLnzgmiV3/iMtPvvPMOu3fvZvz48QB8++23XH311Xz88cece+65\nzJs3j1bh/WsFb9mxg0k7drDMax0OmARa6QX9NFvp9QFtzB8sahHbwqVC1pMmTWLZMn1GOBX07toF\nHb9dD6cNgdJSqE9LM81lsKle/afV/Oxnfowt9jVP8sOyZbBlk6oS88gHQyPmBpjG+/u9qitjppVn\nD0rwjrYA0XzeMCZhGI31NqWah7tl8VL3KhF5XZnSbKbKpnrqRyp87pwgevUnLjM9bdo0hg0bdsRM\nT5kyhVWrVjFs2DBeeeUV+vXrx9SpUxMqVGgi3box3UxY1ATd9IJ+mmPpNVBpAoYB7ds5MBhOVrw5\nYPr06a4c1y1SQW9traqeEY7ZuCU3N/yZ6XG9T8uWcOgQUb+RMI33zGK49lr4wQ+iH9NM44j2fMuW\n0xNeZ9pdpnstIA6mA/DKK+onF750Siip8LlzgujVn7jM9MaNG/ntb38LQHV1NUuWLGHWrFlMnjyZ\nBx98kKeeekrMdKqxejW6LZPUTS/opzmWXh/QHnjjDXjoIdWR+uyzkyAsArot9k1lvW3bqsWJwWZa\nGVL7moPN7hEzHQVz3+xstRjRDlZmOjhnumXLgqgmeffuhsdOS+O5E51O3TkRGaX50089lmGTVP7c\nWSF69SeuduL79u2jQ31l99LSUioqKrjgggsA1dxlszuJZoIgeMTo0Soq9eKLcN11KnVA0Ju2bZve\n3S74iw07ZtrEThm9mhpVC90Ks8pGXZ2Kukcz08GtEdK9MocgCO4Ql5nu2rUrn332GaDyp4855hh6\n1q/a3r9/P1mxKvELgqAd7durFuTXXgsTJjQ0dRBSl2h1ms3IdDBOI7GHDikTDc7MtJ3SeHv3xj5O\nuJm20m8eJzjNQ6rUCIKQSOIy0yNHjuTOO+/k17/+NX/4wx9CSuB99tlnHHvssYnSJySQBQsWeC3B\nEbrpBf00x6P3tNNU2sfq1XDVVaFfo7tNOoxvIikvh0iL7tu0aWymVeTWvuaqKjDXmmdm2mvEAioy\nHSvNI5LhDd5eVwdVVQssI9PRFiA6WYiYePSawwq9NHv9uXOK6NWfuMz0zJkzGTx4MPPmzaOgoIC7\ngwpOPv/885x55pkJEygkjrKyMq8lOEI3vaCf5nj1tmwJM2fC5Mkwfnzkr+MTTbqMb6LYsSPyWlCr\nyLTCvubgyLQd42maWMvI9Lp1qoPQunW237+uDmpqyhzlQnuPXnNY0Vjz44+ra4C5GHHHjlQZX+8/\nd04RvfoT1wLELl268Oabb1o+9+6775JTX8dUSC0ee+yx2DulELrpBf00N1XvqaeqKPXUqSrt46GH\nVDqIW6Tb+DaV+My0fc3BZtoJlpHpqiplpKuqYr4+OGf6qKMes4xMhxu74DrT3qZ56DWHFY01T56s\n7nfsgKFDoXt3+Pe/4ZRTkizNAq8/d04RvfrT5KYtO3fu5ODB0GK0e/fupZf0IxaEtKBVK5gzBz74\nAMaNg+JiOO88r1UJEGSmBwyANWtCVuO1bQtfftm04zs106aJtbMA0Q52FiCG7y+4h900H0FobsRd\nzeOaa64hNzeXo446imOPPTbk1rt370TrFAQhxTnzTHjjgXUMvvwEpo1fZ2sBmeAu335bb6ZzclQK\nRdC3homo5hGcM+0EOwsQ7USPg810rBSDAQOclcYT7LF3Lzz6qHos4yakK3FFpouKiggEAlxzzTWc\ndNJJtIznez5BEJodOb4qcnav44IRVYwdC3fcAeee67Wq9GXr1shpHpEXINrHiZkOTrOwswAx2nFM\nws10pMol4a/9/nt77yVVP2Lz17+qG0hkWkhf4opML1++nN///vfMnTuXG264gauvvrrRTUg9/H6/\n1xIcoZte0E+zW3oLCtSixNdeg5tuanoE1ETG1xlbt0J91dJGRM6Ztq+5okKZcjvU1CgTDfYi09Ew\nc6Zra+Grr/xHzHQs82ua6UmT7L9P4tFrDivsaT7nHJdl2MTrz51TRK/+xGWmq6qqGDRoUKK1CC5z\nyy23eC3BEbrpBf00u6m3dWt45BFVk/qCC+Ddd5t+TBlfZxw6FDlyHNlM29dcUQF5efb2DY5iJ6I0\nnrkAsVu3Wyw7FlpF2e3mTLubrqDXHFbopdnrz51TRK/+xJXmcd555/H+++/z05/+NNF6BBcZPny4\n1xIcoZte0E9zMvT++Mdqtf9vfwuLF8OsWfYNWDgyvhEYOlStNgzj6V1AcGS6W7cjtczatGn8jYEy\nkfY1V1QoU26HcDPdKDJ9+eXqfuRIyM6mbR18A7R6q+EcXvsOstfB1MPQ/m5o0QL+uR8yi7ux4qer\nbUem7eKOqdZrDiv00izXCXfRTW8yiMtM33PPPVx00UXk5eXh9/vp1KlTo306duzYZHGCIDQP8vLg\nscfg7bfh/PPhnntA/hdPIFu3WprpzgBbrV+SmanSJIJxWu1i/37o0aPh52jms6qqYf2jZZrHnj3q\nfudOQH1t2hOgiiPncBRANXQAqM97zgEO7q6jrs5+mofJtm3R9xcEQbBDXGb6xBNPBKC4uJji4uJG\nz/t8PmrDr9LNlKKiItq3b09hYSGFhYVeyxGElGbYMNVB8Y474KWXVEk9uzm3QhQ6dIAdO9jbqgu1\nGdl07AA1tcrsdgiu+x22GtGqFrMTwnOmzVxmK1N78GBDZDo726KcdH6+CjXXU1cH27ar13Suj9d8\n+5167YEDqp55plFNq73fcbh1e0c50ybbt0ffXxYgCoK+BAIBAoEA39tdcdwE4jLTU6dOjfq8L42u\nQCUlJRQUFHgtwxZLly4Naf2e6uimF/TTnHC9YV/VW9EGmIvK5933NGS3sV+reOnBg1zYu3dD27UU\nJ2nz4bnnYMgQZv7oTT7JKGDlSvjX+/C//wu3327/MMpsLgXsaQ7PmW7ZMnKednCaR6tWar8Qwn6n\ne3bB0Z3h/HNh2TK17fxT4eST4dln4cH7YGBVGfuKh9Dypucwvohtfp3mTLvzp8z++KYO9jXPmwfX\nXeeumlik/XXYZXTRawY5y8rKGDJkiKvvFZeZnj59eoJlCMkgEAho8QEw0U0v6Kc54XrDvqqPRkug\nC0C4qYpCALhQow6ryZ4PGRkN5ck2b4Zjj3X2emU2A8RrpnNzQyPQwYSb6ViNDq2i5OHb6uqU2qts\nLkB0Gnn/5htn+9vD/vimDvY1X3+9Sp+ZNs1dRdFI++uwy+imNxk4NtOVlZX069ePJ554gvPPP98N\nTYJLvPDCC15LcIRuekE/zQnXG/ZVvV2qqmB/BbRrGzGgDcALELlwcgqS7PnQogXU1v9zsmkT2Fkn\nFGxCldm0r9nKTFdWqqyTcILNdE5OfGY6/PlTHrmcYcDBOSM5vTZb/SNRv1jx2Bq1gDGYTteGbus6\nqvE+AL790HYmXBq7s3lc7GAop6DHtysKZ/N4+nRvzXTaX4ddRje9ycCxmc7NzeXgwYO0bt3aDT2C\nIOhMnOkXrYCDe+C6IlUXeepUZ22qBUVGRkMqw6ZNId3DLWndWplf83IezwLEYDOdk6OOZ0VwxNpO\nZDoaR9qSH1DfhOTs38mR7yvqFytmEVrIBIDdYdu+s9gHwAD2qZQkN8ig+fc1X71aFZkRhHQgrjSP\nn/zkJ7zzzjtSGk8QhITRoQP85S/wyiswerRanKjJcoSUIdhM79wJnTtH379LF7Wfaaab2gHRjExH\n2tfM0Ik3zSOcgx3yOXioxZEc7Joa6NpFPVddo9qpB9OxI+ze3fBz167w3XeNj+vzQds2sNeyDnf8\nZFJDJ3axm+Zf7WrFCjHTQvoQl5m+++67GTduHNnZ2Vx00UV079690aJDKY0nCEI8XHQRnHWWqkvd\npg3ce6+q3CDEpkULVe7OqomJFZ07KzNt5lbHU1c5+D3MnGkrEpEzDfDVVw3vueLe1axYocosvvee\neu6f/6zf73Po3z/0tS/8STURMlm93Nrw5bWGe+5Sc1CIj/vuUyUwBSEdiKsD4pAhQ9i8eTMzZsxg\n0KBBdOnShc6dOx+5denSJdE6hQQwceJEryU4Qje9oJ/mVNXbtSs8/TRcfLEy10uXAuvWMbF9e1i3\nzmt5tkna+LZqBQMHUpfditpaFZG1k1repQuUlzf8rKLa8WuOluaRKDP91lsNz9fVwYcfTjwSja+r\ngxkzIr/eyT8L7nVBTM3PXHSca25Ku/imkqrXtUiIXv2R0nj17Ny5k6uvvpr33nuP/Px8HnvsMYYN\nG+a1rISiW9ci3fSCfppTXe/ZZ8Py5SqH+pOnqhi+d2/Tkm2TTNLGd+BAWLuW726G2i9g7Vo44YTY\nLzPTPEycdkAMJ1aah5l2kplp32xFq11dVwc9ew4/YqZra6MvfnOSE+7en7HU/sxZo5fmVL+uhSN6\n9UdK49UzefJkevToQXl5OW+99RaXXHIJX3zxRbNKV9GtqYxuekE/zTrobdkSZs+G//c0/PA1+L//\ng9M1yaVO9viaaR5r1tgz0507Q1lZw8/KTNvXHG44o6V5HDignrd6nRVmZNjMA7cqElNXB/37F9qO\nIid6v/hI/c9cY/TSrMN1LRjRqz9xpXk0NyoqKnj11VeZMWMGrVq14vzzz+eHP/whr776qtfSBEGo\n54c/VPf//CdMnqzKsgmhZGQoI+gkMh2c5mGayKoqy+7kjQg3ndHSPPbvd9btMtxMB2Oa8bq6hrbo\nZgTbid5E7SsIQnoTV2QaYOPGjTz55JNs2LCBg0GhCMMw8Pl8vPvuuwkRmAw+//xz8vLy6NGjx5Ft\nJ510EmvXrvVQlSAIVkyZAm/vBr9fLU78n//xWlHqUF2tzOW2bRB0OYuIdZoHPPkk3H9/9N47dXXW\nkelIr4nHTN9wA+zaZZ2eYeZMZ2crM233mHbQMFNREAQPiSsyvWbNGgYPHszrr7/OihUr2LNnDxs3\nbuQf//gHX375JYZm/9JXVFTQtm3bkG1t27alopmFvlatWuW1BEfophf006yd3vr7YcNgyRLVRds0\nXKlIsse3uhqystRjO4awY8fQsVOmdRW1tbFT0ysrG9I2TKLlTMdjpn0+68i0iVpsuYqamsaRaas/\nQ07bibuDXp85hV6atbuuiV7tictM33nnnYwYMYI1a9YAMH/+fLZs2cJrr73GoUOHmDlzZkJFuk1e\nXh779oUWFN27dy9tnFz5NWDOnDleS3CEbnpBP83a6Q163K4dPPEETJwIhYWwYEHqfTWf7PGtrlbp\nGfn59vbPympoPw7m+M2xlTKxezd06hS6LS8vcvpNPGY6IyO2mS4tnXMkMu00zcObCLRenzmFXpq1\nu66JXu2Jy0yXlZVx9dVXk5GhXm5GokePHs1vfvMb7rjjjsQptKCiooIpU6YwfPhwunTpQkZGBjPM\nekgW+xYVFZGfn09OTg6DBw9u1AqzX79+VFRUsG3btiPbPv30U06wk3SoEYsWLfJagiN00wv6adZK\n7+WXswhg5EjVJrH+dvrFPfnbup5c8uue7G7dk9oePUOet3VzqbtEsse3uhq+/hrOPDO+16tL+SJb\nZrq8vLGZbtMG9kVodHLgQENzGDuYaSQtWoQafmgwwTU18POfL2r0fCTCzynSOdo5/3gYwDo+4nMG\noE95R4VG1wk0u64hepsDceVM79mzhw4dOtCiRQuysrLYs2fPkeeGDBkS0dgmivLycubNm8fJJ5/M\n2LFjmT9/fsRyfOPGjWP16tXMnj2b/v37s3DhQgoLC6mrqzuyIjUvL48LLriAadOm8eijj/LWW2/x\nn//8B7/f7+p5JJvc8O9kUxzd9IJ+mrXSu2cPuWCZlOsjqPVzhGoSXpDs8TXLzf3kJ/G9XhlIe5p3\n7WrcYbFtWxWBjnTsDAfhGzPNw1xgaPV8bS20bZvLwYP2mtR4Xc2jFVUMZQOt0Ke8o0Kj6wSaXdcQ\nvc2BuMx0fn4+39b3ae3bty/vvfce5557LqAiunl5eYlTaMGxxx57xMDv2rWL+fPnW+63fPly3n77\nbQKBABPq216dc845bN68meLiYiZMmHAkuv74449z1VVX0alTJ3r27MmLL77YrMriCYL25Odb10cL\nwzBgfwUcPqxSQbLsXOXsdDhJZdatg/Hj6XrUS1x33UB69bL/0qyshlxr00TajUyHm+k2bSKbaacE\nm+lINalralS0e/9+e2baSc60LEIUBMEucZnpH/3oR/zf//0fF198MZdffjlTp05l+/btZGdn88wz\nz3D55ZcnWmdEoi12XLJkCW3atGH8+PEh2ydOnMill17Khx9+yBlnnAFA586deeONN1zVKghCE1i9\n2tZuPqAtsHkz3Ho79O6t2hrn5LiqzluqqmDdOjI7VTH3z85e2qmTijJ36xZajs6OmT7++NBtrVsn\nrmShaWiD87rDNd1+u8qbr6lpXF3ETgfEaIY51fLvBUFIXeLKmb7rrruOpEBMmTKFm2++mSVLlvDS\nSy8xYcIEHnzwwYSKjJc1a9YwYMCAI9Fnk5NOOgkgIaXvRo0ahd/vD7mdccYZLF26NGS/lStXWqaN\nTJ48mQULFoRsKysrw+/3Ux5cABaYNm0as2fPDtn29ddf4/f72bBhQ8j2Rx99lOLi4pBtRUVF+P3+\nRitxA4GAZXvQCRMmeHoexcXFludRWVmZsudxww032P59pMJ5FBcXN3leJfM8iouLbf8+jjkGZs/+\nmnfe8fPjH28guFpnss7DfI9kfs6dnseqVRN44QV1HipyW8yGDSs5dCj678PMmQ4+j2BzGus8MjMb\nTHKk83jtNT/ffbcqLCc6wKFDDeexZEkxNTXw/vsT2LMn9PcBK4GG82gwyJOBBXzwQfC+ZfX7hv4+\nYBowO2zb1/X7bgjb/ihQHLatsn5f9ftoeDaAdZvuCUD082hAnUcobpxHMeHn0UDk8/Dq74c5l7y+\nXtk9D1Njql53w88jWEuq/f0IBAJHvFjv3r05+eSTKSoqanSchGNozs6dOw2fz2fMmDGj0XP9+vUz\nzjvvvEbbt23bZvh8PmPWrFlxv29paakBGKWlpXEfI9k88sgjXktwhG56DUM/zemid98+w7jtNsO4\n5hrDKC8PemLtWsMYOFDdu0DSxre01DDA+MVZzq9Hf/iDYbz7rnr89NOGAY8Yjz1mGFlZ0V83ebJh\nbN7cePuYMdb7n39+6M+XXmoY+/dHPv7nnxtGUZG6ffGF2jZkiGGAYbRpYxgPPaQeX3HFI8avfmUY\nP/+5YZx5ptpmGIaxfr16HHx74onQn1u0aLwPGEZurmH8/vfWzzXlNphS4xEwBlOa8GO7e3skrtd5\nRbpc17xCN73J8GvSATGNuPXWW72W4Ajd9IJ+mtNFb5s28PDDcO21MGECBAL1Ucr69IiYRZXjRIfx\nDW7coiK3t0YtR2dilTMdCasGL61aqWGPlE4RK2f6s8/U/Zgxt7JvH3z7bew8Z7vNXcz3d4PUnxFW\n6KVah89dMKJXf+LugFhTU8OLL77IP/7xD3bt2kWnTp348Y9/zCWXXEJmZtyHTSidOnVil0U3h927\ndx95XhCE9OH002HFCnjgAbjoIpg7CWw0CtSGeNZRdukCX36pHjtZgHjgQOOmLQCvv67MeZcuDdsq\nKlQN6mBMM52VBZ9+CgMGhD5vlTMdbJaffFLdZ2WBuQY9VjfMSG3JrTh0KPqx4uE51HqiFYykmuzE\nv4GL7KAbp2Bv3YIgpBtxud7y8nJGjBjBxx9/TGZmJh07djxSVePBBx9k5cqVdLYbsnCRQYMGEQgE\nqKurC8mb/vTTTwE48cQTvZImCIJHZGXBnXfC55/DnGugBFX5Qy9rY01BgfPXdO8O77+vHgcvQLRb\n+cKKPXtCzfT+/apsXjCmma6ttV60GByZjrQAERo6PkLsBYjhkelo/zD82eFCTju0R1WhOooofdpT\nlAyaMCEEoZkTl5n+5S9/ycaNG1m4cCHjx48nMzPzSKT6hhtuoKioiOeeey7RWh0zduxY5s2bx8sv\nv8wll1xyZPszzzxDfn4+p512WpPfo6ioiPbt21NYWHikbnWqsmHDBo4PX36fwuimF/TTnM56+/WD\nP/4RGAq33gqj74Hzz09sSbRkj+8ppzh/Tc+esHWreqwM9AZ8vtiaI43TL38JB8NqfVt1PzTNdCSi\npXkEv/eOHRuA46NqMnHyD4LdRjBO2EY+G6njOLJi75wiZFHNHr6jjvZeS7FNOl/XkoEuegOBAIFA\ngO+//97194rLTL/22mvcd999IeYxMzOTSy+9lO+++45p06YlTGAkVqxYwYEDB9hfX9R07dq1vPzy\ny4DqxJiTk8PIkSM599xzuemmm9i3bx99+/YlEAiwcuVKFi5cGLHRixNKSkooiCcc5AFTpkxh2bJl\nXsuwjW56QT/N6a7XvASUlMCslfCXv8CsWcpoJ4Jkje/ult15e8A0LhnY3fFrO3ZUpfHAjNROISMj\nuuZoEd1u3Rp3QYxkpsNNd/h7hEemrXjqqSmA0hurKYyTnGk3UGkSfky9OjCYMnoyhC14HyCzS7pf\n19xGF71mkLOsrIwhQ4a4+l5xmWnDMCKmSJxwwglRaz8niptvvpnNmzcD4PP5eOmll3jppZfw+Xxs\n2rSJXvVdCxYvXsxdd93F1KlT2b17NwMGDGDRokUhkep0Ye7cuV5LcIRuekE/zaJXkZMDM2bAf/+r\nahcfdxzccUdjA+iUZI3v83/vTvf7poNzL90oNcLnmxvTlFZWWudLg3UXRCsz3bq1yruOhFXOtBW/\n/vVcLr5YPU7UAkR3/4Tp9ZmrohW30Y/baOW1FNvIdc1ddNObDOIy0z/72c94++23GTZsWKPn3n77\nbX4Sby9bB2zatMnWfq1bt6akpISSkhKXFaU+vZy0RUsBdNML+mlOe71mg6mRIyE7mz7Ai0DVu7D/\nIfDlKNMX0aN16xa1mUyyxvf11+HVV5t+HMOAjIxeMc10tEoebdrYi0wHm+lI+c0tWsSOTB93XMMY\nJ7Kah3vo9Zlbz0DOZaPXMhyR9tc1l9FNbzKwbabNChgAU6dOZezYsdTU1HDZZZfRrVs3tm/fzsKF\nC1myZAmLFy92RawgCEJC2aMWhB2pDVdPq/obh4Bo6XZNWaWXID79FPr2hZYt4z+GaYDNaHAsU7pr\nV2Qz3bYt7NgRum33bpVOEkxeXsPCQyszXV0N2dnR24mfcUbjLowmkQz6FVfAX/9q/ZpYrxcEQbDC\ntpm2qs7x0EMP8dBDDzXaPmTIEGpTIwQgCIIQmfx8Ff6MQp2hIqs11dC2HWRlotzdd99Be+8XZf3u\ndzBzZtOO0bMnbNmi/jfIyIhtpnfuVN0PrWjbFjaGBTLLy1XqTDCtWyuTHYnDh1WKR7TIdOvW6nm7\n1NaG7p/IxaaCIKQvti9DU6dOtX3QRCzsExLP7Nmz+e1vf+u1DNvophf005z2eqOkaJhkAO1Q+dST\n71T+e7q/jDY/HgIxqha5Pb5lZcrP9+nTtOP06aPOzzCgtnY2hhFd8zffwNFHWz9nleZhlRaSlwdf\nfx35PczItFXOdHDU+IEHZgP2xjjcTHsTfbavN3XQS3PaX9dcRje9ycC2mZ4+fbqLMoRkUFlZ6bUE\nR+imF/TTLHrt06cPLFoE774LxcXwBLHrU7utd+ZM1dmxqfTvD598YtZsroyZvbJ5Mwwdav2c1QJE\nKzMdawGincg0wMGDDWMcyxyHm+louGe09frMKfTSLNc1d9FNbzKQduJNpKioCL/fTyAQ8FpKTGbM\nmOG1BEfophf00yx6nfPTn4K5mP222+CRRyKXeHNT7+LFygT37Nn0Y/Xvr1IzDAOys2dmkQz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id0CkRvp07At9/657mkrd9A4GCaiIjIQ9262W/du7ueQ1q3/v2dfx49OrCv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\njwAAAABJRU5ErkJggg==\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": { "image/png": { "width": 350 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='images/mgxs.png', width=350)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations for fine-mesh heterogeneous deterministic neutron transport applications.\n", "\n", "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introductory Notation\n", "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spatial and Energy Discretization\n", "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n", "\n", "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General Scalar-Flux Weighted MGXS\n", "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section $\\sigma_{n,x,k,g}$ as follows:\n", "\n", "$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", "\n", "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://mit-crpg.github.io/openmc/pythonapi/filter.html) on the energy range and spatial zone (material, cell or universe) define the bounds of integration for both numerator and denominator." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi-Group Scattering Matrices\n", "The general multi-group cross section $\\sigma_{n,x,k,g}$ is a vector of $G$ values for each energy group $g$. The equation presented above only discretizes the energy of the incoming neutron and neglects the outgoing energy of the neutron (if any). Hence, this formulation must be extended to account for the outgoing energy of neutrons in the discretized scattering matrix cross section used by deterministic neutron transport codes. \n", "\n", "We denote the incoming and outgoing neutron energy groups as $g$ and $g'$ for the microscopic scattering matrix cross section $\\sigma_{n,s}(\\mathbf{r},E)$. As before, spatial homogenization and energy condensation are used to find the multi-group scattering matrix cross section $\\sigma_{n,s,k,g \\to g'}$ as follows:\n", "\n", "$$\\sigma_{n,s,k,g\\rightarrow g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,s}(\\mathbf{r},E'\\rightarrow E'')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", "\n", "This scalar flux-weighted multi-group microscopic scattering matrix is computed using OpenMC tallies with both energy in and energy out filters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi-Group Fission Spectrum\n", "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n", "\n", "Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n}(\\mathbf{r},E)$. The multi-group fission spectrum $\\chi_{n,k,g}$ is then the probability of fission neutrons emitted into energy group $g$. \n", "\n", "Similar to before, spatial homogenization and energy condensation are used to find the multi-group fission spectrum $\\chi_{n,k,g}$ as follows:\n", "\n", "$$\\chi_{n,k,g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n", "\n", "The fission production-weighted multi-group fission spectrum is computed using OpenMC tallies with both energy in and energy out filters.\n", "\n", "This concludes our brief overview on the methodology to compute multi-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Input Files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import openmc\n", "import openmc.mgxs as mgxs\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate some Nuclides\n", "h1 = openmc.Nuclide('H-1')\n", "o16 = openmc.Nuclide('O-16')\n", "u235 = openmc.Nuclide('U-235')\n", "u238 = openmc.Nuclide('U-238')\n", "zr90 = openmc.Nuclide('Zr-90')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the nuclides we defined, we will now create a material for the homogeneous medium." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate a Material and register the Nuclides\n", "inf_medium = openmc.Material(name='moderator')\n", "inf_medium.set_density('g/cc', 5.)\n", "inf_medium.add_nuclide(h1, 0.028999667)\n", "inf_medium.add_nuclide(o16, 0.01450188)\n", "inf_medium.add_nuclide(u235, 0.000114142)\n", "inf_medium.add_nuclide(u238, 0.006886019)\n", "inf_medium.add_nuclide(zr90, 0.002116053)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our material, we can now create a `MaterialsFile` object that can be exported to an actual XML file." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate a MaterialsFile, register all Materials, and export to XML\n", "materials_file = openmc.MaterialsFile()\n", "materials_file.default_xs = '71c'\n", "materials_file.add_material(inf_medium)\n", "materials_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate boundary Planes\n", "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n", "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n", "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n", "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instantiate a Cell\n", "cell = openmc.Cell(cell_id=1, name='cell')\n", "\n", "# Register bounding Surfaces with the Cell\n", "cell.region = +min_x & -max_x & +min_y & -max_y\n", "\n", "# Fill the Cell with the Material\n", "cell.fill = inf_medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate Universe\n", "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", "root_universe.add_cell(cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", "openmc_geometry = openmc.Geometry()\n", "openmc_geometry.root_universe = root_universe\n", "\n", "# Instantiate a GeometryFile\n", "geometry_file = openmc.GeometryFile()\n", "geometry_file.geometry = openmc_geometry\n", "\n", "# Export to \"geometry.xml\"\n", "geometry_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# OpenMC simulation parameters\n", "batches = 50\n", "inactive = 10\n", "particles = 2500\n", "\n", "# Instantiate a SettingsFile\n", "settings_file = openmc.SettingsFile()\n", "settings_file.batches = batches\n", "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "settings_file.output = {'tallies': True, 'summary': True}\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "settings_file.set_source_space('fission', bounds)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instantiate a 2-group EnergyGroups object\n", "groups = mgxs.EnergyGroups()\n", "groups.group_edges = np.array([0., 0.625e-6, 20.])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use the `EnergyGroups` object, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n", "\n", "* `TotalXS`\n", "* `TransportXS`\n", "* `AbsorptionXS`\n", "* `CaptureXS`\n", "* `FissionXS`\n", "* `NuFissionXS`\n", "* `ScatterXS`\n", "* `NuScatterXS`\n", "* `ScatterMatrixXS`\n", "* `NuScatterMatrixXS`\n", "* `Chi`\n", "\n", "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instantiate a few different sections\n", "total = mgxs.TotalXS(domain=cell, domain_type='cell', groups=groups)\n", "absorption = mgxs.AbsorptionXS(domain=cell, domain_type='cell', groups=groups)\n", "scattering = mgxs.ScatterXS(domain=cell, domain_type='cell', groups=groups)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Absorption` object as follows. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('flux', Tally\n", "\tID =\t10000\n", "\tName =\t\n", "\tFilters =\t\n", " \t\tcell\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", "\tScores =\t['flux']\n", "\tEstimator =\ttracklength\n", "), ('absorption', Tally\n", "\tID =\t10001\n", "\tName =\t\n", "\tFilters =\t\n", " \t\tcell\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", "\tScores =\t['absorption']\n", "\tEstimator =\ttracklength\n", ")])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "absorption.tallies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each `MGXS` object contains the tallies that it needs, we must add these tallies to a `TalliesFile` object to generate the \"tallies.xml\" input file for OpenMC." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instantiate an empty TalliesFile\n", "tallies_file = openmc.TalliesFile()\n", "\n", "# Add total tallies to the tallies file\n", "for tally in total.tallies.values():\n", " tallies_file.add_tally(tally)\n", "\n", "# Add absorption tallies to the tallies file\n", "for tally in absorption.tallies.values():\n", " tallies_file.add_tally(tally)\n", "\n", "# Add scattering tallies to the tallies file\n", "for tally in scattering.tallies.values():\n", " tallies_file.add_tally(tally)\n", " \n", "# Export to \"tallies.xml\"\n", "tallies_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we a have a complete set of inputs, so we can go ahead and run our simulation." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " .d88888b. 888b d888 .d8888b.\n", " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", " 888 888 88888b.d88888 888 888\n", " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", "__________________888______________________________________________________\n", " 888\n", " 888\n", "\n", " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", " Date/Time: 2015-12-02 09:11:05\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", " Loading ACE cross section table: 1001.71c\n", " Loading ACE cross section table: 8016.71c\n", " Loading ACE cross section table: 92235.71c\n", " Loading ACE cross section table: 92238.71c\n", " Loading ACE cross section table: 40090.71c\n", " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", " ===========================================================================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", " 1/1 1.19804 \n", " 2/1 1.12945 \n", " 3/1 1.15573 \n", " 4/1 1.13929 \n", " 5/1 1.16300 \n", " 6/1 1.22117 \n", " 7/1 1.19012 \n", " 8/1 1.11299 \n", " 9/1 1.16066 \n", " 10/1 1.12566 \n", " 11/1 1.20854 \n", " 12/1 1.14691 1.17773 +/- 0.03082\n", " 13/1 1.17204 1.17583 +/- 0.01789\n", " 14/1 1.14148 1.16724 +/- 0.01529\n", " 15/1 1.17272 1.16834 +/- 0.01189\n", " 16/1 1.18575 1.17124 +/- 0.01014\n", " 17/1 1.20498 1.17606 +/- 0.00983\n", " 18/1 1.14754 1.17249 +/- 0.00923\n", " 19/1 1.18141 1.17348 +/- 0.00820\n", " 20/1 1.15074 1.17121 +/- 0.00768\n", " 21/1 1.15914 1.17011 +/- 0.00703\n", " 22/1 1.14586 1.16809 +/- 0.00673\n", " 23/1 1.18999 1.16978 +/- 0.00642\n", " 24/1 1.15101 1.16844 +/- 0.00609\n", " 25/1 1.13791 1.16640 +/- 0.00602\n", " 26/1 1.19791 1.16837 +/- 0.00597\n", " 27/1 1.19818 1.17012 +/- 0.00587\n", " 28/1 1.14160 1.16854 +/- 0.00576\n", " 29/1 1.11487 1.16571 +/- 0.00614\n", " 30/1 1.17538 1.16620 +/- 0.00584\n", " 31/1 1.20210 1.16791 +/- 0.00581\n", " 32/1 1.20078 1.16940 +/- 0.00574\n", " 33/1 1.14624 1.16839 +/- 0.00558\n", " 34/1 1.14618 1.16747 +/- 0.00542\n", " 35/1 1.16866 1.16752 +/- 0.00520\n", " 36/1 1.18565 1.16821 +/- 0.00504\n", " 37/1 1.16824 1.16821 +/- 0.00485\n", " 38/1 1.18299 1.16874 +/- 0.00471\n", " 39/1 1.21418 1.17031 +/- 0.00480\n", " 40/1 1.11167 1.16835 +/- 0.00504\n", " 41/1 1.11545 1.16665 +/- 0.00516\n", " 42/1 1.11114 1.16491 +/- 0.00529\n", " 43/1 1.14227 1.16423 +/- 0.00517\n", " 44/1 1.14104 1.16355 +/- 0.00506\n", " 45/1 1.16756 1.16366 +/- 0.00492\n", " 46/1 1.13065 1.16274 +/- 0.00487\n", " 47/1 1.11251 1.16139 +/- 0.00492\n", " 48/1 1.14731 1.16101 +/- 0.00481\n", " 49/1 1.16691 1.16117 +/- 0.00469\n", " 50/1 1.19679 1.16206 +/- 0.00465\n", " Creating state point statepoint.50.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", " ===========================================================================\n", "\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 4.1700E-01 seconds\n", " Reading cross sections = 8.9000E-02 seconds\n", " Total time in simulation = 1.4728E+01 seconds\n", " Time in transport only = 1.4712E+01 seconds\n", " Time in inactive batches = 1.7890E+00 seconds\n", " Time in active batches = 1.2939E+01 seconds\n", " Time synchronizing fission bank = 5.0000E-03 seconds\n", " Sampling source sites = 3.0000E-03 seconds\n", " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 1.0000E-03 seconds\n", " Total time elapsed = 1.5155E+01 seconds\n", " Calculation Rate (inactive) = 13974.3 neutrons/second\n", " Calculation Rate (active) = 7728.57 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", " k-effective (Collision) = 1.16131 +/- 0.00453\n", " k-effective (Track-length) = 1.16206 +/- 0.00465\n", " k-effective (Absorption) = 1.16096 +/- 0.00364\n", " Combined k-effective = 1.16120 +/- 0.00325\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run OpenMC\n", "executor = openmc.Executor()\n", "executor.run_simulation()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tally Data Processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the last statepoint file\n", "sp = openmc.StatePoint('statepoint.50.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a `Summary` object and link it with the statepoint." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the summary file and link it with the statepoint\n", "su = openmc.Summary('summary.h5')\n", "sp.link_with_summary(su)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the tallies from the statepoint into each MGXS object\n", "total.load_from_statepoint(sp)\n", "absorption.load_from_statepoint(sp)\n", "scattering.load_from_statepoint(sp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voila! Our multi-group cross sections are now ready to rock 'n roll!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting and Storing MGXS Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first inspect our total cross section by printing it to the screen." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-Group XS\n", "\tReaction Type =\ttotal\n", "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t6.81e-01 +/- 1.88e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t1.40e+00 +/- 5.91e-01%\n", "\n", "\n", "\n" ] } ], "source": [ "total.print_xs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the `openmc.mgxs` module uses [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data." ] }, { "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>cell</th>\n", " <th>group in</th>\n", " <th>nuclide</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>total</td>\n", " <td>0.668323</td>\n", " <td>0.001264</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>total</td>\n", " <td>1.293258</td>\n", " <td>0.007624</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell group in nuclide mean std. dev.\n", "1 1 1 total 0.668323 0.001264\n", "0 1 2 total 1.293258 0.007624" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = scattering.get_pandas_dataframe()\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "absorption.export_xs_data(filename='absorption-xs', format='excel')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code snippet shows how to export all three `MGXS` to the same HDF5 binary data store." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total.build_hdf5_store(filename='mgxs', append=True)\n", "absorption.build_hdf5_store(filename='mgxs', append=True)\n", "scattering.build_hdf5_store(filename='mgxs', append=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing MGXS with Tally Arithmetic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects." ] }, { "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>cell</th>\n", " <th>energy [MeV]</th>\n", " <th>nuclide</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>(0.0e+00 - 6.3e-07)</td>\n", " <td>total</td>\n", " <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n", " <td>4.884981e-15</td>\n", " <td>0.011274</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>(6.3e-07 - 2.0e+01)</td>\n", " <td>total</td>\n", " <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n", " <td>1.221245e-15</td>\n", " <td>0.001802</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell energy [MeV] nuclide \\\n", "0 1 (0.0e+00 - 6.3e-07) total \n", "1 1 (6.3e-07 - 2.0e+01) total \n", "\n", " score mean std. dev. \n", "0 (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.011274 \n", "1 (((total / flux) - (absorption / flux)) - (sca... 1.221245e-15 0.001802 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", "\n", "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", "difference.get_pandas_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." ] }, { "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>cell</th>\n", " <th>energy [MeV]</th>\n", " <th>nuclide</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>(0.0e+00 - 6.3e-07)</td>\n", " <td>total</td>\n", " <td>((absorption / flux) / (total / flux))</td>\n", " <td>0.076219</td>\n", " <td>0.000651</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>(6.3e-07 - 2.0e+01)</td>\n", " <td>total</td>\n", " <td>((absorption / flux) / (total / flux))</td>\n", " <td>0.019319</td>\n", " <td>0.000086</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell energy [MeV] nuclide score \\\n", "0 1 (0.0e+00 - 6.3e-07) total ((absorption / flux) / (total / flux)) \n", "1 1 (6.3e-07 - 2.0e+01) total ((absorption / flux) / (total / flux)) \n", "\n", " mean std. dev. \n", "0 0.076219 0.000651 \n", "1 0.019319 0.000086 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", "absorption_to_total = absorption.xs_tally / total.xs_tally\n", "\n", "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", "absorption_to_total.get_pandas_dataframe()" ] }, { "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>cell</th>\n", " <th>energy [MeV]</th>\n", " <th>nuclide</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>(0.0e+00 - 6.3e-07)</td>\n", " <td>total</td>\n", " <td>((scatter / flux) / (total / flux))</td>\n", " <td>0.923781</td>\n", " <td>0.007714</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>(6.3e-07 - 2.0e+01)</td>\n", " <td>total</td>\n", " <td>((scatter / flux) / (total / flux))</td>\n", " <td>0.980681</td>\n", " <td>0.002617</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell energy [MeV] nuclide score \\\n", "0 1 (0.0e+00 - 6.3e-07) total ((scatter / flux) / (total / flux)) \n", "1 1 (6.3e-07 - 2.0e+01) total ((scatter / flux) / (total / flux)) \n", "\n", " mean std. dev. \n", "0 0.923781 0.007714 \n", "1 0.980681 0.002617 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", "scattering_to_total = scattering.xs_tally / total.xs_tally\n", "\n", "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", "scattering_to_total.get_pandas_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity." ] }, { "cell_type": "code", "execution_count": 26, "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>cell</th>\n", " <th>energy [MeV]</th>\n", " <th>nuclide</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>(0.0e+00 - 6.3e-07)</td>\n", " <td>total</td>\n", " <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n", " <td>1</td>\n", " <td>0.007741</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>(6.3e-07 - 2.0e+01)</td>\n", " <td>total</td>\n", " <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n", " <td>1</td>\n", " <td>0.002619</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell energy [MeV] nuclide \\\n", "0 1 (0.0e+00 - 6.3e-07) total \n", "1 1 (6.3e-07 - 2.0e+01) total \n", "\n", " score mean std. dev. \n", "0 (((absorption / flux) / (total / flux)) + ((sc... 1 0.007741 \n", "1 (((absorption / flux) / (total / flux)) + ((sc... 1 0.002619 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use tally arithmetic to ensure that the absorption- and scattering-to-total MGXS ratios sum to unity\n", "sum_ratio = absorption_to_total + scattering_to_total\n", "\n", "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", "sum_ratio.get_pandas_dataframe()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mne-tools/mne-tools.github.io
0.24/_downloads/5ac2a3ff8baa6aba4bf6dd1d047703e2/spm_faces_dataset_sgskip.ipynb
1
6934
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# From raw data to dSPM on SPM Faces dataset\n\nRuns a full pipeline using MNE-Python:\n\n - artifact removal\n - averaging Epochs\n - forward model computation\n - source reconstruction using dSPM on the contrast : \"faces - scrambled\"\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This example does quite a bit of processing, so even on a\n fast machine it can take several minutes to complete.</p></div>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: 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\nimport mne\nfrom mne.datasets import spm_face\nfrom mne.preprocessing import ICA, create_eog_epochs\nfrom mne import io, combine_evoked\nfrom mne.minimum_norm import make_inverse_operator, apply_inverse\n\nprint(__doc__)\n\ndata_path = spm_face.data_path()\nsubjects_dir = data_path + '/subjects'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load and filter data, set up epochs\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'\n\nraw = io.read_raw_ctf(raw_fname % 1, preload=True) # Take first run\n# Here to save memory and time we'll downsample heavily -- this is not\n# advised for real data as it can effectively jitter events!\nraw.resample(120., npad='auto')\n\npicks = mne.pick_types(raw.info, meg=True, exclude='bads')\nraw.filter(1, 30, method='fir', fir_design='firwin')\n\nevents = mne.find_events(raw, stim_channel='UPPT001')\n\n# plot the events to get an idea of the paradigm\nmne.viz.plot_events(events, raw.info['sfreq'])\n\nevent_ids = {\"faces\": 1, \"scrambled\": 2}\n\ntmin, tmax = -0.2, 0.6\nbaseline = None # no baseline as high-pass is applied\nreject = dict(mag=5e-12)\n\nepochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks,\n baseline=baseline, preload=True, reject=reject)\n\n# Fit ICA, find and remove major artifacts\nica = ICA(n_components=0.95, max_iter='auto', random_state=0)\nica.fit(raw, decim=1, reject=reject)\n\n# compute correlation scores, get bad indices sorted by score\neog_epochs = create_eog_epochs(raw, ch_name='MRT31-2908', reject=reject)\neog_inds, eog_scores = ica.find_bads_eog(eog_epochs, ch_name='MRT31-2908')\nica.plot_scores(eog_scores, eog_inds) # see scores the selection is based on\nica.plot_components(eog_inds) # view topographic sensitivity of components\nica.exclude += eog_inds[:1] # we saw the 2nd ECG component looked too dipolar\nica.plot_overlay(eog_epochs.average()) # inspect artifact removal\nica.apply(epochs) # clean data, default in place\n\nevoked = [epochs[k].average() for k in event_ids]\n\ncontrast = combine_evoked(evoked, weights=[-1, 1]) # Faces - scrambled\n\nevoked.append(contrast)\n\nfor e in evoked:\n e.plot(ylim=dict(mag=[-400, 400]))\n\nplt.show()\n\n# estimate noise covarariance\nnoise_cov = mne.compute_covariance(epochs, tmax=0, method='shrunk',\n rank=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize fields on MEG helmet\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The transformation here was aligned using the dig-montage. It's included in\n# the spm_faces dataset and is named SPM_dig_montage.fif.\ntrans_fname = data_path + ('/MEG/spm/SPM_CTF_MEG_example_faces1_3D_'\n 'raw-trans.fif')\n\nmaps = mne.make_field_map(evoked[0], trans_fname, subject='spm',\n subjects_dir=subjects_dir, n_jobs=1)\n\nevoked[0].plot_field(maps, time=0.170)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look at the whitened evoked daat\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evoked[0].plot_white(noise_cov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute forward model\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "src = data_path + '/subjects/spm/bem/spm-oct-6-src.fif'\nbem = data_path + '/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif'\nforward = mne.make_forward_solution(contrast.info, trans_fname, src, bem)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute inverse solution\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "snr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = 'dSPM'\n\ninverse_operator = make_inverse_operator(contrast.info, forward, noise_cov,\n loose=0.2, depth=0.8)\n\n# Compute inverse solution on contrast\nstc = apply_inverse(contrast, inverse_operator, lambda2, method, pick_ori=None)\n# stc.save('spm_%s_dSPM_inverse' % contrast.comment)\n\n# Plot contrast in 3D with mne.viz.Brain if available\nbrain = stc.plot(hemi='both', subjects_dir=subjects_dir, initial_time=0.170,\n views=['ven'], clim={'kind': 'value', 'lims': [3., 6., 9.]})\n# brain.save_image('dSPM_map.png')" ] } ], "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
Patri-meteocat/Meteocat_ANL_collaboration
notebooks/.ipynb_checkpoints/vel_QI_multifile-checkpoint.ipynb
2
18832
{ "cells": [ { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os.path\n", "import glob\n", "import matplotlib.pyplot as plt\n", "import pylab as plb\n", "import matplotlib as mpl\n", "import pyart\n", "import numpy as np\n", "import scipy as sp\n", "import numpy.ma as ma\n", "\n", "from pylab import *\n", "from scipy import ndimage" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def velocity_quality(radar, field='velocity', gatefilter=None, w_speckle=np.ones((3,3)), N_speckle=4, \n", " w_outliers=np.ones((3,5)), N_outliers=9, upper_fac=1.5, \n", " speckle_filter=True, outlier_filter=True):\n", " \n", " ## General function to evaluate quality of the velocity field ##\n", " \n", " Ny, Ny_H, Ny_L, f, N, prf_odd = get_dualPRF_pars(radar)\n", " \n", " v_field = radar.fields[field]\n", " v_data_ma = v_field['data'].copy()\n", " \n", " if gatefilter is not None:\n", " gatefilter_mask = gatefilter.gate_excluded \n", " v_data_ma.mask = (v_field['data'].mask) | (gatefilter_mask)\n", " \n", " aff = ['T', 'True', True, 'Yes', 1]\n", " if speckle_filter in aff:\n", " speckle_filter=True\n", " if outlier_filter in aff:\n", " outlier_filter=True\n", " \n", " list_out = []\n", " dict_out = {'na':v_data_ma.mask, \n", " 'speckle':np.zeros(shape(v_data_ma)).astype(bool),\n", " 'outlier':np.zeros(shape(v_data_ma)).astype(bool),\n", " 'edge':np.zeros(shape(v_data_ma)).astype(bool)}\n", " point_f = nan\n", " out_f = nan\n", " g_data = v_data_ma.copy()\n", " \n", " for nsweep, sweep_slice in enumerate(radar.iter_slice()):\n", " \n", " fix_ang = radar.fixed_angle['data'][nsweep]\n", " \n", " v_data_ma_sw = v_data_ma[sweep_slice]\n", " v_data_sw = v_data_ma_sw.data\n", " v_mask_sw = v_data_ma_sw.mask\n", " \n", " valid_num = np.sum(~v_mask_sw)\n", " \n", " # Identify and count speckle-noise gates\n", " if speckle_filter:\n", " point_f, dict_out['speckle'][sweep_slice] = local_valid(v_mask_sw, weights=w_speckle, \n", " Nmin=N_speckle, mode='mirror')\n", " \n", " # Identify and count dualPRF outlier gates\n", " if (outlier_filter) & (prf_odd is not None):\n", " out_f, dict_out['outlier'][sweep_slice] = dualPRF_outliers(v_data_ma_sw, Ny, Ny_H, Ny_L, prf_odd, \n", " weights=w_outliers, Nmin=N_outliers, \n", " upper_lim_fac=upper_fac)\n", " \n", " # Mask NA values, speckle noise and dual-PRF outliers\n", " vcorr_mask_sw = ((v_mask_sw) | (dict_out['speckle'][sweep_slice])) | (dict_out['outlier'][sweep_slice])\n", " \n", " # Apply edge-finding function to velocity field (corrected by new mask)\n", " vcorr_data_ma_sw = ma.array(data=v_data_sw, mask=vcorr_mask_sw)\n", " g_data_sw, edge_f, dict_out['edge'][sweep_slice] = aliased_edges(vcorr_data_ma_sw, Ny)\n", " g_data[sweep_slice] = ma.array(g_data_sw, mask=vcorr_mask_sw)\n", " \n", " # Retrieve date and time at the beginning of the sweep\n", " datetime_list = datetime_sw(radar, sweep_slice)\n", " \n", " list_out.append(datetime_list+[fix_ang, valid_num, point_f, out_f, edge_f])\n", " \n", " # Returns a list with the fractions and a dictionary with the masks:\n", " return list_out, dict_out, g_data\n", " " ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def local_valid(mask, weights, Nmin=None, mode='mirror', **kwargs):\n", " \n", " ## Find speckle-gates: gates with a number of valid neighbours below a given threshold ##\n", " \n", " if Nmin is None:\n", " Nmin = 1\n", " \n", " # Count number of non-masked local values\n", " k = weights\n", " valid = ndimage.convolve((~mask).astype(int), k, mode=mode, **kwargs)\n", " \n", " mask_out = np.zeros(mask.shape)\n", " mask_out[valid<Nmin]=1\n", " \n", " mask_out = (mask_out.astype(bool)) & (~mask)\n", " \n", " count = float(np.sum(mask_out))/np.sum(~mask)\n", " \n", " return count, mask_out\n", " " ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_dualPRF_pars(radar):\n", " \n", " ## Retrieve relevant parameters of dual-PRF velocity task ## \n", " \n", " pars = radar.instrument_parameters\n", " \n", " Ny = pars['nyquist_velocity']['data'][0]\n", " prt_mode = pars['prt_mode']['data'][0]\n", " Ny_H = Ny\n", " Ny_L = Ny\n", " prf_odd = None\n", " f = 1\n", " N = None\n", " \n", " if prt_mode=='dual':\n", " \n", " f = pars['prt_ratio']['data'][0]\n", " N = round(1/(f-1))\n", " Ny_H = Ny/N\n", " Ny_L = Ny/(N+1) \n", " prf_odd = pars['prf_flag']['data'][0]\n", " \n", " return Ny, Ny_H, Ny_L, f, N, prf_odd\n" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def dualPRF_outliers(data_ma, Ny, Ny_H, Ny_L, prf_odd=0, weights=np.ones((3,5)), Nmin=9, upper_lim_fac=1.5):\n", " \n", " ## Find outliers resulting from dual-PRF dealiasing errors ##\n", " \n", " # Nyquist velocities corresponding to odd and even rays\n", " Ny_odd = Ny_H\n", " Ny_even = Ny_L\n", " \n", " if prf_odd is None:\n", " return\n", " if prf_odd==1:\n", " Ny_odd = Ny_L\n", " Ny_even = Ny_H\n", " \n", " # Footprint (region around the pixel where median is computed)\n", " k = weights\n", " \n", " data = data_ma.data\n", " mask = data_ma.mask\n", " \n", " # Convert masked data to nan and apply median filter \n", " data_nan = np.where(np.logical_not(mask), data, np.nan)\n", " med_data = sp.ndimage.generic_filter(data_nan, np.nanmedian, footprint=k, mode='mirror')\n", " \n", " # Absolute deviation of the pixel velocity from the local median\n", " dev_data = np.abs(data_nan - med_data)\n", " dev_data[where(np.isnan(dev_data))]=0\n", " \n", " # Separate into odd and even rays\n", " dev_odd = dev_data[1::2, :]\n", " dev_even = dev_data[0::2, :]\n", " \n", " # Outlier matrix\n", " mask_out = np.zeros(dev_data.shape)\n", " mask_out_odd = mask_out[1::2, :]\n", " mask_out_even = mask_out[0::2, :]\n", " mask_out_odd[ma.where((dev_odd>=Ny_odd)&(dev_odd<=upper_lim_fac*Ny))] = 1\n", " mask_out_even[ma.where((dev_even>=Ny_even)&(dev_even<=upper_lim_fac*Ny))] = 1\n", " \n", " # Find local medians calculated with the required minimum number of valid values\n", " count_nmin, mask_nmin = local_valid(mask, k, Nmin=Nmin, mode='mirror')\n", " \n", " # Outlier mask (gives number of outliers)\n", " mask_out = (mask_out.astype(bool)) & (~mask_nmin)\n", " count_out = float(np.sum(mask_out))/np.sum(~mask)\n", " \n", " # Return fraction of outliers and mask for outliers\n", " return count_out, mask_out\n", " " ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def maconvolve(ma_array, weights, Nmin=None, mode='mirror', **kwargs):\n", "\n", " ## Convolve masked array with generic kernel ## \n", "\n", " k = weights\n", " data = ma_array.data\n", " mask = ma_array.mask\n", " \n", " # Minimum number of non-masked local values required for the convolution\n", " if Nmin is None:\n", " Nmin=1\n", " \n", " # Data convolution (replace masked values by 0)\n", " data_conv = ndimage.convolve(ma.filled(data,0), k, mode=mode, **kwargs)\n", " \n", " w_valid = np.ones(k.shape)\n", " # Count number of non-masked local values\n", " valid, mask_conv = local_valid(mask, w_valid, Nmin=Nmin, mode=mode, **kwargs)\n", " \n", " # New mask and replace masked values by required fill value\n", " mask_out = mask_conv | mask\n", " data_out = ma.masked_array(data_conv, mask_out)\n", " \n", " # Return the convolved masked array\n", " return data_out" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def aliased_edges(data_ma, Ny, lower_lim_fac=1.5):\n", " \n", " ## Find edges of aliased regions based on the horizontal velocity gradients ##\n", " \n", " # Gradient kernels in x,y (r,az) dimensions\n", " kx = np.array([[-1, 0, 1]])\n", " ky = np.transpose(kx)\n", " \n", " mask = data_ma.mask\n", " \n", " # Horizonal gradient components\n", " gx_data = maconvolve(data_ma, weights=kx, Nmin=3, mode='wrap')\n", " gy_data = maconvolve(data_ma, weights=ky, Nmin=3, mode='reflect')\n", " \n", " # Magnitude and direction of horizontal gradient\n", " g_data = np.sqrt(ma.filled(gx_data,0)**2 + ma.filled(gy_data,0)**2)\n", " \n", " # Edge mask (gives number of edges)\n", " g_mask = np.zeros(g_data.shape)\n", " g_mask[(g_data>=lower_lim_fac*Ny)] = 1\n", " \n", " count_g = float(np.sum(g_mask))/np.sum(~mask)\n", " \n", " # Return fraction of edges and mask for edges\n", " return g_data, count_g, g_mask\n", " " ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def connected_edges(mask, struc=np.ones((3,3)), nmin=10):\n", " \n", " # Find and label connected edges \n", " lab_array, n_reg = ndimage.label(mask.astype(int), structure=struc)\n", " \n", " # Differentiate between small (noise) and large (aliasing) edge regions\n", " n_edges = np.empty(n_reg)\n", " \n", " for i in range(0, n_reg):\n", " n_edges[i] = np.sum(lab_array==i+1)\n", " \n", " reg_s = np.sum(n_edges<=nmin)\n", " reg_l = np.sum(n_edges>nmin)\n", " \n", " f_reg_s = 0\n", " f_reg_l = 0\n", " \n", " # Count fractions in each case\n", " if np.sum(mask)!=0:\n", " f_reg_s = np.sum(n_edges[n_edges<=nmin])/np.sum(mask)\n", " f_reg_l = np.sum(n_edges[n_edges>nmin])/np.sum(mask)\n", " \n", " # Return an array with the labelled edge-gates, a list with the number of \n", " # edge-gates in each region and a list with the number of small and large \n", " # regions and with the fraction of large and small edge-gates\n", " return lab_array, n_edges, [reg_s, reg_l, f_reg_s, f_reg_l]\n", " " ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def write_outfile(table, f, headers=None, mode='w+'):\n", " \n", " ## Write list to an ascii file in table form ##\n", " \n", " f1=open(f, mode)\n", " \n", " if (headers is not None) & (mode=='w+'):\n", " f1.writelines([\"%s \" % hdr for hdr in headers])\n", " f1.writelines('\\n')\n", " \n", " for sw in table:\n", " for item in sw:\n", " if (type(item)==float)|(type(item)==float64):\n", " f1.writelines(\"%6.5f \" % item)\n", " elif type(item)==int64:\n", " f1.writelines(\"%d \" % item)\n", " else:\n", " f1.writelines(\"%s \" % str(item))\n", " \n", " f1.writelines('\\n')\n", " " ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def datetime_sw(radar, sw_slice):\n", " \n", " ## Retrieve date and time at the start of a sweep from a radar object ##\n", " dt_vol = pyart.graph.RadarDisplay(radar).time_begin\n", " \n", " sec_sw = min(radar.time['data'][sw_slice])-1\n", " dt_sw = dt_vol + datetime.timedelta(seconds=sec_sw)\n", " \n", " date_sw = dt_sw.date()\n", " time_sw = dt_sw.time()\n", " \n", " # Return date and time list\n", " return [date_sw, time_sw]\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gate_vid(mask_dict):\n", " \n", " ## Assign an id to velocity field gates based on the mask dictionary ##\n", " \n", " out_array = np.zeros(mask_dict[mask_dict.keys()[0]].shape)\n", " out_ids = ['valid']\n", " lab=1\n", " for k in mask_dict.keys():\n", " m = mask_dict[k]\n", " out_array[m] = lab\n", " out_ids.append(k)\n", " lab += 1\n", " \n", " # Return an array with the labelled gates and a list with the description of the ids\n", " return out_array, out_ids\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_path = '/Users/patriciaaltube/Desktop/VelocityQ/data_process/CDV130618/'\n", "out_path_file = '/Users/patriciaaltube/Desktop/VelocityQ/results/'\n", "out_path_base = '/Users/patriciaaltube/Desktop/VelocityQ/plots/V_filtersON/'\n", "\n", "hdrs = ['Date', 'Time', 'Elev', 'Valid_Num', 'Point_Frac', 'Outlier_Frac', \n", " 'Edge_Frac', 'SmallReg_Num', 'LargeReg_Num', \n", " 'SmallEdge_Frac', 'LargeEdge_frac']\n", "\n", "lab_colors=['lightgrey','white','red', 'black', 'blue']\n", "id_cmap = matplotlib.colors.ListedColormap(lab_colors)\n", "#v_cmap = pyart.graph.cm.NWSVel\n", "v_cmap = plt.get_cmap('RdBu')\n", "\n", "for f in glob.glob(data_path + '*.RAW*'):\n", " \n", " fileout_name = f[-23:-14]\n", " out_file = out_path_file + fileout_name + '_VQI.txt'\n", " \n", " radar = pyart.io.read(f)\n", " frac_list, mask_dict = velocity_quality(radar, field='velocity', speckle_filter=True, outlier_filter=True)\n", " \n", " if not os.path.isfile(out_file):\n", " write_outfile(frac_list, hdrs, out_file, mode='w+')\n", " else:\n", " write_outfile(frac_list, hdrs, out_file, mode='a+')\n", " \n", " figout_name = f[-23:-10]\n", "\n", " id_array, id_list = bin_vid(mask_dict)\n", " \n", " id_field = radar.fields['velocity'].copy()\n", " id_field['data'] = id_array\n", " id_field['long_name'] = 'Velocity flags'\n", " id_field['standard_name'] = 'velocity_flags'\n", " id_field['units'] = ''\n", " radar.add_field('gate_vid', id_field)\n", " \n", " plt.close('all')\n", " \n", " for sw in range(0, radar.nsweeps):\n", " \n", " elev=radar.fixed_angle['data'][sw]\n", " \n", " out_path = out_path_base + 'elev%1.0f'%elev + '/'\n", " if not os.path.exists(out_path):\n", " os.makedirs(out_path)\n", " \n", " out_fig = out_path + figout_name + '_elev%1.0f'%elev + '.png'\n", " \n", " display = pyart.graph.RadarDisplay(radar)\n", " fig = plt.figure(figsize=(8, 14))\n", "\n", " ax = fig.add_subplot(211)\n", " display.plot('velocity', sweep = sw, vmin = -40, vmax = 40, ax=ax, cmap=v_cmap)\n", " plt.xlim((-100, 100))\n", " plt.ylim((-100, 100))\n", "\n", "\n", " ax = fig.add_subplot(212)\n", " display.plot('gate_vid', sweep = sw, vmin = 0, vmax = 5, ax=ax, cmap=id_cmap)\n", " tick_locs = np.linspace(0,len(id_list) -1 ,len(id_list))+0.5\n", " display.cbs[-1].locator = matplotlib.ticker.FixedLocator(tick_locs)\n", " display.cbs[-1].formatter = matplotlib.ticker.FixedFormatter(id_list)\n", " display.cbs[-1].update_ticks()\n", " plt.xlim((-100, 100))\n", " plt.ylim((-100, 100))\n", "\n", " plt.savefig(out_fig)\n", " \n", " out_fig_hist = out_path_fig + figout_name + '_hist_elev%1.0f'%elev + '.pdf'\n", " plot_data=radar.get_field(sw, 'gradient_module')\n", "\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " (n, bins, patches) = ax.hist(plot_data.compressed(), bins=round(3.5*20), color='grey', alpha=0.8)\n", " ax.set_ylim([0,500])\n", " ax.set_xlim([0, 3.5*40])\n", " ax.set_xlabel('Gradient module (m/s)')\n", " ax.set_ylabel('Pixel counts')\n", "\n", " pp = PdfPages(out_fig_hist)\n", " pp.savefig()\n", " pp.close()\n", " #plt.savefig(out_fig_hist)\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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
rabernat/pyqg
docs/equations/notation_sqg_model.ipynb
4
2445
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Surface Quasi-geostrophic Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Surface quasi-geostrophy (SQG) is a relatively simple model that describes surface intensified flows due to buoyancy. One of it's advantages is that it only has two spatial dimensions but describes a three-dimensional solution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The evolution equation is\n", "\n", "$$\n", "\\partial_t b + J(\\psi, b) = 0\\,, \\qquad \\text{at} \\qquad z = 0\\,,\n", "$$\n", "\n", "where $b = \\psi_z$ is the buoyancy.\n", "\n", "The interior potential vorticity is zero. Hence\n", "$$\n", "\\frac{\\partial }{\\partial z}\\left(\\frac{f_0^2}{N^2}\\frac{\\partial \\psi}{\\partial z}\\right) + \\nabla^2\\psi = 0\\,,\n", "$$\n", "\n", "where $N$ is the buoyancy frequency and $f_0$ is the Coriolis parameter. In the SQG model both $N$ and $f_0$ are constants. The boundary conditions for this elliptic problem in a semi-infinite vertical domain are\n", "\n", "$$\n", "b = \\psi_z\\,, \\qquad \\text{and} \\qquad z = 0\\,,\n", "$$\n", "\n", "and \n", "\n", "$$\n", "\\psi = 0, \\qquad \\text{at} \\qquad z \\rightarrow -\\infty\\,,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The solutions to the elliptic problem above, in horizontal Fourier space, gives the inversion relationship between surface buoyancy and surface streamfunction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat \\psi = \\frac{f_0}{N} \\frac{1}{\\kappa}\\hat b\\,, \\qquad \\text{at} \\qquad z = 0\\,,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SQG evolution equation is marched forward similarly to the two-layer model." ] } ], "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
darkomen/TFG
medidas/11082015/Análisis de datos.ipynb
1
511331
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción.Se implementa un regulador experto. Los datos analizados son del día 11 de Agosto del 2015\n", "\n", "Los datos del experimento:\n", "* Hora de inicio: 14:27\n", "* Hora final : 15:08\n", "* Filamento extruido: 537cm\n", "* $T: 150ºC$\n", "* $V_{min} tractora: 1.5 mm/s$\n", "* $V_{max} tractora: 3.4 mm/s$\n", "* Los incrementos de velocidades en las reglas del sistema experto son las mismas." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importamos las librerías utilizadas\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numpy v1.9.2\n", "Pandas v0.16.2\n", "Seaborn v0.6.0\n" ] } ], "source": [ "#Mostramos las versiones usadas de cada librerías\n", "print (\"Numpy v{}\".format(np.__version__))\n", "print (\"Pandas v{}\".format(pd.__version__))\n", "print (\"Seaborn v{}\".format(sns.__version__))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Abrimos el fichero csv con los datos de la muestra\n", "datos = pd.read_csv('1119703.CSV')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Almacenamos en una lista las columnas del fichero con las que vamos a trabajar\n", "columns = ['Diametro X', 'RPM TRAC']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Diametro X</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1526.000000</td>\n", " <td>1526.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.721607</td>\n", " <td>2.363879</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.299929</td>\n", " <td>0.909141</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.206868</td>\n", " <td>1.497500</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.482145</td>\n", " <td>1.497500</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.631253</td>\n", " <td>2.165000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.986820</td>\n", " <td>3.500000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>2.560314</td>\n", " <td>3.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Diametro X RPM TRAC\n", "count 1526.000000 1526.000000\n", "mean 1.721607 2.363879\n", "std 0.299929 0.909141\n", "min 1.206868 1.497500\n", "25% 1.482145 1.497500\n", "50% 1.631253 2.165000\n", "75% 1.986820 3.500000\n", "max 2.560314 3.500000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mostramos un resumen de los datos obtenidoss\n", "datos[columns].describe()\n", "#datos.describe().loc['mean',['Diametro X [mm]', 'Diametro Y [mm]']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representamos ambos diámetro y la velocidad de la tractora en la misma gráfica" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0x36cb2d0>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SSVRCP7XbyH1PHMB80ce+owvH7Di6m/nkVitEREREREQd9Q8PfREvzu3BWevOwEffcDEA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oeS3G7nJ5+umqgqySeaV3PbGlprFKVCKphLemgtinX73Oo86JWWdhuMrXZV1Xrk0xvjxJrPUB/Z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\ntFvAOH97zUyt+WytNxZK8zihsgUPHduNOw58F4A75xQZ5NOfwWvdqg3dV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Dy0srvlb5VS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I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\nt5L0tr1vjJ675tAm9eXtq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"text/plain": [ "<matplotlib.figure.Figure at 0xb5ee990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].plot(figsize=(16,10),ylim=(0.5,3)).hlines([1.85,1.65],0,3500,colors='r')\n", "#datos['RPM TRAC'].plot(secondary_y='RPM TRAC')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xa809910>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa855ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "En el boxplot, se ve como la mayoría de los datos están por encima de la media (primer cuartil). Se va a tratar de bajar ese porcentaje. La primera aproximación que vamos a realizar será la de hacer mayores incrementos al subir la velocidad en los tramos que el diámetro se encuentre entre $1.80mm$ y $1.75 mm$(caso 5) haremos incrementos de $d_v*2$ en lugar de $d_v*1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparativa de Diametro X frente a Diametro Y para ver el ratio del filamento" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0xa931f10>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5rwaUg5/mFKt3nw51dC86Zl8XZGkBGR7pHxIZFK8edjocs3j78yJIzzKf3qfT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\nO11bXkMO6MpaN7L8wKLUMeXec87V4WU68Oztree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"text/plain": [ "<matplotlib.figure.Figure at 0xa8fe1b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos['Diametro X'], y=datos['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Filtrado de datos\n", "Las muestras tomadas $d_x >= 0.9$ or $d_y >= 0.9$ las asumimos como error del sensor, por ello las filtramos de las muestras tomadas." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datos_filtrados = datos[(datos['Diametro X'] >= 0.9) & (datos['Diametro Y'] >= 0.9)]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#datos_filtrados.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Representación de X/Y" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0xa9d7e90>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5rwaUg5/mFKt3nw51dC86Zl8XZGkBGR7pHxIZFK8edjocs3j78yJIzzKf3qfT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\nO11bXkMO6MpaN7L8wKLUMeXec87V4WU68Oztree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"text/plain": [ "<matplotlib.figure.Figure at 0xa9554b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos_filtrados['Diametro X'], y=datos_filtrados['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analizamos datos del ratio" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 1526.000000\n", "mean 1.016194\n", "std 0.135635\n", "min 0.632548\n", "25% 0.940269\n", "50% 0.999491\n", "75% 1.077269\n", "max 1.655858\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratio = datos_filtrados['Diametro X']/datos_filtrados['Diametro Y']\n", "ratio.describe()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xaa427d0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AYqhIwEVG2j7MS35ijothisWEKsPOdxwDmwMDbMNOFwaNu/7hM8BZUyEDDCsf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5AAAg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jl9429v095jRfp9VhlX4A3ITSqHyJvxQThrNat7veEoheL9lZjZKkerQSDCJSUzwztNCASXB5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\nAFiSJElJMQCWJElSUgyAJUmSlJT/B5aaXmMA+MHUAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa9fae90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rolling_mean = pd.rolling_mean(ratio, 50)\n", "rolling_std = pd.rolling_std(ratio, 50)\n", "rolling_mean.plot(figsize=(12,6))\n", "# plt.fill_between(ratio, y1=rolling_mean+rolling_std, y2=rolling_mean-rolling_std, alpha=0.5)\n", "ratio.plot(figsize=(12,6), alpha=0.6, ylim=(0.5,1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Límites de calidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculamos el número de veces que traspasamos unos límites de calidad. \n", "$Th^+ = 1.85$ and $Th^- = 1.65$ " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_u = 1.85\n", "Th_d = 1.65" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_violations = datos[(datos['Diametro X'] > Th_u) | (datos['Diametro X'] < Th_d) |\n", " (datos['Diametro Y'] > Th_u) | (datos['Diametro Y'] < Th_d)]" ] }, { "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>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1469.000000</td>\n", " <td>1469.000000</td>\n", " <td>1469.000000</td>\n", " <td>1469.000000</td>\n", " <td>1469</td>\n", " <td>1469</td>\n", " <td>1469</td>\n", " <td>1469.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>63.554323</td>\n", " <td>151.313070</td>\n", " <td>1.721209</td>\n", " <td>1.706638</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.356450</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.279066</td>\n", " <td>0.864951</td>\n", " <td>0.305449</td>\n", " <td>0.297587</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.913973</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>63.200000</td>\n", " <td>149.500000</td>\n", " <td>1.206868</td>\n", " <td>1.195617</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>1.497500</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>63.400000</td>\n", " <td>150.600000</td>\n", " <td>1.470675</td>\n", " <td>1.471450</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.497500</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>63.500000</td>\n", " <td>151.300000</td>\n", " <td>1.619783</td>\n", " <td>1.609366</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.942500</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>63.600000</td>\n", " <td>151.900000</td>\n", " <td>1.998289</td>\n", " <td>1.954157</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3.500000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>64.400000</td>\n", " <td>153.200000</td>\n", " <td>2.560314</td>\n", " <td>2.609260</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>3.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO \\\n", "count 1469.000000 1469.000000 1469.000000 1469.000000 1469 1469 \n", "mean 63.554323 151.313070 1.721209 1.706638 1 1 \n", "std 0.279066 0.864951 0.305449 0.297587 0 0 \n", "min 63.200000 149.500000 1.206868 1.195617 True True \n", "25% 63.400000 150.600000 1.470675 1.471450 1 1 \n", "50% 63.500000 151.300000 1.619783 1.609366 1 1 \n", "75% 63.600000 151.900000 1.998289 1.954157 1 1 \n", "max 64.400000 153.200000 2.560314 2.609260 True True \n", "\n", " RPM EXTR RPM TRAC \n", "count 1469 1469.000000 \n", "mean 0 2.356450 \n", "std 0 0.913973 \n", "min 0 1.497500 \n", "25% 0 1.497500 \n", "50% 0 1.942500 \n", "75% 0 3.500000 \n", "max 0 3.500000 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_violations.describe()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0AA7FAF0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AAE0C90>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AB110B0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AB2F750>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AB59A30>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AB79EB0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0B038090>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0B03C7B0>], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pU6YzbdpXemWyat26NVy8qJ7fO2zYx9pvIbZu/UP7QcTf/wC7dr2I37tz5zam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1Y/funbz22stVlqFWox6EyXHxFaWGHnhgIr/9tpbp019XloWGtuTxx5+q1uPv7++Ps7MzcXGx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T19AGhubpYWsNmzlWOkpqbW9tuQyZOnAmCeasrFW42TWra2tnZ8+eUGA8O7CvT1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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xaab4390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_violations.plot(subplots=True, figsize=(12,12))" ] }, { "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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
bioinformatica-corso/lezioni
laboratorio/lezione15-26nov21/lezione-biopython.ipynb
3
43468
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Biopython\n", "\n", "Biopython è un package che mette a disposizione funzionalità in ambito bioinformatico, principalmente manipolare sequenze, leggere i formati standard tipici della Bioinformatica (ad esempio `EMBL`, `FASTA`, `FASTQ`) e accedere alle banche dati." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Installare il package `Bio` di Biopython." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "!conda install -y -c conda-forge biopython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importare il package `Bio`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import Bio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contiene basta usare la funzione `help()` e passare come argomento il nome del package." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(Bio)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cosa vediamo?\n", "\n", "- `Seq` e `MutableSeq`, classi per rappresentare una sequenza biologica (DNA, RNA o proteina)\n", "- `SeqRecord`, classe per rappresentare una sequenza biologica annotata\n", "- `Bio.SeqIO`, interfaccia input/output per formati standard (`EMBL`, `FASTA`, `FASTQ`, etc.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Oggetti `Seq` e `MutableSeq` per rappresentare sequenze" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le classi `Seq` e `MutableSeq` appartengono al modulo `Seq` che definisce le classi per manipolare sequenze biologiche. `Seq` rappresenta una sequenza immutabile mentre `MutableSeq` rappresenta una sequenza mutabile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importare il modulo `Seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Bio import Seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contiene basta usare la funzione `help()` e passare come argomento il nome `Seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(Bio.Seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importare le due classi `Seq` e `MutableSeq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Bio.Seq import Seq\n", "from Bio.Seq import MutableSeq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contengono basta usare la funzione `help()` e passare come argomento il nome delle classi." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(Seq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(MutableSeq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Costruzione di un oggetto di tipo `Seq`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'espressione:\n", " \n", " Seq(primary_string)\n", " \n", "restituisce un oggetto `Seq` che rappresenta la sequenza primaria specificata da `primary_string` passata come argomento.\n", "\n", "Costruire una sequenza `my_seq1` che ha sequenza primaria `GTGGATTGCCGGAAATTT`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1 = Seq('GTGGATTGCCGGAAATTT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stampare la sequenza." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convertire la sequenza in un oggetto di tipo `str`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(my_seq1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lunghezza di una sequenza" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La funzione `len()` restituisce la lunghezza della sequenza passata come argomento.\n", "\n", "Ottenere la lunghezza della sequenza `my_seq1`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(my_seq1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Qualche metodo degli oggetti di tipo `Seq`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `lower()` e `upper()` restituiscono la versione in minuscolo e in maiuscolo della sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la versione in minuscolo della sequenza `my_seq1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1.lower()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `find()` restituisce la prima occorrenza di una sottostringa nella sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre la prima occorrenza di `GATT` nella sequenza `my_seq1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1.find('GATT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `count()` restituisce il numero di occorrenze non sovrapposte di una data sottostringa nella sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creare la sequenza `AAAAAA` e contare il numero di occorrenze non sovrapposte di `AAA`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('AAAAA').count('AA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `count_overlap()` restituisce il numero di occorrenze sovrapposte di una data sottostringa nella sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contare il numero di occorrenze sovrapposte di `AAA` nella sequenza precedente." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('AAAAA').count_overlap('AA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `tomutable()` restituisce una copia mutabile (oggetto di tipo `MutableSeq`) della sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creare una sequenza mutabile a partire dalla sequenza immutabile che ha sequenza primaria `ACTTTGAAAG`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACTTTGAAAG').tomutable()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accesso alla sequenza" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'espressione:\n", "\n", " my_seq[my_index]\n", " \n", "restituisce il carattere alla posizione di indice `my_index` della sequenza `my_seq`.\n", "\n", "Accedere al quarto carattere della sequenza `my_seq1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1[3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'espressione di *slicing*:\n", "\n", " my_seq[start:end:step]\n", " \n", "restituisce i caratteri della sequenza `my_seq` a partire dalla posizione di indice `start` fino alla posizione immediatamente prima a quella di indice `end`, con un passo `step`.\n", "\n", "Accedere alla sottosequenza di `my_seq1` che va dal quinto al decimo carattere." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1[4:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere tramite un'operazione di *slicing* il reverse di `my_seq1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1[::-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verifica della presenza di una sottostringa in una sequenza\n", "\n", "L'espressione:\n", "\n", " my_str in my_seq\n", "\n", "restituisce `True` se la stringa `my_str` occorre nella sequenza `my_seq`\n", "\n", "Verificare la presenza della stringa `CC` nella sequenza `my_seq1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'CC' in my_seq1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attraversamento di una sequenza\n", "\n", "L'operatore `in` può essere utilizzato per attraversare i caratteri di una sequenza, nel seguente modo:\n", "\n", " for my_char in my_seq:\n", " do_something \n", "\n", "Attraversare la sequenza `my_seq1` e stampare ogni singolo carattere." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for c in my_seq1:\n", " print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Concatenazione di sequenze\n", "\n", "L'operatore `+` permette di concatenare sequenze.\n", "\n", "Concatenare due sequenze di DNA uguali a `ACGT`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGT') + Seq('ACGT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ripetizione di sequenze\n", "\n", "L'operatore `*` permette di ripetere sequenze.\n", "\n", "Ripetere due volte una sequenza di DNA uguale a `ACGT`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGT') * 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confronto tra sequenze\n", "\n", "L'espressione:\n", "\n", " my_seq1 == my_seq2\n", " \n", "restituisce `True` se la sequenza primaria di `my_seq1` è uguale a quella di `my_seq2`.\n", "\n", "Confrontare due sequenze con sequenza primaria `ACGT`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGT') == Seq('ACGT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Complement e Reverse&Complement di una sequenza di DNA o di RNA\n", " \n", "- Il metodo `complement()` restituisce il complemento della sequenza invocante.\n", "\n", "Costruire la sequenza `ACGTGAGGACCCTTT` e ottenere il suo complement." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGTGAGGACCCTTT').complement()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Il metodo `reverse_complement()` restituisce il reverse&complement della sequenza invocante.\n", "\n", "Costruire la sequenza `ACGTGAGGACCCTTT`, e ottenere il suo reverse&complement." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGTGAGGACCCTTT').reverse_complement()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTA BENE**`: complement()` e `reverse_complement()` tengono conto dell'alfabeto ambiguo di DNA e di RNA (cioé dello IUPAC CODE).\n", "\n", "L'alfabeto ambiguo di DNA o di RNA è `{A,C,G,T|U,R,Y,W,S,M,K,H,B,V,D,N}` con `T` se DNA e `U` se RNA. Cioè è l'estensione dell'alfabeto dei quattro nucleoitidi con lettere che rappresentano ambiguità tra nucleotidi.\n", "\n", "Precisamente:\n", "\n", " R \tA or G\n", " Y \tC or T|U\n", " S \tG or C\n", " W \tA or T|U\n", " K \tG or T|U\n", " M \tA or C\n", " B \tC or G or T|U\n", " D \tA or G or T|\n", " H \tA or C or T|U\n", " V \tA or C or G\n", " N \tany base\n", "\n", "Costruire la sequenza `BBBB` e ottenere il suo complement." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('BBBB').complement()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trascrizione di una sequenza di DNA\n", "\n", "- Il metodo `transcribe()` restituisce il risultato della trascrizione della sequenza di DNA invocante, intesa come sostituzione di tutti i caratteri `T` con caratteri `U`.\n", "\n", "Costruire la sequenza `ACGTGAGGACCCTTT` e ottenere la sua trascrizione." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGTGAGGACCCTTT').transcribe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Il metodo `back_transcribe()` restituisce il risultato della retrotrascrizione della sequenza di RNA invocante, intesa come sostituzione di tutti i caratteri `U` con caratteri `T`.\n", "\n", "Costruire la sequenza `ACGUGAGGACCCUUU` e ottenere la sua retrotrascrizione." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGUGAGGACCCUUU').back_transcribe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Traduzione di una sequenza di DNA o di RNA\n", "\n", "- Il metodo `translate()` restituisce il risultato della traduzione della sequenza di DNA o di RNA invocante secondo il codice genetico. La lunghezza della sequenza invocante deve essere un multiplo di tre.\n", "\n", "Costruire la sequenza `ACGUGAGGACCCUUU` e ottenere la sua trascrizione." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ACGUGAGGACCCUUU').translate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Costruire la sequenza `ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG` e ottenere la sua traduzione." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG').translate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Specificando l'attributo `to_stop` uguale a `True`, la traduzione viene fermata al primo codone di stop incontrato." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la traduzione della sequenza precedente fino al primo codone di stop che si incontra." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG').translate(to_stop = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sequenze mutabili\n", "\n", "Le sequenze mutabili sono oggetti di tipo `MutableSeq`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creare la sequenza mutabile `ACTTTGAAAG`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq = MutableSeq('ACTTTGAAAG')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cambiare in una `T` la prima base." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq[0] = 'T'\n", "my_seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Qualche metodo degli oggetti di tipo `MutableSeq`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `remove()` rimuove dalla sequenza invocante la prima occorrenza del carattere passato come argomento." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Costruire la sequenza `AAACCCTTTGGG` nella variabile `my_seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq = MutableSeq('AAACCCTTTGGG')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rimuovere dalla sequenza `my_seq` la prima occorrenza di `C`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq.remove('C')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `reverse()` opera il reverse sulla sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Invertire la sequenza `my_seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq.reverse()\n", "my_seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `complement()` opera il complement sulla sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trasformare in complement la sequenza `my_seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq.complement()\n", "my_seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `reverse_complement()` opera il reverse&complement sulla sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trasformare in reverse&complement la sequenza `my_seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq.reverse_complement()\n", "my_seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `toseq()` restituisce una copia immutabile della sequenza invocante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere dalla sequenza `my_seq` un oggetto immutabile di tipo `Seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_seq.toseq()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## L'oggetto di tipo `SeqRecord`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La classe `SeqRecord` appartiene al modulo `SeqRecord` e permette di rappresentare (e manipolare) sequenze annotate.\n", "\n", "Importare il modulo `SeqRecord`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Bio import SeqRecord" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contiene basta invocare la funzione `help()` passando come argomento il nome del modulo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(Bio.SeqRecord)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La classe `SeqRecord` rappresenta una sequenza annotata, cioè un oggetto di tipo `Seq` con l'aggiunta di informazioni." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Bio.SeqRecord import SeqRecord" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contiene basta invocare la funzione `help()` passando come argomento il nome della classe." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(SeqRecord)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un oggetto di tipo `SeqRecord` contiene i seguenti attributi:\n", "\n", "- `seq`: oggetto di tipo `Seq` che rappresenta la sequenza biologica\n", "- `id`: oggetto di tipo `str` che fornisce l'identificatore univoco (*Accession Number*) della sequenza\n", "- `name`: oggetto di tipo `str` che fornisce il nome della sequenza (può anche essere l'*Accession Number* stesso)\n", "- `description`: oggetto di tipo `str` che fornisce la descrizione della sequenza\n", "- `annotations`: oggetto di tipo `dict` che fornisce le informazioni sulla sequenza. Le chiavi sono i nomi delle informazioni e i valori sono le informazioni associate\n", "- `letter_annotations`: oggetto di tipo `dict` che annota la sequenza lettera per lettera. Le chiavi sono i nomi delle informazioni e i valori sono liste che forniscono le informazioni per ognuna delle lettere della sequenza\n", "- `features`: oggetto di tipo `list` che contiene oggetti di tipo `SeqFeature` che forniscono le *features* annotate sulla sequenza\n", "- `dbxrefs`: oggetto di tipo `list` che contiene oggetti di tipo `str` che forniscono le cross-references alle banche dati in cui è memorizzata la sequenza\n", "\n", "La classe `SeqRecord` è il tipo di oggetto che viene manipolato dalle funzioni di input/output del modulo `SeqIO` di Biopython, che è il modulo per leggere/scrivere sequenze nei formati standard (`EMBL`,`FASTA`, `FASTQ`, etc.), ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Costruzione di una sequenza annotata \"da zero\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Di solito gli oggetti di tipo `SeqRecord` vengono ottenuti dalla lettura di un file in uno dei formati standard della Bioinformatica, ma una sequenza annotata può anche essere costruita da zero tramite il suo costruttore `SeqRecord()`, a cui può essere passato come argomento un oggetto di tipo `Seq`.\n", "\n", "Costruire la sequenza annotata con sequenza primaria `AGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAA` nella variabile `annotated_sequence`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence = SeqRecord(Seq('AGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAAAGCCGTTTTAAAA'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stampare le sequenza." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(annotated_sequence)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In questo caso, solo l'attributo `seq` non è *unknown* e contiene la sequenza passata come argomento.\n", "\n", "L'attributo `annotations` contiene un dizionario vuoto." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assegnare all'attributo `annotations` il dizionario `{'type' : 'dna_molecule'}`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.annotations = {'type' : 'dna_molecule'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'attributo `letter_annotations` contiene un dizionario vuoto." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.letter_annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'attributo `features` contiene una lista vuota." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Associare a questo punto alla sequenza annotata l'identificatore univoco `AA00000` e il nome `AA00000`," ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.id = 'AA00000'\n", "annotated_sequence.name = 'AA00000'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aggiungere la descrizione `Questa e' una sequenza annotata di prova`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.description = \"Questa e' una sequenza annotata di prova\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(annotated_sequence)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la sequenza primaria della sequenza annotata come oggetto di tipo `str`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(annotated_sequence)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la prima base." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere le prime dieci basi." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la versione in minuscolo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence.lower()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formattazione di una sequenza annotata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il metodo `format()` degli oggetti di tipo `SeqRecord` restituisce la stringa ottenuta formattando la sequenza annotata invocante nel formato specificato dalla stringa passata come argomento.\n", "\n", "Formattare in `FASTA` la sequenza annotata `annotated_sequence`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "annotated_sequence" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(annotated_sequence.format('fasta'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Il package `SeqIO`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`SeqIO` è il modulo per leggere/scrivere un file in uno dei formati standard (`EMBL`, `FASTA`, `FASTQ`, etc.)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Bio import SeqIO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per vedere cosa contiene basta invocare la funzione `help()` passando come argomento il nome del modulo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(SeqIO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Funzioni principali\n", "\n", "- `read()` legge un file composto da un solo *record* e restituisce un oggetto di tipo `SeqRecord`:\n", "\n", " read(file_name, format)\n", "\n", "- `parse()` legge un file composto da più *record* e restituisce un generatore di oggetti di tipo `SeqRecord`:\n", "\n", " parse(file_name, format)\n", " \n", "- `write()` scrive record di tipo `SeqRecord` in un file in un certo formato:\n", "\n", " write(records, file_name, format)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ESERCIZIO1**: Leggere l'unico *record* del file `ENm006.fa`, che contiene una sequenza genomica di riferimento, e assegnarlo alla variabile `fasta_record`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record = SeqIO.read('./ENm006.fa', 'fasta')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `seq` dell'oggetto `SeqRecord` restituito." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.seq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la sequenza primaria come oggetto di tipo `str`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(fasta_record.seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `id`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `name`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `description`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `annotations`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `letter_annotations`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.letter_annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `features`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre l'attributo `dbxrefs`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.dbxrefs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scrivere il record in un file in formato `embl` dopo essersi assicurati che l'alfabeto sia quello del DNA." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record.seq.alphabet = Bio.Alphabet.DNAAlphabet()\n", "SeqIO.write(fasta_record, './prova.embl', 'embl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ESERCIZIO2**: Leggere i *record* del file `ests.fa`, contenente frammenti di trascritto (ESTs) relativi a un gene umano, e assegnarli al generatore `fasta_records_gen`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_records_gen = SeqIO.parse('./ests.fa', 'fasta')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I *record* (oggetti `SeqRecord`) possono essere estratti uno alla volta dal generatore tramite scansione con un ciclo `for`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stampare uno dopo l'altro i *record* separandoli con la stringa `//`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for r in fasta_records_gen:\n", " print(r)\n", " print('//')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In alternativa, i *record* possono essere estratti in una lista (da processare in seguito) invocando la funzione `list()` e passando come argomento il generatore stesso.\n", "\n", "**NB**: una volta che il generatore è stato *consumato*, esso risulta vuoto. Quindi prima di costruire la lista occorre invocare di nuovo il metodo `parse()` per ricostruirlo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_gen = SeqIO.parse('ests.fa', 'fasta')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere dal generatore la lista dei *record* e assegnarla alla variabile `fasta_record_list`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_list = list(fasta_record_gen)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere il numero dei *record* (numero degli ESTs) contenuti nel file `ests.fa`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(fasta_record_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estrarre per il primo EST:\n", "\n", "- la sequenza primaria (come oggetto di tipo `str`)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(fasta_record_list[0].seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- l'identificatore univoco" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_list[0].id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- il nome" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_list[0].name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- la descrizione" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fasta_record_list[0].description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la formattazione `FASTA` del primo EST e stamparla." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(fasta_record_list[0].format('fasta'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ESERCIZIO3**: leggere il file `input.fq`, che contiene quattro reads in formato `FASTQ`, e assegnarli alla variabile `fastq_records`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fastq_records = SeqIO.parse('./input.fq', 'fastq')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la lista dei *records* (come oggetto di tipo `list`) e assegnarla alla variabile `fastq_record_list`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fastq_record_list = list(fastq_records)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la sequenza primaria (come oggetto di tipo `str`) del primo *record* (read)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(fastq_record_list[0].seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere l'identificatore del primo *record*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fastq_record_list[0].id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere il dizionario delle annotazioni per lettera del primo *record*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fastq_record_list[0].letter_annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la formattazione in `FASTQ` del primo *record* e stamparla." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(fastq_record_list[0].format('fastq'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ESERCIZIO4**: leggere l'unico record del file `M10051.txt`, che contiene una sequenza di mRNA in formato `EMBL`, e assegnarlo alla variabile `embl_record`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record = SeqIO.read('./M10051.txt', 'embl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ottenere la sequenza primaria del record (come oggetto di tipo `str`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(embl_record.seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Produrre tutti gli attributi del record." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.name" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.description" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.annotations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.letter_annotations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.features" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embl_record.dbxrefs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'attributo `features` fornisce una lista di oggetti di tipo `SeqFeature` che rappresentano le *features* annotate sulla sequenza.\n", "\n", "Un oggetto di tipo `SeqFeature` possiede i seguenti attributi:\n", "- `type`: stringa che definisce il tipo di *feature* rappresentata\n", "- `location`: oggetto di tipo `FeatureLocation` che fornisce la localizzazione della *feature* sulla sequenza\n", "\n", "Un oggetto di tipo `FeatureLocation` possiede a sua volta i seguenti attributi:\n", "- `start`: oggetto di tipo `ExactPosition` che fornisce lo start della localizzazione\n", "- `end`: oggetto di tipo `ExactPosition` che fornisce l'end della localizzazione\n", "\n", "La *feature* con `type` uguale a `CDS` rappresenta la coding sequence della sequenza. \n", "\n", "Accedere quindi al terzo oggetto della lista delle *features* (cioé la coding sequence della sequenza di mRNA) e recuperare lo start e l'end della coding sequence." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cds_start = int(embl_record.features[2].location.start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cds_end = int(embl_record.features[2].location.end)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NB**: la start position viene fornita 0-based mentre l'end position viene fornita 1-based.\n", "\n", "Effettuare lo slicing della sequenza per ottenere la coding sequence." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cds_sequence = embl_record[cds_start:cds_end]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Formattare la coding sequence in formato `FASTA` e stamparla." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(cds_sequence.format('fasta'))" ] } ], "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }
cc0-1.0
netmanchris/PYHPEIMC
examples/.ipynb_checkpoints/Working with Network Assets-checkpoint.ipynb
3
21356
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Serial Numbers, How I love thee... \n", "\n", "No one really like serial numbers, but keeping track of them is one of the \"brushing your teeth\" activities that everyone needs to take care of. It's like eating your brussel sprouts. Or listening to your mom. You're just better of if you do it quickly as it just gets more painful over time.\n", "\n", "Not only is it just good hygene, but you may be subject to regulations, like [eRate](https://en.wikipedia.org/wiki/E-Rate) in the United States where you have to be able to report on the location of any device by serial number at any point in time. \n", "\n", "> Trust me, having to play hide-and-go seek with an SSH session is not something you want to do when government auditors are looking for answers.\n", "\n", "I'm sure you've already guessed what I'm about to say, but I\"ll say it anyway...\n", "\n", "> *There's an API for that!!!*\n", "\n", "[HPE IMC](http://www.hpe.com/networking/imc) base platform has a great network assets function that automatically gathers all the details of your various devices, assuming of course they support [RFC 4133](https://tools.ietf.org/html/rfc4133), otherwise known as the Entity MIB. On the bright side, most vendors have chosen to support this standards based MIB, so chances are you're in good shape. \n", "\n", "And if they don't support it, they really should. You should ask them. Ok?\n", "\n", "So without further ado, let's get started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing the required libraries\n", "\n", "I'm sure you're getting used to this part, but it's import to know where to look for these different functions. In this case, we're going to look at a new library that is specifically designed to deal with network assets, including serial numbers." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyhpeimc.auth import *\n", "from pyhpeimc.plat.netassets import *\n", "import csv" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "auth = IMCAuth(\"http://\", \"10.101.0.203\", \"8080\", \"admin\", \"admin\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ciscorouter = get_dev_asset_details('10.101.0.1', auth.creds, auth.url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How many assets in a Cisco Router?\n", "\n", "As some of you may have heard, HPE IMC is a multi-vendor tool and offers support for many of the common devices you'll see in your daily travels. \n", "\n", "In this example, we're going to use a Cisco 2811 router to showcase the basic function.\n", "\n", "Routers, like chassis switches have multiple components. As any one who's ever been the ~~victem~~ owner of a Smartnet contract, you'll know that you have individual components which have serial numbers as well and all of them have to be reported for them to be covered. So let's see if we managed to grab all of those by first checking out how many individual items we got back in the asset list for this cisco router." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ciscorouter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What's in the box???\n", "\n", "Now we know that we've got an idea of how many assets are in here, let's take a look to see exactly what's in one of the asset records to see if there's anything useful in here. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'alias': '',\n", " 'asset': 'http://kontrolissues.thruhere.net:8086/imcrs/netasset/asset/detail?devId=15&phyIndex=1',\n", " 'assetNumber': '',\n", " 'boardNum': 'FHK1119F1DX',\n", " 'bom': '',\n", " 'buildInfo': '',\n", " 'cleiCode': '',\n", " 'containedIn': '0',\n", " 'desc': '2811 chassis',\n", " 'devId': '15',\n", " 'deviceIp': '10.101.0.1',\n", " 'deviceName': 'router.lab.local',\n", " 'firmwareVersion': 'System Bootstrap, Version 12.4(13r)T11, RELEASE SOFTWARE (fc1)',\n", " 'hardVersion': 'V04 ',\n", " 'isFRU': '2',\n", " 'mfgName': 'Cisco',\n", " 'model': 'CISCO2811',\n", " 'name': '2811 chassis',\n", " 'phyClass': '3',\n", " 'phyIndex': '1',\n", " 'physicalFlag': '0',\n", " 'relPos': '-1',\n", " 'remark': '',\n", " 'serialNum': 'FHK1119F1DX',\n", " 'serverDate': '2016-01-26T15:20:40-05:00',\n", " 'softVersion': '15.1(4)M, RELEASE SOFTWARE (fc1)',\n", " 'vendorType': '1.3.6.1.4.1.9.12.3.1.3.436'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ciscorouter[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What can we do with this?\n", "\n", "With some basic python string manipulation we could easily print out some of the attributes that we want into what could easily turn into a nicely formated report. \n", "\n", "Again realise that the example below is just a subset of what's available in the JSON above. If you want more, just add it to the list. \n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Device Name: router.lab.local Device Model: CISCO2811\n", "Asset Name is: 2811 chassis Asset Serial Number is: FHK1119F1DX\n", "\n", "Device Name: router.lab.local Device Model: VIC2-2FXO\n", "Asset Name is: 2nd generation two port FXO voice interface daughtercard on Slot 0 SubSlot 2 Asset Serial Number is: FOC11063NZ4\n", "\n", "Device Name: router.lab.local Device Model: \n", "Asset Name is: 40GB IDE Disc Daughter Card on Slot 1 SubSlot 0 Asset Serial Number is: FOC11163P04\n", "\n", "Device Name: router.lab.local Device Model: \n", "Asset Name is: AIM Container Slot 0 Asset Serial Number is: \n", "\n", "Device Name: router.lab.local Device Model: \n", "Asset Name is: AIM Container Slot 1 Asset Serial Number is: \n", "\n", "Device Name: router.lab.local Device Model: \n", "Asset Name is: C2811 Chassis Slot 0 Asset Serial Number is: \n", "\n", "Device Name: router.lab.local Device Model: \n", "Asset Name is: C2811 Chassis Slot 1 Asset Serial Number is: \n", "\n" ] } ], "source": [ "for i in ciscorouter:\n", " print (\"Device Name: \" + i['deviceName'] + \" Device Model: \" + i['model'] +\n", " \"\\nAsset Name is: \" + i['name'] + \" Asset Serial Number is: \" + \n", " i['serialNum']+ \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why not just write that to disk?\n", "\n", "Although we could go directly to the formated report without a lot of extra work, we would be losing a lot of data which we may have use for later. Instead why don't we export all the available data from the JSON above into a CSV file which can be later opened in your favourite spreadsheet viewer and manipulated to your hearst content.\n", "\n", "Pretty cool, no?\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keys = ciscorouter[0].keys()\n", "with open('ciscorouter.csv', 'w') as file:\n", " dict_writer = csv.DictWriter(file, keys)\n", " dict_writer.writeheader()\n", " dict_writer.writerows(ciscorouter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading it back\n", "\n", "Now we'll read it back from disk to make sure it worked properly. When working with data like this, I find it useful to think about who's going to be consuming the data. For example, when looking at this remember this is a CSV file which can be easily opened in python, or something like Microsoft Excel to manipuate further. It's not realy intended to be read by human beings in this particular format. You'll need another program to consume and munge the data first to turn it into something human consumable. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "firmwareVersion,vendorType,phyIndex,relPos,boardNum,phyClass,softVersion,serverDate,isFRU,alias,bom,physicalFlag,deviceName,deviceIp,containedIn,cleiCode,mfgName,desc,name,hardVersion,remark,asset,model,assetNumber,serialNum,buildInfo,devId\n", "\"System Bootstrap, Version 12.4(13r)T11, RELEASE SOFTWARE (fc1)\",1.3.6.1.4.1.9.12.3.1.3.436,1,-1,FHK1119F1DX,3,\"15.1(4)M, RELEASE SOFTWARE (fc1)\",2016-01-26T15:20:40-05:00,2,,,0,router.lab.local,10.101.0.1,0,,Cisco,2811 chassis,2811 chassis,V04 ,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=1,CISCO2811,,FHK1119F1DX,,15\n", ",1.3.6.1.4.1.9.12.3.1.9.3.114,14,0,FOC11063NZ4,9,,2016-01-26T15:20:40-05:00,1,,,2,router.lab.local,10.101.0.1,13,,Cisco,2nd generation two port FXO voice interface daughtercard,2nd generation two port FXO voice interface daughtercard on Slot 0 SubSlot 2,V01 ,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=14,VIC2-2FXO,,FOC11063NZ4,,15\n", ",1.3.6.1.4.1.9.12.3.1.9.15.25,30,0,FOC11163P04,9,,2016-01-26T15:20:40-05:00,1,,,2,router.lab.local,10.101.0.1,29,,Cisco,40GB IDE Disc Daughter Card,40GB IDE Disc Daughter Card on Slot 1 SubSlot 0,,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=30, ,,FOC11163P04,,15\n", ",1.3.6.1.4.1.9.12.3.1.5.2,25,6,,5,,2016-01-26T15:20:40-05:00,2,,,0,router.lab.local,10.101.0.1,3,,Cisco,AIM Container Slot 0,AIM Container Slot 0,,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=25,,,,,15\n", ",1.3.6.1.4.1.9.12.3.1.5.2,26,7,,5,,2016-01-26T15:20:40-05:00,2,,,0,router.lab.local,10.101.0.1,3,,Cisco,AIM Container Slot 1,AIM Container Slot 1,,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=26,,,,,15\n", ",1.3.6.1.4.1.9.12.3.1.5.1,2,0,,5,,2016-01-26T15:20:40-05:00,2,,,0,router.lab.local,10.101.0.1,1,,Cisco,C2811 Chassis Slot,C2811 Chassis Slot 0,,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=2,,,,,15\n", ",1.3.6.1.4.1.9.12.3.1.5.1,27,1,,5,,2016-01-26T15:20:40-05:00,2,,,0,router.lab.local,10.101.0.1,1,,Cisco,C2811 Chassis Slot,C2811 Chassis Slot 1,,,http://10.101.0.203:8080/imcrs/netasset/asset/detail?devId=15&phyIndex=27,,,,,15\n", "\n" ] } ], "source": [ "with open('ciscorouter.csv') as file:\n", " print (file.read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What about all my serial numbers at once?\n", "\n", "That's a great question! I'm glad you asked. One of the most beautiful things about learning to automate things like asset gathering through an API is that it's often not much more work to do something 1000 times than it is to do it a single time. \n", "\n", "This time instead of using the *get_dev_asset_details* function that we used above which gets us all the assets associated with a single device, let's grab ALL the devices at once. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_assets = get_dev_asset_details_all(auth.creds, auth.url)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1013" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len (all_assets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## That's a lot of assets!\n", "\n", "Exactly why we automate things. Now let's write the all_assets list to disk as well. \n", "\n", "**note for reasons unknown to me at this time, although the majority of the assets have 27 differnet fields, a few of them actually have 28 different attributes. Something I'll have to dig into later. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "dict contains fields not in fieldnames: 'beginDate'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-e4c553049911>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdict_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDictWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdict_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriteheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdict_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_assets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/csv.py\u001b[0m in \u001b[0;36mwriterows\u001b[0;34m(self, rowdicts)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrowdict\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrowdicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dict_to_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrowdict\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 159\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/csv.py\u001b[0m in \u001b[0;36m_dict_to_list\u001b[0;34m(self, rowdict)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwrong_fields\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m raise ValueError(\"dict contains fields not in fieldnames: \"\n\u001b[0;32m--> 149\u001b[0;31m + \", \".join([repr(x) for x in wrong_fields]))\n\u001b[0m\u001b[1;32m 150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrowdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfieldnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: dict contains fields not in fieldnames: 'beginDate'" ] } ], "source": [ "keys = all_assets[0].keys()\n", "with open('all_assets.csv', 'w') as file:\n", " dict_writer = csv.DictWriter(file, keys)\n", " dict_writer.writeheader()\n", " dict_writer.writerows(all_assets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Well That's not good....\n", "\n", "So it looks like there are a few network assets that have a different number of attributes than the first one in the list. We'll write some quick code to figure out how big of a problem this is. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The length of the first items keys is 27\n", "The length of index 39 is 28\n", "The length of index 41 is 28\n", "The length of index 42 is 28\n", "The length of index 474 is 28\n", "The length of index 497 is 28\n", "The length of index 569 is 28\n", "The length of index 570 is 28\n", "The length of index 585 is 28\n", "The length of index 604 is 28\n", "The length of index 605 is 28\n", "The length of index 879 is 28\n", "The length of index 880 is 28\n", "The length of index 881 is 28\n", "The length of index 882 is 28\n", "The length of index 883 is 28\n", "The length of index 884 is 28\n", "The length of index 885 is 28\n", "The length of index 886 is 28\n" ] } ], "source": [ "print (\"The length of the first items keys is \" + str(len(keys)))\n", "for i in all_assets:\n", " if len(i) != len(all_assets[0].keys()):\n", " print (\"The length of index \" + str(all_assets.index(i)) + \" is \" + str(len(i.keys())))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Well that's not so bad\n", "\n", "It looks like the items which don't have exactly 27 attribues have exactly 28 attributes. So we'll just pick one of the longer ones to use as the headers for our CSV file and then run the script again.\n", "\n", "For this one, I'm going to ask you to trust me that the file is on disk and save us all the trouble of having to print out 1013 seperate assets into this blog post. \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keys = all_assets[879].keys()\n", "with open ('all_assets.csv', 'w') as file:\n", " dict_writer = csv.DictWriter(file, keys)\n", " dict_writer.writeheader()\n", " dict_writer.writerows(all_assets)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# What's next?\n", "\n", "So now that we've got all of our assets into a CSV file which is easily consumable by something like Excel, you can now chose what to do with the data.\n", "\n", "For me it's interesting to see how vendors internalyl instrument their boxes. Some have serial numbers on power supplies and fans, some don't. Some use the standard way of doing things. Some don't. \n", "\n", "From an operations perspective, not all gear is created equal and it's nice to understand what's supported when trying to make a purchasing choice for something you're going to have to live with for the next few years. \n", "\n", "If you're looking at your annual SMARTnet upgrade, at least you've now got a way to easily audit all of your discovered environment and figure out what line cards need to be tied to a particualr contract.\n", "\n", "Or you could just look at another vendor who makes your life easier. Entirely your choice. \n", "\n", "@netmanchris" ] } ], "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 }
apache-2.0
ES-DOC/esdoc-jupyterhub
notebooks/thu/cmip6/models/sandbox-2/land.ipynb
1
173496
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: THU \n", "**Source ID**: SANDBOX-2 \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:40" ], "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', 'thu', 'sandbox-2', 'land')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. 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.land.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&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.land.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&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.land.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "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", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "*Land surface soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
jurgjn/relmapping
annot/notebooks/Fig2S3_import_Gu2012.ipynb
2
203288
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:46:49.925503Z", "start_time": "2018-05-18T15:46:41.103973Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "os.getcwd(): /mnt/beegfs/scratch_copy/ahringer/jj374/lab/relmapping\n" ] } ], "source": [ "%run ~/relmapping/annot/notebooks/__init__.ipynb\n", "def vp(fp): return os.path.join('annot/Fig2S3_tss/', fp) # \"verbose path\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://www.sciencedirect.com/science/article/pii/S0092867412014080\n", "> Table S1. trans-Splice Sites, Transcription Start Sites, and csRNA Loci for Protein-Coding Genes and Transcription Start Sites for pri-miRNAs, Related to Figure 2. Analysis of C. elegans CapSeq and CIP-TAP, containing lists of trans-splice sites, transcription start sites, and sense and antisense csRNAs derived from protein coding genes. Also included is the list of the transcription start sites for pri-miRNAs.\n", "> \n", "> For C. elegans analysis, reads were mapped to the genome (WormBase release WS215)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:46:56.675864Z", "start_time": "2018-05-18T15:46:49.927729Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "88936 records, ~20,000 not assigned to an annotation:\n", "True 69109\n", "False 19827\n", "Name: assigned_to_an_annotation, dtype: int64\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>chromosome</th>\n", " <th>strand</th>\n", " <th>start</th>\n", " <th>reads</th>\n", " <th>distance_to_transcript</th>\n", " <th>transcript</th>\n", " <th>covered_by_CapSeq</th>\n", " <th>transcript type</th>\n", " <th>assigned_to_an_annotation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>I</td>\n", " <td>+</td>\n", " <td>4578</td>\n", " <td>6</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>I</td>\n", " <td>+</td>\n", " <td>6302</td>\n", " <td>12</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>I</td>\n", " <td>+</td>\n", " <td>9332</td>\n", " <td>6</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>I</td>\n", " <td>+</td>\n", " <td>9421</td>\n", " <td>12</td>\n", " <td>-992.0</td>\n", " <td>Y74C9A.2.3</td>\n", " <td>Not</td>\n", " <td>coding</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>I</td>\n", " <td>+</td>\n", " <td>11294</td>\n", " <td>10</td>\n", " <td>-201.0</td>\n", " <td>Y74C9A.2.2</td>\n", " <td>Not</td>\n", " <td>coding</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " chromosome strand start reads distance_to_transcript transcript \\\n", "0 I + 4578 6 NaN NaN \n", "1 I + 6302 12 NaN NaN \n", "2 I + 9332 6 NaN NaN \n", "3 I + 9421 12 -992.0 Y74C9A.2.3 \n", "4 I + 11294 10 -201.0 Y74C9A.2.2 \n", "\n", " covered_by_CapSeq transcript type assigned_to_an_annotation \n", "0 NaN NaN False \n", "1 NaN NaN False \n", "2 NaN NaN False \n", "3 Not coding True \n", "4 Not coding True " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#!cd ~/relmapping/wget; wget -m --no-parent https://ars.els-cdn.com/content/image/1-s2.0-S0092867412014080-mmc1.xlsx\n", "fp_ = 'wget/ars.els-cdn.com/content/image/1-s2.0-S0092867412014080-mmc1_B._TS_sites_for_protein_genes.csv'\n", "df_ = pd.read_csv(fp_, skiprows=11)\n", "df_['assigned_to_an_annotation'] = df_['transcript'].map(lambda x: x == x)\n", "print('%d records, ~20,000 not assigned to an annotation:' % (len(df_)))\n", "print(df_['assigned_to_an_annotation'].value_counts())\n", "\n", "df_.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Using a cutoff of one CapSeq read per 10 million total reads, and a requirement for a YR motif, our CapSeq data predicted approximately 64,000 candidate TS sites genome wide (Table S1B)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:46:56.829750Z", "start_time": "2018-05-18T15:46:56.678534Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coding 67528\n", "ncRNA 612\n", "Coding_pseudogene 398\n", "snoRNA 277\n", "pre-miRNA 182\n", "snRNA 71\n", "RNA_pseudogene 36\n", "scRNA 4\n", "miRNA 1\n", "Name: transcript type, dtype: int64\n", "67528 records with annotated as \"coding\"\n" ] } ], "source": [ "print(df_['transcript type'].value_counts())\n", "m_ = df_['transcript type'] == \"coding\"\n", "df_ = df_.loc[m_].reset_index(drop=True)\n", "print('%d records with annotated as \"coding\"' % (len(df_.query('transcript == transcript')),))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:04.929278Z", "start_time": "2018-05-18T15:46:56.832558Z" }, "code_folding": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "67529 annot/Fig2S3_tss/Gu2012_tss.bed\r\n" ] } ], "source": [ "# Raw (Gu et al., 2012) TSS sites (=many assigned to multiple transcripts)\n", "df_gu = pd.DataFrame()\n", "df_gu['chrom'] = 'chr' + df_['chromosome']\n", "df_gu['start'] = df_['start']\n", "df_gu['end'] = df_['start'] + 1\n", "df_gu['name'] = df_['transcript']\n", "df_gu['score'] = df_['reads']\n", "df_gu['strand'] = df_['strand']\n", "df_gu = df_gu.sort_values(['chrom', 'start', 'end', 'start']).reset_index(drop=True)\n", "\n", "fp_ = vp('Gu2012_tss.bed')\n", "write_gffbed(fp_,\n", " chrom = df_gu['chrom'],\n", " start = df_gu['start'],\n", " end = df_gu['end'],\n", " name = df_gu['name'],\n", " strand = df_gu['strand'],\n", " score = df_gu['score'],\n", ")\n", "!wc -l {fp_}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:07.164521Z", "start_time": "2018-05-18T15:47:04.933747Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42812 annot/Fig2S3_tss/Gu2012_tss_unique.bed\r\n" ] } ], "source": [ "# Collapse TSS annotations by chrom/start/end/strand (raw TSS assignments are to all \"compatible\" transcripts)\n", "fp_ = vp('Gu2012_tss_unique.bed')\n", "df_gu.groupby(['chrom', 'start', 'end', 'strand']).agg({\n", " 'name': lambda l: os.path.commonprefix(list(l)).rstrip('.'),#lambda l: ','.join(sorted(set(l))),\n", " 'score': np.sum,\n", "})\\\n", ".reset_index().sort_values(['chrom', 'start', 'end', 'strand'])[['chrom', 'start', 'end', 'name', 'score', 'strand']]\\\n", ".to_csv(fp_, sep='\\t', index=False, header=False)\n", "!wc -l {fp_}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:09.305133Z", "start_time": "2018-05-18T15:47:07.168509Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14363 annot/Fig2S3_tss/Gu2012_tss_clustered.bed\r\n" ] } ], "source": [ "# Cluster TSS annotations using single-linkage, strand-specific, using a distance cutoff of 50\n", "df_gu_cluster50_ = BedTool.from_dataframe(df_gu).cluster(d=50, s=True).to_dataframe()\n", "df_gu_cluster50_.columns = ('chrom', 'start', 'end', 'transcript_id', 'score', 'strand', 'cluster_id')\n", "\n", "fp_ = vp('Gu2012_tss_clustered.bed')\n", "df_gu_cluster50 = df_gu_cluster50_.groupby('cluster_id').agg({\n", " 'chrom': lambda s: list(set(s))[0],\n", " 'start': np.min,\n", " 'end': np.max,\n", " 'transcript_id': lambda l: os.path.commonprefix(list(l)).rstrip('.'),#lambda l: ','.join(sorted(set(l))),\n", " 'score': np.sum,\n", " 'strand': lambda s: list(set(s))[0],\n", "})\\\n", ".sort_values(['chrom', 'start', 'end', 'strand']).reset_index(drop=True)\n", "df_gu_cluster50.to_csv(fp_, sep='\\t', index=False, header=False)\n", "!wc -l {fp_}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:31.321459Z", "start_time": "2018-05-18T15:47:09.309437Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/ipykernel_launcher.py:64: RuntimeWarning: Mean of empty slice\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "13054 of 42245 sites with CV values via promoter annotation\n", "26764 of 42245 sites with CV values via \"associated gene\"\n" ] }, { "data": { "text/plain": [ "coding_promoter 5416\n", "pseudogene_promoter 28\n", "unknown_promoter 111\n", "putative_enhancer 1276\n", "non-coding_RNA 22\n", "\\n\\nother_element 109\n", "Name: name, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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K2nbmfSh1MOkP7HnZObcDBwK3SlqhXmSWmL4LOIdUAuJi4ALgUdKCWFuVtO3q67R7FsPw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WwVletMO3bq7G1tO1VVHdUTWwn+XbEnN+ZnXBLZnj0+EXuWlCab4YQ+vrXfcnt1biDmIHfEJISb\nLSraqsjfvvCyhDJEgkyyJYQYWGs4lsYYpKJziljeifQIHl6ask2JdHcQQgghxpT0r/Q+irmSda07\nCbQ2skNVsWCzhVkOHN5gOZxXpNrtJaPZmU6D7vVlJ7OezKjar6C2jVfQniCmwx5Wq43s91nqPuMr\nvu6IR4hJQAusdncQwv28LaEss1EKIQZVtASZqGQKienCx9chv7NhKMgHPiGE8BhFW5VwIM3dcQg3\nsRPUmc/G9v2ctZvYD+BoaZln+jQ7tauqJltV1Y7R7G5rfNOqQ+uK2wO19uLxCXhq0zjULo1NM8OK\nvlf/ZI3qqL/KunuJu+ISYhKQG5vC6xLK0j9ZCDGg1ggszdOld/JUogFljolRz3buhWYo2xR5/xNC\nCM+wHKQ1l7dzmJjZvo9VhgKOqHZKAW1XyYks8849nY5O497R7Csl0JJQvik/eWWoIWecwp2yYjrs\nJ6p00ytRFL3r8pVdx4q1OLTuikuISUD6KAsvSihL/2QhxBAKl0ql61Q0z4TN3TFMETJpjBBCeIaV\n7g5ATB5dDSxtzSHZXEWOqtKiWiwx5j371ljyCw6pDkflSPfjq1F9P1xVmvnjtNN7QDWMZ8xTSUqT\nrbFUF9/vfDxgfFFu1Atvt7poqyI3Vbyc9ySUIQKQPkdCiH6ap2FpiZLq5Kko3SRVWiM0U9mmhLk7\nCCGEEBdNJlsVfWlNJ8ls2wVdreSoKjb7mfrlpu05021n63eoqmoZ6Y4emFW/dtea4/V+GkfpeAY8\nVcS32vzPaCOiXJf5qpbSxbYSaTsjvF0QUrDi9bwpoSx3EYUQAzq+UKqTp6pkC/rhR4luC90dgBBC\niIsmFcpiQKqNcEMumR2HOOWwcgSHw896rGCjee/+OtViOTzS/cwPNieXbzo2Y16Qadd4xjvpqaqq\ns+qiu9DOcV28xZJ92l0hCTHJSNsLL+dNCWWZkE8I0Y/ZH1tzNH7ujkNcmFA7+gibtL0YodnKNkUq\n8YUQYooq2qrMQIpkxDDsBlLbdrO08zgHVAeVaqdxliln9zJryYm9qqqeGck+ArRqwO61x9d/K7lu\nF6im8Y55MoowOiqqdDH1KC6P9auq40vGl+cMsZkQ3mStuwMQ7uVNCWXpnyyE6Kd8DhYUaZswlaXL\nxHwjpQXmDjtKCCHEZCXVyWLErHWsbM0hxlJHtqpisFXVrDFtzwmwN7fkqKpqH8k+vpd6Zv0nq05U\n6xXHqXEOd9JJbrKdLtXN6JVMj1Tb8mIdjXJTRwin+e4OQLiXdySUFSUCpAJRCNGbCmpNEj7ujkNc\nnPlGaVkyCunKNkVuoAghxNS03N0BiClGxdd4nKy2PXTaDOxSbfZgy+HcTMvBI6WqzVY4kl0sCzWm\nndyYH5Hkb9k73uFOJomtNk29NqxXUdrtpreN7opHiEkotWir4h05RTEgb/nlR7g7ACHE5HN2Bhar\nn/TgnermmJAZhkcuCJjp7iCEEEJckHR3ByCmJtXK9I6DrDfkUeSwUeBoa5tr2p6T3lVRmaOqautw\n24foHSG5G4rW3D+zPhtU60TE7G4+Jl2oDe35yfdUtfMO09uL3RiSEJONLzDL3UEI9/GWhHK4uwMQ\nQkw+FWmo7o5BXLyZVrkpMEoZ7g5ACCHEBZnt7gDE1GZrZV7bTuaZTrJbVanvKjuZacrZ3eXoMIxo\nAr5H557Oent5WalWUT16YrpAi+NMlTbWgHL+qa5Ue1VeIOYgd8YlxCQk7fS8mCSUhRBeyeyPrUkm\n4/MIviraBAteUS0zRhKUbUqwu4MQQggxckVbFQVIcXccwiMo5irWteYQbG0kW7VYQ8z7Dqy35B7L\nU+32k8NtvCHCMO9EVn5AnK/14EQE6w5JzbaKMv2MXp8t7ze+It8bhOhPJqn0Yt6SUA5zdwBCiMnl\n1GwsaGQyPk8x34jN3TFMMUnuDkAIIcSoxAP+7g5CeBAHgZ35ZLXvp95uYp+9sXGxaXvOTFtt3Q5V\nVYfsFRzpYw8vzCxcfltc0w4Y2QR/U0lSs62rURMa3/OzRnWcvcK6R9pdCNGfVCh7Mc9PKCuKBghx\ndxhCiMmlOlkm4/Mk80zDjxG9zHJ3AEIIIUYl1d0BCM/kMJHQvo/VhnxyVZt6ylpYvNG8e2+zw2Q+\nMNR2ioLy1PyqjS8vKS/QoJ6dqHgngt7k42tXtMk9P6/qOnpci0Pm7BCiP6lQ9mKen1B2Vid7w+sU\nQoxQUxRmi7/03fUk8Va5zo/SdGWbIpVuQggxdUj/ZDGuuhpZ0ppDsrmKHIfRHGDetWelpah4v+pw\nDNkv+aqo9kVFWQWaafqu3ImKdTzp7WpHjRLb5brsAeOLse6KR4hJThLKXswbvoBLuwshRC91CTjc\nHYMYW9Fd6NwdwxSjIFXKQggxlUhCWUwErekkmW270HS1kmOrqVtm2p4Tbm9o3KGqatdgG8X42qJO\nbCxYtCW6NRvUKT3pdUKL7US5Lu7ca/BTLScW2U6kuTMmISaxmKKtSqi7gxDu4Q0JZZmQTwjRS30c\n8siahwl2oPN1yI2CUZrl7gCEEEKMmCSUxYRRbYQZcsnsOESlw+wotuQd22jef7BStXYdHWwbjYLm\n74srsv6yoPKwgto0kfGOpZQmm6FZE5LY8/MWy446d8YjxBQgVcpeShLKQgivYvbHZgzG191xjIV/\nFTPn2pf401+OsMzdsUwGcVbsfMgSfsif2CkTRIzADGWbIr3EhRBiapCEsphwdgMpbbtZ1nmcA442\ng96UvXORtax8l6qqjYNt89nYluVHNxRaQ3W2/ImMdazojX44FM1MAFTV8SXjy5IsE2Jo8m/ES3nD\nI8LS8kIIL/U4pP4ZLquCZDME+kFnWBfVmflsv30BBSPZx9/yWLyzkktazMx0qOgD9DTOjeTA19bw\nQYgvtsG2e/4oC3dWsbHZRGKXHT9fHR0R/lR/Zg7/uWY2FT3jPq0gPqeSJadaSTdYmWa1E+SrpWN6\nEKXXz+XDy5KpGmj/NgfKG8XcEupLzb1LOOy67v0yZu2qYsnZThJaTCRY7IT46Wh99Wa+PdzrffM4\nqe+f5LKGTpKtdgJ9tHSG+XF6UxKfjPSc3fIqj5hsRA60zldL+2u38E3XZZ1WtI/uZktJI6u6HASE\n+1H1+QW8NtBrf6WQ9L8f46u3zuN3dyyk1xeVeCu2isvI5ShV7OVm1vITtEzpxy7HmQaYCZS5OxAh\nhBCDK9qqKEDysAOFGCfWOlZaz2ANmEO2qp5aaqupsfkuWbxTExK8XlEUpe/4mf5dsSc35kfddCQl\nO7s5JMsdMV8IjUPtqlPPt7uIVFtzYxxNUrghxNCkkMdLeXZC2fnmJv1chPBCW+Cad+EzfmBIh2MR\n0NYOQWUK8YX1zIHhk6M/2M51uWfYrFWwJIZxxF+HobaD1EN1XPfge2Q8eRVPhPrRq5+czYHyrY+4\nvbSZDX46mhNDyQ3QYzBYCanvJLmogUTXhPKzedzRYiYpzI/KWWHk+mqxnO0kobKNFb/Zz9Lqdv58\nz2Ly+sb2lyOsaLMQv3k2z2j6fIzffoqVRQ1cqoA92Jc6i52QkZyzH2VzzcFaPuOjxZAQwrEgH9qM\nXQQ1GJk50nPWQ6fBtGg6n/Rd7qvF3HfZj3O4sbCByxJCOBLsS0tpE6t/f5CvJ4Tw8JxptPWMazTi\n+68i7kwKY3/fZDJAvBUHGmAB77OP/+YDVnANQ85QLpiFJJSFEGKySwD83B2E8HIqPsbjZJnKqQ9a\naDuh7j+0XhsZXui7eKFe0Wr7VSjqNOjeXH4y69mayP1fLUqYC5O/z2pMh/1EpTbmXI7kTtPb/T63\nCiH6kQplL+XZCWUIAemVKoS3+Tosexc+MwuKd8BTiWABcCio79+A2ulAP9w+PiknIfcM1+g0GL+f\nyU+XxtII4FDh2x/zueONbHp8H1f9cCNvu273i91cXtrMhqQw9v3sUp4P9MHuut7U1fuatGA6+zfO\n4pkVcTS4Lv/jIVa+W8oX3i7hzlsyyO+7n301bNRpMN02n34zal+Zwp4rktm7Op7aQB/s177En4Z7\nvX85wrKDtXwmOpDin13KU9GBznM2WNzD0Wsw9j03A3GocLyRzJRw9jxxFc8BvFFM7l/z+N/Xiln1\nfxv4sGfso7u40aGi/+ZaXh5oX/FWnKn1LI5yECPFZElCeVgJyjZFqz6s2ocfKoQQwk2k3YWYNFQr\n0R2HiNaFURS4oEV1fJqdqp+blq2Ln7FUUZTgvuPvjm9atS7cULVx35zaTrs23R0xj1Ryk61xtybI\n+e9NVTtvN72z2M0hCTEVSELZS3l6D2XpnyyEl+kC5Rm4UQfWf8NfepLJAI0xWBxaNP56hk2eZVey\nBFDmTmNXTzIZQKPAN9fyBqDm15NltXOuPvisAb8Dp9nip6PlkUt5oW8SGKDvsb+5lu19k8kA9y/n\nQKCe+i4HQXuqmeG6bm81MU0mUhJCONa3QhrgkiRqLk2meqDjD8TmQPmonBu1CtaHNvCXvsnkgeIe\nKxWtBNtVfGaGnq/aviSJUwAtpvNtM/59nNklTWRdM5t/JIRiHGhfsdbu9zR/bESTRwepFBMzHnF7\nED0Q5+4ghBBCDEkSymLSsbWS0baT+caT7LMWn0gz79zT6eg07h1o7OxAy8zyTfnJy0M7cyY6ztHQ\ndQZaVEUTBzDbXpUXgDnQ3TEJMQXMLtqqeHpuUQzA0yuUpX+yEF7mD5DSDtMy4PBsMP4AFuRDnC90\nzdBTvAlGNFOzwepsExEVQL9JR6IDsfhoMVjtBO+qYsYlSdQAvF7MIruKb3oE2XYHyjNHWFpnINpP\nh3lZLGWbuseNlEZxJnF1Ghyuy3dXO/tUJYVTOpr9DeY/paQYu5iWEMLhuGCMfz/GgspW4vRauuZF\ncWpzGuWj3adDRffUQVY1mojw1WJJieD0tWmc8OnT0zgpjA6NgrW6nXOzae845fz/4f40AbSZ0b9c\nwF0JIRy5d0n/iuweUTaX97QYTlLHWoqYSzpnRhu/l5kOVLs7CCGEEIOShLKYrBRLFessNXQGZlgO\nOcz7Vuliow/5zMuIUjSaRNeBvhrV9+NVJzKfqIje/cPSuMWgTLpkbYMtVul5jvF+48vSZkaIkfHF\n2UZv1N8ZxdTm6QllqVAWwsvsxpmMDIWORHioEZfq3ip4vpHSH2Tyx6RwDEPtJ0DvXN9oZFrfdfWd\n+FrtBAGUNRPTk1CuaGUWgFbB/sW32eY6MV12JbxaxJGfXsLfwv2xDvc63islqcNKrJ+O1nUzOe26\nrqLV+cVyfhSVw+1nJIobnOcs0IeOe9/ioXbL+XO2swpeKx7ZOXNlsRP6nzLudd3PPwtp/Nw8nr0h\n/XwiXKPA3GnsLGrgki+/i3+QL62lTazSaTDfONfZruJnu/iM1U7AN9by0lDHDLGj1aqodgWFWZwi\nFzhDGrBjxCfDO0W5OwAhhBBDSnV3AEIMyUFgZwFZGn9qghbV2+z1jdN95mfs0EZHrVEUxdd16FeT\n6tddGtlx8vIDaarZoZk0f9sRnfaKGu30IACNaj9zhXXPEnfHJMQUEocklL2OpyeU+/VwEkJ4tmZn\n73T2QWYoND4Cj98BFTsVwr8ewC1nO5n3oxzu+9tn+NVQ+1kdz7GjZ7n6eCPr886wY3GMs1oW4LE9\nXA/OVhcGK+eqK4xdzmvOsbNcGepH9b1L+PPyOOoO1RL7Yj6fr25n6Q+zsTx5Fc8OdeyadgKeO+pM\nxl6Rwj/7VvUarEQAJISen7DuYnR0V2OXNJIZoKfxrkU8vmkWFcWNRDx/lJvPGEZ2znpkRLFnfjSl\ni2OojfTHkl/PtDdL2HSiiQ3PHuXBMD9+7lqt/f1MXn90F10nmlhRayAwzI/qz83j1fQoWt8rJamw\ngcs2z+aviaEYfriDLQX1ZFrtBIf6UXNLBi9fO4eTABpQ4qxYq33xIar73Jic50oMSRLKQggxuUmF\nspgSHCbi2/cRr5/myA3sKojThATU+S1b0qT4+i5zHbcgxJRSvumY8dL9c3YXG/zXuSteV0nNtprD\nSuBcgNVdR0s0qNI2TYiRixx+iPA0np5QlsdUhPAy9vO94ZXfwp/uwJm4vCqSSnUDT/3X2/y40Uja\nuwGki48AACAASURBVCdIHqqVw5Y0yrefIudEE5nbsvlBYihH/HR01naQ2mJmVrAPtR1W4jTK+XYU\nquo8tkah6weZ/G52JO0AV6VyKimM33/7Y35c3sLq4gb+nR5F60DHbTLi89CnfLmzi+iF0XzwxaUc\n7jvGYnMmsWODBu4lPFqO7rhVUO5bxp96kr0bZlK3MHrk56zHDzfyjuvPmYnUZiby4nc/wVJQz+Wv\nFHLtpiSe6lkf5IPtJ5fwBvCG63adVrQvHGNrXDD59y/nwM93c+nhOq5dFss76VGUvV/GNc/k8pX0\nKP4vNYIOgHgr9mpfILL73HQ5K8nFkPyUbUqQ+rA64gp0IYQQEyrW3QEIMRpdjSxpzcHun2Lc4zDs\nztAnJuzRp6WmKIoyvWdMgFYN2Lv2+LqflMXufKx8+gpQ3PrdXWMI6URRogAeML4o/+aEGJ1+T/UK\nz+fpjbMloSyElwmBToBQaOhJJgO0TMMe6kdXfAiFAIUNzvYUQ/nVFbx4RTLPBftQd6qV5ccbydJq\nsN25kCci/J1tKML8nIlMAF+dM4kZGUB5TzK5x5xptEUGUAEoB07Tq6dcjyYjPv/7EQ80mUidF8VH\nP72U1wcap9M4J+LrsPZ0ebs4AXrnOQvU09C3z/Noz9lQbkwnG6C+c2SVVo/u4lqTjbCvruJFgEOn\nuSI6kOM/3Mjbt86j+Our+Ztdxef5o2zq2SbB2l3Nbew+N9rh24sIAKLdHYAQQoj+uic6CnV3HEJc\nAK3pJBvadqE1Ha22Gbfn+NibW7JVVe010fNDqXUbPlx5olKvOMaklduFau5y5pD9VPOJhbbSNHfG\nIsQUJAllL+S5FcqKouBsDi6E8CJpcBbAD0yuy1u7Gx/4dSd9rXZ8RrK/B1axB9jTd/lrRdwFsGoG\np3qWRQdyprQZfLUDVw77ap2JW4u9fyK4vhPfb3/MA41GZi+M5oPBksndr6G9zQJ1BgLjQy6+SnlG\nsPOc6bW9z5nL8UZ1zgYzM8SZfLerw1+bPykn4ehZrrgsmRfTo2g9a8DPbCdsdqCzrzLAguk0+2gx\nNBiJ61k2vas7odzc3YpEfz7hL4YUhfQ9E0KIySiU7jZbQkxFqo0wQx6Z2iD7SdWYG6SLDi31XbrI\nruh083rGrAwzzjm5Mb89c9/cfadMvqsnOsYAq6O+nqgwgGvNO+oASSgLMTrS8sILeW5C2ZlMlg9f\nbnQJ3FgOiU0w3QRBOugKgablkPdL2D6vu5IU4GOIvBweGWxf8+FQPjw9kuPuhrDfw5L9sKABYjoh\nVA+WWKj6LGT/EnIH2q4C/L4Cm/bB8naIUEEJhuZlkPcL+HQRvSck+w4sfgdW1EK8EUJsoA+Glnio\n/Ap89F+MzYRpYnTugtJfg70ZottAGwp2gPZwtACNRueEc9GBNF7oMV4pIMNkI3JaACdcW1esmsHx\n3dXQYj6f4HTVszw+5Hw/ZoC6Dvy/8wkPNptIXhzDf368iTeHOn5UIKfPdpJxvJGYFXE0XOjr6LEp\nidI3jmM3WInutKIN9KFX5chYnDOA7adIBgjQDx2zxYbmb3lsjQ6k5MFV7HZdZ3P0ft9yqL2T88H2\n7idvqnD2vQujesQBPsIjWAf5MKSnnf/jmyPe10cspoBLMDATB3r8aCSeA9zABwRgG3S7j1lIIRvp\nIBE7fujpIJhqVvMfVlBxbtxR4slnCWdJx8w0bASho4NwSlnDhyyhasSxOkkfZSGEmJxkknHhEewG\nUtr2gE9M20F7S840n7SUHN2smQsVRQkDCNE7QvI2FK3+ZnF89tPV09aCMiZP4o1EYpPtZLHiPxdV\ntX/J9MrciTquEB5EKpS9kCcnlP3dHYC3y4bLpkNVGhSHQbsZfMsh+T24Ngc2/AcezYQW122ioGYZ\n5PXdVwbO9gIj8QPY9ClcFQKNqVASAe31EFEESx+D9P3wcQ686rrNSfBfAd9tgekxULkS9gKUwOwP\nYfMeWLsDfrqM89WOH8KiapiVAKcioE0HtjqILoYlX4Tle+Dvf4Vdoz5x4qIsAMN8OHQMVt0MWz6E\nNx0KqjEI/SuFpJ8xkKHTYNqS5mzjAHDLqzxishH54018z3XyvbMG/KYHYXbd/+Faov5VzB0KOG7O\n6F1FvCmJmueOUtZkIvXJ/az/yqrzv/8n97PeYCU2QE/DZcnnq5pr2gn47id8tdVM4vJY3np4I+8O\n9xozoigpqOfyE00kA/kXdqbOmxWGITGMQ6daWfXobra4JrQHO2emLrR5Z4jy0WJf5pLU3llF7KxQ\n2hJCe1dOHztLxL+Pc1t3/PuHiufR3VxtsBL9rXX8oWfZ9CDMfjpaq9qYZ7Gh8dXheKOY2TYHflEB\n1PaMC7Z330g87Uxek0jJqE6GFhNJfNJvub7338GQXuA6TrIZDRaiOYIvBppIpZTreIoM7ucJAp1t\nS86xo/AMt1PLBnxoZjq5+GLARAitJFNFYq+E8kfcgYEkAqlkOrnosNBKAvWs4C2W0sifubz/tXQI\n8iFQCCEmJ0koC49iPcMK61msAWdOVvlWVrf5LVtcoAkOWt+z/pfpNVmbo1sLbjqSGmFXlQGLNMaa\npjO8FUUJn+ZoOTzd0bRs+C2EEH1IhbIX8uSEsvRPdrOz8OA0+lfiXQ7XfwxXfxuu3gv/cF0XD9Xv\nwdsXc9ylcOoaeOwbUOq6/F/w7ufhOzvhsmdg/xc4X8H3TdjQAtOXwp7D8Fyf/d2dC2t+AJnvcj7Z\n9yG8ONDrewlm3AnfexE++zjs7amQFRPnWXj1Ekj6CK5JgNnxGk6efo+omnaWKODYPJsX4oLPt3dQ\nu59m0Gt7/662ZXOXwUpkVCCV/jqMLSaiazpYqKpor0jh+WtmuyT4ut2/nOd/uYdvfVzOnQX1LJkW\nQG2Tkdg6Awu0CtbbF/Csj7a7LQPwg+3c32omMUBPgwqaH+7g2r77zEwk9xKX3sZbZlPyejHGU63M\ng/7VzHuriXm5kKtcl3XZCfjK+9zd8/PXVvParLDzVfdfXc2r//cJSXlnuOaeN5kdE8SpNjMRg52z\nkibCHtnFNn8dTf+8me/1LP+knOW5Z7gqOpCSUF8afbWYW8xEne5ggUNFHxtE/tdX8+GAvzicCenD\ntVyTmcirC6fT7LpuRRwf7Kzi1v9+h2/GBFFR1swqrYLlrkVs7xkT1PMbPEsGWoysHHVC2cgdF3EN\nyiOBk1yDFiO38VNSu6u6HcDf+BzVbOINrup3jNe4nFo2EMM+7uZ5/PpcNyzOCvtzZrGfhTxDWp9q\n73dZyUG+wH7uZAP5/fYzOB9lmxKmPqwOOFmkEEIIt5GEsvA8Kj7GErJMFdaGoOYD+CRNy/VdOC9E\n0WpTADZGGuaXZOU3b9g792CdxWfFeIfTanE+2Han6a2RFxAIIVxJcYoXkoSyGDcDJVsBtsKhj+Hq\nM+M0CdRgLS1ugjOL4NBB2PAezHFNKNd2XwAvhaN9t7sUjubCmlYIdl0+2Ou7DU5/BeoaIKEAgteB\nJGgm2BLo2AM/uw82H4Ul++0k6wyY44LJv24O77kmgmvaCTDbCI/0p2xeVO/f1bwo8g/WsqGiheU2\nB36+OjpmhnLkpnQ+2Dhr4Kr51fGc3baRnzyTy5aaNuafNZDuo6UzMZQDn1/Au2sTOOM63mB1/u0Z\nu4g6XMeWgfY5LYBG14RyuD/WtEj2FjVw6Z5qYvrus85ASHkLa1yX2VV8XJe1mnkblzYuKeF0/PJy\nfvb7g2wub2FJYT3JOs3A52woi6ZzvNHI9CYTCY1Gku0OfPRaTNMCKFsay74vLWefZpBmRFY7ytOH\n2RoZQMXX17Cj7/r/XcunZht++fVkFTcwM8yPmpsz+GdqxPknBwIcaCghmg6SmcknBE3wpHz5LAEU\nEth1LpkMzilwb+INnmAjp8jCxjvoum8stOBHCVvwoYWtvDBgEti3z7LPnk+i97KZA+RzLWaiKWIG\nS0fV+iIKuV4JIcRkIwll4bFUK1Edh4jSlTYWBZ7JafNbNHeHNjZmpaIoAdN87BFFmYXh9xckZr9S\nF74eFO3wexw9vV01tNgjI1HUzs+b3l08HscQwgtIhbIX8uSEsrS8mKRehoUAMzmfIOvRCqFbndXC\nQeFguALKbx9Fu4vhaLurhXV9qoZToHY/8CksoE/LjU+7482E4pEc4y2IboHpfmBYCW1jE7kYrXQw\ndrc2efXoSjqrU7onaevj0wpSAeXyFN7ru+7LK9lLd/uT0ZgfTcvjV/LCSMa6VveOxm3z+eThHWT9\n+zhZaxN4xXXdjemcuDGd+0a7z4RQjI9e5jxnw41dHEPT27f1P8YN6ZTekN776YCR8tGiPn8Djw62\nXqPAD7L4D/CfwcYEONCwh0wUbGTy6aiDUNHxLqtoIwI9FuI4zSpOnEv+DsdECAAhA/SbDsOCDgM2\ngilkBou6r4F7WIQDX+LIxoHCByylmWh8MJNK2blxI6V0X9+0OEa1XZ+bZkIIISaFCHcHIMR4s7WR\n0bZTxXyqeHfA/MpSv+WLLRp/v5WKgvKnBZVZ101vzbszLynOgTLmBUmxrfaSKsUvfba9Mi8A87qx\n3r8QXkIqlL2QJyeUpUJ5krgJLjeCXyf4V0BiDaROg5rfwPt9x1ZARgVk9Pz8PPA9KHkOnt1I78ff\nR6sC/I7BUkC9GYpc1z0Ju7Jh5WFYHwczZkOZCkopzG6E2Gvh3z8boHoZ4BGYuxNmd4H2LEw73p2A\n/io8r2eESSgxroyB3RO1DaCogbRQX2puX0DBRMZ0sRbH0DQvik8LG9hU3MAH6VFSWQpQVU841WQx\nk+29KoRHqotQDnLvuZ8LgRwayeJZ1o4gUe7XXfXdPsCHqlZ8sREEQC0x5xLFZ5gFgAY7T7Kt18SA\n+cBOjnA3fxtRtfVBkjARi55W5o36ZlzAKMcLIYQYf1KhLLyGpZp1ltPGzsDqPQf9F8Xt8klPS1I0\nmhmbo9sWF2YW1q/bOzevuUs3plXEmo6IehRl2ZeML0tBmhAXLrxoq6LJeE4dbUGLmMIkoSzG3ftw\nhZHuqj0gGQr/CX9b5PK4fSRYr4R3b4W89Th7gr4N8b+Baythzg3wtUL4cRwX9vi6HdgCdxkhZDXs\nuIneLQKmgS0ffn0V3HoAMusgqWddBhy+c4CJAnt8Aumfcr5fbQC0fRue/UGfpLVwH4s/gzRZgEcv\n47WJjGUsfWUV7z51CEtZM5GSUHaqaCZKSeJ99ZoLqE6eyR5mUUoytYRg4RTT2MsmTrOBj3mQQH4+\nbLXwHI5RztVUs55ydpB8fpJH/sX1dPfrxuRSMW/prgwu50qCqOYK/sxs6igllh18nkaW8ncs3M+z\nQx67kQA+6U6GL+OfI66qPm/AKn4hhBBuJQll4V0cBHYWsNFUVlsTtPTsyYDV80s1kRHrYv26oks3\n5kfemZe04z8NoVmgDPr5fjQMplg0qr3ucuteaXchxIXT4Hy/ahpuoPAciqp6aAGlolwCpLo7DHHe\nYQh+BVL+AjdawO938Lt7GLq/pxE0qfCtOki6FV55mQtIEgGZcPNOuCweSo/BE+F9+h8XQuDVcH8T\nxHwRXrkPihygPA3pT8OtXeD7W/j1fXBqsGPUgs+HMP3XcEU+rNgCb75N/zYKYuJ9eANdVj/07o5D\nTIx7krE16sfwhunf+CyVXE4keTzAU8OOf5rbOU0mGsxM5wg+dNJEKgZm4U8dJuJYzN+5np0A/J7/\npoFlaLDwBR5iBu3n9lVDKH/lxzjw4V6+w8xBbhx04MPTfIV2UpnFB9zN6xfwShvUh9U3LmA7IYQQ\n46Roq/Jn4IvujkMId9FHkhe0OqTdf8WiUMVHvwjgldrwQ/cXJCarKBfVEkZRVZtPwZq85V0Fhj+3\nb9s4JgEL4b3mZDynnnB3EGLiDPoYuAeQR1YmmWXQ8QvIew2eMEPgN+Ge4bYJAMdVsAvgKKRdyHE3\nwU074bIEKN0Dv+2bTAa4E26uhrSvwAtPwKF0MM6Dzifg0IPw9y7w/SncNNRx4sB6N1Qfg2eSoegd\n+MzTkHghMYux1aVnXCbxEJNTsH3UvYOHto5sAFqZPaLxX+RFlvIcAdRxluVUk4UGG5fyBMHdbSgC\nz08kiA9GAEIo75VMBoinjRAqAIUTg1xPOvDhLzxAO6kk8tEFJpOdUQkhhJhspEJZeLWuJha3vNu+\nvvn5ne3W0opPVFVtvDWuZXnehiJziM5+US3rpnWoJRZ8Mh40vjhjrOIVwotJH2UvIy0vxIS7BJoj\noa4BEvIhaIFL64uBTMeZeLGCz2iPlQm37IRLE6FkH/wuZpCWGcXOyfi4D0r6rvtvKHkUODOK5PAK\nKCiHee9D2hehcrRxi7HTpceuaiWh7E1C7WPcuzyqO/nrwHfE21zHHmBPv+U7uQuAOS5PO4RyhtOA\nvjux3JeeTgC6Bqiyb8WXv/IA7cy+iMrkHv7KNkVRH/bUR5eEEGJKkoSyEKAxl7PBUlXRFrj49MGg\nSxfqZ4YEZ57ceCzqpiOp2TnNwVkXtNeOyFo/LPr5trILKlwSQvQiCWUvIxXKwi0MEAbgw/CVhAch\nGSCakU+wZQfWwG074dIkKDoAvx0smdw9Xg9wnO5epi56lmkGqGwezNnuD/+6Ebw+Mb7M/tjdHYOY\nWCFjXaF8zHkNws/Z3/2CZZOBlUhCOdGrdcUcjgNgIG7A7XqWT+vTk6wZf57hq7Qzm2T+c5HJZHD2\nd5b3TiGEmFwkoSxEN9VGqOGQ9bKG3x+KN+3Jf1+n2svfWl6W9av06n2gto12f+bOGPt15u114xGr\nEF4ocvghwpN4ckJ55JVkYsy9ATEHXSbi69EFyuVwvQmCZ8DJOTgr8v4ISW30ryL9OczZAZcBfB72\nua77GCIV+FMIPOK63A6shjv3wcYUKDgAv4+GrqHinQmlAN+BLV2cn8DNDMp34FqAFLqTPkAL6H7b\nneju62lI3A2ZCqifg8KhjivGnzlgjKtVp5iyZoKvfYk/fusjbnV3LBPlgiqUC4ilgYB+yyuIYC+3\nAZDA/l7rHuERfsifKO/z4allgCdkSoliN3cADtb3SfwuooZgyjARy5us77XuTdZjIhZfGljiUtXc\nSAB/5Wt0kMxs3uIu3hzNyx2CtL0QQojJRRLKQvRhN5DS8m7j1Y2/z26znqp97974hnkH1hW3Bmjs\n/Z42HYrZGhb1JdMr6eMVpxBepl9xnvBsntzywpOT5ZPeyzDvNbgpAUqjoSEEOlsg5CTMboOoAGh7\nHF7oGf8I3Pg1iEuGkkiclXtVMKMS5gJshjcfgHLXY9i7E7+aPlXA18KWQ7BeB9ZZUH0nXN03vkVQ\n/Sjk9fz8Y3j9bkjJgzWxkDinO3l8HNKbIdYPDL+Ac5NVNYL+Qfj2D+HMDKiaBi0W8KmF2FMwB+Am\n+NcNcOZiz6W4ON5eofzOCRYDyqr483/vni7oQhLKeSznJFcRRgmBNKLDjIEomliAip4I8rmRD/ts\n5bz5pO3zN/YP7sJEJKFU4ouRDqJpZCEqWpbxPCuo6Hf8zTzPa3yLXO7kFEsIpZZ2YmlmARqsbOJZ\ndC6v6wXux0AivjSgouHvzhtfvSwgl0XUjPJM9E+qCyGEcCdJKAsxCOsZVjY8fdwasKAyO/XaRYHl\nm/KXXnNw9s4j7YEbhts2yEhFoL3dFu1ojp6IWIXwAtJm0st4ckJZGX6IGC83QHEV7DwJqfkQb4EA\nPVgioH4NvPNr+CSd8/1CL4d9ObCkGmadgCAHaAOgfR4c+gJs/xqU9T3GDpgBsBIOui4/0927xwY+\nnwyQTAY4C3tdE8q3welI+PH34aoSSN/nrDAmGJrXwvZfwPvrOP+IegxYN8NbhZBWDmlFEASoQdC6\nCPbfDzvuZ4CkkZhwJi+vUC5sYIleg+HaNLxmxl3dhfzGkzlOO9NpJ4F2knHggxYToZSRyj6uYV+v\n25SNBGAlnGDKSHRpXwEwk3xOsIEzLMeBHzo6iOYI6/iAhd2T8vU1l7PcwU/4kC00MJ9W0tHRSTQH\n2Mi7ZPS5OWXq7lFmIYoytgy4z1AaLyChLBXKQggxuciNPiGGouJjPGa63HR8X0PoZXGffLLWPv+J\nypjd20rjFoMy6OcatWNa5e3md/rPTyGEuFCenF8UA1BUT5x7R1EU4IvuDkOMr0y4eS9kHoHvDjex\nn/BeBcvoPJXmnUmyug7873uHx5LDOfDEVTzn7ngmyuvhdP4tepx/5x+zkF18mSx+yyYuaobxSeaA\n+rDqNdXsQggx2RVtVcxIKz8hRkwbrimK+Nz8qqKQ+FlXHpyjtzg0KQONCzyZ9n529YOZ/ljkpo0Q\nY+O7Gc+pj7o7CDFxPLUthKe+LuGiBNJWw05JJouhODTe+7TCmyUsUEG3LJZcd8cykSbkDaCaNAKo\n8bBkMsj7pxBCTDZe3bpLiNGytzgyGp46dlXCv3MaSlYePjMn0LR7oHGLjKfskkwWYkxJywsv46kl\n6V6bQPImZ+Gn7o5BTH4Ojfe2vDh2lsVaBctn5lLk7lgmkmYifuP38NoEHMUdJKEshBCTi2P4IUKI\nvswlhg38Isf48RWVO382Y91HfzgdkwmKL4DWqqm/tePTWHfHKISHkYSyl/HUL46SUBZCAKAq3nk9\naLegq+1gflwwBSG+2Nwdz0Ty1De2CSKnTwghJhepUBbiQjkIaH+/8soH/v3P9H+EHnhfpzgqAXyN\nvicusR5Y7O7whPAwklD2Mp76xdFTX5cQYpRUr0wnw7+Pk2FX8V04Ha/rhytvABfFS//FCCHEpCUV\nykJcJEenLX7Rm7s+s6fkueY4R+ee9YbjRg2qfGQUYmx5agcEMQj5hQshPJq3JpSP1LFEAdtn5pDv\n7lgm2oS0vPBcXvovRgghJi2pUBZijATWNi/5qPaPjk6tvsPdsQjhgeR7hJfx1Ltykk4QQgCgeOHV\nwGpHqWpj4fQgSmKDMbk7nonmkI8yF0Mq4YQQYnKR67IQY0sTaO8KdXcQQngguQHqZSShLITwaN5Y\nrfr2CdK6HATNiyLX3bG4g1c1jB57krgQQojJRb6gCyGEmArk/crLeGpCWb4Qu9FiuCcAHqsFH3fH\nMhmkwZfD4Cdt0qTeLRSH991g2lfDEkDdkuZ9/ZNhkArlP3IPP+ExOuS6hAP4Bd/n13xzgLVe9+9F\nCCEmOfmCLoQQYiqQ9ysv46k9lOULsZv8BRKPwqrPwmtxYO1ZbgblG7DiLchqhmgr+AVBaxKUfQc+\nvAXqXPfzEsz4FVxSBYkdEG4FvwDomAZnPws7HoXckWZnfwlp34JvDLb+Enj/E3jDddk7EP01uKUa\nUvRgWQBHX4LXE8HSd/uF8IVKSCmAbQkDrH8I3twKD90Ll/wLPhph2GKMKA7v6+V0qpXFkf6cTI3A\nK/vD9bujeIREzrCKDF4j+Px1qZ8X2Uwp1wFwKY+zgeO91lcSxm7W0EIC7czEwjRA4fM8RBoNowry\nbdZwmLsHXb+IF7mBnH6vYwc3YyABPQbi2c/NvItfnw9vDuAJ/hcHOr7Gz9H2eU/UAEt5i138Dx+w\nlCs50mdrIYQQk4dcl4UQQkwF8qCol5GEshhTj8D1PmD+PWS7Ll8O/1UIy4OgZSHk+oO5Cmbkwprb\nYGUF/ObbUNIzfjvMLITF8VCRAicDwdQCoUWw8DG4/yPYnwd/HU1sCXBiHpzou3wDlLr+XAs+t8HX\nusB3BextgvC9sPEaCC6EP7uOfQgW5MPKn8LjAyWTAe6Cmh9B4btwTS1kuybaxfjT2bzrevBBGYlm\nG+HLYvnY3bG4S78K5RyuR4uZzb2vS73kMpMyNqPBggPfAcecYBYnuB5Q8aURLSbsBFxUsJHkEU5N\nv+WJVPb6uYow3uHr6DCSwE7amMFJNvMyeu7mX73Gvs4mOkjis/ykXzK5x2Uc5TB15HI9l3PE5Xkl\nqSwQQojJRa7LQgghpgJ5v/IynplQVlUVxeuKEt3uLYiugPQVsCsaunqWPw2JhbA8EmoL4GcxLgnV\nL8Lav8DWp2Cza0L5ETj4Z9jb9xgV4LccvnMUVv0JPr0PTo00vnlw4j14e7hxv4CFBoj4FTz29e5k\n81LYmgtrcyF4Cc6qz5Pg/wTcsRx2fY8+lYx9bIG9T8L8h2DlX2HXSGMWF8/f6F0VyjurWAJwZYp3\n9k+GPqVcJUTTSjpx7CLw/HWpFyM6PuRegqgkkHrOsHrAcWmcIohfMpcawjHzON+gjbSLCnYWeVzb\n/1rXzx5W4cCXW/kRKTQB8Dhfp4qNOPjXuYRwOZEUcQOzeYd5vZ/86CeRvRznRnYz16Ua2+smcRRC\niElOKpSFEEJMBZJQ9jKe2kMZ5MPXhHsS1gHKDXDIdXkhRAHMheMxfapzv46zx6sBglyXTxvkcYkk\nMM+DIoBjED2G4Z9TBREAd0FFz7J53YnrQ93rAG6FmwFehteG2+dDkKeFrg+c50hMID8vSyifbGFJ\nqC81S2K7k45eyKxx+Z3vc16XSO99XerlZW7EQiRb+BtDPeGSSCtrKCMc8xiGOzIGItHTcS6ZDBDO\nKRz4UO9y/XyLO/HnLDfzwbD7XMVBAPJZ77K0c8xiFkIIMRbkC7oQQoipQFpeeBnPrFB2siGTwk2o\nQkhXwLEVyl2Xr4baJ4HjMKce9K7Vy0/CQoCMYSp8e5wBn2KYA7AWTo8mvlqIuh02doB/NLRdD2Vb\noL7vuARoBvgHJD4IJwGKIRFgefe6n0L6YVj3ffhdyggq+qaBLQYqayHlJPiPZBsxNvxM3jUZ4ks3\n8bC7Y3C3Nq1LQrmedMDB4t7XpXN2M4cqLmEe/2QO9WyfoCB7NJDAqwRgQ08wLSyghERa+40LpJku\ngjlFBLOc1yFaSESDlWgMAPyb9bSRxvU8gn4EN1WTaEZPK82k46DnFrNx7F6cEEKIMSBFMkIIIaaC\ngZ8GFR7LkxPKZiShPGFqwaceEiKgrm+P4M9B7R/g451wWSr8aB4cCwBzNcSdhHnz4eDL8O+BLAls\n0AAAIABJREFU9vsuRP0OVjtAaYGQIljQCWGXwnu3jzKhfAxWHYNVPT8/A6TDkTfghTkuSZT/hfy/\nQsu34X9eg/1NEFYEy+ZC7hLoqAbfn8Odi2D/jyB/pMdPhsrTkPo8pGyDgtHELi6cn9Gjn8QQA2jv\nSSh34EMnCQRQN+BkfM34k83dhFDGTXw60XECUMWlvX4+hIMZ7OJ2XiHA5S7/WvZRyjW8xP8SwxHa\nmEEbc0nkIzQ4eyzn81mSeZ9FA/RkHkwIp2hiMcXEdrfIkAplIYSYXKRCWQghxFTQ4u4AxMTy5ISy\nCQhxdxDe4hCEqaAJgbaB1ufAq1vhzD/gln2wsWf5dKj8LOwdbKK6QxD9Pmzp+VkDthvhtX/CRyON\nLRE6rofXb4X8ddDUCvrXIfEPcH0xLL0UQirgMX33o+4JYHkBHv8G3HoA1ujAugqyX4LXAW6BG+2g\nfwle3gER/w23lUO6BhwZkPsGvJRE/0fiI7vPTYVL2wwx/nzN3lWhLFwqlGsJAzT4DHxd4p98DhuB\nXMevJvy2QyT/n737jq+yvP8//rqyEwIEwpa9lAMi4EClDlytq+7VIX7bfttqa4e2/f46Y2tb7V62\ntUPraa1WcaJWQRkKMgXCChvChhBIyB4nuX5/3OeYELLXdcb7+Xicx0nOuc99v3MI5+R8zud8rnzO\n5FkmkcNpFFJIKhsZy1pu4gAX8xQp3McTH24/gkKu5jcs5lb2cTGJlDCaN7mN1wF4hU+RRAG38Qbr\nOY353EkRY4ijkiEs55O8QEojRYmU4H1zlL7AXptl1VkgIhJe9LgsIiKRIN91AOle0VxQ7v4ZlzFs\nX3AGclojH5euAS6GO5bBpR+HVx+E5SOh/CUY9ijc/hB8ZRs8+29Y1PC2WbApC75QDPFLoO9jcN4r\ncNMEGL8KHu/diq6N2+HQ7dQtTjUMKs+ETXfDzinw/QMw9gcw+RFYF9rmRjhyI/y+4b5+DeOWwyUP\nwl/GQ9ml8PVS6PUA/L0Ykp+Eu66DxE3w14a3zQh2/h1vMC9aulacxcRXU1OTqMJyrPiwQ/lE8P9a\nYiNjHOYxlcOcz2SeYayDP34uZDvBRT8BSKeKoazmDHbxL35AHuexjrkndRufy27O5Ren7Ot1pnOc\niVzHzwgQz2t8hQTKuJg/cowBbOJW/kOAe3jxlNsmB++bUtLRuAsRkXDU+JuiIiIi4UUF5RgTzR8F\n14zabtQz2GFcDYkNr7sXLlgKl30EFrwCb10EhcOg8quwYw48lgBVs+HmfZDczP5rroajb8Ab18Oc\n7TD5M3BZRzKPgorpsBJgKYxrafs8SHwY7vbBml/C2kdhQh4Mvxte+jlk/xlWXAlv58DZbwYXIqyv\nIjiCJUWdJt0uqVIfF40lxfHBNw+Sg598qG3wuHSUNFbyKXqzhRt5t9sDNmckBfQLjtLZ3vLjEgfp\nyVruYCTvcA65vMN5VJPBJfybmWziVhYyiBXs4TJKGhkDVRO8bxKpQuMuRETCUcwusisiIhFFz1cx\nRh3K0inGQTFAKfRoeN2S4MJ7F8HWhtedC0V94XAeDJ8HAz8Le1s61q2w8VW4ab23OF+rR180pm8w\nd0UzxeyQ2+CGCkh7Gp4F2AiDAWbWy3w27HkDWAKDr4aj9W9fELxv+kNRRzJL2yVXUlOuvvCYURwf\nfLO0r/f/m6oGj0sH6UuAdE5wBj/iL43uZD5fZz4wkee5jfldGrih5OAie9UtPy7xEp8gkVJuYw4A\nBd7jEpPqPZb2Zw+HmMFu+nNmg9nzlcH7pifFqENZRCQc6QW6iIhEAnUoxxgVlKVTnAcnUqH4GAxq\neF0g+Ht2pIlRD2XQEyCtlYuObIcMgPhOWKRkE4wGOK1B8behx2HUYrjiXnhyarAIbYPXldb7f1TW\nSId2yP7gfXMxbVgwSzpFcvmH/1xR5cXNnP5UNg/ccDp//dw0VrvOEw7+spaz7LPcx+X8hhlsIYFi\nyhs8LvWmlCEsaXQHxxlPBQPow0ZSKWRQ2xb/7BQFjAKgT/OPS8xlGvlM5aP8kh7BTz7Y4LiPChJI\nD3ZoB5p+XKKEQYBlPAdQh7KISDhSQVlERMJduc9v1ZwSY1RQlk4RD4yE7Zth2hvQ/9p6BdpJsH07\nTH4Frvw2rB1TbxzJ3XBxCfRJgxM3wcHQ5b+DsZ+B3T0bFI3XQfqf4GaACwh+LDxoA6TvgPSxUHIm\nwQ4/4Pcw5l7YlcjJRcX7YPpGOCcOAvfRdDHuBMR/F2aNhQ1/DI7IAJgMB58DZsPke2AfwPxgN/ZH\n6s1sDtkLo1Og5HYcFKhi0A/gzKfhsjwYUr6YHimJnMhMZc+143jn2vHsas8+X9nCuCfW8iBgpgzi\nvw/P5NXGtnt9G6Nf3cq1x8oYXWNJSE/i6OSBvP/181mQFH/y7+HBYlKfyuaiQyUMyy9jWGkVAy3E\nfXoyv7l9IluayhKoxby8mdt7J7P/M1NP/v396lvcs6uAC5q67Xc+QtYFwzjc8PKn13PmolwuO1HJ\nkKoaeqQktO8+u302Py0PkNnYdcnxFL1wO9+sf1lpFfGPvs91W/OZXl1LWp8U9n7iTF64YvSpn1h4\nbhMTnl7P1+6YyGOfmnzyYwDArWez+vV97GUZt3EhPyaD7eQzjW30Z3zwcWkkBXyefzUa/nHu4TAD\nmMbbXNT0/d9q++lNMakM5AR9641iWsZYLmDHSdvWAs/xMYoZTQIlTGdTk/vNower+ATDWHTSfjI5\nyF5gOZO5juUAHGAyhgCjGhSoy0mglGH0YC+ZlKOCsohIOFJBWUREwp2eq2JQNBeUNUO5m10BazbD\ntGdg4rX1Ftj7Hby7GKbnw9Az4eGJsC4dynbD8D1whoHa++DZlHoF35/AXd+BXsNhZ384Hge1eZC5\nHSYFIOl0b2bx+/WP/y2Y+RZc9zF4/U14LXT59+Cz3wUzEnZlQkEVJObCiEMwKg5qPgtPX9HMA+Ct\ncH0JZMyB39a//P9gy+9hz5tw3TmQWQHJm+AcH6xuOO7iFRhYDH3PhcVaGa7rXQY3L4SPpkDJRMhO\nTKLwQApD9hcx5fHVTNt7gn/cey4r2rLPvFKSn9nA/8Qbqmps06MI/Nmc9eJmvhhnqB6ZwQepiZTm\nFjJ5yV5u31PImD9de/KCjVvyyVy2n1sAUhIoSIqnpLKGXi3l+fsazj1RydBrx/FEnGl8G19/5qcm\nnPpYOLhn3RsuId+dz83r8/hoYhwlIzLITkukpKCcAe29zxLiKD9r4KmjIpLjT32z7+H3uHnTUa4Y\n1os1PZMp2H6M8/+4igeG9SLr9H51ixHll5H8Yg6fHpXBisaKyQAV8dRyJm+xnM8zl3MZzRrymcYG\nJjL+1IU/2+xx7vnw67Jg5/M8bmFB8OeawhLOr1fgfZ2bOMwFnM1TXM+yDy+fyzd5lyP0JpdUCqkm\nlQLGUMZpxFHFRTxBn2beGH2RO4mnilt5+aTLr2AFG7meNXySw4yilP6c4HRGMu/DjuWQlYzHksBQ\n1gQvKWjPXSIiIl1KL9JFRCTcadxFDIrmgrI6lLvZw7DmCShaCOdTr6A8DCrXwM+/CFesgmnZcF4t\nxKdAiQ9WfwXmfQFy6+/rFpi3CKYehGE7wFcLCSlQMhK2XgvLfwUftLYwezm8mw0TcmHMZm/shkmH\ngqmw9AGY/6lmRlD8E4YugKvugX/PgML618UDs+FP/wt3rfM6nWunwrLZ8FzD/TyG1y36ZTqhoCXN\nWgW9FsFVaVC0BH40FYorDTVvX0t8aETEoj18vK0F5Z8u5s5ALalnD+HNlQe4sbFtjpSQMmcbnwZq\n7zuXX101hj0ARZW8ev+bPLiviLP/uppzPn82H4RuMz6T43efxW/OP429w3pT1lJ3ccjy/VyaEEf5\nXZNY29Q2d01i/pRBLb8Q3X6MXuvzuCo5nqKfXcmPxvQJzh6mbqxGW++zxDjKHrq07o2dptRa2JLP\nxWP6sPS3H8MP8PJm1j6ZzTde2Mz0717EvNC2jy7h5lpL4jcv5D9N7a80nhouYR2rKGMzl/Alfs1a\nith98uNSux1u5N8mn6kffn2UrdCg87gxI5nHcUZxnDMI0ANDLUkcZyiLuJS3GdvMH2ULOJMjnMfl\n/IbeVJ50XQ+quYHf8TZ3cIAZxFPJcOZzJ6+csp9NXIAhwKUfvjnX/IgNERFx4bjrACIiIi1QQTkG\nqaAsnaY31FwB8+fATU/BsHuCYyDAKyq/AW/gnVr0Z1gBbSv4BbuSTylgvQxz8U5tdjfsvxvua+r6\ni6BwC/y5uX0UQMJyuGAkbLlb85O73GrItGCGwu7QvOvkSuITKwncMoGtT6+nojLgze1uLX82Z+0s\n4MJrxvJkjQ0u+NaI5zcxraqGnqMzWBYqJgP0SiZw/Xhe8a/jgWX7ubR+QXloL8pu87VttMKyfQw6\nVs6YURms6J0SnJ3bATuOkwmYfmnsrl9MBmjvfdZauwvpWWNJGt6b3aHLLhtF7pPZUFBeNzbjlS2M\n23qMS246g78M69304nElcVhSCTCAbA5xIbvpz2jms5WbyGYYU+oelxr1RZ4Cnmry+of4Qht+vKb3\ndw8vtmk/9V3GBi5rJsckDjKJ3zS7j0P0JJ8pDGY5gykGTtgsW9XsbURExAW9SBcRkXCnT9PEoCYL\nI1FAIy8ceALe6QnHfwofd50lXHwRLimD3g/DbNdZYsElcCQOAvth5IZ6C0H2PEH1K1sYF6glZVA6\nm1u7v50F9JyzjU+f1pPsljp0tx3nDADfgFNn314/nu3xhqpjZYwuqerYm3nv7/OOM6oP25vbbsFu\nJv3kPT76yGKufCqbKUdKSGlsu0kDOGIgkF/GyNzCkxfPbM99BlBrSfjzKqY//B5X//x9LntxM6dX\n1XDKcI5RGRTHGar2FTEidNmiXO/rPqneHyYnKkj8z0buHtaLNZ+Z2nRHNkBx6KMLg9gJQA5ncAPv\nkMRx3tPj0ofe5GrAcg1zgpeoO1lEJDwdcR1ARESkBXrzMwZFb4eytQGMCRDNP2MY6geBh+DJd+D0\ng5A0BGK+4y0JAp8Df3OjNaTzTICyW+Gl5+G26fDQJMjuBaU7l9BnbxVnD+xBzjdn8HRr9/eL9/mU\ntZhvXNjybQorvJm6ozNOffGXnEBtWiL5xVUMWX+Efhc2sihea+0uZBzApP51XdCNWZjLJ+p//+pW\nKmYM4+VvXHjy6IdhvSmbMZyXluzltgfn8dCI3mSnJVJaUE7//cWc1db7DKCyht7/3cFnQt8v3gvP\nbyL/zok8ddOEukJ4nIEz+rE45yiXfekNUtOTKdx+jOkJcVTcfIa3COYjS7ihqoa0By/k2ZaOeyK0\n6OFIclkLHGY8aSxiJk+yk9MpJomeMf64VAukcYILeJKhH86oVkFZRCQ8nbLQs4iISJhRQTkGRXux\ntQJO7raTrvcAbH+A5jsnY8m/4F3XGWLNczB/FBz7LcxaBRcBUAk9Esk7ZwjLGo51aMofVnDhgWKm\nfPx0/jq2b8u3qa4hFSAjtfFPSCTFe5cXlJPW6h+mESVV9AUY1rtuwbr6xvZh26QBbDj/NHaPyKBo\nx3Ey5u5k6soDXPfuHu5KiKPma+ezuP5t/m8G8wf24Nicrczafjx4n9H2+wzA15+lkwawfcogDmam\nUrkhj36vbmXmtmNc9NQ6vpKRws9mjqp7g+X7F/PSo0uo3naMcw+W0CMjhX13TmT2hP4UvrmdUZuO\ncsW143hyRG9KHlrEdRvzuLiqhp69U9h/u4//XH96sBsZ2J8U7ILuH7xvyr37igvYzgV6XAK8zybd\necoYIBWURUTCkM9vS3NmmSJoecFeERERRzTyIgapoCwiUedmuOoVuOkjsOC7sPAsKHopiX4/SuS2\nN7bz2b0nGPbTy5ufYZt9mMwFudwxvBer/3caqzsjl8UrdhoT7KJtp8oAPQAGpzc+S/j+6Syt//20\nweRPG8zb/1rP4ec38eXFe7nxvnNZkhRfl+Mni7lqxX5u8vVnwe0TWTgqg6LVhxj0n43c1Nr7LOSh\nS3m9/vcXj+DgxSP497fnU7kxjyuf28T1M0fVzR5PTyLw48t4GXi5/u1Kq4j/13pmDenJhi+ew8qf\nvc/lqw9x/dmDeX1Cf3a8tYNrnljLVyf057uhgv++ZLyhF5nB+6ZazwGtYFFXgYhIODuECsoiIhK+\n9FoiBkXzDGWg6YWbRCQ6/QLGvwy3nA7r3oPZH4X8QVB1XxUHfzWTx1ISKNyQx5VrDtGvuf38fgWz\n4gzV35zBM609dmKwA7mw3OtUbqi6xpthnJHSsRnvCXHeQnzFVSS25XafnsyGlAQKq2pIX7aPIaHL\nX9rM+OX7ueW0Xqx79ApmTxtMfp9Uqq4Yzd5fXMmfW3ufteTmCV63fl6pN7KjJY8u4fryABlfm86/\nAT44wFUDerDloUt57Y6JbH7gfP5RY0n65zpmhm6zPylYUC4L3jfxMT7eonUKbJYNuA4hIiJNOug6\ngIiISDNUUI5B0d6h3OjHwUUker0BkwGmwtaG142soKRfGrv3FzE1+zDDpg1u+omvoILhgVpS73+T\nXzV2ffZhrrn+Wa45rSfZj1/nddtmpHC4sIIRuwoZCOytv31lgLiyavoZqJk8sGNPuCkJFJ2ohEMl\n9Bjaq21vnCXHU1wRIKO0mqTQZasOevfZ6D6n3md9Uqlq7X3WkuG9vC7iGktyS9vO38WwdUe46orR\n/HtCfwqPlJBSUUPGuB7eXGWAMwdyPCmekqNlXnG8ylBbmBB8XjvudXGT2PpRHTEsz3UAERFpluYo\ni4hIOFNBOQZFe0G5wHUAEele1cHHtePQs+F1Aw9gy6u9yxPjqGluP+P6sqy6tq7oGlJYwcD8Msb1\nTmZf/x7sOa0n+0LXje/LltxCpufkMRFYVf92r21jXI0lqV8a29OT6FA3aP8eHDhSim9LPoPOHdL6\n2beHikktrmIQYMf1rZtzFaj17rPiylPvM4DW3mctWZjLaIC0xOYzVwaI+0c2swb0YOtXpvN+/etC\nWUNqbV2Xdn4CAQj+m+31Fkgko+7fR5qk+ckiIuFNHcoiIhLOdrsOIN0v2gvKha4DiEj3Og+2L4WZ\ni+Gi9+G9GfUeB/62g2nHqhkTZ6i+ZGTdQm63z+an5QEyH57Jd6YM8gqtP7+S5xrb/2MruWDuTsaN\n6sOGh2fyav3rbp/ImkV7uGV3IefO28nCq8awB6CokoTXtnEjwAVDWdTRn9HXn60b87hy2zFGAxvq\nX7f9GL0KK0luWGjOLyP5h+9yT60lcWAPcsZlUhS6bnwm27fkMzPnKBdtPsp7E/rX3WfPbGDisfJT\n77PyauKzD9M/KZ6as+sda/FeBo/szYlhvU/unF5/hL6vbOGuYP4Vzf18j77P1SVVDPjWDP4Uumxg\nOhUpCRTuPcHEygBxyQnUvryZcYFaUvqneS+0jyTWKygf8IrXjDi161pOccB1ABERaZYKyiIiEq7y\nfH6r2lsMivaCsjqURWLMz2HNK7A5FybMhB9OhLV9oGg/DN5ezZmAmTGMl4b3pjR0m9BieYnxHevA\nHZhOxfXj+ddLm/nCn1bx4Js7WJWaQNnuQiaXVDFoWC9Wf24aHzS83bfnc2tZcPG4wyWMBZi3k4++\nv4/zAaYOIvueKWSHtr9uHFtf2kxZbiET4eSi9oY8Bv0jmwf7prKrTwqH0pMoLqok42AxEypr6J2W\nyNGvTudf9W/zP1NYs3w/m/NKmfCdBfxweC/WpidRlF/O4IPFjd9nW4+R8dMl/DA1gWPP38Z3QpfP\n38U5aw/zsQE92No7mfzkeCoKKuh/oJgzay2Jg9PZ8MD5zGvqPly8l8GrD3LNxSOYPXkgx+tfd+4Q\n5i7eyx2ff51vDkpn947jTI83VN59FgsBDiXVW+zwCD7iKeM8FZRbcNxm2aKWNxMREYd2tryJiIiI\nE1tcBxA3orugbG0lxpRD4wtkiUj0SQS7Hv7wObh0MZy7CaYGICkFSsfCxpmD+e/HZ7ArtP3+ItIq\nAvTJTGXHxP4d/1TDPVPIzkzll3O2cU1uIdNqLYnpieR9ZBjPf/0CFsSZU2+z8zjTygNk1r/sSCm+\nUPm2TwrHoK6g3CeVqvGZLMs5yuVL9zHowmEcDl03PpOj4/qyOK+UEfuKOKu6htT4OKp6JnFk8kAW\nfvEcFgzoQWX9YyXEYR+7mj/8fiWXbsrj3L1FTK2pJSkxntLB6Wy8fBQL7phETmt+/rMGsiW/jIHH\nyhmWX8bo4H7K+6WxY9pglt97Dssbuw8Aqmowf1vNrMw0dj9wwamd3N+4kAUVAVI25HHJ5qMMz0hh\n/20+nh/b15uTvDfJe2OArQygmNEMZz7pWpSvBbmuA4iISIta9RwsIiLigBp4YpSx1ra8VSQz5jrw\nFmwSETk6iMoVM+sWhfvnOibPzuFLd07iD588k40us7VF9mEysxbxo9Mzea+p8Ryx5rtDqVzfg2T+\nwa3sZSafJIuxWiCiBS/ZLKv7SEQkjOXMMnFAKZDiOouIiEgDD/r89teuQ0j3i3MdoBtolouIfCjz\nCElxgbrRFjlHGd87mf2RVEwGmDKIYxP7s2DrMS7afJQM13nCwf5k4tlPb/ZxCcNZqGJyi0pUTBYR\nCX8+v61FHWAiIhKe9PwUo6J75IVHc5RF5ENxFtM3n8r8QaQBPHoFL7jO1F5fnc4bf/6Ayh3HyZzQ\nCeM6Ilk11B5PIIGDZDKauVzNfNeZIkCu6wAiItJqOcBZrkOIiIg0oIJyjFJBWURizqB9kD/IdYqO\nG5hOxUOX8rrrHOHgeAIBIInz2MV5dTOypVm7XQcQEZFW0xxlEREJN1XoNUXM0sgLEYk5g/eRTC1R\nPkA+thxOqhtjIq1SAXWLOYqISNhTQVlERMLNDp/f6nVYjIr+grK1ZUCl6xgiEj6SK4nPzKPCdQ7p\nPNtTqHWdIcLssVnRviqviEhUUUFZRETCzRbXAcSd6C8oezT2QkROMmobxnUG6TybUol3nSHC6KNp\nIiKRZQfeR4tFRETCheYnx7BYKShr7IWInGTgAZKTKqh2nUM6R04qia4zRJAyYL/rECIi0no+vw0A\n213nEBERqUcdyjEsVgrK6lAWkZMYMEN3q9MnGuQnUFUWrw7lNthqs6xGhIiIRB6NvRARkXCiDuUY\npoKyiMSs0VtJxmpxvki3I4WA6wwRxAKbXYcQEZF2UUFZRETCiTqUY1isFJTzQEUjETlZSjkJfY9q\ncb5ItznFdYKIstdm2RLXIUREpF1UUBYRkXBxxOe3J1yHEHdio6BsbRVw3HUMEQk/o/QhnYi3KU3j\nLtpAxQgRkcilx3AREQkX6k6OcbFRUPYcch1ARMLPoP2kaHG+yBWA2h0pJLnOESEKbJbd5zqEiIi0\n2zbQmCcREQkLa1wHELdiqaB80HUAEQk/BszwnVqcL1IdTKK6xmBc54gQG1wHEBGR9vP5bRWww3UO\nERER4H3XAcStWCooH3YdQETC09gcUuOrqXGdQ9pue4r+3VqpDNjuOoSIiHTYB64DiIiIAEtcBxC3\nYqegbG0FmqMsIo1ICBA3eqsW54tEm1JdJ4gYm2yWVfFdRCTyLXYdQEREYt4On98ecR1C3IqdgrJH\nc5RFpFFjckhNqNJcwkizMY1E1xkiQBVayElEJFq86zqAiIjEPHUniwrKIiIACTXEjd6qWcqRpMxQ\ncyhJBeVWyLZZttJ1CBER6Tif324F1BUmIiIuaX6yxFxBWQvziUiTRm8mRV3KkWNrKiqStqwELcYn\nIhJt3nMdQEREYpo6lCXGCsreHOUC1zFEJDwl1BA3ZrO6lCPFsnTXCSLCSs1OFhGJOiooi4iIK/k+\nv93iOoS4F1sFZY/GXohIk0ZvISWpgmrXOaRly3qS5DpDmMuzWXaH6xAiItLpVFAWERFXNO5CABWU\nRUROEl9L3NgcFZTD3cFEKgsTSHCdI8wtdx1ARES6xAbguOsQIiISkzTuQoDYLChrjrKINGvUVlLT\nijWfN5yt7oHGODRvt82yh12HEBGRzufzW4te0IuIiBvqUBYgFgvK1pYDha5jiEj4MmCmLQUs1nUW\nadz7PWPw+av1aoEVrkOIiEiXetd1ABERiTnlwGrXISQ8xOoLco29EJFmZRwnedguylznkFNVGGpy\nUkl2nSOMbbJZtsh1CBER6VKaoywiIt1tlc9vtYi9ALFbUN7rOoCIhL+Jq0nVAn3hJyeVSmswrnOE\nqQpgjesQIiLS5dYCxa5DiIhITNG4JflQrBaU94OKRCLSvIQa4s5aQcB1DjnZ8nTXCcLauzbLav63\niEiU8/ltDZpjKSIi3UvPO/Kh2CwoW1sD7HMdQ0TC38CDpPY/qNEX4WRZT5JcZwhTW2yW3eM6hIiI\ndBuNvRARke5SCyx1HULCR2wWlD25rgOISGSYspzk+GpqXOcQOJBIZWECCa5zhKEi9AeeiEisUUFZ\nRES6yyqf3xa6DiHhI5YLyntABSIRaVlyJfETstEYgTCwuodGkDSiFlhgs6zuGxGR2LIKKHcdQkRE\nYsIc1wEkvMRuQdnaauCA6xgiEhlG7iCt32GNvnBtSU91Jzdirc2yea5DiIhI9/L5bRXwjuscIiIS\nE15zHUDCS+wWlD25rgOISOQ4ZzEpKWVUuc4Rqwrjqd6cRrLrHGEmD1jrOoSIiDjzgusAIiIS9fb4\n/HaD6xASXlRQ9j4qLCLSooQAcdMXQlyNHjdceK+nivkNBPBGXej3UUQkds0Bql2HEBGRqKbuZDlF\nbBeUra1AYy9EpA16FpF05ioqXOeIRf/NINF1hjCzzGbZItchRETEneACSfNd5xARkaim+clyitgu\nKHt2uA4gIpFl2G7Shu7WPOXutC+JygPJJLnOEUa22Sy72XUIEREJCxp7ISIiXaUYeNd1CAk/Kih7\nYy8CrkOISGSZvILUnoXqVO4ub/emxnWGMHIQeM91CBERCRuvoNczIiLSNeYGF4EVOYklkhGAAAAg\nAElEQVQKytZWA3tdxxCRyBJnMdMXkZBQpRdwXa0G7PxeWowvqACYp7nJIiIS4vPbY6h7TEREusZL\nrgNIeFJB2aOxFyLSZinlJJy9hAC1WNdZollOKhVFCcS7zhEGyoE3bZY6BERE5BQvug4gIiJRpwIt\nyCdNUEHZsw/QC3QRabP+R0g5ayXlrnNEs7kZrhOEhQDwls2yJa6DiIhIWHoJ0KdXRESkM831+fX6\nQxqngjKAtTXALtcxRCQyDdtN2unrtEhfV6g01CxNj/lxFxZYYLPsUddBREQkPPn89giwxHUOERGJ\nKrNdB5DwpYJynRzXAUQkco3LIW3kVhWVO9vKdCqr42L+uWqZzbK5rkOIiEjY09gLERHpLJVo3IU0\nI9ZfpNexNh844jqGiESuSWtIG5KronJnejMj5p+nNtosu9F1CBERiQgvgtZ1EBGRTjHP57dFrkNI\n+Ir1F+oN6UW7iHTItGWkDdqronJnKIynekMaKa5zOLTFZtmlrkOIiEhk8PntAWCF6xwiIhIVXnAd\nQMKbCson2w0qBIlIx5z9PqkDDuixpKMW9IrpxVI32Sz7nusQIiIScVQAEBGRjqoA5rgOIeFNBeX6\nrK1Fs5RFpIMMmHPfI7X/IRWV2ysAtS/2jdnu5PU2y77vOoSIiEQkzVEWEZGOesHnt4WuQ0h4U0H5\nVJuBGtchRCSyGTDnLSJ16G4VldtjeU8qixKId53DgWybZZe7DiEiIpHJ57e5wErXOUREJKL9xXUA\nCX8qKDdkbTmwy3UMEYl8BsyU5aSNX08pVovktMUzmTFZTF5ms6yKACIi0lFPuA4gIiIRK8fnt0tc\nh5Dwp4Jy47Q4n4h0mvGb6DF1GRWmllrXWSLB5hQq9iWT5DpHN6oFFtosu8F1EBERiQrPACWuQ4iI\nSERSd7K0igrKjbH2KJDnOoaIRI/T9pB6/gKq4qs1Uqclz2W6TtCtAsBcm2W3uw4iIiLRwee3JcCz\nrnOIiEjEKQf+5TqERAYVlJumLmUR6VSZR0m5aC41yeVUu84Sro4kULU6PWYW4ysC5tgsu891EBER\niTp/dR1AREQizmyf3xa4DiGRQQXlpu0CLaYlIp0rvZiki98kLv0Ela6zhKOX+sZMB3cu8JLNsvmu\ng4iISPTx+e0HwBrXOUREJKJo3IW0mgrKTbG2FtjsOoaIRJ/kSuI/MpfEwXv0plV9JXEE5mZEfXdy\nLbDCZtl5NstWuQ4jIiJR7W+uA4iISMTY6PPbpa5DSORQQbl5m0GLaIlI50uoIe7spaRNXUp5fCBm\nunKbNa83lTUG4zpHFyoDXrdZdp3rICIiEhP+DZS6DiEiIhFB3cnSJiooN8faMrzRFyIiXeK0PaRe\n8ga1vQqocJ3FpWqofbFvVHcnHwRetFn2sOsgIiISG3x+W4xXVBYREWlOGVqMT9pIBeWWrQWs6xAi\nEr3Syki86C2SR2+mlNrYfLxZ3pOKogTiXefoImuBN2yWLXcdREREYs4fXAcQEZGw95zPb0+4DiGR\nRQXlllhbAGx3HUNEopsB48umxwULqEyqoNp1nu5UC/bZTBJc5+gCJcCbNsuuslk2Jt8oEBERt3x+\nuxFY6DqHiIiENY27kDZTQbl1VqNZyiLSDTKPkjLzdeL6H4ydBftWplO+L5kk1zk6US2wDnjeZtl9\nrsOIiEjM+73rACIiErbW+fx2hesQEnmisSOs81lbjDGbgYmuo4hI9EusJn76u6TlDaZi/bnEVfSI\nqmLrSQJQ+9cBJLrO0YkOA0tslj3uOoiIiEjQa0AuMNJtDBERCUPqTpZ2UYdy660FAq5DiEjsGHCI\nlMteI/H09ZTFBahxnacrLOxF+dHEqCgoVwDv2iw7R8VkEREJJz6/rQH+5DqHiIiEnRK0eKu0kwrK\nrWVtGbDRdQwRiS1xFjNuE2mXv4YdtI+oWtStylD7VH9SXOfoBFvxxltsdR1ERESkCX+H2BmnJSIi\nrfJnn98WuQ4hkUkF5bbJBqpchxCR2JNcQcI5S0idMY/K9BNUus7TGd7IoKIogXjXOTrgODDHZtl3\nbZatcB1GRESkKT6/LQD+6TqHiIiEjTLgl65DSORSQbktrK3CKyqLiDjR5xjJl/6X5EmrKEuqoNp1\nnvYqM9Q80y9iu5OPAe8AL9ose9h1GBERkVZ6FCL3bwcREelUj/v8Ns91CIlcxlrrOkNkMSYBuBNI\ncx1FRGJbrcHuG03FDh/x5emRtXDfP/tRNjsz4h5HjwBrbZbd6zqIiIhIe+TMMn8BPu86h4iIOFUO\njPb51Rwj7aeCcnsYMxGY4TqGiEjIoaGUbzsTU5wR/l2/J+KpnjWGhBqDcZ2llQ4Ca2yWPeg6iIiI\nSEfkzDLDgB0QWW9Ei4hIp/qdz2+/5jqERDYVlNvDmDjgDqCn6ygiIvXlD6Bi62RsQX9SXWdpyp8G\nUPZmn4joTt6L15F8xHUQERGRzpIzy/wJuNd1DhERcaICrzv5kOsgEtlUUG4vY8YDl7qOISLSmBMZ\nVG07k0DeYFJsfPjMy89LoOpzo0m04dudXA3sBHJsls13HUZERKSz5cwyp+E91yW7ziIiIt3ujz6/\n/bLrEBL5VFBuL2MMcCvQx3UUEZGmBBKoPTCCin1jMIV9ScFxIfdng6lY0issx3IcArYCu2yWDbgO\nIyIi0pVyZpk/ACooiIjElipgjM9v97sOIpFPBeWOMGYkcJXjFCIirVKZQmDvGKr2jyS+tFf3dyXl\nJlFx/6iwKiYX4HVo7bBZtsh1GBERke6SM8sMBnZBWD0vi4hI13rc57caeSSdQgXljjLmo8AI1zFE\nRNqiuBdVe8ZSfWg4SZWpJHb18WrAfn0E1btTnC8CVIRXRN5ps+xxx1lEREScyZllfgt81XUOERHp\nFtXAWJ/f7nUdRKKDCsodZUwP4Hbo+oKMiEhXKE2nOm8I1XmDoaAfSYEkEjr7GPN6U/qHQfTo7P22\nQglwEG+kxUGbZYsdZBAREQk7ObPMILwu5bBdyFdERDrN33x++3nXISR6qKDcGYyZBFzoOoaISGco\n7kVV3hACRwdDQSbJNYnEd2h/cQQ+M4a4irhuWRywhGDxGDikURYiIiJNy5llfgU84DqHiIh0qQAw\nzue3ua6DSPRQQbkzeAv03QAMcB1FRKSzncigqqAfgeIMbHEv4sp6El+RQiJxrVvg79eDKFvYm7RO\njlULFAMngqfjqIAsIiLSJjmzzABgN3T687SIiISPf/j89jOuQ0h0UUG5sxjTF7gZuqUDT0TEqVqD\nLe1JdVEGNcUZ1Bb3xpT2JK46ifjqROJqE7yu5m0plD84ot0fpQ0AZdQVjYvqfV1ss/QEJiIi0lE5\ns8zPgW+6ziEiIl0iAJzh89udroNIdOn0OZkxy9rjGLMemOI6iohIV4uzmJ5FJPUsAhpZ1qHWYKuS\nqcq+lLmMoBZvznxC8DweqMH74yZ0qq7/tc2ygW75QUREROTnwL1AuusgIiLS6fwqJktXUIdyZzIm\nHrgN6OU6iohIGFiBtetchxAREZHm5cwyjwD/z3UOERHpVCeA8T6/zXMdRKKPxjN0JmtrgMWuY4iI\nhIF8YL3rECIiItIqP8d77hYRkeiRpWKydBUVlDubtQeAba5jiIg4VAu8iz4CIyIiEhF8fluAOpRF\nRKLJBuAx1yEkeqmg3DWWAxWuQ4iIOLIea4+5DiEiIiJt8iSwzHUIERHpFPf7/LbGdQiJXioodwVr\nK9AfYyISm04Aq12HEBERkbbx+a0F7sNbOFdERCLXcz6/fdd1CIluKih3FWu3A/tdxxAR6UYWeC84\nT15EREQijM9vs4E/uc4hIiLtVgo86DqERD8VlLvWYiDgOoSISDfJxtpDrkOIiIhIh3wfOOw6hIiI\ntMuPfX57wHUIiX4qKHcla4uB913HEBHpBoeBD1yHEBERkY7x+e0J4Juuc4iISJttB37tOoTEBhWU\nu5q1W4EdrmOIiHShCmA+1lrXQURERKTjfH77NKD5myIikeUrPr+tch1CYoMKyt1jMVDkOoSISBdZ\nhLWlrkOIiIhIp/oSGt8nIhIp5vj89i3XISR2GDWUdRNj+gM3oCK+iESX9Vi73HUIERER6Xw5s8wv\ngG+4ziESDl7eAd9b2vw2cQY2fNr7+jvvw6s7m99++iB48qrWHb+qBl7Y7u1zfwlU1sDgHnDBYLjH\nB0PST97+aDn8fBUsPwwGb7tvnQOZqafu+3dr4dkt8OoNMDCtdXkkrFQAE31+u8t1EIkdCa4DxAxr\nj2LMSuB811FERDpJHrDSdQgRERHpMj8E7gJOcx1ExLUz+sJ9kxu/bnUerDgMFw2pu+zyYXBaj8a3\nf20X7CuBi1r5PytQC5+ZB2uPwujecM1ISIqHjcfg31tgzk54+moYm+FtX2vhSwtgZyHcMAYqauD1\nXbC3GP59tVf4Dtl8DJ7cCD84X8XkCPYLFZOlu6mg3J2sXY8xQ4DhrqOIiHRQFfAO1ta6DiIiIiJd\nw+e3JTmzzNeB511nEXFtQl/v1JhP/Nc7v3V83WWXD/dODRVVwZObIDEObhzTumPP3+sVk88fBH+7\n8uSC8GPZ8Of18NQm+PEM77KN+bDpGPx0hldQBhiaDn9c5xWhJ/fzLgvUel3X5w2CW8a1LouEnT3A\nI65DSOzR+IXutwgocR1CRKSDFmGtHstERESinM9vZwNvu84hEq62F8C6fK+795JWdBy/ttPrGL5i\nOPRJad0x9gX/6r546MnFZIDLhnnnxyvrLjsYXN3kzH51l4W+PlTvL/i/bfS6ln94QetySFj6qs9v\ny12HkNijgnJ3s7YCeAdQV5+IRKpNWJvrOoSIiIh0my/jfTpJRBp4fpt3fvNYiG9FheWF7d75beOb\n366+0CiLJQe8cRb1LdrvnV8wuO6ywcFRGznH6i7bGPx6cHDW8o5C+Mt6+Pq0U+cvS8R42ue3r7oO\nIbFJIy9csDYPY5YCH3EdRUSkjfKBLlmEzxizBG/O/BnW2h1dcQw5lTHmISAL8Ftr73GbJroZY94G\nLgOmWGs3uM4jItJaPr/dljPL/Aj4sessIuGkIgCv7/a6hlszMiL7KGwrhJG9vAX5WuuS07yO5nf2\nwo1zvOJxYhxsOg5r8uCTZ8AnTq/bflIm+PrCQ8u9URkVAW+G8qRM71QTHHVxVn+46/SmjythbT9w\nv+sQErvUoeyKtTnAdtcxRETaIDQ3uaazd2yM+TgwA/hPS8VkY8x1xpgnjDGbjTEFxphqY8wxY8wq\nY8wfjTFXGGPiOztjVzHGTDHGPGSMucd1lu5kjDnDGPMdY8w8Y8xBY0yVMeaEMWalMea7xpiMVuyj\nlzHmx8HfhbLg78F8Y8ytLdxusjHmi8Hfo/XGmIAxxhpj/tPFuX+M97eX5tyJSCR6BFjsOoRIOHlr\njzcT+aIhdV3BzZkd7Ga+tY3zio2B314CXzoLcovg6S3wjxxYeRjOGQDXjjq5Ozo+Dv54mVeInpsL\n7x2AK0fAY5d5xW9/jjeq40cXePn/bzGc+wxMfdpbzO9IWdvyiROf9fltoesQEruMtbblraRrGJMA\n3Ag0MdpfRCSszMfanZ29U2NMHLAe8AETrbWbm9huPPAsMK3exQGgCOjFyZ+62QLMstau7Oy8nS1Y\nSP4H8K619lIHx3+Ibu5QNsbMAJbUu8gCJ/D+HUMvh/YDV1trNzaxj6HAe8Co4EUlQAp1vwePW2vv\nbeK22cBZjVz1nLX2zi7O/R5wEXCRtXZJY9uIiISrnFlmOLAOaPFNP5FY8Mk3va7jx2bCzGHNb1tc\nBTNf8BbCW3hr6+cnA1TWwLeXwOID8M1zvGOlxnvdxz9d6c1M/s3FcFkjiwA2tKcIbn4N7p8C90yE\n+xfCqsPw7fMgPRF+shIGpMGzV3uFbAlLj/v8jf+dK9Jd1KHskrUBvAUuNI9MRMLd+q4oJgd9FJgI\nLGmmmDwVWIFXTC4Avgf4rLWJ1tpMIAkYDXwBWAucAVzYRXml4xKBauA/wLVAL2ttHyAd+CRwFBgK\nvG6MSW14Y2OMAV7AKybnAjOstT2BnsC38NYp+KIx5n+bOH41kA38He93Zm535A56Inj+9VYeU0Qk\nbPj8di/e46ZIzNtR6BWTB6XBxa1ZjG8XlAfathhfyN83wNw98NWpcPt46J8K6Ulw0Wnwm0u8IvUj\nq1rej7Xw/aUwvg/c7fOKywv2eYXlG8bA5cPha1NhQz6sONy2jNJtdgHfcB1CRAVl16w9AcxHi/SJ\nSPjahbVdMjc56HPB80bHDRhjegIv4nVDbQemWmt/Ur/4bD27rbV/tdZOA+7Cm/cs4WkH3qzsu6y1\n/7XWlgBYa8uttc8Atwe3G1Hv6/puAKbjPXfeZK1dGrx9hbX2F8Dvg9v9yBiT1Mjtz7fWTrXW/q+1\n9q9Aa18ydTQ3wMtABXC9MWZAK48rIhI2fH77PPCU6xwirs1u52J8t7dhMb6Qdw945+c1Mnf5jL7Q\nO8nrUi6saH4/z2yB9fnw8IXe6IudJ7zLffU+M+3L9M53aphCOKoFZvn8ttR1EBEVlMOBtfvQPDIR\nCU+HgYVdtXNjTCZwPd7ogNlNbHYvXidqDXCbtXZPS/u11v7HWvt0g2ONDM7JbXLWkzHm0uA2ua39\nGRrcPs4Y82ljzNvGmKPB+boHjTHPGWOmN7K9xRt3AXBJKF+906VtOPZ0Y8wjxpjlxpgDwWPnGWPe\nammmcHez1u631u5q5vpFeJ3HAGc3sskng+fvWGuzG7n+l3i/U4PwFsFruP92zQHvhNxYa4vwOqIT\nqfs5REQizf14b7KJxKTKGpizyyvK3tyKecjrj8LWAm8xvsaKwi2pCv7lcryRgnFVDZRWe18nNrOK\nyIES+O1auHcyjA0NrQn+VVxVr72tqtNXS5FO9EufXyPTJDyooBwurN0KfOA6hohIPSeAuV2xCF89\nM/EKa9uttUeb2ObzwfM3rLXrujBLhwQ7qecC/wSuADKBcmAwXrfqUmPMlxvc7AjeDGjwRikcaXBq\n1UgkY0w6sBz4f3idu/3xumD7440UmW2M+Ut7fzZHjgXPG3tpdGnwvNFRFdbaA8Cm4LenFJS7WHO5\nQ94Pnl/VxVlERLqEz29L8N4UC7jOIuLC3NzgYnyntXIxvmB38m0tFJ+Lq2DXCTjaYFG8acHPNP1t\nw6kF3z+ug4CFSZnQI7HpfWctgxG94LOT6i4bEywsL9pXd9mi/SdfJ2HjA7yxfyJhQQXlcGLtGryF\npEREXCsH3sTayi4+zozg+erGrgwuvDYm+O0bXZylo0KF5PV483V7WGt7A32A7+C96P5dcGE3AKy1\ng4CvBr9daq0d1OC0tJXHrgX+izfq4zQgxVrbK3js+/EWrPu8Mea2Dv+U3cAY0xcIvdzZ2OC6AUC/\n4LebaFpO8NzXuema1lzuBkJvIF8YXJRSRCTi+Px2Jd6iriIxp7UFYoCSKngzFxLjvDnFzXlnL1z/\nKvxm7cmXf2GyN6t5+WG47lX40XL42Sq487/w942QEu8tqtdk3m3ewnsPXwgJ9f7yGNHLm+n88k54\n4F34wVJ4fD2c2Q+mt6OTWrpMCXCXz2+rXQcRCUloeRPpZouBNKAV67OKiHSJAPAW3kfzu1roT9/1\nTVw/od7XTW3jnDHmCuBGvHEHM621x0PXWWsLgUeMMTXAz4BvA9d15vGttWV4ReyGlxcCjxljTuAV\nvO+j6dEi4eT7QDLeH88vNLhucL2vDzazj9B1g5vZprM1l7u+UKd9L7zf8eYK4yIi4exRvE9bXOI6\niEh32VkIa/Javxjf67u9xfiuHtn2xfhCBqbB7OvgiY3w3gF4eYfXTdA/FW4c43Udj+7d+G2PlMGv\nVnvbTOh76vUPXwhpCbBwn9fpfMlQ+N50MKZ9WaVL3O/zW40ZkrCignK4sdZizDt4xQYt1iMi3c0C\n82l6/ERnCxX7mlpAr/6fvQWNbWCMmQk828hV+6y153YgW1vMCp4/Vb+Y3MAzeAXlmcaY+PbO8W2n\n14Ln5zs4dpsYY64CvhL89geNjEKp/8HS8mZ2FfqwaHpnZWtOK3LXV4A3Ezwe7/+ACsoiEpF8flub\nM8t8Cu9N3z6u84h0hzEZsOnu1m9/5+neqTVuGuudGtM3Bb55jndqi4FpsPyupq/vlQSPfKRt+5Ru\n9R+f3z7lOoRIQ/qYZTiy1usOrJurKSLSXd6nFYvedaLQ6IJGi8WtlAwMbOTUv2PR2uTC4PnXjTGH\nGztRN+YgDW++cqcyxiQYYz4bXITvkDGmst4ihKH7N4UwfsFvjPHhvTkQB7wO/Laxzep93eQCi92p\nlbk/ZK21eDPKoe7/gIhIRPL57X7q1jsQEZHOkwt80XUIkcaooByurK3Am4fZXPeViEhnWoe1OS1v\n1qmSg+dNLT5Xv9u30UKotfYta60JnYD/7cyArRTqtO5N48Xt0CkkrTMPHlyU713g73iL8A3C64A9\nSt0CfyGtWDqm+xljRgHz8LrSlwF3BguvDZXU+7q5+zF0XUkz23RYG3I3FFqnPbWrsomIdBef374A\nPOk6h4hIFKkBPunz2xMtbinigArK4cybX/oWWj1ZRLreTqxd4eC4oYJxU+tIb6739eQuztIRoefT\nG+oXt5s55Xby8b+P1yWdjzd+Y6C1Ns1aOyC48F/9CX9hNxEvuPjifLyc2cA11trSJjavPzd5SDO7\nDV13qOMJG9fG3A2F3iA51hXZREQc+Aqw3XUIEZEo8aDP3+oFukW6nQrK4c6bwfgO3sx9EZGucAhY\n5OjYodnJTXUf7wd2Br89ZdG5NvrwzTljTFNLojSxnEmLQh3AvnbevqNuC57fb639p7U2r8H1Axve\nIFwYYwbhFWVHAVuAq4KLCTYqOJs49HszsZldh/4tuqTrvq25G9w2mbrO5Kbmh4uIRBSf35YCdwGV\nrrOIiES4J3x++zvXIUSao4JyJLB2L7DEdQwRiUqFwDzcLdK2NXg+qplt/ho8v9YYc1YHjlW/2De0\niW3au4jfsuD5Le24begNw450Dod+nrVNXH9FB/bdZYwxmcDbwHhgF3B5C4vZhSwMnl/ZxH5Po67Y\nPL+jORvZf3tzh4wMnlvq/g+IiEQ8n9+uRvOURUQ6Yglwn+sQIi1RQTlSWLuFugWdREQ6QwHwOta6\n7CR6P3je3HrVfwZ2A/HAbGPMiPYcyFpbgrewBcANDa8PFgk/1559A08Fz88xxjS77rcxpmE3dmgB\n1qbGfrRGaLbamY0cLx34bgf23SWMMb3xZg9PAvYBl1lrDzZ/qw89Ezy/qok3GR7AK9Afoq743Ck6\nmDsk9MbFZmutRl6ISFTx+e0/gV+7ziEiEoH2Arf4/Lap9WVEwoYKypHE2jWoqCwineMY8BrWljnO\nEfr0xVRjTHxjG1hri/E6fwuBccAaY8z3jDET6m9njBlgjPkU8NVmjvd88Px7xpiPG2MSgrc9H2+8\nUFJ7fghr7VvAS8FvnzTG/NAYE1qoD2NMH2PMDcaYVzn1Rfam4LnPGDO9PcfH65YF+LUx5hJjjAke\n91y8Dt1+7dmpMeYhY4w1xjS6yJwx5qng9blt3G8P4A1gGl7R9zJr7Z427OJVYAXe3zEvB//9MMYk\nG2MeBL4W3C7L2lP/IDfGpBlj+oVO1C0OmVT/8mAxvjNzh4QKyovbcVsRkUjwLbw330REpHXKgBt8\n/lNG14mEJRWUI41XVF7uOoaIRLSjeJ3JFa6D4L1JtgvoAVza1EbW2rXA+cAaoC/wMJBjjKkyxhw1\nxpTgzTH+F17n6GbgC43s6tHg8TLwipIlwdsuC+73Kx34We4GXsHrpP4BcNAYU2iMOYG3+OArwMcb\n+dm2A+8BCcByY8wxY0xu8HR+K4/9PbxZvMPw5mGXBX+ulXhdy3d14OfqCrcAM4Jf9wKWGGMON3F6\nqeGNrbUWuBWvc30UsMwYUwyUAL/E+/vmcWvt35o4/rfw/h+ETncGL7+pweWPdWbueq4Jnj/XzDYi\nIhHL57c1wB1okT4RkdawwCyf32a7DiLSWiooRyJr16OZyiLSPkeANxyPufhQsDD4ZPDbO1vYdive\naIzrgX/gzZ4twysOV+AVm/+MNy94orV2biP7KAAuxJvLfBDvefAY8Ae8rtP9HfhZSq21NwHX4XUr\nH8BbeC0J2IE3puFWGp+JdjPwJ7wCaTowInhqavHAhsfeBZwHPA3k4RW1C4F/A+daa7uqSyzUhb2q\njber//dHD7xFA5s69W1sB8EFG6cAP8VbFC8BKMYbcXG7tfbeNmbqltzGmHOAsXhvbCzqgowiImHB\n57eFeG+kFrW0rYhIjHvY57cvuA4h0hbGey0vEcmY8cAldGwhJxGJHYeAt7C22nWQ+owxQ/BmGxcD\nQ2yYFLulecFxIQV4hdUp1nuzU1pgjPkV3ozn71hrH3GdR0Skq+XMMtcCc1Azk4hIY14CbvX5VZyT\nyKIn9Uhm7Ta82Zi1rqOISNjbD7wZbsVkgOCCZn/B6+j8H8dxpPXOxuumflnF5NYJLuj3WRofpyEi\nEpV8fvsG8A3XOUREwtB64G4VkyUSqaAc6byPOc8DalxHEZGwtReYi7UB10Ga8TDe/Nv/Cy2UJ2Hv\n4uD5w05TRJavAL2BnwYXmxQRiQk+v/0N8EfXOUREwshR4OM+vy11HUSkPTTyIloYcxrwUbwZkiIi\nIbnAO1gb9p9kMMbcBJwFPGWtzXUcR6TTGWO+DPQBfq7RLiISa3JmmXi80RfXtLStiEiUqwYu9/nt\nYtdBRNpLBeVoYswg4GN4C0CJiOwEFkZCMVlERESiX84sk463uPhZrrOIiDj0eZ/f/s11CJGO0MiL\naGLtYeANQF1PIrINWKBisoiIiIQLn9+WANcCB11nERFx5LcqJks0UEE52lh7FHgNKHcdRUSc2Qy8\niz6CIiIiImHG57cHgOsAzQ0VkVjzJPCA6xAinUEjL6KVt5L81UAv11FEpNtYYPtIPNkAACAASURB\nVCXWrnMdRERERKQ5ObPMFXiNMCmus4iIdINngU/5/PoEqUQHdShHK2tPAC8DB1xHEZFuEQDeVjE5\nMhlj7jHGWGPMItdZREREuoPPb98BbgaqXGcREelirwB3q5gs0UQF5WjmrSD/X2Cj6ygi0qXKgDlY\nm+s6SGsFi6ftOS1qsJ++xpjvGmOWGmOOG2OqjTFHjDHrjDHPGmO+YIwZ3UyOm40xrxhj9hljKo0x\nRcaYbcaYt40xWcaYS4wxpsvvkDBhjMkwxjxkjHnIdRYREYl+Pr99E7gVqHadRUSki8wF7vD5bcB1\nEJHOpJEXscKY8cBFQLzrKCLSqfKBuVgbUXMIjTGHm7iqL5D4/9m77zDJqmr94993SAMMOScZBBUK\nIwLKVRQERLwgiIGkDvpTFBVBvQIX9QrmgIIgiKgwJZKDBAOgIjkIKCI2GYacYQJhAjPr98faRdcU\nVdVdPd1THd7P85zndJ2zzz67TldXd6+zam1gJjCtyf6rI2LX0sdbgPOBVev2Tyff55au23ZeROzS\ncP6lgLPI0kA1s8l6jssx/w3XFSJial/PaUFI2hs4EbgsIrYaynP1MY6JwL0AETFmAulmZtZdPZO0\nK3A6sGi3x2JmNoguA3aoVMNzXNmo4wzlsSLiDrJG2fPdHoqZDZopZGbyiAomA0TE6s0W4OrS5PQW\nbWrB5OXpDSbfCewFLBMRy0XEBGANYHfgHJpnPR1BBpPnAN8FJgLjI2JFYAJ5A+5HwGNDcwXMzMys\nplKNc4CPAHO7PRYzs0FyLbCjg8k2WjmgPJZEPE4GVx7v9lDMbIHdSMTFxJj96NTuZDB5FvCuiDgl\nIp6t7YyIRyPi9Ij4APCx+gMlLQvsXR4eEhFfjYj7onxkJyJeiIgrI+JA4BVk1rOZmZkNoUo1Tid/\nP7vGqJmNdP8kM5Of7bOl2QjlgPJYE/E8mal8R7eHYmYDMpsscXFjtwfSZa8r65si4sF2DSNelhXw\nGmDx8vXv+zh2dsTAJs+QtI6kH0u6RdKMsvRI+rWkrTvop1Y/emKL/RNrbZrsG1cm/PubpKdKjekn\nJP1H0gmS3lPX9lJKuYuG89aWQ1uc+2hJt0t6vjzHGyUdJGnpxvaNz0fSRpKqpYb1HEnn1rVbVdKP\nyvV7TtLM0u5qSd+UtG5/r6GZmY0MlWr8Fvgk4LqMZjZS/Qd4d6U6tCXzzLrNNarGooi5wKVITwFv\nBVwn02xkmApczBDX8x1h1pCkWnbxAKwF3DaYAwKQ9AHgJGDJsmkm8CKwUVm2IctsDLWTgD3rHk8D\nlgVWBiplubDse5qsyb1yedxY7mO+DAtJuwInA+PLphfIQP0mZdlL0nYR0apsyJbAccBSwAzy+tT6\nXhe4hixdAvkR6Onk92ttYAvg4XK8mZmNIpVqnNgzSYuR7/H+P8XMRpK7gG0r1Xiy2wMxG2rOUB7L\nIv4N/In8yLiZDW/3A+c6mPySG8r6FcB3JC3RwbH/IYOfAD9qlfk7UJK2AE4jg8l/AzYHloqIZcgy\nHe8HLhnMc7YYxzvIYPI84IvAshGxPBkAXpP8WPGVtfalPvVmdY8b61cfXtf3ZuRzXAz4AbAuORHi\nUuSNyuvILPLftBniscD1wOsiYtly7JfLvm+QweS7gHcAi5f61kuWfr8NtJrY0czMRrhKNY4H9uv2\nOMzMOnAfsE2lGv4b1cYEDTypy0aNrCe6PbBCt4diZi8TwD/GSomLUnbhnUA1IvZu0248cBNZvgIy\ne/sSMpB5PXBdZImfVscfBvxfeTi3HHc18Hfg2oh4YAGew3VkEPlyYNuIaDYpYOMxewMnApdFxFYN\n+2q/qNeLiClNjp1IKVUREarbfiAZ7L0wInbo59ib9tWk3ZXA24AvRcQRTfavANxCBq43i4gb6vbV\nns89wGublCRBUg+Zyb17RJzen7Gbmdno0zNJB5AT6ZqZDWcPA++oVOPubg/EbGFxhrJBxHTgXODO\nbg/FzOYzHThvrASTOxERM4F3AX8om5YHdiUDqJcAUyWdX7KFmzkUOIQs47AI8F/A/wBnAPeXGsOf\nl7RYJ+OStCEZTAY4sD/B5CFUm0xwVUmD9vte0vpkMPkFWpSciIhnyE/AAGzXoqufNQsmF7Wxr9Fi\nv5mZjQGVahwJHNTtcZiZtfEAmZnsYLKNKQ4oW4qYQ8TfgL+Sk36ZWXfdCpxFxOPdHshwFREPR8SO\nZCbr/5LB5UfK7sWAnYCrJO3f5NiIiO+RNXknkdnB/yGzlSFrCx8NXCJpqQ6G9dayfjoiruvwKQ22\nv5Dv55sAl0r6iKQ1B6Hf/yrrxYF7JT3abAF2L+3WadHPNW3O8cey/oGkYyRtLWnJNu3NzGyUqlTj\nh8DXuj0OM7MmbgG2qFRj0OdkMRvuHFC2+UXcDZxFb1DGzBauF4ALibiCiBf7bG1ExG0R8f2I2DEi\n1iQDzIcBz5OT+fxE0iYtjp0eEb+JiE9ExGvJCen2IIPLAG8HvtPBcFYr6/sH8lwGU0TcBexLvqa2\nJCfoe0jSvZJ+LulNA+y6ljW8CPl8Wy1Ll3atAvJPtDnHD4DzyaD1Z8ms8+mSrpb0FUnLD3DsZmY2\nAlWq8R3yd9rcvtqamS0klwNbVqrxULcHYtYNDijby0U8C/yerCU6r8ujMRtLpgBnEtH1YORIVgLM\nhwI7kDWox5FZyP05dmpEnAZsSm9QeVIHJSOG1Wz0EXECsB5wAHAe8BQwEfgMcKOkQwbQbe1a/DMi\n1I9l7xb9tAwKRMSsiNgZ2AL4IXAt+b2sPb5D0hsGMHYzMxuhKtU4DtgFeK7bYzGzMe8s4N2VqidM\nt7HLAWVrLiKIuImsrew3SbOhNQe4jIiLydrANggi4nJ6a8O/usNjZwInl4crAKv089DarM6v6OR8\nfagFXse32L9cu4Mj4rGI+GlE7EI+j82B35HB729Jen2H43msrF8ladEOj+1IRFwbEQdFxBbk92EP\nMvt7FeBXQ3luMzMbfirV+D2wFeCSYGbWLT8DdqtUY1a3B2LWTQ4oW3sRTwJnAzeT2WFmNrgeBc4m\n4vZuD2SUqmUxDaQ2fH0GVH+Pv7asV5T01rYt+692U2/tFvs3629HpXb09cCHgAfJvwPeXtfkpU+l\nSGqVbV2rfTwBeHd/z72gIuK5kj2+T9n0ZklLtzvGzMxGn0o1biDnLPDfTma2sB1SqcZ+lWr4k9w2\n5jmgbH2LmEvEteTHpZ2tbDY45pFlZS4gYnq3BzPSSNpMUtvMXEkbA7WyCDfVbV9Z0hv7OHYcsFt5\neF9EPNOfcUXEbeT3FeCHkhbrz3F9+HdZ79y4Q9ISZDmLl5G0eKsOI2IumRkPsETdrvrXYtM6xeU5\n1gLnP2gX1JW0ZBljR9qNnawJDZlh3a6dmZmNUpVq3EtOEntVt8diZmPCi8DelWp8r9sDMRsuHFC2\n/ot4HGcrmw2GZ4BzibiJCP8sDcxuwH1lcrltJS1T2yFpJUn7An8hf889x/zlEVYH/inpz5L2lrRu\n3bHjJW0FXEz+owpwVIdj+xL5R+eWwIWSNq3rf2VJu0s6ueXRL3dGWX9K0sdrAdoSMP8jsGaL474r\n6SxJu0hasW4Mq0k6iqytHMCfa/siYirwcHn48TZj2g+YBbwWuKJ8DxYt/Y+TtLGkrwF30zuJXydu\nkfTdcuNg8dKvJG0OHF3aXN/fQL+ZmY0+lWo8DWxL1jI1MxsqzwE7VapR7fZAzIYTOZZhAyKtBryT\nFhlsZtbUPPKGzI1khqg1kHQp+d5SbTOZG5K+BxzcsHk6sCiwVN22qcDuEXFR3bEbAj3MP4HeLOB5\nsk5vvWOB/SI6+1ibpN2ByfRm/75A1kKeUB7fFxET69rvDZwIXBYRWzX0tRhwBfCWsunFMtZlgaeB\nT5D17okI1R13JLB/XVfTyee8TN22r0bEdxvOdxjwf+Xhc8CT5esjI+LIunY7AKfSW8N5NjCjjKs+\nM3tiRNxXd1ztD4/1ImIKTUiaWtfvXGBaGXet3yeBbSLi5mbHm5nZ2NEzSQJ+DHyx22Mxs1HnCeC/\nK9W4vtsDMRtuhnQyHRvFIh5DOht4PfBG5g8emNnL3Q9cQ8S0bg9klDgE+D3wHmALYENyojaRf/jd\nClwE/DIinqg/MCJuK1nJOwHvIN/H1iEDoTOAKWSd4BMjy/10LCJOk3Qdma387tL/vDKuq4CTOuhr\njqTtgK+TtY/XJAO95wCHtTn0CDJDeBtgIzJTeAngAeBq4JiIuKLJcd8s/e8FbADUMrjnu4EYEX+S\n9GoyW/m9pe3yZBD/duBC4Mz6YHIHdga2J78/rwBWIwPWt5JZ2UdEfmrGzMzGuEo1AvhSzyTdB/wE\nfwrXzAbHPcD2lWrc1e2BmA1HzlC2BSctBWwOvLrbQzEbhqaSgeQHuj0QMzMzs9GsZ5J2BU4Gxnd7\nLGY2ov0DeG+lGo91eyBmw5UDyjZ4pFXImqOrdXsoZsPAbPIPkVvosFyCmZmZmQ1MzyRtAVwArNTt\nsZjZiPQ74GOVajzb7YGYDWcOKNvgkzYgM5Yn9NXUbJS6Hfg7ES90eyBmZmZmY03PJL2KDCq/pttj\nMbMRYx7wNeD7pZSOmbXhgLINDWlR4A1lca1uGyseB67GtV3NzMzMuqpnkiYAvwD27PZYzGzYewrY\no1KNP3d7IGYjhQPKNrSkCWS28gbdHorZEHqezEi+o9sDMTMzM7NePZP0KeAoXFfZzJr7B7BrpTqg\niaTNxiwHlG3hkFYj6yuv0u2hmA2iecC/gX8QMafbgzEzMzOzl+uZpNcDZ+JJxM1sficCn61UY2a3\nB2I20jigbAuX9GpgM2Dpbg/FbAHMA+4AbiJiercHY2ZmZmbtlRIYxwN7dHssZtZ1M4H9K9U4vtsD\nMRupHFC2hU8aR2YHvAFYrsujMevEi8BtwM2EZ/01MzMzG2l6Jmkf4Ke4BIbZWHUH8OFKNf7V7YGY\njWQOKFv3SALWA94IrNzl0Zi1MxvoAf5NxAvdHoyZmZmZDVzPJL0BOAOXwDAba04GPlOpOjnIbEE5\noGzDg7Q2GVhes9tDMaszE7gFuIWI2d0ezGgn6UrgrcCGEXFXt8djw5Ok2h8u60XElG6OZSAkrQvc\nCdwUEZt3ezxmZmNVzyQtQ5bA2L3bYzGzIfcCsF+lGr/u9kDMRotx3R6AGQARDxLxe+BcYEqXR2P2\nPHAtcAoR/3AweehJeh/wNuC0xmCypHGStpZ0oKQzJN0rKcrymQGeb+e6PgZ0Z1XS4pI+I+kESf+Q\n9LCk2ZKml8ffk7RGP/t6p6TfSrpP0kxJT0i6UdJPJL2ySftNJX1L0oWS7pI0TdIsSQ9JOk/SLgN5\nTjb0IuI+MjtmM0kf7PZ4zMzGqko1ZlSqsQfwGTKJwMxGp1uBzR1MNhtczlC24UlagcxYXh/f+LCF\nZwbwL+B2IuZ2ezBjhbKu+s1ABdg4Im5t2L888EyLw/eNiOM6PN8EsoTJOrVtEaGOBp39rA48Urdp\nLjAdWB6o9TcNeH9E/K1FH+OAY8h/ZmumAhOARcvjj0bEbxuOOw74dN2mZ0v7+nqQZwN7RMScDp7W\nsCfptvLlNhHxUFcHM0CSXkXWY7+TfM37/cbMrItKCYwzgVd1eyxmNmiC/Dv74Eo1nuv2YMxGGwfq\nbHiKeIYMwJwG/IecDM1sqEwFLgVOJ6LHweSFbntgY+DKxmByneeAK4AjgD2BRxfgfN8ig8nXLUAf\nkNlMRwK7AmsBi0fEimRQ971kwHA54MwSFG+mFkyeARwArBQRK5Q+NgC+BNzX5LhrgC8CbwaWiYhl\nImJJ4BXAj0qbDwAHL+BzHHYiYsOyjMhgMkBE3AlcDryGfK2YmVkXlcm53gxM7vJQzGxw3AG8o1KN\n/RxMNhsazlC2kUEaT2YvvgZYpsujsdFhHnAvcCsRD3d7MGOZpLPJoOznIuLYJvtF/r6aV7dtCrAu\nHWYoS9oE+DtwE3As8GsYWIZyP861PlAr3/GxiDipYf92wMVkZvNWEXHlIJ77JOAjwD0Rsf5g9WuD\nR9I+wC+A8yLCJUrMzIaJnkl6D1lbeZ2+2prZsDMXOBw4tFINl7IxG0LOULaRIWJmqWV7KvB78o6j\ns5ZtIKaRmam/JeKvDiZ3l6SVgJ3Ij6Sd2axNpHnN9nV4rnFkAE/AvuRNhSETEXfTW6qj2YSjh5T1\nCYMZTC6ub3PetupqS0+UtJGkqqQHJM2RdG6T9juVus2PlhrSj0u6QNL2fZynIun00v4FSbdJOkzS\neEmHljFMbje+Fv2uL+kXku4p9aifkXS5pE9KWqTFMZeWPveWtGQ5/+1lXI9LOq2UqWj1XHaW9EdJ\nj5Xr9HQ5/lRJu7U47GzyNfjfklZtd63MzGzhqVTjQvKTU78g/z4xs5HhX8BbKtU42MFks6G3aN9N\nzIaZDAA+jHQV8Eoya3n17g7Khrm55GSPzkYefrYGFgPuiIgnhvhcnwc2BY6LiOslbTyUJ5P0GmCF\n8vDehn1rAVuVh5OH4PT/1ey8HdoSOA5YiizJMd9NPEmLAScCe9Vtng6sAuwI7CjpRxFxYGPHkrYF\nLqC35vN0YD3g/4B3kyVoOiZpR/LGRK3facDS5blsCewmaZeIlh99XBa4CngTMIsM+K4C7AZsJ2nz\ncqOg/pzfoffmAOS1WhJ4dVm2Bk5vPFFEPFXqQVdatTEzs+6oVGMG8JmeSToN+BU5r4uZDU+zyZJ2\nP6hUR9fcIWbDmTOUbeSKmEPE7UScT9Za/gc5MZVZzWNk3d2TnI08bL2trG8cypOUAO63gSeYP/g3\n2OcZJ2mNkpX6h7L5fjJ4Wu+tZT0LuFHSPpJulPScpGmSrpH0uRK07e+5J0h6vaRjyAAowM8W4Okc\nS2Y6vy4iliUDy1+u2/9DMpg8haxrvUxELEeWJfo0GST+iqQ9Gsa5MvmePZ4sP/K6ctyE0t9rmX+S\nwn4pJUZq/V4GbBgRy9eNZxawLfDTNt0cRt4EeA8ZiJ4AvAN4EFgR+F7DOSfSW6f6e8AqEbFsqWe9\nGvBBel8HzdxQ1lv25zmamdnCVanGpcDryTkThvSTTWY2INcCb6pU49sOJpstXM5QttEhYjr5j/kN\nZODo1WS2m1/jY88M4E7gjvK6sOFt87K+eYjPczQZWPxCRDzTV+NOSfoV8P+a7LoJ+HBEvNCwvVY+\n4SHgBDIgC1kiYxky4PxWMqN2h1YZtZLWBh5osmsm8N1mNak78DiwQ23skZMu3F3O+yrgC+SElttE\nxD21gyLiWeB4SVPJrNuvAqfW9bsfsFLpf/uImFqOmwOcIulFBpatewgZBL4beG9EPF/6nVXGA/nx\n5U9I+n5E3NWkjyWA7Rr2XSHpAOAs4H2SFo+I2WXf5uTN+dsiYr4bFRHxOFnW4uw2Y/5XWb+lg+dp\nZmYLUaUazwNf7JmkU8jfI2/q8pDMDJ4n/8Y8qlJd8NJ4ZtY5Zyjb6BPxEBF/A34LXA482uUR2dB7\nFugBLiDiVCJucDB5xFijrJ8cqhOUMgjvJ0sZVIfoNNPIjPj6YPVNwH4RcWeT9suX9XpkMPls4BUR\nsSJZduEAssTElmRWVCtzy3kfIz/uRznueyxYdjLAz5oEwms+Rv4NcW59MLnBOWRW8MaS1qjbvmtZ\nH18LJteLiDOAVn02VSZu/EB5eEQtmNzgV2QAX2TmcDNntQg0n0/W0VwC2KBue+19ZjlJS3Uy5qL2\nul+jbSszM+u6SjWuBzYDvog/FWnWTZcAr6tU40gHk826xwFlG70iZhNxWymJUQsu34cn8xsNgrxR\n8HfgLCJOIeJKIh7p8riscyuX9aBnDQNIWho4hvy537dk2Q66iPhyRKxeAsLLAXuQpROukHR4k0Nq\nv39FZtTvHhEPlL5eiIif0htI3rshIFt/3kfKeVcn6/a+BvgNWbrhpgWsE31Nm321Gs0fLJPxvWwh\ny0TUSnasAyBpCbJmMEC7iQg7naTwleR1B/hbswZlYsdLy8NNWvRzfbONJXv68fJwhbpd1wFPkwHh\na0rpkvX6P+yXXvcrt21lZmbDQqUacyvVOBLYCPhdt8djNsZMB/YBtq1UWyY0mNlC4nIANjZkttpt\nwG1IiwJrAusCryA/Im3D30wyQHU/8AD5MXYb+ZYo69ltWw3cN8mf859ExL+H6BzzicyOP03SFWTm\n/JclXRUR9f941mc2HRsRzW50/QT4H/J39VbMXzai2XnnAXcA/6+Um/gS8FtJby77OtVuksRagHtC\nWfpSy95dgd5gersbQJ3WO1+l7uuH2rR7sEn7ejPaHFubLfylutYR8YykjwInkzU2fwFQAuoXAydE\nxGX96HNxSeMG+H0yM7OFrFKNB4FdeyZpJ7Ks1rpdHpLZaDaPnMT665Wq58QxGy6coWxjT8SLRNxP\nxBVEnEx+LPvvZBBibncHZw2eJssGnEdOrHcJEXc5mDyqPF3Wy7dtNQCSNgD2JzNLDy+T1r200BvM\npm774oN1/oh4iN7spU807K7/Y/j2Fsc/Qm9JhXU6PP3RZf1GBl7rsd37Ye3vh/0jQv1YLi3tNcCx\ndGKJvpsMnoj4IzCRzJg5g/zerk6WBblU0vFtDq9lO091MNnMbOSpVOMCYGPgG7S/KWlmA/NH4A2V\navw/B5PNhhdnKJtFPEnWsbypZC+vAawNrAWs2M2hjUFzyKzF+8gsZNenG/2eJINvK/TVcADWBhYB\nVqXvjNfaP4GHAYcO4hhq2bLrN2z/T93X/SnD0Wmpjvos3fWBGzs8vi+PkeU1Kn01bPA0mWUyjnyv\nbTUZY6c1heuzqdeltzxFo7WbtF9gETEN+GVZkFQh62B/CviUpPMi4g9NDq297oeshriZmQ2tSjWe\nA77ZM0k/B74GfAYYtBvUZmPUjcBXKtVoWsrMzLrPGcpm9TJ7+QEiriHiLLL28sXAP4EH6P14si24\nuWTQ5z9kXdMziDiRiAuJuNXB5DGjlp3bSd3ZkaT2vBpfzzcAtQnpNmx2oKQ1yQn6IG+yDOS8zc49\nGGr1lXeStFjblnUiP13QUx6+vU3TdvuauYfe67l1swaSxpGlQwD+0WH/HYmInojYB7i2bHpni6YT\ny/q2oRyPmZkNvUo1nqhUY3/y9/pvyRuoZtaZKcBewGYOJpsNbw4om7UT8TwRU4i4nog/EfEb4BTg\nz2SQ+UEcZO6PIDMTbycn2zoHOJGIc4m4iog7iJjatgcbra4q600Hu+OIuLRdGQbg43Vta9sP7W//\nyk80tNv/KmCX8vCKhrHNIcsjAHyuRV9fLOtZ5GzWtX4XkdRX6YivlPWLtJ9cb6Cq5D/KawL/266h\npMbs81oZkE9JWq5J+w/w8ozutspki+eUh/tLWqpJs0+SnzwJ4KxO+m+lHyVSXijrVmU4NivrK1rs\nNzOzEaZSjXsr1fgoWXLqj90ej9kI8QzwZWDDSjVOqVSHZiJtMxs8DiibdSriWSLuLUHmP5Yg86lk\nkPkmMsg81mv8zgDuJrPzLiCDx2cRcRkRPUQ8ieuFWrqyrN8kaZFWjSQtJ2nl2kLv76+l67dLGtT6\nuZL2lhRlmdiw+yhJR0n6L0nj645ZXtLewGXAkuTPwxFNuv9m2bcBOYnf2uX4JSV9gSyZAHB0ZGme\nmnWAGyR9onZMOW6cpDdKOpkMntaOfWZgz761iLgVOLI8PEzSMZJeWTeWCZK2k3QScGbD4UeTN5hW\nA/4kaeNyzKKSdgdOpDfbuBPfBZ4jg9x/kPSa0u8Skj4FHFXa/Toi7hpA/83sK+kiSXtKeqlMR3kN\nHEJvRvRFjQeWmwJvLg8dUDYzG2Uq1bi5Uo3/Jj+lcm1f7c3GqFnA4cD6lWr8pFL1XDlmI4VrKJsN\nhogZZGDo3pe2ScuQE41NAJYp69qyFCP7hs4s8vnWlmfne5zZl2b9cQNZruCVZPDtry3anUfzsgGH\nl6Xm4+Qs0AvDUsAkYD9gnqRp5KRz9RMMPgp8KCIebDw4Ih4q2bi/Az4A7CppKvkeUSsjcR5wSJNz\nbwL8GkDSTPJncBnmz4SdDBw40CfXDweSAfN9gc8Cn5U0gyxnsxy9E/BdWn9QRDwhaU/yuW0B3FKu\n3fgy/qvIAOvBdHBzLiLulrQHmfm9FXBbuZ5L03s9/0pvoH4wCHh3WZD0HFkLvv41cHyZuK/R20q7\n+4DrBnFMZmY2jFSqcTmwRc8k7ULe/Nyoy0MyGw6C/OTvVyvV6LS0m5kNAw4omw2V3iDzy2Vm2tLM\nH2SuX5ahNwCysAUZxKkPEjtgbEMiIkLSCcC3gd1pHVDullrW6UPkhJH1vg/cCryLzDJejfy5fQy4\nBfgDcEKZsK2piPizpNcDBwHbl/M9R5bU+TVwSinnUO9hYDdgG2DzcsxKZPmdu8kSFydGxFUMoYiY\nSwaRTyYnINqSnGAR4H6yTvG5ZOC48diLJG0KfIOsebw0eUPuVOAHZYEOM5Uj4gJJryOD3duR2crP\nA/8GfkN+P+Z20mcfTiHfH7cFXk9+LyaQr5XryWzo81scu3tZn9Dke2xmZqNMpRrn9kzSBcDe5ATA\na7c9wGz0+itwYKUaQzqnhZkNLfl/GLNhKie6WrQsi9R93Wpb4+MgMwVbLbMbljnAbAeLbWErk89N\nIW9YrFkmbhsWJF1IBnr3i4ifdXs8Y4WkK8iJ+T4eEZO7PJxBV2pmP0jeCHhlRDzQ5SGZmdlC1DNJ\n48lPOB0MrNjl4ZgtDPPIUoiHV6pxZV+NzWz4c0DZzMy6TtLRwOeBfSPiuG6PB3LyO3KCkBnA+hHh\nCTgXAklbAFeT/3hMHI3BVkkfB04AfhERn+n2eMzMrDt6JmkCmbG8P/lpJ7PR5gVyMucjKtW4o9uD\nMbPB44CymZl1naRVyXINTwKviogXuzwkJG0G/B04ICJ+2u3xjCaS9gFWBk4HpkTEXEkTgF3JCQxX\nBE6LiD26OMwhIWkcWSplHWCDiHi4y0MyM7Mu65mkccCOZJ3/rbs8HLPByPS7wwAAIABJREFU8ARw\nDHBMpTrf5NJmNko4oGxmZsOCpPcDbwAmR8SULg/HhpCkbwNfLQ/nAtPICepqk5XeBGwXMfr+AZG0\nNvBJ4D8RcWa3x2NmZsNLzyS9kQws7wEs3uXhmHXqDuAnQLVS9af7zEYzB5TNzMxsoZL0WuDjwDvJ\nSYlWJCcj7AHOAo6LiBe6N0IzM7Pu6pmk1YHPkhPfrtLl4Zj15Qrgx8D5laqDTGZjgQPKZmZmZmZm\nZsNQmcDvI2TW8sZdHo5ZvbnAOeREe3/v9mDMbOFyQNnMzMzMzMxsmOuZpO2ALwLvAdTl4djYNZ3e\nifbu7fZgzKw7HFA2MzMzMzMzGyF6JmlD4HPAbrgchi0cAfwVmAycU6m6NJnZWOeAspmZmZmZmdkI\n0zNJiwLbAXsCuwATujsiG4XuIYPI1Uo17u/yWMxsGHFA2czMzMzMzGwE65mkpYD3kcHl9wCLdXdE\nNoJNJ2sjTwYu9yR7ZtaMA8pmZmZmZmZmo0TPJK0IfBDYC9gS11u2vs0E/gCcCvyhUo2ZXR6PmQ1z\nDiibmZmZmZmZjUI9k7Q2sAeZufzGLg/HhpcXybrIpwK/q1RjepfHY2YjiAPKZmZmZmZmZqNczyRt\nRGYt7wG8ssvDse6YTgaRLySDyE90eTxmNkI5oGxmZmZmZmY2hvRM0sZkreUdyLIYi3d3RDZEAvgH\ncBEZRL6mUo0XuzskMxsNHFA2MzMzMzMzG6N6JmlpYGsyuPwenL080j0OXEwGkC92FrKZDQUHlM3M\nzMzMzMwMgJ5J2gDYBngXsBWwalcHZH2ZA1xNbxbyTZWqAz1mNrQcULaOSZoI3AsQEWrYNxmYBBwW\nEYd22O9WwN+A+yJi4gIPdIhIuhR4J/DxiJjc3dGYmZmZmZkNnZ5Jei2Zwbw1+X/Qit0d0Zg3D7gN\nuJwMIF9SqcaM7g7JzMaaRbs9gG6StAvwu/LwzxHx7m6Ox0Y2ScsDBwB0Gkw3MzMzMzMbjirVuAW4\nBTi6Z5LGAa8H3gy8sSxvAJbp3ghHtbnArcCNdcu/KtV4rqujMrMxb0wHlMlM2pptJK0dEQ92bTQj\nxxzg9m4PoovuJ5//tIbtywPfKF8fujAHZGZmZmZmNtQq1ZgH3FQWAHomScD69AaYa8ta3RjjCPYi\n8B9yEr364PELXR2VmVkTYzagLGkl4L+B54FzgT2BjwDf7+a4RoKIeAjYsNvj6JaI+Fi3x2BmZmZm\nZjYclHq9d5XlrNr2nklahZcHmV8DLNKFYQ43s4AeMmhcCyDfXKnGzK6Oysysn8ZsQJkMIC9G/sL7\nRXk8CQeUzczMzMzMzBZIpRpPAH8uCwA9k7QksDGwHrA2sE5Z15Y1GB1ximfIT7beV5b6r+8DHvfE\neQNXN3fTehExpWybSM71VI2Ivbs0NBuhJG0NXAJ8OCLO7PZ4BpuktcibfodHxNcHo8/R8EY9ULVy\nFycDV5Bv8BtK2jwi/t7uQElLA/sCu5KZuksBD5N1pU4HzoiIOQ3HCPgw8DGy3tQKwJPA3WQd599E\nxFNNzvV24PPA24FVgGeBfwK/Bk6LJrMqSloPOJCcmXcdIIAnyDfXi4BfRsSTde3HlXFNIuthLQtM\nBR4HrivP58K69hNpMSlfwzjGA/8L7AasC8wA/gp8IyLuaHVcO+XcXwbeXZ7bXOAO4AzgZxGd15KS\n9IbS5zvIP2Bmk9frDnKSg+Mj4vm69pfSMClf3bZam8bvy8smKRzIc5G0DFmneRfg1cAS5OvoYXJC\nw2pE3NLhJTAzMzMzMxtypXzDDWV5mVKjeXVeHmiuDz6vSSaHdcM88v/aacBDzB8kfilo7EnyzFqr\nxU/axZMWphITOwL4F3Wfsij7JjN/udxGG0XEbX30/1HgN+XhpyLiVw37lwc+RX6K401krGcRYLuI\n+Esffa8MHATsRMbdZpHxuosj4qBau4h4SNJxwJclHR8RD7Trtz/GZEBZ0sZkUPcp8iKHpFPJb8Ik\noGVAWVIF+AMwsWx6kQzyrleWnYCrgCl1xyxHvii3LZuC/AW0OvnLcEvyDubkhnP9gAwM18wg6/Ru\nU5b3SdorIubVHbMJcCm9kyLMAZ4DXlGWd5IB6Qt7u+UkMkO7ZhoZVF4ZqJSlvn1/LEEGON9KBmhn\nkgHx3cu4d4iIyzvpUNKu5A2A8WXTC8DiwCZl2UvSdhHxWAd9vpcseVL7g2QW+UdC7fu5Pfnc275B\nAE+Tgd2Vy+PGMTy7oM+lvI6uJr8flHFOA1YjA+FvJoPSB/cxVjMzMzMzs2Gn1Gh+uCzXNWtTajZP\nAJYk/59qt261bzHyf7+ZZan/eib5/9kMYDr5P9f0sjzrzOJh6yFgI14+15FZX3YnJxfdq1nSZvFT\nMvGy0ZNNtr1E0jrA0WRMaEKLZhOBH5avHyx9rtZ+yCDpTWTS6ErAxWRsazzwSjKh9aCGQ34E7Ad8\nHdinr/77MiYDyvTeXajPJD6ZvNi7S/piRMxuPEjSimRwcR0y4v9F4E8RMbtkjr4B+AQZZK53MhlM\nfoEM9v0mIqZKWpy88/AhMqBcf679yWDyE+REb6eWY8aTQeufki/6m4Hv1R16OBlMvg7YNyL+Wfpb\nigxE7kXdG6ykd5DB5HlktuyvI2JGyahencyefV2ba9nKvuQv6Ull7HMkvZHMrN4EOEPSRhHxTLtO\n6sa5GXAaMA74AXAs8EB5vCl5Pd5C3vXZvoNxHl3G+Xvgy7XMaUnLkt/Pj5J/ULQVEbs2ZG6vPgTP\nZX/ye/gE8HHgooh4UdJi5BvQrmRg28zMzMzMbFQqAd0ZZTEDoMR2+koEM2vmc+QNo9+1aXNkrbxK\nf5W42olkMus5wP+0aHofGTP8Z0Q83Y+saCStAFxAJia+LSKubdj/sk9xRMTDkv5MJjB+JSIW6ObL\nuAU5eCSStAg5+R7AKbXtEfFv4N/AimTAtpmDyWDyk8CWEXFeLfAcETMi4sqI+EREPFh3vveSk/8F\nsGtEHBURU8sxsyPiloj4RkScV3fM8sC3ycD0jhHx87pjZpZ6LruWPr9SAtM1by3r/WvB5HLc8xFx\nQ0R8MSKuadL+4og4MiI/mhPpkYioRkSrF307ywH7RMRvakH7iLiJDJA+Rd5t+VwH/R1BBn6/EhEH\nR8T9ZYxzI+I6YAfyLva7JW3anw4lrUreuQH4ZH0ZjoiYHhFXRMQ+nb5p9MNAn0vte/XjiPhDRLxY\nxjonIu6MiB9ExC8HeaxmZmZmZmZmTUnaXNLpkh6SNEvSI5IulvThJm0/LOlySdMkvSDp35L+V9IS\nLfreVtIVkp6T9LSkcyVt2KLtRElRgnH12yeX7RMlfbqcc6akxyQdXz4J3Ky/7SVd1Xju+v46vljZ\nb0i6VNKakk6S9Hi5FjdK2rNJ+63KMYeWa/2HMp75xiDpzZLOLv3NknSfpGMlrdGkz9pzWE/S5yX1\nlGsyRdIhJRCKpA9J+nu5Bo9L+llJcmz2vLaRdGEZ20xJd0j6fv31rX2PKOVCyxhqy6UN/a1dzndP\neT5PSTq/JOg1nvvQ0sdWkvaUdJ2kZyVN6cf3Y0Pgv4DzI+KFvtp36AvAu8iEwJblWSPimYj4a0R0\nkiD4RWAt4KuNweTS55yXHwJkcuNSZILqAhlzAWUy43YN8g7AVQ37Ti7rVncCPlrWh0fEQ/0838fK\n+qL6OsR9+ACZCn9lq3rO5QVzD1mL+c11u6aX9cveNFqotV9VWTdmsNxHXcC+ptRu/kV5+MH+dCRp\nfeBtZIb3cc3alEznP5WH2/VzjDPIzGzo//VaIAv4XDr93pqZmZmZmZkNCUmfIssy7lLWPyZLhK4K\nfLah7XfJOac2ImMFPwMEfBe4qDGjUtIHyY/zbwqcScYRVgKuIctTduqHZfkXcAxZIuNTNMlKlbQb\n8Eeynm3t3CuUc08cwLkbrUBer9eRGay/IZPdTpb0lRbHbEHO/zUeOAGokuVFkbRj6W8n4C/AT4Db\nyU+O39Am+H04cBhZ9vU4Mj7yHeAbkr5QznFX2fcomRR4RGMnkj5NTr75NrLswpHkp6cPAq4uSZOQ\nJSMOI+NFlK9ry+S6/jYBbiJfQ7eTnyy/gJz36kpl4mYzXy7X5n7y9fWnFu3q1UrTXtlHux0kHSTp\nfyTtovxUe0uSNgK+D/y003Kv/bQnWfL0JEkVSfuV8X1QUqvSGtAbB+1v3Ky1iBhTCxmND+B7Tfa9\ngvwBmgOs0rBvYjkugNd1cL4p5Zj9Ojjm1+WY58kf2lbL7NLuw3XHnlC2TSdfvG8FFmtzrg3IelEB\nXE5mb6/Zx/heuhZN9k0u+6ptjn93afMisHjd9q3K9ikN7T9a177d9Xi2tDu2g2t9STnmMeBrZBH0\nRfo45tJyzN79vS6D8VzImxNRXqMnkZnMy3T7Z8qLFy9evHjx4sWLFy9evIythSzHOIcMHG7cZP/a\ndV9vUf6XvR9YvW77omSgMIBD6rZPID/ZPAfYtKHfI+iNzUys2177f3xyQ/vJded+RcO5Ly/7Nq/b\nvgxZknQW8IaGvr7f7NwdXrfa8WcA4+q2r1eu5WzglXXbt6o75tNN+ptAfop+LvlJ+vp9B5XjLm5x\nTaYAa9VtX7709RxZanOjun1LAD3luqxat702Edx0YMOG8xxbznN8w/ZLaRE3Kd+Xu8jSo+9s2Lcm\neSPgEWCJuu2HlvM8B7ypw+9HLUb45hb7a9eqcZkOfK7Nc7iBDIYv2TDGT/ZjTLVzbtti/wpl/+3k\nzYN5DWN7Enhvm/6fAR4fyOu3fhlTGcol1X7n8rBZ9uz95B2fRZl/kjqYvyD2/R2ctnZcJ8fUMlCX\nLMe3Wmp38JaqO/Yr5J2pZcg3j2uA6ZIukbSvpCXrTxQRd5F3rV4gJwc8CXhI0r2Sfq4s8j0Q7TK4\na/sWIX8Q+lK7HovQ/nosXdot1dhBG58EbiXvoH6LnLBwavkYyUckDXad8QE/l4j4DXA8eRf3I+Qd\n06mS/inpm80+ymJmZmZmZmY2BPYlYyffioj/NO6MulKg5FxTAN+OiEfr2rxIZpXOI/83r9mZLEd6\nSkTc0ND1oQxs4r1vlphP/blPLA83bzj38sDJEfGvhj6+TfOJ2To1FzgoImqfmCYi7gWOIuM8H21y\nzE0R8Ysm23cmM7dPj4grGvb9mAwabyfpFU2O/VbUffo+stTq+WQc4ucRcWvdvllkhvniZJZ5zUfK\ntp9FRGMN66+Snwz/qFqUNWniv4H1gaMj4rL6HRHxMJllvjqwTZNjj4+60q/9VLsuj7TYfzmwGxk4\nX7KMrVYW9meSmk1u939kdvveMfhlNCDjV5Sx7EfG/lYn401fIUvQnl2ypJt5FFilVfmS/hpTAWXy\nRVC7YDc31GuJUsvlHWV/Y9kLLbRR9n5fjogI9WOZXDswIp4C3k6mrx9FBkgXB7Ym7w7dImnt+pNF\nxAnk3bADgPPIO4ETgc8AN0o6ZJCfX6fXsnY9/tnP67F3fzuOiHuA1wPvJ4O1t5J3+N5LBtev6+Pj\nAp1aoOcSEZ8GXgt8k7yrN4vMqv46cKekBf/YgpmZmZmZmVl7tTl++lNWYJOyvqRxR+RcRg8C69WV\nRqi1v6xJ+2lkOYRONQamAR4o6/pEt1pS3ctKIETEswM8d6P7SwC50aUNY6jXtBwq7a/ti2RAtFWf\nza7Jw2V9Y5N9teBzfUyp3fmfIWNS44Gmta+b2KKs1y21kedb6A3+NwuWtrpG7axU1s802xkRJ0TE\nGZFzX82MiHsi4sf0JqF+RzlXG5A1xYFDyLmvrmnW5yBYpG7904j4UUQ8FhGPRsThZCxwPBnja6ZW\nq3nlBRnEWAsot50lscGbJL2u7vGjdV+v20E/jy3AMZUOjnlJpL9ExP4RsQn5Ivk0+aJ5JU1q3pQX\n308jYhdgFfKH9Hdk8Pdbkl7f4TDWbLOvlkk7lxY/tA1q1+NVQ5AxTES8GBHnRsSnI6JC712dmeSb\n4zcG8XQL/Fwi4j+REzluTd453YmcUHJpoNpYe8rMzMzMzMxskNWCv/2ZX6o2MVurLNBHGtrV1o81\naQvzx2f6q1lm8YtlvUjdtr7O3Wp7J/p6Xs0mCmz1nPt7bZdvsq9ZpveL/dhXH3NYkPM3UwvwfoiM\nxTQutUBus8S/gbwuahnEHWXrRsTvydf+ypTYXYnxnATcQSb9DZX6ONrLaoDXbdu8yT7ITGvofe4D\nMmYCypI2IGduhMzoXKHNckFp91IAOiKm0PvibFUAvJnabIudHFO7i/FOSSu1bdkPkTNGHk/eJYEy\no2ab9hER15M/wA+Sr5O3d3jadueo7bslImb3o6/a9ZhA1l8eUnV3dY4sm9perzovfVylNitqE4P6\nXCJidnkj+1DZtAbwqgXt18zMzMzMzKyNWoB2rX60rQUnV2+xf42GdrX1ak3atutnMEzv49yttnei\nr+fVLJgbLY7p9NoOtsE+f63dzn18mvuwJse2ukbtPF7WA4m9PVHWtZKlE4BXk9nTMxuqIdQSFX9Z\nth3JAEXEI/S+TpvdKKkFnJdssg/yub5Ib6bygIyZgDK9weF/RcS/ImJqq4WcxRNgr/rUdfJOA8CX\nJfXnTRNytk6Ad0t6Tz+POZMsJj4e+FG7hpJWqPt6XB9Zr7W7Dy/VrpG0eKvGETGXLII/3zH9NFHS\nHo0bJa0I1GrMnNm4v8U4bqM3MP8DSUu3aitpyf7W5pG0WJvALzS5Xn2YXvd107tvC/Jc2n2vmP/O\nUqffKzMzMzMzM7NO1P6v3aEfbWt1bbdq3FGS/9YG7i3xGIB/lPXLkrvK3Fhv7GiknamN9WVJdaUc\n5mCc+xWSJjbZvlXDGPqj3bVdlN7n8Y/G/YOk3fmXJ6/XTLK8aM3csn+RxmPofV1tOXhDbOvmsu5v\nSQ7gpdfhhvRObghZkvTXLZbadbqyPF7Qchi1EiOvbbKvtm1K444Sg1oLuDkiBhKAf8mYCCiXoGGt\nqPk5/TjkAjKQujqwfd32H9Cb0n6FpPfVgnySJkjaStJpDTWK/1QWkUWx96vVBZK0uKTXSfqxpF1q\nB5Q6yP9bHn5c0hmSXnqRSBov6e2SjgGuqjvXssBdkr5a+l2ktB8naRvgO6XdRXXHfFfSWZJ2KcHe\n2jlWk3QUWVs5gD/347rVm0beeXlpYrtSNuMisqTG42RN5/7aj/zhfC157bet63ecpI0lfQ24m947\nYH3ZmKwpfYCkV9eCyyXQ/AHgS6XdRS17qFN++dXqDX18CJ7LXyQdJekdqptcUdLG5CygkB8n+Xd/\nxmtmZmZmZmY2QD8nsxy/Lull5Tob4iInlPXXJK1S12YR4HAyNvXruvbnkVmWe0ratKHrQ2leEmKw\nnEfGM/aS9IaGfV+j/6Ub2lmETDB7KSYnaT3gC+Q1/W0HfZ1LZpruIemtDfsOIMue/qV+QsJB9lsy\nfrZfuTlQ71tknOq3ZVK/mqfKutlEgeeRsZDPSWr6SX9JW0haasGG/ZJLy7rx2iFp9SbPqXZjYTKZ\nBPqXKBNNRsQLEfHJZgs52SFAtWw7fQHHfUxZf62u9ngtiF8rt3Fak+M2J19/f1vA8zPo9WiHqa3o\nrWF8dl+NI2KqpEvIYPIk4I9l+1OSdiiP1yNf6HMkPcf8byoH1/UVkvYkf8jfSRbHPlLSNPJNsPYG\nMl8QMCKOLnc8vkmWM/iQpOfJQGT9cVMahr8uOfPot8vYZpT2tTs/99AbKIV8DXygLEiaTga/l6lr\n87WIuKXV9Wrh5+R1Pwn4laRZ5BsJwPPAh0qB9n6JiBskvR84lSwm/2dgdnl+yzJ/DZ9O7rJUyJrS\nRwCz6r6Xtet7A3kt++tX5IyeP5b0TeDJsv3IiDhyAZ/LsmQwej9gXnkNLUlvrZ/ngY+WwvtmZmZm\nZmZmQyIieiR9FjgO+Kek84A7yY/TbwrMALYuba+W9EPgQDKp6yzyU9k7kIlWV1L36eyIeFbSPsDp\nZBLW6WTy1NtL+8uBdwzR85pentdvgaslnVHO/V/AG8iJAt9JXcnLAbgZeAtwo6SLyZjNbmQs4sCI\nuLuD8T4r6RPkJ8Avk3QmcD/wZrLM5qPknFpDIiKmSDqADHD+o1yvJ8hrtAVwG3BQw2F/JeNc50j6\nI/mJ6/si4qSImCNpVzKx7w+SriYnQnweWAfYjAySr1G2LahLyLIR25M3DOptCPxN0jVkhvXjZHbv\ndmQC6j3AJwdhDEg6nN5J8mpZ5V+R9JHy9bkRcW6tfUT8RdLRZHzoFkm10r07khn/59JbMaFerfRq\nn7HRvoyVgHKt3MUdEfGffh5zNvmC2lnS8rWPXkTEv0tG6H7ALmR9lPHkC+lm8g3vwfqOSoD6XcBH\nyEzpN5JvGI8Ad5EFs8+nQUR8u7wpf558I16brM3ySDnX+cyfcT2dfPFsS77ZrU1mAz8H3E6+oI6O\niBl1xxxB3v3ZhqzzsgZZMuEB4GrgmIi4op/XrN6sMub/BXYn7zw9Qb5xHBoRt3faYUT8SdKryWv/\nXmAD8g13anl+FwJnRsR9/ezyVuCD5PV6CzmR4ErkdbyF/F4e3886zzXfJK/3XmV8tRsZ893FHOBz\n+WRpuxV5Q6NWo+g24C/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f01f81622e8>" ] }, "metadata": { "image/png": { "height": 285, "width": 714 } }, "output_type": "display_data" } ], "source": [ "# Overlaps to TSS clusters\n", "df_regl_ = regl_Apr27(flank_len=150)[['chrom', 'start', 'end', 'annot']]\n", "\n", "gv = yp.GenomicVenn2(\n", " BedTool.from_dataframe(df_regl_),\n", " BedTool.from_dataframe(df_gu_cluster50[yp.NAMES_BED3]),\n", " label_a='Accessible sites',\n", " label_b='(Gu et al., 2012)\\nTSS clusters',\n", ")\n", "\n", "plt.figure(figsize=(12,6)).subplots_adjust(wspace=0.5)\n", "plt.subplot(1,2,1)\n", "gv.plot()\n", "\n", "plt.subplot(1,2,2)\n", "annot_count_ = gv.df_a_with_b['name'].value_counts()[config['annot']]\n", "annot_count_.index = [\n", " 'coding_promoter',\n", " 'pseudogene_promoter',\n", " 'unknown_promoter',\n", " 'putative_enhancer',\n", " 'non-coding_RNA',\n", " '\\n\\nother_element'\n", "]\n", "#plt.title('Annotation of %d accessible sites that overlap a TSS from (Gu et al., 2012)' % (len(gv.df_a_with_b),))\n", "plt.pie(\n", " annot_count_.values,\n", " labels = ['%s (%d)' % (l, c) for l, c in annot_count_.iteritems()],\n", " colors=[yp.RED, yp.ORANGE, yp.YELLOW, yp.GREEN, '0.4', yp.BLUE],\n", " counterclock=False,\n", " startangle=70,\n", " autopct='%.1f%%',\n", ");\n", "plt.gca().set_aspect('equal')\n", "#plt.savefig('annot/Fig2S5_tss/Gu2012_annot.pdf', bbox_inches='tight', transparent=True)\n", "annot_count_" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:46.834176Z", "start_time": "2018-05-18T15:47:31.325170Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/ipykernel_launcher.py:64: RuntimeWarning: Mean of empty slice\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "13054 of 42245 sites with CV values via promoter annotation\n", "26764 of 42245 sites with CV values via \"associated gene\"\n" ] }, { "data": { "image/png": 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gldx0ATc92IfbGmdDPGYui8fU3ENE8kc8Zt5DFgZWgBPfeHyStbZlNOdOjXbV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\nz0LPAc+WrJ+LMjRho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"text/plain": [ "<matplotlib.figure.Figure at 0x7f01f40bac88>" ] }, "metadata": { "image/png": { "height": 221, "width": 470 } }, "output_type": "display_data" } ], "source": [ "df_regl_ = regl_Apr27(flank_len=150)[['chrom', 'start', 'end', 'annot']]\n", "\n", "gv = yp.GenomicVenn2(\n", " BedTool.from_dataframe(df_regl_),\n", " BedTool.from_dataframe(df_gu_cluster50[yp.NAMES_BED3]),\n", " label_a='Accessible sites',\n", " label_b='(Gu et al., 2012)\\nTSS clusters',\n", ")\n", "\n", "plt.figure(figsize=(8,4)).subplots_adjust(wspace=0.2)\n", "plt.subplot(1,2,1)\n", "v = gv.plot(style='compact')\n", "v.get_patch_by_id('10').set_color(yp.RED)\n", "v.get_patch_by_id('01').set_color(yp.GREEN)\n", "v.get_patch_by_id('11').set_color(yp.YELLOW)\n", "\n", "plt.subplot(1,2,2)\n", "d_reduced_ = collections.OrderedDict([\n", " ('coding_promoter', 'coding_promoter, pseudogene_promoter'),\n", " ('pseudogene_promoter', 'coding_promoter, pseudogene_promoter'),\n", " ('unknown_promoter', 'unknown_promoter'),\n", " ('putative_enhancer', 'putative_enhancer'),\n", " ('non-coding_RNA', 'other_element, non-coding_RNA'),\n", " ('other_element', 'other_element, non-coding_RNA'),\n", "])\n", "\n", "d_colour_ = collections.OrderedDict([\n", " ('coding_promoter, pseudogene_promoter', yp.RED),\n", " ('unknown_promoter', yp.YELLOW),\n", " ('putative_enhancer', yp.GREEN),\n", " ('other_element, non-coding_RNA', yp.BLUE),\n", "])\n", "\n", "gv.df_a_with_b['name_reduced'] = [*map(lambda a: d_reduced_[a], gv.df_a_with_b['name'])]\n", "annot_count_ = gv.df_a_with_b['name_reduced'].value_counts()[d_colour_.keys()]\n", "\n", "#plt.title('Annotation of %d accessible sites that overlap a TSS from (Chen et al., 2013)' % (len(gv.df_a_with_b),))\n", "(patches, texts) = plt.pie(\n", " annot_count_.values,\n", " labels = yp.pct_(annot_count_.values),\n", " colors=d_colour_.values(),\n", " counterclock=False,\n", " startangle=45,\n", ");\n", "plt.gca().set_aspect('equal')\n", "#plt.savefig(vp('Gu2012_annot.pdf'), bbox_inches='tight', transparent=True)\n", "plt.savefig('annot_Apr27/Fig2S3D_Gu2012_annot.pdf', bbox_inches='tight', transparent=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-05-18T15:47:46.893457Z", "start_time": "2018-05-18T15:47:46.837662Z" }, "collapsed": true }, "outputs": [], "source": [ "#fp_ = 'annot/Fig2S4_TSS/Gu2012_not_atac.bed'\n", "#gv.df_b_only.to_csv(fp_, header=False, sep='\\t', index=False)\n", "#!wc -l {fp_}" ] } ], "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 }
gpl-2.0
alpinedatalabs/ODST
notebooks/D2. K-Means Clustering - Data Exploration.ipynb
4
8689
{ "metadata": { "name": "D2. K-Means Clustering - Data Exploration" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "K-Means Clustering - Data Exploration\n", "==========================================\n", "***\n", "\n", "###UN Data on Countries of the World\n", "\n", "We are going to explore or dataset which we get in a csv format but may have missing values.\n", "We need to be able to drill down on useful dimensions to explore after cleaning up the data.\n", "Since we only have one observation per country, we may not have the option to use columns where there are many missing values as we are effectively going to drop many countries when we drop rows with missing values.\n", "But then how did we drop such rows before? Because in those cases there were many observations per individual entity and dropping some did not eliminate an entity altogether.\n", "\n", "So first we import the data and explore the columns and types - this time rather than doing it manually we are going to use the facilities in our software to do that.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "df = pd.read_csv('../datasets/UN.csv')\n", "print('----')\n", "# print the raw column information plus summary header\n", "print(df)\n", "print('----')\n", "# look at the types of each column explicitly\n", "print('Individual columns - Python data types')\n", "[(x, type(df[x][0])) for x in df.columns] " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "----\n", "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 207 entries, 0 to 206\n", "Data columns (total 14 columns):\n", "country 207 non-null values\n", "region 207 non-null values\n", "tfr 197 non-null values\n", "contraception 144 non-null values\n", "educationMale 76 non-null values\n", "educationFemale 76 non-null values\n", "lifeMale 196 non-null values\n", "lifeFemale 196 non-null values\n", "infantMortality 201 non-null values\n", "GDPperCapita 197 non-null values\n", "economicActivityMale 165 non-null values\n", "economicActivityFemale 165 non-null values\n", "illiteracyMale 160 non-null values\n", "illiteracyFemale 160 non-null values\n", "dtypes: float64(12), object(2)\n", "----\n", "Individual columns - Python data types\n" ] }, { "output_type": "pyout", "prompt_number": 3, "text": [ "[('country', str),\n", " ('region', str),\n", " ('tfr', numpy.float64),\n", " ('contraception', numpy.float64),\n", " ('educationMale', numpy.float64),\n", " ('educationFemale', numpy.float64),\n", " ('lifeMale', numpy.float64),\n", " ('lifeFemale', numpy.float64),\n", " ('infantMortality', numpy.float64),\n", " ('GDPperCapita', numpy.float64),\n", " ('economicActivityMale', numpy.float64),\n", " ('economicActivityFemale', numpy.float64),\n", " ('illiteracyMale', numpy.float64),\n", " ('illiteracyFemale', numpy.float64)]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see that we have 14 columns with country and region being string types and the rest being floats. We also see that the country column has 207 values, ie this is data on 207 countries.\n", "The region columns also has 207 entries, but the rest of the columns have many missing entries, indicated by number of non-null values less than 207.\n", "\n", "We see that tfr, lifeMale, lifeFemale and GDP, and infantMortality are the columns closest to 207. That is, if we use these columns we will only drop a few countries and not whole clusters as we might if we used educationMale and educationFemale. On the other hand were we to use educationMale and educatonFemale we would have to drop almost 2/3 of the data. So we focus on the columns with non-null values close to 207.\n", "\n", "So our short list is now, country, region, tfr, lifeMale, lifeFemale and GDP, and infantMortality.\n", "\n", "We suspect that there is clustering of lifeMale, lifeFemale and infantMortality according to GDP and we are going to pull out the heavy machinery of K-Means sofwtare to analyse this in detail and look at the clusters. \n", "\n", "We don't know in advance how many clusters there will be which is different from the iris example where we had a 'species' label and there were three unique species.\n", "\n", "So while using our KMeans software we will also look at some analytical measures to decide what the right number of clusters might be after looking at multiple such possibilities from 1 through 10 candidate clusters.\n", "\n", "Finally, to be able to apply the KMeans modeling software we convert each field in our file to a scientific float format that the numerical algorithms expect.\n", "\n", "Onward!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: \"Charis SIL\", Palatino, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 120%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " }\n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 2, "text": [ "<IPython.core.display.HTML at 0x1092b1890>" ] } ], "prompt_number": 2 } ], "metadata": {} } ] }
bsd-2-clause
metpy/MetPy
v0.12/_downloads/0c4dbfdebeb6fcd2f5364a69f0c6d4a8/Skew-T_Layout.ipynb
1
3885
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nSkew-T with Complex Layout\n==========================\n\nCombine a Skew-T and a hodograph using Matplotlib's `GridSpec` layout capability.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nimport metpy.calc as mpcalc\nfrom metpy.cbook import get_test_data\nfrom metpy.plots import add_metpy_logo, Hodograph, SkewT\nfrom metpy.units import units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upper air data can be obtained using the siphon package, but for this example we will use\nsome of MetPy's sample data.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n\ndf = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),\n skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n\n# Drop any rows with all NaN values for T, Td, winds\ndf = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'\n ), how='all').reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will pull the data out of the example dataset into individual variables and\nassign units.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = df['pressure'].values * units.hPa\nT = df['temperature'].values * units.degC\nTd = df['dewpoint'].values * units.degC\nwind_speed = df['speed'].values * units.knots\nwind_dir = df['direction'].values * units.degrees\nu, v = mpcalc.wind_components(wind_speed, wind_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a new figure. The dimensions here give a good aspect ratio\nfig = plt.figure(figsize=(9, 9))\nadd_metpy_logo(fig, 630, 80, size='large')\n\n# Grid for plots\ngs = gridspec.GridSpec(3, 3)\nskew = SkewT(fig, rotation=45, subplot=gs[:, :2])\n\n# Plot the data using normal plotting functions, in this case using\n# log scaling in Y, as dictated by the typical meteorological plot\nskew.plot(p, T, 'r')\nskew.plot(p, Td, 'g')\nskew.plot_barbs(p, u, v)\nskew.ax.set_ylim(1000, 100)\n\n# Add the relevant special lines\nskew.plot_dry_adiabats()\nskew.plot_moist_adiabats()\nskew.plot_mixing_lines()\n\n# Good bounds for aspect ratio\nskew.ax.set_xlim(-30, 40)\n\n# Create a hodograph\nax = fig.add_subplot(gs[0, -1])\nh = Hodograph(ax, component_range=60.)\nh.add_grid(increment=20)\nh.plot(u, v)\n\n# Show the plot\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
jegibbs/phys202-2015-work
assignments/assignment13/GitHubRepos.ipynb
1
3851
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Turning In Your GitHub Repos" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The purpose of this assignment is for you to \"turn in\" your Github repos. In addition to being used to turn in your project, this assignment will be assigned a grade that reflects your usage of Git/GitHub." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Homework GitHub repo" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Throughout the quarter you should have been pushing your weekly homework to a public Github repo.\n", "\n", "1. Make sure all of your homework is pushed to this repo.\n", "2. In the following markkdown cell, paste the URL to that repo. This should be something like https://github.com/ellisonbg/phys202-2015-work" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "00acb1f05c2e0b17272076cd16e1d65b", "grade": true, "grade_id": "githubreposa", "points": 5, "solution": true } }, "source": [ "https://github.com/jegibbs/phys202-2015-work" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Project GitHub repo" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "To turn in the notebooks for your project, go through the following steps:\n", "\n", "1. Create a new public GitHub repo, named `phys202-project`. If you need a refresher on creating a repo, have a look at this [tutorial](https://help.github.com/articles/create-a-repo/).\n", "2. Clone the repo onto dirac1. When you do this, you should do it in a directory that doesn't already have a directory with the name `phys202-project` and which itself is not another GitHub repo.\n", "3. Copy your project materials into the new GitHub repo on dirac1.\n", "4. Commit and push your changes to GitHub\n", "5. In the following markdown cell, paste the URL to that repo. This should be something like https://github.com/ellisonbg/phys202-project\n", "\n", "Before turning this in you should check the following:\n", "\n", "* Make sure it will be obvious to me what each notebook contains and which order I should go through them in.\n", "* If there are cells or notebook that take longer than 30-60 seconds to run, you should put a uppercase bold warning in a markdown cell immediately above the cell: **THIS CELL TAKES XXX MINUTES TO RUN**. I won't run these cells.\n", "* You should make sure that your notebooks will run without error wth a cleared kernel, from the top to the bottom, *leaving out the long running cells*. This will require you to save the output of long running cells to disk and load them back for analysis and visualization purposes. Yes, I will run your code!" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "e2204fa9a8e531e9578600dd34f48cee", "grade": true, "grade_id": "githubreposb", "points": 5, "solution": true } }, "source": [ "https://github.com/jegibbs/phys202-project" ] } ], "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
maasg/spark-notebook
notebooks/viz/Geo Data (Map).snb.ipynb
6
7179
{ "metadata" : { "name" : "Geo Data (Map)", "user_save_timestamp" : "1970-01-01T01:00:00.000Z", "auto_save_timestamp" : "1970-01-01T01:00:00.000Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "customLocalRepo" : null, "customRepos" : null, "customDeps" : null, "customImports" : null, "customArgs" : null, "customSparkConf" : null }, "cells" : [ { "metadata" : { "id" : "FF3EB7842862456C84257C9192BA27D4" }, "cell_type" : "markdown", "source" : "# Geo Data " }, { "metadata" : { "id" : "C94A076687C7468B9821FFDE46712EE1" }, "cell_type" : "markdown", "source" : "## Lat long points (EPSG3758)" }, { "metadata" : { "id" : "C7FFC3B6B19A432FAF05759A8B2848FB" }, "cell_type" : "markdown", "source" : "We can create implicit `latLon` points with a simple sequence of tuples" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "50B8A8184BA247168C4D856E438806A0" }, "cell_type" : "code", "source" : "val points = Seq((51.31, 0.71), (51.31, 0.72), (51.31, 0.73), (51.507222, -0.1275))", "outputs" : [ ] }, { "metadata" : { "id" : "E26FA4C999D542A1990112E72B14FDB4" }, "cell_type" : "markdown", "source" : "So that calling `widgets.GeoPointsChart` on them will place markers on the map" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "3AD8F406E18149EFA4D54A9A80E4C3F1" }, "cell_type" : "code", "source" : "val w = GeoPointsChart(points)\nw", "outputs" : [ ] }, { "metadata" : { "id" : "F3CF12D364C44650B98190C7E0EF9F98" }, "cell_type" : "markdown", "source" : "### Add Paris" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "C9D0F26AC9E8420286863664E2A8CFF6" }, "cell_type" : "code", "source" : "w.addAndApply(Seq((48.85, 2.34)))", "outputs" : [ ] }, { "metadata" : { "id" : "544F89AC7B3A45D288C2704608C0780B" }, "cell_type" : "markdown", "source" : "### Keep Liège" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "3B1C482423AD4B9FA40237699A9EE891" }, "cell_type" : "code", "source" : "w.applyOn(Seq((50.65, 5.57)))", "outputs" : [ ] }, { "metadata" : { "id" : "D8E778574DDA4F81AFDBE3BC79ACFF86" }, "cell_type" : "markdown", "source" : "## Give geo data a group and a value" }, { "metadata" : { "id" : "915E04FD5D5940989B752C7009724A27" }, "cell_type" : "markdown", "source" : "It's common to have geo data assigned some group but also a value, these generally result into a radius on the map and a group color" }, { "metadata" : { "id" : "17F24538F53D4C22802363AF63D04028" }, "cell_type" : "markdown", "source" : "We can create such dataset using 4-uples" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "660DC89607A64D9E8B4DD81AFC45C622" }, "cell_type" : "code", "source" : "val colored = Seq((51.31, 0.71, 20, \"red\"), (51.31, 0.72, 10, \"blue\"), (51.31, 0.73, 100, \"yellow\"))", "outputs" : [ ] }, { "metadata" : { "id" : "EE66EE3E32BB498793243890859D305D" }, "cell_type" : "markdown", "source" : "So we need to tell `GeoPointsChart` which fields to use for what" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "5D83C321BE8748A1809F43B0017CE343" }, "cell_type" : "code", "source" : "val fancy = GeoPointsChart(colored, latLonFields=Some((\"_1\", \"_2\")), rField=Some(\"_3\"), colorField=Some(\"_4\"))\nfancy", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "368D70B101D247609135FAD4860D1E25" }, "cell_type" : "code", "source" : "fancy.applyOn(colored)", "outputs" : [ ] }, { "metadata" : { "id" : "49D3E44603CB4F88AACE7BCE03AF22F3" }, "cell_type" : "markdown", "source" : "## Use your types" }, { "metadata" : { "id" : "876DD2061387426486E53D431B2385FE" }, "cell_type" : "markdown", "source" : "You can of course use your own type to represent your data in a more convenient manner..." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "B81B031960D3474096F1CB03F24328B3" }, "cell_type" : "code", "source" : "case class GeoData(lat:Double, lon:Double, value:Int, group:String)", "outputs" : [ ] }, { "metadata" : { "id" : "D797BC33531646F087235EBF4DA1DC2F" }, "cell_type" : "markdown", "source" : "Let's generate some abstract data" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "83D2FEE7901A440DAC37CBFEF91CF6EA" }, "cell_type" : "code", "source" : "val groups = Map(\"female\" → \"red\", \"male\" → \"blue\")\nval data = List.fill(100) {\n val lat = scala.util.Random.nextDouble * 160 - 90\n val lon = scala.util.Random.nextDouble * 340 - 180\n val value = scala.util.Random.nextInt(10)\n val group = scala.util.Random.shuffle(groups).head._2\n GeoData(lat, lon, value, group)\n }\nTableChart(data, maxPoints=3)", "outputs" : [ ] }, { "metadata" : { "id" : "DA539C2D794D4A2095D86094A671AB8C" }, "cell_type" : "markdown", "source" : "And map the fields onto the graphics aesthetics" }, { "metadata" : { "id" : "64A8D26BAF9541958005BA00DBBCD284" }, "cell_type" : "markdown", "source" : "__Note:__ by default, as for the other Chart, only the 25 first items will be rendered" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "5E2D0D5B8C634DCE8477B245A443C055" }, "cell_type" : "code", "source" : "GeoPointsChart(data, latLonFields=Some((\"lat\", \"lon\")), rField=Some(\"value\"), colorField=Some(\"group\"))", "outputs" : [ ] }, { "metadata" : { "id" : "2BDDA2B2F2A04BCF81E50EC2F899F902" }, "cell_type" : "markdown", "source" : "Let's add all points by specifying the `maxPoints` and extend the plotting area a bit using `sizes` " }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "253454E3F576401A8D14DB2C07881C8F" }, "cell_type" : "code", "source" : "GeoPointsChart(\n data, latLonFields=Some((\"lat\", \"lon\")), rField=Some(\"value\"), colorField=Some(\"group\"),\n maxPoints=100,\n sizes=(800, 600)\n)", "outputs" : [ ] } ], "nbformat" : 4 }
apache-2.0
raghu-icecraft/ml-practice
Titanic_Challenge.ipynb
1
155818
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Titanic: Machine Learning " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exploring Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "# TODO: Dont hard code the path\n", "# Either define a variable to store the path or let input file be in the same folder\n", "# 2nd approach is better -- Did this now.\n", "\n", "train_df = pd.read_csv('train.csv') # training data in a pandas' Data Frame object\n", "test_df = pd.read_csv('test.csv') # test data\n", "\n", "full_df = [train_df, test_df] # complete pandas' Data Frame object" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "# first 10 records\n", "train_df.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Moran, Mr. James</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>330877</td>\n", " <td>8.4583</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>McCarthy, Mr. Timothy J</td>\n", " <td>male</td>\n", " <td>54.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>17463</td>\n", " <td>51.8625</td>\n", " <td>E46</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Palsson, Master. Gosta Leonard</td>\n", " <td>male</td>\n", " <td>2.0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>349909</td>\n", " <td>21.0750</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n", " <td>female</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>347742</td>\n", " <td>11.1333</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n", " <td>female</td>\n", " <td>14.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>237736</td>\n", " <td>30.0708</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", "6 0 17463 51.8625 E46 S \n", "7 1 349909 21.0750 NaN S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C " ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "# last 10 records\n", "train_df.tail(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>881</th>\n", " <td>882</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Markun, Mr. Johann</td>\n", " <td>male</td>\n", " <td>33.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>349257</td>\n", " <td>7.8958</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>882</th>\n", " <td>883</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Dahlberg, Miss. Gerda Ulrika</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7552</td>\n", " <td>10.5167</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>883</th>\n", " <td>884</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>Banfield, Mr. Frederick James</td>\n", " <td>male</td>\n", " <td>28.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>C.A./SOTON 34068</td>\n", " <td>10.5000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>884</th>\n", " <td>885</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Sutehall, Mr. Henry Jr</td>\n", " <td>male</td>\n", " <td>25.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>SOTON/OQ 392076</td>\n", " <td>7.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>885</th>\n", " <td>886</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Rice, Mrs. William (Margaret Norton)</td>\n", " <td>female</td>\n", " <td>39.0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>382652</td>\n", " <td>29.1250</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>886</th>\n", " <td>887</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>Montvila, Rev. Juozas</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>211536</td>\n", " <td>13.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>887</th>\n", " <td>888</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Graham, Miss. Margaret Edith</td>\n", " <td>female</td>\n", " <td>19.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>112053</td>\n", " <td>30.0000</td>\n", " <td>B42</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>888</th>\n", " <td>889</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n", " <td>female</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>W./C. 6607</td>\n", " <td>23.4500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>889</th>\n", " <td>890</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Behr, Mr. Karl Howell</td>\n", " <td>male</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>111369</td>\n", " <td>30.0000</td>\n", " <td>C148</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Dooley, Mr. Patrick</td>\n", " <td>male</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>370376</td>\n", " <td>7.7500</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ " PassengerId Survived Pclass Name \\\n", "881 882 0 3 Markun, Mr. Johann \n", "882 883 0 3 Dahlberg, Miss. Gerda Ulrika \n", "883 884 0 2 Banfield, Mr. Frederick James \n", "884 885 0 3 Sutehall, Mr. Henry Jr \n", "885 886 0 3 Rice, Mrs. William (Margaret Norton) \n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "881 male 33.0 0 0 349257 7.8958 NaN S \n", "882 female 22.0 0 0 7552 10.5167 NaN S \n", "883 male 28.0 0 0 C.A./SOTON 34068 10.5000 NaN S \n", "884 male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 female 39.0 0 5 382652 29.1250 NaN Q \n", "886 male 27.0 0 0 211536 13.0000 NaN S \n", "887 female 19.0 0 0 112053 30.0000 B42 S \n", "888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n", "889 male 26.0 0 0 111369 30.0000 C148 C \n", "890 male 32.0 0 0 370376 7.7500 NaN Q " ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "# <Raghu> for now since we are starting, lets go with mean.\n", "# Here in this example, they have used a formula making use of mean and std dev of training data for Age.\n", "# https://www.kaggle.io/svf/560373/fcf6c03312081da830b2ab2cb26b4a1a/__results__.html#6.-Age\n", "# We can do these later to improve efficiency of learning and predicting. </Raghu>\n", "\n", "print(train_df['Age'].value_counts(dropna=False)) #How should we handle these? Drop NaNs? Replace wtih mean?" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "NaN 177\n", " 24.00 30\n", " 22.00 27\n", " 18.00 26\n", " 28.00 25\n", " 19.00 25\n", " 30.00 25\n", " 21.00 24\n", " 25.00 23\n", " 36.00 22\n", " 29.00 20\n", " 32.00 18\n", " 26.00 18\n", " 35.00 18\n", " 27.00 18\n", " 16.00 17\n", " 31.00 17\n", " 34.00 15\n", " 23.00 15\n", " 33.00 15\n", " 20.00 15\n", " 39.00 14\n", " 17.00 13\n", " 42.00 13\n", " 40.00 13\n", " 45.00 12\n", " 38.00 11\n", " 50.00 10\n", " 2.00 10\n", " 4.00 10\n", " ... \n", " 28.50 2\n", " 63.00 2\n", " 0.83 2\n", " 30.50 2\n", " 70.00 2\n", " 57.00 2\n", " 0.75 2\n", " 13.00 2\n", " 59.00 2\n", " 10.00 2\n", " 64.00 2\n", " 40.50 2\n", " 45.50 2\n", " 32.50 2\n", " 20.50 1\n", " 24.50 1\n", " 0.67 1\n", " 70.50 1\n", " 0.92 1\n", " 74.00 1\n", " 34.50 1\n", " 14.50 1\n", " 80.00 1\n", " 12.00 1\n", " 53.00 1\n", " 36.50 1\n", " 55.50 1\n", " 66.00 1\n", " 23.50 1\n", " 0.42 1\n", "Name: Age, Length: 89, dtype: int64\n" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "train_df.describe() #Only 38% of passengers survived, average age is 29.67." ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>714.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>446.000000</td>\n", " <td>0.383838</td>\n", " <td>2.308642</td>\n", " <td>29.699118</td>\n", " <td>0.523008</td>\n", " <td>0.381594</td>\n", " <td>32.204208</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>257.353842</td>\n", " <td>0.486592</td>\n", " <td>0.836071</td>\n", " <td>14.526497</td>\n", " <td>1.102743</td>\n", " <td>0.806057</td>\n", " <td>49.693429</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.420000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>223.500000</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>20.125000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>7.910400</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>446.000000</td>\n", " <td>0.000000</td>\n", " <td>3.000000</td>\n", " <td>28.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>14.454200</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>668.500000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>38.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>31.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>891.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>80.000000</td>\n", " <td>8.000000</td>\n", " <td>6.000000</td>\n", " <td>512.329200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "train_df.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "train_df['Pclass'].plot(kind = 'hist', rot=0, logx=True, logy=True) \n", "#Large majority 3rd class, small portion 1st, even smaleler portion 2nd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c229c22d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f2c22cf9610>" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "train_df.plot(kind='scatter', x='Age', y='Fare') #Outliers?" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c22350f10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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qd1GiJKrVSNh/+kvcfl2KU5xq40xcBbstPlBNt4FsFgr3xUCkYIKIcW4D4eZn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\n4g9rijBW75zHLdahEsV+L1d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"text": [ "<matplotlib.figure.Figure at 0x7f2c22673790>" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='Age', y='Fare', data=train_df, hue='Pclass') \n", "#Probably not outliers/errors in data, likely just very expensive tickets since they are 1st class passengers?" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "<seaborn.axisgrid.FacetGrid at 0x7f2c22cff110>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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XUlNTw8qVKxkYGADgK1/5CgcOHMCyLK655hqWLl3Kgw8+yGOPPYbX62XmzJncfffdebcl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\nTDEtfnakhZfDIX49cQYdUlKqGRiOwjYT1IXLuWzC+TSWNLg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"text": [ "<matplotlib.figure.Figure at 0x7f2c2221d490>" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.residplot(x='Age', y='Fare', data=train_df, dropna=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c22163510>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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MrUY6XX6jRSigWMCnqsNcPdcumMWxxu5QwUMIhN5T9F5I54CfgDqQ4FMZzlhy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\ncru8vJyWlpYRj//www/59a9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"text": [ "<matplotlib.figure.Figure at 0x7f2c22134310>" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.boxplot(x='Pclass', y='Fare', data=train_df)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c220b8890>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f2c220723d0>" ] } ], "prompt_number": 56 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Cleaning data" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Replacing NaN values of Age" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# </Rachel> I went to the link provided above for this calculation, but was confused by the Warning message\n", "# I found a similar method on Github that shows the distribution before and after the random values are generated </Rachel>\n", "\n", "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n", "axis1.set_title('Original Age values - Titanic')\n", "axis2.set_title('New Age values - Titanic')\n", "\n", "# plot original Age values (drop null values and convert to int)\n", "train_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)\n", "\n", "# get average, std and number of NaN values\n", "average_age = train_df[\"Age\"].mean()\n", "std_age = train_df[\"Age\"].std()\n", "count_nan_age = train_df[\"Age\"].isnull().sum()\n", "\n", "# generate random numbers between (mean - std) & (mean + std)\n", "rand_age = np.random.randint(average_age - std_age, average_age + std_age, size = count_nan_age)\n", "\n", "# fill NaN values in Age column with random values generated\n", "age_slice = train_df[\"Age\"].copy()\n", "age_slice[np.isnan(age_slice)] = rand_age\n", "\n", "# plot imputed Age values\n", "age_slice.astype(int).hist(bins=70, ax=axis2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c21f7d350>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VnZ0tu90uu92uF154IRiZAQAA/Coue3mDaU77mBAkaVu+23tYU+v97Pm5A79p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\ngLOj6yMAAAAAGAxX1AAAgE+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"text": [ "<matplotlib.figure.Figure at 0x7f2c22cff610>" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "# Distribution looks good - replace Age vector in original data with new values\n", "train_df[\"Age\"] = age_slice\n", "\n", "# Show number of missing Age values\n", "train_df[\"Age\"].isnull().sum()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "0" ] } ], "prompt_number": 58 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Replacing NaN values of Embarked" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Fill missing values with most common value\n", "train_df['Embarked'] = train_df['Embarked'].fillna('S')\n", "\n", "# Show number of missing values\n", "train_df['Embarked'].isnull().sum()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "0" ] } ], "prompt_number": 59 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Replacing missing Fare values" ] }, { "cell_type": "code", "collapsed": false, "input": [ "train_df['Fare'] = train_df.Fare.apply(lambda x: x if x>0 else pd.np.nan) # Replaced zeros with NaNs\n", "train_df['Fare'].isnull().sum() # Checked to make sure they are now recognized as null" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "15" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "m = train_df.groupby('Pclass').mean().Fare # Calculated mean for each group/class\n", "m" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "Pclass\n", "1 86.148874\n", "2 21.358661\n", "3 13.787875\n", "Name: Fare, dtype: float64" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "train_df['Fare'] = train_df.apply(lambda row: m[row['Pclass']] # Replaced NaNs for Fare with the mean value for each class\n", " if pd.isnull(row['Fare'])\n", " else row['Fare'],\n", " axis=1) \n", "train_df['Fare'].isnull().sum() # Checked to make sure there are no longer missing values" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "0" ] } ], "prompt_number": 62 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Convert Embarked & Sex to numerical categories" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Transform Embarked (S = 0, C = 1, Q = 2)\n", "train_df['Embarked'] = train_df['Embarked'].map({'S': 0, 'C': 1, 'Q': 2}).astype(int)\n", "\n", "# Transform Sex (male = 0, female = 1)\n", "train_df['Sex'] = train_df['Sex'].map({'female': 1, 'male': 0}).astype(int)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Specify variables for prediction model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# With the target variable \"Survived\", I would recommend starting with Sex, Age, Pclass and Fare as our predictors...\n", "# Let me know what you think!\n", "\n", "# <Raghu> Yes, lets start with these we have to engineer the data (fill NaNs) and clean the data \n", "# to make it less overfitting. </Raghu>" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 64 } ], "metadata": {} } ] }
gpl-3.0
topix-hackademy/pandas-for-dummies
01_SERIES/CSV-Reader.ipynb
1
20256
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Read Data From CSV\n", "\n", "Method:\n", "\n", " read_csv" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>surname</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Alex</td>\n", " <td>Comu</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Valeria</td>\n", " <td>Gela</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Alex</td>\n", " <td>Comu</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Valeria</td>\n", " <td>Gela</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Alex</td>\n", " <td>Comu</td>\n", " <td>22</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name surname age\n", "0 Alex Comu 22\n", "1 Valeria Gela 91\n", "2 Alex Comu 22\n", "3 Valeria Gela 91\n", "4 Alex Comu 22" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asd = pd.read_csv(\"data/input.csv\")\n", "print type(asd) \n", "asd.head()\n", "# This is a Dataframe because we have multiple columns!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create a **Series** we need to set the column (using **usecols**) to use and set the parameter **squeeze** to *True*." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.series.Series'>\n" ] }, { "data": { "text/plain": [ "RangeIndex(start=0, stop=74, step=1)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\"data/input.csv\", usecols=[\"name\"], squeeze=True)\n", "print type(data)\n", "data.head()\n", "data.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the input file has only 1 column we don't need to provide the *usecols* argument." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.series.Series'>\n", "0 Alex\n", "1 Pippo\n", "Name: name, dtype: object \n", "\n", "0 Alex\n", "1 Pippo\n", "2 Vale\n", "3 Fra\n", "Name: name, dtype: object\n" ] } ], "source": [ "data = pd.read_csv(\"data/input_with_one_column.csv\", squeeze=True)\n", "print type(data)\n", "\n", "# HEAD\n", "print data.head(2), \"\\n\"\n", "# TAIL\n", "print data.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On Series we can perform classic python operation using **Built-In** Functions!" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Alex', 'Pippo', 'Vale', 'Fra']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(data)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 'Alex', 1: 'Pippo', 2: 'Vale', 3: 'Fra'}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict(data)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Vale'" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(data)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Alex'" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(data)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "['T',\n", " '_AXIS_ALIASES',\n", " '_AXIS_IALIASES',\n", " '_AXIS_LEN',\n", " '_AXIS_NAMES',\n", " '_AXIS_NUMBERS',\n", " '_AXIS_ORDERS',\n", " '_AXIS_REVERSED',\n", " '_AXIS_SLICEMAP',\n", " '__abs__',\n", " '__add__',\n", " '__and__',\n", " '__array__',\n", " '__array_prepare__',\n", " '__array_priority__',\n", " '__array_wrap__',\n", " '__bool__',\n", " '__bytes__',\n", " '__class__',\n", " '__contains__',\n", " '__copy__',\n", " '__deepcopy__',\n", " '__delattr__',\n", " '__delitem__',\n", " '__dict__',\n", " '__dir__',\n", " '__div__',\n", " '__divmod__',\n", " 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'set_value',\n", " 'shape',\n", " 'shift',\n", " 'size',\n", " 'skew',\n", " 'slice_shift',\n", " 'sort_index',\n", " 'sort_values',\n", " 'sortlevel',\n", " 'squeeze',\n", " 'std',\n", " 'str',\n", " 'strides',\n", " 'sub',\n", " 'subtract',\n", " 'sum',\n", " 'swapaxes',\n", " 'swaplevel',\n", " 'tail',\n", " 'take',\n", " 'to_clipboard',\n", " 'to_csv',\n", " 'to_dense',\n", " 'to_dict',\n", " 'to_excel',\n", " 'to_frame',\n", " 'to_hdf',\n", " 'to_json',\n", " 'to_msgpack',\n", " 'to_period',\n", " 'to_pickle',\n", " 'to_sparse',\n", " 'to_sql',\n", " 'to_string',\n", " 'to_timestamp',\n", " 'to_xarray',\n", " 'tolist',\n", " 'transform',\n", " 'transpose',\n", " 'truediv',\n", " 'truncate',\n", " 'tshift',\n", " 'tz_convert',\n", " 'tz_localize',\n", " 'unique',\n", " 'unstack',\n", " 'update',\n", " 'valid',\n", " 'value_counts',\n", " 'values',\n", " 'var',\n", " 'view',\n", " 'where',\n", " 'xs']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(data)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Alex', 'Fra', 'Pippo', 'Vale']" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(data)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Alex 12\n", "Pippo 23\n", "Vale 66\n", "Fra 12\n", "Name: age, dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\"data/input_with_two_column.csv\", index_col=\"name\", squeeze=True)\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Alex 12.0\n", "asd NaN\n", "Name: age, dtype: float64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[[\"Alex\", \"asd\"]]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Alex 12\n", "Pippo 23\n", "Vale 66\n", "Name: age, dtype: int64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"Alex\":\"Vale\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
DaveBackus/Data_Bootcamp
Code/IPython/bootcamp_sandbox.ipynb
1
2664
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Data Bootcamp sandbox \n", "\n", "This IPython notebook was created for the NYU Stern course [Data Bootcamp](http://databootcamp.nyuecon.com/). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 3.5.0 |Anaconda 2.4.0 (64-bit)| (default, Oct 20 2015, 07:26:33) [MSC v.1900 64 bit (AMD64)]\n", "Pandas version: 0.17.0\n", "Matplotlib version: 1.4.3\n", "Today: 2016-01-07\n" ] } ], "source": [ "import sys # system module \n", "import pandas as pd # data package\n", "import matplotlib as mpl # graphics package\n", "import datetime as dt # date and time module\n", "\n", "# check versions (overkill, but why not?)\n", "print('Python version:', sys.version)\n", "print('Pandas version: ', pd.__version__)\n", "print('Matplotlib version: ', mpl.__version__)\n", "print('Today: ', dt.date.today())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.options.display.mpl_style = 'default'" ] }, { "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": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This is an IPython command: it puts plots here in the notebook, rather than a separate window.\n", "%matplotlib inline" ] }, { "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Quadrocube/rep
howto/04-howto-folding.ipynb
1
130134
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# About\n", "\n", "This notebook demonstrates stacking machine learning algorithm - folding, which physics use in their analysis." ] }, { "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy, pandas\n", "from rep.utils import train_test_split\n", "from sklearn.metrics import roc_auc_score\n", "\n", "sig_data = pandas.read_csv('toy_datasets/toyMC_sig_mass.csv', sep='\\t')\n", "bck_data = pandas.read_csv('toy_datasets/toyMC_bck_mass.csv', sep='\\t')\n", "\n", "labels = numpy.array([1] * len(sig_data) + [0] * len(bck_data))\n", "data = pandas.concat([sig_data, bck_data])\n", "\n", "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, train_size=0.7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training variables" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "variables = [\"FlightDistance\", \"FlightDistanceError\", \"IP\", \"VertexChi2\", \"pt\", \"p0_pt\", \"p1_pt\", \"p2_pt\", 'LifeTime', 'dira']\n", "data = data[variables]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Folding strategy - stacking algorithm\n", "\n", "It implements the same interface as all classifiers, but with some difference:\n", "\n", "* all prediction methods have additional parameter \"vote\\_function\" (example folder.predict(X, __vote\\_function=None)__), which is used to combine all classifiers' predictions. By default \"mean\" is used as \"vote_function\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from rep.estimators import SklearnClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define folding model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from rep.metaml import FoldingClassifier" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "FoldingClassifier(base_estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n", " max_depth=3, max_features=None, max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2, n_estimators=100,\n", " random_state=None, subsample=1.0, verbose=0,\n", " warm_start=False),\n", " features=['FlightDistance', 'FlightDistanceError', 'IP', 'VertexChi2', 'pt', 'p0_pt', 'p1_pt', 'p2_pt', 'LifeTime', 'dira'],\n", " ipc_profile=None, n_folds=4, random_state=None)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_folds = 4\n", "folder = FoldingClassifier(GradientBoostingClassifier(), n_folds=n_folds, features=variables)\n", "folder.fit(train_data, train_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default prediction (predict i_th_ fold by i_th_ classifier)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] }, { "data": { "text/plain": [ "array([[ 0.2383179 , 0.7616821 ],\n", " [ 0.27117691, 0.72882309],\n", " [ 0.02837497, 0.97162503],\n", " ..., \n", " [ 0.03500956, 0.96499044],\n", " [ 0.35562003, 0.64437997],\n", " [ 0.91160588, 0.08839412]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folder.predict_proba(train_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Voting prediction (predict i-fold by all classifiers and take value, which is calculated by `vote_function`)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# definition of mean function, which combines all predictions\n", "def mean_vote(x):\n", " return numpy.mean(x, axis=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using voting KFold prediction\n" ] }, { "data": { "text/plain": [ "array([[ 0.01176629, 0.98823371],\n", " [ 0.04199359, 0.95800641],\n", " [ 0.01372648, 0.98627352],\n", " ..., \n", " [ 0.06014284, 0.93985716],\n", " [ 0.85454863, 0.14545137],\n", " [ 0.71465495, 0.28534505]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folder.predict_proba(test_data, vote_function=mean_vote)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of folds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again use `ClassificationReport` class to compare different results. For folding classifier this report uses only __default prediction__." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Report training dataset" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] } ], "source": [ "from rep.data.storage import LabeledDataStorage\n", "from rep.report import ClassificationReport\n", "# add folds_column to dataset to use mask\n", "train_data[\"FOLDS\"] = folder._get_folds_column(len(train_data))\n", "lds = LabeledDataStorage(train_data, train_labels)\n", "\n", "report = ClassificationReport({'folding': folder}, lds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Signal distribution for each fold\n", "\n", "Use `mask` parameter to plot distribution for the specific fold " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { 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"output_type": "display_data" } ], "source": [ "for fold_num in range(n_folds):\n", " report.prediction_pdf(mask=\"FOLDS == %d\" % fold_num, labels_dict={1: 'sig fold %d' % fold_num}).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background distribution for each fold" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEZCAYAAAC99aPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJztnX28HGV1+L8LElCCJPKWxJ/k7hUaUJQrQaoGuANFJRYR\n", "lSitKAFKIm3zolWsVbwbRSm2pXnBFqLABUE0VglogSKauZhoVNAAoiCy94aX8CKQQBIJr/P748zs\n", "zsw+szuzO7szu/d8+SyZnXnmmTNzZ5+Xc85zDiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiK\n", "oiiKoiiKoihK+hwNXA88BLwMnBbjnPcA64FngD8Bq4ED2yWgoiiKkoydOny93YE7gUXAs4DToPwB\n", "SMdhAwPAccBuwA3tE1FRFEXpFrYCH2tQ5mTgRaDg23cMMpt5TZvkUhRFURLQ6RlJUtYB24CzgJ2B\n", "PYC5wC+Bp7ITS1EURckDcWYkAO8AHgdeAF4CbgP2aaNciqIoSgLyPiPpR2wklwOHAxbSAa0iqO5S\n", 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prediction is used" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] } ], "source": [ "lds = LabeledDataStorage(test_data, test_labels)\n", "\n", "report = ClassificationReport({'folding': folder}, lds)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAjQAAAEZCAYAAACAUb92AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXFWd9/FPJSFBGyUqSAiDpIPsLmFwHFCEAwoPOqiA\n", "uI0KkVEZF2R55sHhASVuKPiogCKCCK2oIKiD6GhcKZUMjDIKqIgKTYMQwiJEQgwQknr++N2y71br\n", "PV3n1q3v+/WqV9/11O/W7a7763POvQdERERERERERERERERERERERERERERERERERERERERERERE\n", "RAZjH+BK4E5gI3BkzjY7At8AHgTWAv8D7DyoAEVERGQ4zRrge40BNwLHAuuARmr9OLACuBXYD9gN\n", "OBl4eIAxioiIiHRtDXBEatlXgIsDxCIiIiJDbpA1NO3MAg4GfgcsB+4Ffg68JmRQIiIiIu2ka2gW\n", 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[] } ], "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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
mne-tools/mne-tools.github.io
0.13/_downloads/plot_objects_from_arrays.ipynb
1
6185
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Creating MNE objects from data arrays\n\n\nIn this simple example, the creation of MNE objects from\nnumpy arrays is demonstrated. In the last example case, a\nNEO file format is used as a source for the data.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Author: Jaakko Leppakangas <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport neo\n\nimport mne\n\nprint(__doc__)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Create arbitrary data\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "sfreq = 1000 # Sampling frequency\ntimes = np.arange(0, 10, 0.001) # Use 10000 samples (10s)\n\nsin = np.sin(times * 10) # Multiplied by 10 for shorter cycles\ncos = np.cos(times * 10)\nsinX2 = sin * 2\ncosX2 = cos * 2\n\n# Numpy array of size 4 X 10000.\ndata = np.array([sin, cos, sinX2, cosX2])\n\n# Definition of channel types and names.\nch_types = ['mag', 'mag', 'grad', 'grad']\nch_names = ['sin', 'cos', 'sinX2', 'cosX2']" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Creation of info dictionary.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# It is also possible to use info from another raw object.\ninfo = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)\n\nraw = mne.io.RawArray(data, info)\n\n# Scaling of the figure.\n# For actual EEG/MEG data different scaling factors should be used.\nscalings = {'mag': 2, 'grad': 2}\n\nraw.plot(n_channels=4, scalings=scalings, title='Data from arrays',\n show=True, block=True)\n\n# It is also possible to auto-compute scalings\nscalings = 'auto' # Could also pass a dictionary with some value == 'auto'\nraw.plot(n_channels=4, scalings=scalings, title='Auto-scaled Data from arrays',\n show=True, block=True)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "EpochsArray\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "event_id = 1\nevents = np.array([[200, 0, event_id],\n [1200, 0, event_id],\n [2000, 0, event_id]]) # List of three arbitrary events\n\n# Here a data set of 700 ms epochs from 2 channels is\n# created from sin and cos data.\n# Any data in shape (n_epochs, n_channels, n_times) can be used.\nepochs_data = np.array([[sin[:700], cos[:700]],\n [sin[1000:1700], cos[1000:1700]],\n [sin[1800:2500], cos[1800:2500]]])\n\nch_names = ['sin', 'cos']\nch_types = ['mag', 'mag']\ninfo = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)\n\nepochs = mne.EpochsArray(epochs_data, info=info, events=events,\n event_id={'arbitrary': 1})\n\npicks = mne.pick_types(info, meg=True, eeg=False, misc=False)\n\nepochs.plot(picks=picks, scalings='auto', show=True, block=True)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "EvokedArray\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "nave = len(epochs_data) # Number of averaged epochs\nevoked_data = np.mean(epochs_data, axis=0)\n\nevokeds = mne.EvokedArray(evoked_data, info=info, tmin=-0.2,\n comment='Arbitrary', nave=nave)\nevokeds.plot(picks=picks, show=True, units={'mag': '-'},\n titles={'mag': 'sin and cos averaged'})" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Extracting data from NEO file\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# The example here uses the ExampleIO object for creating fake data.\n# For actual data and different file formats, consult the NEO documentation.\nreader = neo.io.ExampleIO('fakedata.nof')\nbl = reader.read(cascade=True, lazy=False)[0]\n\n# Get data from first (and only) segment\nseg = bl.segments[0]\ntitle = seg.file_origin\n\nch_names = list()\ndata = list()\nfor asig in seg.analogsignals:\n # Since the data does not contain channel names, channel indices are used.\n ch_names.append(str(asig.channel_index))\n asig = asig.rescale('V').magnitude\n data.append(asig)\n\nsfreq = int(seg.analogsignals[0].sampling_rate.magnitude)\n\n# By default, the channel types are assumed to be 'misc'.\ninfo = mne.create_info(ch_names=ch_names, sfreq=sfreq)\n\nraw = mne.io.RawArray(data, info)\nraw.plot(n_channels=4, scalings={'misc': 1}, title='Data from NEO',\n show=True, block=True, clipping='clamp')" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.12", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
bsd-3-clause
ledeprogram/algorithms
class2/donow/Kandrach_Sasha_2_donow.ipynb
1
1945
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import pi " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#volume_of_sphere = (((4/3) *math.pi) *5**3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#print(volume_of_sphere)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "math.pi \n", "\n", "radius = float(input('Please enter radius:'))\n", "suface_area = 4 * math.pi * radius * radius \n", "volume_of_sphere = (4/3) * math.pi * radius * radius * radius \n", "\n", "print(\"\\n The surface area of the sphere = %.2f\" %surface_area)\n", "print(\"\\n The Volume of the sphere is %.2f\" %volume)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Example in class:\n", "\n", "# def volume(r):\n", "# if r<0:\n", "# return False\n", "# return(4/3) * pi * (r**3)\n", "#volume(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0