Full Code of dylewsky/Data_Driven_Science_Python_Demos for AI

master ca8091d23569 cached

116 files

211.9 MB

11.7M tokens

1 requests

Copy disabled (too large) Download .txt

Showing preview only (46,696K chars total). Download the full file to get everything.

Repository: dylewsky/Data_Driven_Science_Python_Demos
Branch: master
Commit: ca8091d23569
Files: 116
Total size: 211.9 MB

Directory structure:
gitextract_1f08lisc/

├── .gitignore
├── CH01/
│   ├── CH01_SEC02.ipynb
│   ├── CH01_SEC03_Rotation.ipynb
│   ├── CH01_SEC04_1_Linear.ipynb
│   ├── CH01_SEC04_2_Cement.ipynb
│   ├── CH01_SEC04_3_Housing.ipynb
│   ├── CH01_SEC05_1_PCAGaussian.ipynb
│   ├── CH01_SEC05_2_OvarianCancer.ipynb
│   ├── CH01_SEC06_1.ipynb
│   ├── CH01_SEC06_2_3_4.ipynb
│   ├── CH01_SEC07_1.ipynb
│   ├── CH01_SEC07_2.ipynb
│   ├── CH01_SEC07_3.ipynb
│   ├── CH01_SEC08_RSVD.ipynb
│   └── CH01_SEC09_Tensor.ipynb
├── CH02/
│   ├── CH02_SEC01_0_InnerProduct.ipynb
│   ├── CH02_SEC01_1_FourierSines.ipynb
│   ├── CH02_SEC01_2_Gibbs.ipynb
│   ├── CH02_SEC01_2_Gibbs_Movie.ipynb
│   ├── CH02_SEC02_1_DFT.ipynb
│   ├── CH02_SEC02_2_Denoise.ipynb
│   ├── CH02_SEC02_3_SpectralDerivative.ipynb
│   ├── CH02_SEC03_1_FFTHeat.ipynb
│   ├── CH02_SEC03_2_FFTWave.ipynb
│   ├── CH02_SEC03_3_FFTBurgers.ipynb
│   ├── CH02_SEC04_1_SpectrogramChirp.ipynb
│   ├── CH02_SEC04_2_SpectrogramBeethoven.ipynb
│   ├── CH02_SEC05_HAAR.ipynb
│   ├── CH02_SEC06_1_2DFFT.ipynb
│   ├── CH02_SEC06_2_Compress.ipynb
│   ├── CH02_SEC06_3_Denoise.ipynb
│   ├── CH02_SEC06_4_Wavelet.ipynb
│   └── CH02_SEC06_5_WaveletCompress.ipynb
├── CH03/
│   ├── CH03_SEC01_Compress.ipynb
│   ├── CH03_SEC03_1_Underdetermined.ipynb
│   ├── CH03_SEC03_2_AudioCS.ipynb
│   ├── CH03_SEC04_Matrices.ipynb
│   ├── CH03_SEC05_1_RobustRegression.ipynb
│   ├── CH03_SEC05_2_LASSO.ipynb
│   ├── CH03_SEC06_SparseRepresentation.ipynb
│   └── CH03_SEC07_RPCA.ipynb
├── CH04/
│   ├── CH04_SEC01_LinearRegression.ipynb
│   ├── CH04_SEC02_1_GradientDescent.ipynb
│   ├── CH04_SEC03_1_OverUnderDetermined.ipynb
│   ├── CH04_SEC04_1_CompareRegression.ipynb
│   ├── CH04_SEC05_0_Fig4p16_Pareto.ipynb
│   ├── CH04_SEC05_1_CrossValidate.ipynb
│   ├── CH04_SEC06_1_kFoldValidation.ipynb
│   ├── CH04_SEC07_1_ModelValidation.ipynb
│   └── CH04_SEC07_2_RegressAIC_BIC.ipynb
├── CH05/
│   ├── CH05_SEC01_1_FischerExtraction.ipynb
│   ├── CH05_SEC02_1_Fig5p7_Fig5p8.ipynb
│   ├── CH05_SEC03_1_Kmeans.ipynb
│   ├── CH05_SEC04_1_Dendrogram.ipynb
│   ├── CH05_SEC05_1_GaussianMixtureModels.ipynb
│   ├── CH05_SEC06_1_LDA_Classify.ipynb
│   ├── CH05_SEC07_1_SVM.ipynb
│   └── CH05_SEC08_1_Trees.ipynb
├── CH06/
│   ├── CH06_SEC01_1_NN.ipynb
│   ├── CH06_SEC04_1_StochasticGradientDescent.ipynb
│   ├── CH06_SEC05_1_DeepCNN.ipynb
│   └── CH06_SEC06_1_NNLorenz.ipynb
├── CH07/
│   ├── CH07_SEC01_SimulateLogistic.ipynb
│   ├── CH07_SEC01_SimulateLorenz.ipynb
│   ├── CH07_SEC02_DMD_Cylinder.ipynb
│   ├── CH07_SEC03_SINDY_Lorenz.ipynb
│   ├── CH07_SEC04_Koopman.ipynb
│   └── CH07_SEC05_HAVOK_Lorenz.ipynb
├── CH08/
│   ├── CH08_SEC01_CruiseControl.ipynb
│   ├── CH08_SEC07_1_LQR.ipynb
│   ├── CH08_SEC07_2_KalmanFilter.ipynb
│   ├── CH08_SEC07_2b_Obsv.ipynb
│   ├── CH08_SEC08_1_TransferFunction.ipynb
│   ├── CH08_SEC08_2_SandT.ipynb
│   └── CH08_SEC08_3_PlantInversion.ipynb
├── CH09/
│   ├── CH09_SEC02_1_GramianPlot.ipynb
│   ├── CH09_SEC02_2_BalancedTruncation.ipynb
│   ├── CH09_SEC03_ERA_OKID.ipynb
│   └── CH09_SEC03_Fig9p5.ipynb
├── CH10/
│   ├── CH10_SEC03_ESCfixed.ipynb
│   └── CH10_SEC03_ESCsinusoidal.ipynb
├── CH11/
│   ├── CH11_SEC01_1_Fig11p1.ipynb
│   ├── CH11_SEC02_1_HarmonicOscillator.ipynb
│   ├── CH11_SEC03_1_NonlinearSchrodinger.ipynb
│   └── CH11_SEC05_1_Invariance.ipynb
├── CH12/
│   ├── CH12_SEC01_1_GAPPY.ipynb
│   ├── CH12_SEC02_1_GAPPY.ipynb
│   ├── CH12_SEC03_1_GAPPY_ConditionNumber.ipynb
│   ├── CH12_SEC04_1_GAPPY_Variance.ipynb
│   ├── CH12_SEC06_1_DEIM.ipynb
│   └── CH12_SEC06_2_DEIM.ipynb
├── DATA/
│   ├── CC2.mat
│   ├── VORTALL.mat
│   ├── allFaces.mat
│   ├── beethoven_40sec.mat
│   ├── burgers.mat
│   ├── catData.mat
│   ├── catData_w.mat
│   ├── census1994.csv
│   ├── colors2.mat
│   ├── coolcolors.mat
│   ├── coolcolorsBW.mat
│   ├── dogData.mat
│   ├── dogData_w.mat
│   ├── fisheriris.mat
│   ├── hald_heat.csv
│   ├── hald_ingredients.csv
│   ├── housing.data
│   ├── lettersTestSet.mat
│   ├── lettersTrainSet.mat
│   ├── ovariancancer_grp.csv
│   ├── ovariancancer_obs.csv
│   ├── testSys.mat
│   ├── testSys_ABCD.mat
│   └── testSys_Fig9p5_ABCD.mat
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*.doc
*.zip
*.rtf
*.eps
*.pyc
*.mdl
*.slx
*.m
*.doc#

Python_Codebase_Notes.txt
UTILS/*
auxiliary book code files/*
*/.ipynb_checkpoints/*
*/*/.ipynb_checkpoints/*
*/__pycache__/*
*/*/__pycache__/*
*/.DS_Store
*/*/.DS_Store

CH01/dog.jpg
CH01/None0000000.png
CH01/temp.mat
CH02/CH02_SEC04_2_SpectrogramBeethoven/*
CH02/None0000000.png
CH03/CC2.mat
CH03/jelly.jpg
CH03/mustache.jpg
CH05/catData.mat
CH05/catData_w.mat
CH05/dogData.mat
CH05/dogData_w.mat
CH05/census1994.csv
CH06/catData_w.mat
CH06/dogData_w.mat
CH06/CH06_SEC01_1_NN/*
CH06/CH06_SEC06_1_NNLorenz/*
CH06/extra/*
CH08/CH08_SEC07_3_LQG_Simulink/*
CH09/*.mat
CH09/CH09_SEC02_2_BalancedTruncation/*
CH09/CH09_z_extra_BT_ERA_OKID/*
CH10/CH10_SEC02_GA_PID/*
CH10/CH10_SEC03_ESC_Krstic_ex1p3/*
CH10/CH10_SEC03_ESC_Krstic_ex1p3/NEWkrstic_example_1p3_wnoise.slx
CH12/old_extra/*



================================================
FILE: CH01/CH01_SEC02.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.image import imread\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "plt.rcParams['figure.figsize'] = [16, 8]\n",
    "\n",
    "\n",
    "A = imread(os.path.join('..','DATA','dog.jpg'))\n",
    "X = np.mean(A, -1); # Convert RGB to grayscale\n",
    "\n",
    "img = plt.imshow(X)\n",
    "img.set_cmap('gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "iVBORw0KGgoAAAANSUhEUgAAALEAAAD3CAYAAABSKLW0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOy9uY9kWZand54tbm5uZm7m5kssWVlZmVPgoDEAR6JKZQQKBBqgSFDin0BKA4IAQYEKRQIzpEQMCAwpz6AFDhVKVNhCowkM0KiubnR1VMbqm61ubtuj4P1d+94Ni4gMjx70ZCMe4HB3s7fc5Sy/8zvn3leUZRlfj6/Hz/mo/V034Ovx9fjS46sQfz1+9sdXIf56/OyPr0L89fjZH1+F+Ovxsz++CvHX42d/fBXir8fP/vgqxH8HR1EU/0FRFP+qKIp3RVFcF0Xxb4qi+IfZOf9VURSvi6IYFUXxvxZF0fq7au+/78dXIf6JR1EUjb/F2w0i4l9HxD+MiCcR8f9GxL/Ss/6TiPinEfFPIuJXEfFDRPz3f4vP/3t1FF8zdh8+iqL4q4j4nyPiv4gHgeuUZbn+d/CcYURcRcRZWZZXRVH87xHxV2VZ/jd/8/0/iYh/WZbl07/tZ/99OL5a4k8f/3lE/KcRMdgnwEVR/H9FUdx+4Oef/8Rn/McR8bosy6u/+f8fRcSf6vs/jYgnRVGcfklH/r4ef5su8u/r8T+VZfniQ1+WZfkffsnNi6L4RUT8s4j4r/VxNyJG+p+/e/Fgsb8eOr5a4k8fHxTgLz2KojiPiP8rIv55WZb/h76aRsSx/ufvyb+rtvycj69C/Onjo0FDURT/tiiK6Qd+/pePXHcSDwL8r8uy/B+yr/9tRPxj/f+PI+KN4MbXQ8dXOPGFR1mW/+hzrymK4jgi/k1E/D9lWf7TPaf8bxHxL4qi+JcR8Soi/tuI+Bdf0s6/z8dXS/x3c/xnEfEfRcR/mVnuX0ZElGX5f0bE/xgR/3dE/O5vfv67v7PW/nt+fKXYvh4/++OrJf56/OyPr0L89fjZH1+F+Ovxsz++CvHX42d/fJRi+6M/+qNK1Fer1aIoiliv17HZbGKz2cR2u43NZhMREUVRREREWZZRFEU0Go1oNBpRFEUURRFlWcZms4larRZlWUa9Xk+fbbfbKMsy/S6KIv3Nsx2EFkURtVot/URErNfr9Cz+bzQaUa/X0/NrtVpsNptYrVaVdnI/nrler9Nztttt+r3dbqNWq1X6RZ8jIlarVazX6/TMWq0W9Xo9ms1maidtYyw8DoxL/vyyLFN76/V61Ov19Fxfw7xwL4+Zx4rnctBH5ow2lmUZq9XqPRmgfxGR5o/7bzabdC1jUBRFbDabyrX+3vJTr9crY0U//vAP/7DYJ6efxRPTUA4aYEHzeTTeQkyjGDT+534WjPyg0/lnvqfbse+ebovvl9/Xn9NHFJa2Mxn5OCCQEZEExefW6/WkELSD/7metrm9+8aSe9DnXNHdJo8Nc5P3l3v63vz/oXnZN2b5HOVznc9NLgOfc3x2ssMP3teAiKhYSAvUxxrJRGCV9gnsh9rB+Z7YD33Os3Ir5fvy2x7B3+fK6XtaqPeN177nW0H4fL1eJ8XYN/F85/HfN6a5EH/onPwZH/p739znAoon4PCc+jzm3M9+DOX7SSHeJ4R8/iFByM/1/54Ed27fRFl4PGn77r2vHbZyvh/W9EPXfagfOYR5jNX40P0/Nlb+bp8F/ZgX+dg4eezxnvl47Ds/9wz5sw1VrPT2BvvukY9zRFQ89oeOjwrxYrHYa1VGo1HM5/OYz+exWCxisVjEcrlM2KzRaMTBwUEMh8M4PDyMg4ODaLfb6R5gunq9nqzPdruN+/v791x1RCRc22w202BauYxl7aLBnPSh2WymySqKHbbfbrfRbDaTmweT5Z7m4OAg4WHO5Zmr1aqCRfmO/xuNxl7I0Wg00rPX63WsVquo1WoVPM9kGjNjALbbbQX3MxaMD9jdY8LB85bLZWy32zg4OIijo6N0TaPRSG1kzPfBMhumXMl9rgXbsU9R7PAyfeUwnHmUEN/e3iatssv40z/903jx4kW8e/cubm9vYzKZJMFlcLvdbjx58iTOz8/jyZMncXFxEZ1OJ9rtdurg4eFhxQrkwogwMUmtVisNgoW5Xq/HcrmMiIj7+/tKoMEktdvtaLVaURRFNJvNaDabMZlMUiBGQLLdbqPdbqdBR2giIo6OjiJih2/v7u5iuVzGarWK+/v7pFAIuJUPBSRI2m63sV6vUz+3220sl8tYr9dxeHiYnoPwE+jd398nQW82m7HZbFI7rRyLxSIODg6i3+9Hq9WK7XYb0+k0RqNRrFar1D4Uc7PZJCE+ODiIiAePxXguFos0HzYgCB/tOTg4SMJPHw2TcvhJHxk7n8O45N7ns4TYLtfm/fb2Nl6/fh2Xl5cxHo9jPp/H0dFRNJvNKIoiVqtVzOfzWC6XsVgsUif6/X50Op0kFAgLAnJ0dFSxjgiwo9u8fXzvAUMRELb8OgYwH9DcejqgoQ3L5TI94/7+PubzeRIKxojJZlIRFISUMbUg8MOktlqtZKFzFgFFsDI76EQgsKYIKazMYrGI7XYb3W43Wq1WHBwcJEWmrbbktpL7IBXnWohp03q9Tj+5AuChUEYH0HjHD8FHHx8VYk+y8ShuCEviweW33R6Nub+/T+4qIqLX61UsVbvdrliVPIiylc7xMpPITx7wIJymA3NqyJNPnzxpBFxcyxjsUy6320KIEOfP5dkIAmOXCyqCZgiB0tjlY009X54rG4k8wIzYeRu+R8HwNDyfeTaUBI7wDEPHHFbRZwfRzBMK9anjo0KMC2TSze/SQQbr4OAg7u/vk9YxWKvVKlarVcxms7i5uYl2u53ue3x8HAcHB9Hr9aLZbMbh4WHlOcaWzWYz3ddWywLi6Di3iuYmsbRwmQ5E4HMtsFhE3Ct9yycbZeJ7t9/wAeG1IPKcbrcbnU4nnQdcMfQgzkAJfU8LOcqF9c2tYbPZjFarlSwicc1ms0mGp9VqpTY2m83UTyu7+22IQJv3xRU2VDwLjwFcQpm/SIixmGgJNzSG8aAxwQRN9/f3cX9/H8vlMmazWUwmkzg6OkoaB/4aDodpIDudTkREUgaw7uHhYdzf3ydcbLwYEQm6rNfruLu7i4ODgzQBxrUID4NrZViv1yn4y5mUiAc8end3l8aj2+2msQBvo7Q59YTFxkrhgUypOdGSf46iDAaD6Ha7ERExmUxS33PPd3BwkAI2lAChpC0IMUK1XC6T8Ldarcpve4eIKo2GQBoq5UHsh2i8HPbY49q4fIxF+qgQ+wZmDHq9XhwfHydBI9jIQTwHVohBwnowkBEPTMhkMoknT57E4eFhEj7cDZ00Q8CxXC6TsBMAIizGyQyc7+HIOMd7tMHwwXgNYcGaIOicj/AzFq1WKymhsSrWjzGEpUFYDLl6vV4cHR3FarWKg4OD6HQ66dmMj7E0xoHnMqbb7TYped7n1WpVUQhn8Wiz55qxs/JhfVFCviObyb2Qg/x/e8xPHR8VYt/cwjwYDOLi4qISUMxms6T5psaYLBpO59G47XYb8/k8Xr9+HT/++GM8efIkTk5O4vz8PA4PD6PVakW3201Wg0E1JbVcLqNWqyVYwoHi8BxzmPSp1WrF3d1dUgDjWCylLRluHMuEZ3F/jAUjIl3HNcACxhQrulwuK8aCZ/p+tlSNRiN6vV5SHgdHtuwYAtqJQNIWB262sECMHOIRoDoesBA7rW+cbYhjS5xbdcYQAf4iis0u0QPe6XRiMBjE3d1dzOfzmM1m6XsLfm7Z+B5txL2uVqsYjUaxWCxiPB7H+fl5bDabOD4+TvQQk4GVcn1BxAPc6Ha7Cdqs1+uYTqdpQCygtrrgeg+uJ5s2I1yHh4fJipl54HoE38El1+K6sXRMGG4+p5oQKgeTwBnuyzXw1PZypvkcIzjwMq/OZ7ayVl6fsy8YtAwAcRzAIZB5wEzf8kCcz8HyjxJiF8Y42u52u3FychJ3d3cxm81iNpvF3d1dmiQmrd1ux+HhYRweHka73U4Den9/n6y0J3M2m6WOQLcROBLcmfy3hUKoYDgIZrBApmsQPITblglBNt/L/7j0sixTfwhGHQ/kVsneyUbBASpjZoqQ723xUBgUhPG24ME5c08HtUAO2mzKEKU1S2BDRj+gTf1jb22l9ph47vhtQ+FYhIP2+bPPEmIidUe0RVHE0dFRHB8fJytMQOVGYeVQBDceC4pLMohfLpcxnU7j8vIy7u/v4+DgoBJdLxaLBC8iHrhmUzHGUlg4W0yCN0Me00RmH2ypGFwE4PDwsOLiPciGTu4nnKipSNOF9lYec08ilpEJxm3n1nG5XFYYHwuxoYy5a+bX1hJhNLb1MxF2W/sctjmeQSkNFXwveyHzzF9kiXMhjniwZN1uNwaDQSVDVpZlLBaLihDj3u0q+ZxGb7fbRCuRPJjNZrHZbNIkIJjz+Tx6vV7Cl8fHx3F8fFxhKxyI2RohYKZ3SBzwPfgYgQYTY7VRBqJ8hP7u7u49XhR3yjPI7GElEVaMBR4Eiil3974XXsJ0pt23IZgVLHf9eYLIcMFBl62omRC8gGMf7kFcYRaLOck91IfaZ8X8oJx+WITfp9K4EVBhMBhU6BSsnDFWRFS03RbBbpOULkzHfD6vJFXu7u7i7u4ucalM/nA4jPPz8xgMBnF0dJRwMUpnTMigMZBgbBTCkbexnlkUPiNbh2VE4E1b2TrCMS8Wi0qKmpgApoNnOJDix8EU0IrzbGQwEr6n4QkH3s18MmPg+zm5wTmmColtbI2Zf3O99lp5gG/IY+Nmw/QoISbyznERtQhoHgNFvp4gw5YHAabjdqNgWuoZIiJZcIQHrplBwyLNZrPERw8Gg4iIGA6HFby63e6Ki7CwDDYCC0xAWJh0vIjrCcDv0+m0ggHtfnOmg7GzpXEmi/EB+hweHiblN93I+G+3D2njdrv9HoSiL41GI46OjipswWKxSJ7lQ6lg7sP8Egs4fsiFGIuLnKC8TgKhcL4OoYW5cICX05yPEmJrBw0z/USnIPrv7+9jPB7H3d1dLBaLuL29TQMGf2pNZIIODg7i7u6u4pIczS4Wi4Rn6TxWmio6EhEEg91ut+KCwY8OLGmHsanZEwSBSQSTm1Lcl8HCsnAPBBR+mfuiGPP5PFlnvIiLgPAgHitDEfC1P2MMc4ORw0IoS4/FfD5P7ceYYFHxtAik+WRbUvrrOef5breZHEMbw5svEmKb+5yzM/ZDsJ49exZHR0cxn89jPB6nYGC5XCZ3u09YttttHB4eJqvHdZwDFOF7OkqSA6Ghba1Wq1LphRKtVqtKoMNPTjNZgc11Y6W5l/G23R7XmN1h4p3OPTw8THQUVt+ZMlNl7kutVqtUlVmocoWyYJjmw/q5ug/jg7K7fzzL4+Ug0zDCRi/HspxnI2UGzIyH5+PRQoz1dPQasbMWTAju5ODgIFnG6+vr6Ha7MRqN4u3bt/H69eskAFhHW+PVapUmnuxSbkH5noEhCMLCYYlwsY1GI/r9fgoii6KI4+PjCl1FQIYAkMVCwHDNDDQDjxUju8j3trK4QlNpjB/ZNp5NW4+PjxNdiTIjuLAiQCXaBhNBnxBKvCGC3Ov1otPppDnlfkdHRxUPiEJFRHQ6nZR0Ip64u7ursDjG/Z5j+o5XQRgdADpWov2cQ78/dXxUiKfTaepUHtQxiVjX9Xod7XY7BWidTidOT09jOp3Gq1ev4uTkJHHKxmRYTwSPiY6ICmaz+3Ugg0UAWsxms1QaitDS3rIs4+zsrJLJMzFPvYVrLsyZRjzECYZRnU4narVaYhwscLSL8UP57aqZZMaViUd5mWDTZEAABBeLakzMMZlMknKT/azX6zGfz1M9NPdwpo4+5EpJX3MqzMJmGg5v7u/3WVcndUxF5gjgs4U4xzP8plbY9cNlWSZhiXjQqH6/H/f399Hr9VJyBMvHxOKaT09P08QTxN3c3KREit2jEyXmQufzeYxGo2TFYDL43m4/IpIgcy+sHFbCq3zLsozDw8PEPrjuFiVjjGxNHOggdJ48hM4BFsrg4iKn2o05XdjDffFWh4eHyfPUarVkYFznYtdt1oYYBIHFG8Ky2AiYUTATYdYBQTXnznzyuaEHwmzlfZQQm6j2Q7CkwAjcK64biMAknJ6exrNnzyqFOnQGwe52uymKvru7i9FoFP1+P8bjcUwmk1gul1Gv11PaNdd8XBgJGKzH1dVVgirO+KFA0+m0UpJInxhEPBFwCdiCGzfWxwW64suu09wvbYA/ZYx9fVHsyh+Z0NFolFiRXq+XlMDpboRvvV4nDMxzMEDw+0AthJkgNsfVCLEVx3MMvHRhlvtpvt3BPdbfjISN5hdjYlfpm19kwswNmpuMiBS4gLu63W7CsDQIgZ9Op8nFcQ5LmVjxQQp5NpslHAWWtibDIABNyrKM169fJyF+9epVGhTOOT4+TgX6TIwrwRzUUDvBxBITYKUQCOO9fVaW68xuYPVchWcIwkSbAkThXCWGFzFL4ja4XWVZVqBLLlCmHBkvezN7IgeDrs/IgzmUOY8VcrIAhTDu/mwhZtDtErEOeQYMIcYNfahTCA8ROOvsxuNxjMfjpNHdbjclOZgs6jBMiY3H48Qfc3+s8Wazidvb20Sp2SISqZ+fn8fZ2VmyTLACnU6noqRY3l6vl/qI5cHSI9h2/U5MREQ632MKRTafzyveDy9gys8MAXQighZRXRBgao65hMd3lhAhRbHM5+YshNc50kYsNEJpug9r7MDNCkl/7fmcIzB9+CghtlvLKQ8GyQ0xFrTGYqXAVnQUbEkQdnd3l7ApGowQ1+v1hG9JXBDIwSNDPQE5wJXz+TwNDOfy3bfffhuXl5fR7/fj5OQk/Zyfn8fx8XHCwbZ0eToWXO9nw9SgiLbssClYXNdWNBoP5ZWNRiNZIAIwJpRxpC8Weq7xAlg+R/idEoaO5HvTY84TMLfALh9mYbiPPTjtYB6REdetIEdWQOYwzzR+lhATCHhwjO0QTKyaO+UGuRHOPq3X60TxvHv3Lp3LQBwdHUW73Y7JZJI+63Q6KXi8u7uL6+vr5IZtDWnnarWK6XSaoM1kMnlvKRVepNPpxC9+8Yu4uLiIX/7yl/HNN99ExEMhFAI6n8/TWGCJr66uYrlcxmQyifF4HKvVKo6OjhLuRngPDw+j1+slIfUyI9dE2FrRD8av3+/H0dFR8jCMy93dXZoXFAuvyeeui+a+YGl+26LTFvppGGSGCW/qVdQWbJTCBg4jhyH0b6f9UYJHsxOYcwuyrW3EDjc7HYmLM2a1peF7Bo9Vt8fHx4nuIsDpdDrRarUSbjPh76DHTAETioYj5BYGLPnbt29jPp8nt//mzZu4uLiIN2/exJs3b+Lbb7+tBKBYWtiAer0eV1dXcX9/Hzc3N3F1dRV3d3cxGAyi3W6n/tRqD/z1kydP4tmzZ/HkyZM4OzuLk5OTlJ732jieCd5lQjEYQCmsKbXTpr7yOg4HZRYwglWuY37hd6EcURrXjQCxEO7NZpPqV3J6zLCB5xCAcg7tAK45gH+UELsAG8GK2OX886DBCyrZUIUMkDvhyiYmAwGFnViv10m4zLlSqsmgHR8fxw8//FBJryJIRPEIUsRuQ5iiKKLf7ycaKiISxbZareLNmzcxmUzixx9/TP0mjd5sNuPo6Ci1+/7+PjqdTozH47i6uorZbBb9fj+5S8cF19fX8Vd/9VdxcnISw+EwhsNhNJvN6HQ6cXJykjKeWP+cuyVpgeID78bjceKqwb5W7hxfIshk/gwjeB6eDeHCknsFD3PuYBsBtRcwzUasACRKwtjYLV3jXhb4Rwmx05n+zJqD2+r1eslCOH1pstvwAp65Xq8nK4LlAANCBS2Xy0q9AIPZarWi3+/HcDhM8KQsyyRQt7e37xVVw58CVY6OjlJt8MHBQUyn05RR224fNhxZLpfRarViPp9Ht9tN9czX19fpGXkmy4kdhLjVasV4PI7FYpGCVAr5T09P4+nTp0mY+/1+REQKaBFcJvfk5CSxO+/evYvtdpuYG/pHH3NBy7NsCArPwkPCCTP2nncH+eanEVzTdTzXyR9oUTw0FtrMB0pqBuOzhRh3TYOtlRHVpdpQZWi3sa0DPbSb8xaLRaq1IP1ZlmUqsAGHIsxs0sJqEQbK52DJgS7ACdrKsieuxQKAkREwWBcU0oU1WPDxeBzb7TYmk0kaHxfv5PQfQr9YLBIu7nQ6Cd+D+1kQenZ2FhcXF8m6YoG3211NCsLgbB9BGAEc7e52u8nacT2eMue0jXvNNpgLzgN44JDjDY48cYFyG2JwGGPnmdzPEmKXMvqBuBMEAJxMR+2W0V4slKNYLDYpUKzivvQyltZtccS/Wq1SUmQymSQB9Mrp7XabrDfaz244zhxhMaC2SJ7AvzryZ3xwi05eRES6P/2Zz+epXzl9h6t+9+5dtFqtBFkc6BweHka/308eBgak0Xgou4TxaTabKRW9b7GnkyIWsrwWGt7abBFzbQNm2i9PlxuOuh0wI5atffDV8c1nC7FpGWMSm3+nFWm4iX3n5Dkv5xRxx2STxuNxTKfTCoOQpzeNm4AhwAL2rkCh2u12sj64WnOg0FZ5BL0vwUN7vDCT72lPThP5vjlX7m0MGo1GMhBcM51O4+bmJgkXXoI5QfiNQfe1gedTNESpgA0D7UPhTKnacDieyZkorjW37IM5tDXOhd1j7gzfh46PCjG0mYXYk50T9ky0ExvurBtiorwoikT0Q02xyHSz2aSajMVikSAH5+IJwI1OJOAZbDko8gEeYWW4DjwM3Ih4qBWJ2K05tDIyeVhmp7X9mzZ7wuzWsWJkG3H1XIsxIftZr9cr6wtNezoGwdLmsQ3KaM4bJQXTOlFhViq3lvaYwETaz1hbGfYpvMeKtuSJkg8dP2kbK3N0tVotWUjTWmhqPpG564RFICGw2WzSZiCDwSAGg0H0er1ETxEwYWm8dBwcxeRPJpP028/FHUdEeiaCzWA6vUtJpAtksJRe1WxBtLBF7Phu8JyV2J/zdx50oWSLxSKm02mMx+M4ODiI6+vrtL8GY4jy7RtzezC+B4rQdwSLH57Pb1tqsw02bBY6C55ZCyCex89K7mdYYZj7RwmxLU3eSL53Js9WCSEBt9Ip8CsdsSsiqXF3d5cCJWOsvETSWsySHoTN6VP+zq2mhdu8c6fTSbQbq0ZIXfsaR+sm8AmGLdR25xYgTxheBYxLcDUajRLkAMbc39/H+fl5qvd13QrWnb/N43uZFYq0b4xsXS20DvwwBg7YKSqyVXX/Tec5yPN5tvSWpQ8dnxRiUzEmzNEwN8j/my9kogxD0C4mrNlsJmaBibI1i6jyiHlbSD+z5g4FcvDi9Xq42nwyjKOZ1HyHIBP+Xmxpi2E+1MJgY8DfKIONBBMIkwGFdnNzkxRrvV7H6elp9Pv9FIBxLYLsBAnzAHtkoc2hoYU4F2AnJuizIST3yefPlK3lJj/HsYQN3qOEOHcTtnz8b/CfTwYTlGMirBbuxUQ8v7ESfEcQ6doLJsoriBuNRrKirVYrFcnDptgSwD6gkCwgteVlomg/wjOfz5NAORizINr6OaPJmDki9/5rHBYWJxnevHkTRbFLy/d6vYrxQFkc0Tu1TZtZALAvmNrHzVohmUt7RZTEfXSsRJ+5NqK6Za3va0P1RZjYGmG8hAD58IMjqhuBGAsiNKa1yHrBuaKxuHjfh4nE4lI0H7Fbnn98fByNxsNqh5ubm5T9IjBzGtrtN0TBfTno4SjLMiaTSYI8jIcV3jAFSwjO9X2ABggxiuYkQu51OI9YwYbB8QhuODc0hmiGZxay3GtaJjhc/Rax25rBVBtjgjAaZvHdPmOJh8w9/b7jJ2FiC7MF2pqar4Pju7we2RaZjlPwjsCyP4MhAZaRRAYWMY/s4XEp8vEKlFarlei2Wq0W0+k0WXFgBMEjSuhJZ2DJcuEZbIFyGMXhQhi+ZwycHoZf5yBlzmcuQ2UFDOWfJH/wMJyPVyMLSXo/Z4g+1lZbVoTL7WSMXYrgoNDjYoG2peZ77g8cyRFAfnxUiL2WypY4r4RigHDbUFdMujM5CDmdgDpjElzbkMMYp7GbzWbldQMOZFgQORwOK1lBnskzSKdScprXD2NBrShYTV7zgIu1APsg4GRtnq0lirperyv11MAPUtztdjvRasAj8+skemzdGGu2LnC0z5jCUxsfm/3JAyugnRelluVujzxjYOYIeIMxc97AAm5jyXeMua39ZwtxHk0iSFhNNu9zWZ0zSEyQI+Ic/Ftj7+7u4u3bt8ky8z3fRUSqOQaTjkajKMuHeoiTk5NoNpvxB3/wBzEYDOLk5CTevXsXb9++fU85XWln4aUPDlQIXNig5f7+PqWPc2WzN0KIyRw6/QuMwINQ6tlut6PT6SThpRoOfrzVaiXhPzo6Sm+oKssyce1YMpgLhBoszzMRXgQU3tkQCmtrQTK1lgf+tqB4v9yj5zJmFoc5McRwfcdnC7EDoRxW5Mt3SA543wLjJ7tn3Fe9Xq/ser5arZJ7ZPJw/6ZYGo1GTKfTyoLHk5OTODs7i06nEz/88EMqaGdiGTwYDOqMmTSsKbAgYrcptncHYnd7lBTrhNUAc/t/CwqCziSjNLj3TqcT5+fnMRwOo16vV9bR8Zuaj5OTk3j69Gn0er0Ke+TAkX0sCOam02mCYWYtEE7gEoLJ+Oa4Fm/iIJ55RhgRaENR5o/znPDA6hI3WckeLcTuXM7vuTgIC8t2rN53wDUOHlyUBC3s9/up/JHyQI6yLNNeDLi/6XSaoMDBwUE8f/48vvvuu+h2u/GLX/yisk1/o9Go1AQweQiOS0Sp54CvBQ4BOQaDQWy32xgOh/H69evKxO+rb/aEQc2B+6l68+u6nj59Gt98803aWqBWqyXrGRGJguz1etHv9ys7hFrogGoICuPvdDn4H6hBzQvBmV27aVXgInPDGDhg4zuzC5Yn/rbFNTw1e/NFQswk0xHwlxcyYiXNpXpZExQajTHd4uqnzWYTo9Eo1ex2u904OjpKncMlw5kyCbhA1spxXVmWMdv+lUIAACAASURBVBqNYjKZpNJOuGjqFWgP/azX6+n+XiFN1ZzPM9ajP0yWC4mwUFhsrAyRPFav1+tViuW90vvt27dJqK6vr2O1WsXTp0+TVfakmyKLiBQL5Atqc/aEucutOfPM2rw8PuIHAXTADuvkbC0yY0ts1gY4hhKYP36UEEe8/4pVhBTBdFUXkTEddJYJDWfizGJst9u4ubmJt2/fxmw2SwNCrS2Whl04KcmM2O1GhGViged0Oo3r6+t49+5dXF9fx2w2S4wFgwVUsCBtt7sttUy3wXTkMUAenKD8XMPhIIi6XxgGrKZ36ImItIMPS7fq9Xrc3t5WSjip43ZRkqvwWETrdtBmxtmpYCsXeJZnM064/VywTMkh7LTHzzC8M2fvw8bT7Na+45N7sdmt8L/5O08cjWESbLEc3fuFJ2j78+fP4/vvv4/5fB7T6TTdy7wjy4Mo3ez3+3F2dhaDwSB+9atfxcnJSTQaDwtDJ5NJXF9fx3q9TsyAN/Bj0IAOLhw3xcfgebceAi2Ex9apKIrEfuQKbXhA3MCzT09PE4yA0aCs1GWtZVnG+fl5/PrXv46zs7Ok1J58vAe0JJ+TUkconCLn3t4XxLSohX273VZWlXCuU9KMBc9xwseMl5UB4edvIN6njk8uT6KRYESYAA8Mk48ggDdd9meNsjKgHAQxJycn6d4ODBkQziewefbsWVxcXMTFxUXimS8vL+Pt27fxu9/9LkajUQWjUafLZMMR1+v1VHscsXulGJaJgYe2om+uXmNCgCs+XPIIK+HXQTx9+jS+++67OD4+TjEBE2i33mw24+nTp/HLX/4yeQ5bM8YMq8pWZGBYylU5wKROvHjMgUD0D8+a9/tjB4LtebCBRK44B8+dJ1o+dHwSTuxrjBuB68L6cJ4HCNBvNiPPDMHrnp+fp3tgxRF8k+skLrrdblo9jAX98ccf482bN/H69etYrVYJHjBJTMR2u03shYM6kgJ2ifxN8Nbr9eL09DRub29TH+xmGRcCUa/ANoQgoALaRFQDtIhdzUhRPLwv5eLiIp48eZKSPwgAh/+2xTOdaOwMRrdF9XzjMczIMLcO1vK8QUQkgXR5gUtAeb7lxUEfv23QPkuInVfPsZRhQ65lpo5qtVr6TYOdzKAz9Xo9Tk9PYz6fp8kB+xFQYJFwZdBo1B1Pp9OYTqfx9u3buL6+TkEFAwiFBb7DWnIOK64JDMnmjcfj9HwWqcLNsqKD/jBePA+hYQLtorH+3W43WeiyLNMLeCzsCAiLSrFYwJJ9DJI5Vqw479P2HNkY2cgQ9/DcnEaL2JUS2Lta8S2YKIMVK+fYuaep2E9Z408uT8K62BoQ7TNJ5vbyBuWUGgLlYIMX2Tx79iyOj49jMBjEu3fv0v3hhHnpTMTDLposqOT1B7xCjBXH9AH8GrHD7qScb29vkxLd3t6mPSpM9Hs/BbvsbrebVjwbGzNehhjQawRMCDCBK14C4XIdgrHiYDCIJ0+exPHxcWy324pS+WU4ZlbMlqDYKK8VwAX0ObVl92+D5e9yDG2oYCtPv6x0TgIZEtGPRwuxgXeOvZwA4TO0xpGoBwqNcpoyT1XC+9JwdyLnP80g2H1b8aDHsN5HR0cJs3r/BHDjdrt979W8/I1gGyvbQ9hFmkZyAIvFd7BDPyxoeAEYB39PwqbRaFRqQ7C4tmi0x9YsZxYs+MavhhG+L9fsm2PG3ec4sGcsHfjhaRFip6X3Zfk+S4g5cnPOgFowiaAR7Dxt6/MZDNNt3pjDlJ2fDyYlsHSyhUFpNpvpjUoRkTZgwcIhjLhSbwcQ8RDQsd0/ffUk4GYJXHu9Xkq1myay28TqoKTeAYjAFzaFZfi22mTtGo1GnJ6exunpacLTjC3K5TJIsKkZHsYExYio7jXheMUJB4Q7H/dcTnJZMXRxcE5/rAB+Jn2gfR+DFD9pf2LjLCyWP+Mg3RpRXbVA491YXCadYjeb0WgUs9ksKYJx3HK5TKloLPJ6vU7LkfAG1BPwfM4zPNhsNqmmFuoOfLvZbNJrAMj8Gd8RGIGh6/V6ogXzA0HGAh8dHcVgMKjUa6xWq7i6ukoWE4tk3MxC2G+//TZOT0+T9aWW2MyB6TOCVD6P2O3447qInPJC8N1nF/7Y4+TeF4WBprSh4571+sMawYjqO/Is9BgGK9BnC7GzcnnEaEGn03QY7JtPpBuTu87pdJpcvflVB0e+HuuGoBM1e3Ep5xEY0ScE00kMYzesKIEW2SdbKCwsLtv1uaaj+Jt2Y/npi42AKScvycJ6m1Uxu2GXyz0ZG/ppq4nX9GdOQLg93A/BM/zzb/fHRU1YUfrlQNewwZZ9H+thKPOenH7wm9gtMcqxEJbIHfGDTfzTOadnERZDDTqNYNdqu/0bsCpYYSJ5nucJpx3gRRIfeZqXXeWd3bLFBdu5HdvtNlWZ2UquVqtot9vp3rZkWBOEygplugmLjBHA2kVUt5DCg+CVqCcBKjjYslAw7q6Lpq9WcCuD2YR98MEWnEDfQgwkstcmDrCFtfXPKbacxfhsIeYhtsSm1RBUNItBcrDnwcgHwEJPYQvf4T7NihCYYQmxznCR9gIMCrsLUb2FdcX6R+xWJBjTuq6DPoLJ6/V6Slj4/HxRrOGVEylYSPpOWylyArNieZnkRqOR2j2ZTCq7ATEnvmfEzjN4vsqyTMyFz8fS2uOxgxJtd6bOaW68FXU0k8kk1VtbToBoQFIbN/qBgbMXeDSc8D6/eWDm4A5rhUun83ax1mKDeIK5y8vLSnGRs2p0bLVaJWWJ2FkbNuCD2aBwiGewMQuVY1gVE/scZVkm+g2XjTItl8u0RxpF6GTdUB7aELFjbIA5QBMLOJVstqQInKvowJ0ILfwuqW8LHoKaQzcnUcw4GKdaGTgPg+ExNcOBcYC3psDfGV+ua7fbFY9ICW+ehzCE+CIhxqK58baswIaIqLyxEwGn4x+KOPMAA3fDuUw4gSHPYtDhWVm9gBIhrFdXV5XdhKiqQiDYv4G/qWdmhQqCvFgsUoLh5OQksQO2YFBdtKkoimTpsYZYLLtaTyZjSvuxbA6ksV7dbjexOiRHbOl9PkxEHuHjqVzLjNKZPuUehi3AhohIK2dc+MRc5Ol3oNlms9uCCyPBWNEXC+6jMTF5dFyMhdhuMyIqtRIWOqdczVwg2Ov1Oi318WoRLDmJASJ8qCQGENfEBoS1Wi2VL7569Squrq6Sa0MhyXpdXFwkq0m/Li8vYzKZxGw2i1evXqUtYnF7WGDqEkiq1Gq1yq6VeAyoNJZvUR46Ho8jYpfoOT4+ThbNdRv0ifuOx+MYjUZxdnaWjIh5WY8xq0XoM8rAXJVlmbYOM08P1DCHjdBiDGwljYEXi0WMRqNYLBYJs5sKNIdNXQf43vXlCHzuHT5biLGiEe/voeWgwJbRwUqeLvZBx5gkAjtSyUAQhNSBHG1w0oSJc44e/vX29jYJcb1eT2nler2eLBrXesnR4eFh4pDNmiBg7NaOIHId597f3ydOG+UGVuRBHV6JQJPEDdtBgdnhknu9XhwfH6eieM9LDgHxEPYeCAxQCOgUERXoh/dC4AnUuN6e2kF1ngCxPCFTDoR9vY3jx/jhnyTEXuzJw2iUqRZwm3lYLEreOMMQIMP9/X2MRqNKls68ozG0MRpuk00IccW8eMZr1/AqlFGSrgYmcE9qmFk9QV8IxoAEedRtRgEYgvChjAiDEw0IOzXG3J9MIhNPu4gVEHbTYJ4fjAyerN1uJ/iBQFnIeUZOs/Ec+oVhcrmBBT1itzACvLuPGMgxvFksC64F/FFCbMHLb+aJ4OFoNILlibCWQoIvFou4ubmJFy9exG9+85sYDofx7bffpgnHyhlPouFe+4bFdaADziUQI4Fyfn4ez58/j+fPn8fZ2Vn0er1Uo0zF23q9rrAjETt+l9UnHh/aAxYGR1vRmXwCxVqtluqinWIGXqEo4N+IB/eKpzL08Lh74lEk5o/2mXECwsE6ANd8mCHgWpdj5vQebbEFdvqb5zJ/yI6NF9fYUDxKiOloDvBzsp8UsbdMLYrdPg80GmtFZL5arVJ6mCyU3R3unvsaPhh7+wUsHjgshKNvryTGcq5WqwprgfsdDAaJMtpsNumFL7AxBID2BCgZwZItD9QcPCnu28oIHjZ1yNjzHIIhoBibHwJjrECmInNWxu4da4sQ43395ib2/LBnNquAFaWePGdDHAvRd3sC5o3DvPGjeeJcu3N8a8YA6+OGOFnhoI/smN13URQxm83S7o/UF+BiOd9u2wLEMhxcLDCCIApogwW7vr5OrxaYzWYxmUwiIlIfsPxYOqCKNwEHU1JOad4VXMu4EaQcHR2lXT8JGhk/7klw64CKFS+MJXwxy66cGUQomC+CI4Qqh2hYUsbNUBCmBONjLtwGzXSry2WdfaQtXEsfrWSmBmmzLfJnC3EejPEAlri4asxRpzGTaTF+O0jC2i6Xy7i+vo7Xr19HrfbwXuh+v1+x/gRGPJcI99WrVzGdTpMybDabGI/H6X6TyaRikW9ubhIVdnFxEaPRKFlSBBEvgmVmInCDBH7D4bCSBKG9TpaYLqJACesClPE7TthqACtIiamXRhFHzGazVCBkfpWxB0p4jWBElUHJYxNXyeHNgHhmECx0zqxhPYFEGBxyAIwhON1BoO9tCvejcvqxLx3552DbOAdtc1GLgzCwjksGcdO4RKww7+6YzWaV7BwBide1GVeToMhTthGRBmuzeXjb0jfffJOs/GazicvLy3j16lWihcqyTK8VgDnBCtIWgiXgDJ4AlgHKLa+3QEhROJYiub2kykejUWy326TkuNeIqOB410tD//mtSK4RYYxgYRAsBBYvghA7dQzkQgZcipoLpRkVl+76mWySjnGzxXUsZaz82UJsOMH/NBg6ioGhAXaLuRJY6OkMfPBgMIjb29vUYa5xDarLFLGsWDGnb7Hqr169ijdv3iSLsN0+vB4MfpK9HsgsEeDg9tmY0KWZQIOcPcHiASNI19rFcj1CBXeLsBMs8V66TqdTeRfIev3wch/KM4lHwN8ceerfFtM7EtVqtSSwd3d3MR6P4+bmJs0pxVQ2GqZUzTzY0ICxeS6wETrPK+WtvPtkLWIHgx4lxE4qYOLtJrwXmCGFqRNn6vIole+JkH2dNY/B99b5CBe7qFtQV6tVsszOGOZpYT7nO6xvt9uNwWAQp6en0Wg04t27d2mp/OvXr9MEE0ThihFYFNrLingGfaHfrqnAgnMOvLtx/tXVVRLmiEhC53uQwIjYbftFG2BcgE1YvHy/Zbb6QoCtFF7r6Mo0p8nxshG7uhJKSnnBEPXUCLIVzlz3FwV2JrcR4nq9ntbBMQDGwVgdW2JHyrbYLtRBWBE0XDfCZq1Eg8mAkcyAq6zVailT5agX3AXEIZFydHQUm83DmzBPTk4SNdfv96MoisqLX7BStiLgfCseXgSLlUMNrKgVHeFzkT4Tz+aC5noRFCwV92QcEHyeTZ85gGPz+TxBEwwEQsaBZ+H+zI2ZB+YG2YCndlYS5SQziEyg/B5TY+JH88TGnxZiBM8CzETa8lIUZEomT5xwPR2F/uI+uCUUARzJBMCvmtsk5WrN5mD1Ay4NLjtXFCYLAYSvfvbsWQoqN5tNcvksbEVooQbNxeYY2NE6WNarogkgI95/payV+Pr6Ok5OTlL7sfq2ZPzYiBCXUF/iF15aaJgHVwBaERhL8C+ehlU1nU4nWV8gpOk2jJfn2KxXxBfwxEwKg8EAmZ4xuQ/p78QGAo/bsMDwGfUHTBIdYgsmZ3G2220qeMF6wPO6ao6Vz856kWgh9++ExmAwSJbCCywZWBgK3l7KG5XI6lkpsUAWKu4HFWdhwjM4re5YwtE6XC3z8PLly3jy5EmyeLjtw8PDZLFNWyFoFmBe8m6rZ16Z67yjprOcKCTneokYuxTRJuAb/Xc2z/NMv20oHy3Edvfc3Av7GKSchchduTFwbhlWq4e9154/fx7ffPNNDIfDVOztwUcJvL8YFB3Qh/vjNo2lEdjj4+Not9uJfYBtgA1xvxwlG/PjpZi4vCCKCY6oQiAmwxx7DgvILqJ4vNYANgSP5+SQeWEMyHa7rWzTheBSqIMHccbNFXCu+gPmEANw0A5SzFhf9gPBI+Ur3DmMgemDDxTq0ZbYew1w4DbMB6JdOY4xBvb5aBhk/2w2S5knrCHunHw+AUW9Xk9BB5NFUIi7A1dD5RD9Y6X6/X76m8CHAMpCaK7SQSiTZuzuKN3nmmFxuSZKyG/wLHTeZrOp7M+GYICJvV8zr35wAAcm5VVqxrMoAtbXWTGnlaEOgUFUCvoA9oGjWfnS6XQq0MHshiGCZcICnGcbH42JHSHyf8Ru6by1HjfM/r223ta03F0w4exuiav1lqf8j5Dd3d2lrJUjYH7MzWLJwGi9Xi8Gg0FlU0T6xG8HSnbrzvnj/v3DZDG5DgZx48a2uFVYA/ZXjti9FMcJAoTTAaHHMhcC+sEPQrnZbCovhacvMB6wTVyDEON5jOWZezwbhoitvmi/oRNCCy3JuCOoeCnTtY+2xPsuzqkPazDa5oDKgw3W9X0IAMnOwRTYxdkq0i5TZmi+n8l3R0dHqS6jXn94Oz0FNfTBGa6i2FWkOdtFhs4cqXGjo2ruzeTlSuGicz8nH3vYBKxZjh0dr1hIqA+2h2QuEVrXNNBe2mD+2StRHLAzbsAIZxNJvth6WtHos7OceRuMx/fBkJ8sxJQU5owCA2TLSgTrqDOnnDwRfMaAADEIvKCyEHwSHBE7bhOX5/pbLAVQiN/8cB4YdTqdpv/pKwOLV2BS6TcKYqoMy4mA0QYmgWutgFgfrHJZlsnN8x0K6WAToWLcGVOUBAF3cIT3pA2MGQEZuN2c8Xq9TrAFLwhscNKk1Xp46TuvmOh0OimlbUbFga/nCOXbx2Tx+ccE+ZNbu9pC5RkVHoIQOFduHhCLwACicbhXU16Odg03fK+yLBPWAyN7l3oHYkwSEw2zAY9sN4lAuljJGNP0ERQY45TzmwRRCCZKDh4mAwfUYPygufBEXjuIINM/7gtGph3NZjNl/hAm43bG25k4xz55cMu1eAagAvEI3tABaM7z57LDuPmz3CLTn5zy+ywh3sfR5W4dISODhSb7fPColYKJctIAAc+/j6juVez/c8yKlcNK4E4tLPDLCDhuzRNtKGVMh6A6xRqxe90slsXu2m4/X8JlloDAyRYsZ3MI/lipgpdCYTx+jLuFwIaBtDLt9uoSP9fjztIurLETRvztktkcDuaC67H2/HtsTRZ8thDnwsuBNnuQaagHhf9znJM3DvzoRZQInUsobTmAEViviB3dY7LdrrYsy8pKD4TThSsO2BgDviuKIiVj4EqxMrngYUX8vZUA+OIAiZXMCDj1DdRgMB64fYQGL2ehMnSzAUBIuAfex5y4vR/1DjzPryVz0Y/rTiwzH6LM8s85n+eb0tx3ro9PFsW7McbEDgqMBc1aeNIMNRBsik+g2Jy1wTI5kWAKjODEuX1TSVRIEdxhHQ176AcDixWyAG82m7TM3JO9Xq8rtcX85jqe4bG0ZfZKCg7DJui4iF1BjwUYgd5sNvHu3bu4uLhIUMWctWESkAB4BDRj/Ogj1pc5pN9YYSwxisM4Wzg9prbCNmA5o2N5sYLtU4afLMT7aI19SQsmIOcA/TuP0k2hcJ7pI9NKprDMjOwL6CKqONJLxGmHYQqTasuDa0RRyH6RhmagwfT8nyc7jO2ZcLB/jvGAKNzHVKEVjhLH7Xab6Ez/5CuGoctQVuoh7DEZC4+xg3IMCe+R7na7yUswTt77zkJraEC/HOCZIPAc5vd5tBBjAfnbnbJmo7G4ZwdwXI/gIKgQ7RS9IGxeX0fgFRGpjsD8syccQSXixjIa9jAxq9UqCSMCZQHAVdIHEi7GzygZk+I0M0ICuwPccgBIO1wUjyBxTyxmXqtg1uH+/j69YxraiqX5znI6jkG4GBtbYzwkRqLdbld26AQ2EDAy12ZBTP8xH4aali0H/qZjTR3aCH22EBtoW0NooN1yu92OZ8+epe8Y4EajkWAD1M1yuYyrq6sUDK7X67i9vY3xeJzeBtTtdhOsWK9327xStO7BZhJx1wwE9Q2mser1evT7/ZQAwZJ7ww9WiSCkYFLn/2kLa+1ub28rgSDKhfXECPDWUOo+EGYCRd7+hGICqahRJlFDX8lW0i7GxDXPWF8LHXPqclqXdEbsqEyKg/IlYKvVKm3OyFYEJLxcL8EuoCiYaUrm0gyWqVva/WhLHLHDJ86mwCi4loAaXAQ2IlLdLRaUfdEWi0W8efMmLRJFs+kogsyzjM/QUNgFfnCnrrcwc4BlpT7iQzUA1FccHR0lgYqI9DwoOmAP3oK0r90zE+L9HrAoBK/T6TRub2/j6uoqFSLBsy6XD29P+uu//uuUpST9fHBwkHY+ms/nCft7jvJ6BdqGkKNMjUYjbm5uYjweJ2oQxazX6/HixYu4v7+P6XSa9mMm/d1sNivvOXHKH4N3enqatvs6Pj5ORf0OBimMor3G27lQf5YQO2jyZ/vAttdnsbCQyjaudwTsa/OImI5R3mi+12Q/7t8pZ7TabUXRoJGcWLAV8yCiPMAJFBdrjaUFC7L/m3f3NKRCsfyicuCUC8gjdmsbUX7umcMnvqeG18kD2oSwrdcPhfTstAQc4OWXV1dX8fr161gul2nh7M3NTRRFEX/xF3+RIODz58+T5cWCowgoh6lDCvkJghFe3o3S6XSSQppVMYVHTPSrX/3q84XYSQKT+HxmIM8eEUy406wkIcB+DCqujGj48vKyUgd8eXkZ7969S64MSxuxW5WM+8dbkEFCUItitw0sQl6r1ZK7Y4Ao1kboERgsLoqKZUB4fD2Tl+NQ2tFsNtM2AA6E6/V6KgV9+vRpKsafzWZxe3ubIARKQdEShoA9PrzHBAqCsCLk3h2UtXz0Dwhnqg6vxVpG/ke5iqJIQkwfSUEzLo5HyALWarU0r61WK+bzefLAKLit76MzdtzQ1hNLCEXDhE4mk2TJCEhwtywCZdUxnWeSmMybm5vodruV4hO2MV2v12lXdNN0ETsKCibB/DXFSHxngcXtegkPASE4GWUDQjA5KCipdsbEgaepRMaJklEC13q9nlY/n56exnA4TMq6WCwqeyvnmBGYxH0JMPGCVMNxLJcPW29h1QnewKaDwSBZc5S03W6nl2T+/ve/j/V6HS9fvqx4KuYZKAnV5+VHxAWMI3t2AD+63W4cHx9Hr9eLev1hfxBWWX+REOfcb8SuBsKZoPV6nVwQA83AF0WR6lCpiyBQwxKD54qiqGxqwvNs0S280GHgsel0WlleT6BBcTbUGc+ijbjCHI5gMbGq7XY7KXOn09n7MnOvzthXysrWAASdzn6xNApc6oAL6MME9/v9pJS0m7ECazNmeB8EE8scERVFRTFMbxFQE7CROnfK3d4YYSUNToDv4BJl99jTR4LHbrebgmAH658txE4J5tkeomPTQcZym80maaVJemeSzGCAbxG0XHMRfgZiH1vCd1hrb9JN+80JM5lYOSupSXk/C7iAcqKQzi6Zr2bszE0ztowFQS21uMQSETsvww+bxSBwjAnb12L52SmIZ9FPIB2xR6Px8Kpc8/OOd1Aq4GK+ChrlddxjmpA2813Eg0dALjzWnE+/mfsPFdT/JCF2pZQFBOEmQMAt3dzcxHq9Tu4J7ERAQaCGhcPKIFBErqaAsADeyopnExhE7Fb18nej0di7sgC37q1ITbaDKc1wmP+kcAgFNnPjMYnYvYIWSAaUcDIDDIkVv7m5ifl8nlZYbzabhCsjHtzqcDhMHoh5GY1GcXNzE83mw/7Ml5eXlQwYc2ZLXKvVKvtD8LkVmi2piCNyxgY4hhUdj8eJqWE8nfgACuJlaBt4Hpnj3oapjxJid94D4sl1mtbkfVEUaaedPPK25bNClGWZNrm+vb1N+wpjrYEwuDMSFAQi3IM2DQaD5EbRdnPHuH2EiX5iGVG4zWaTIv2i2O2XABxhPIBZLi+128US5u40x/hYLIp0HCg7QUTf1+t1/P73v09vcprP5/Hy5cuo1+tpOzAEAd45ItLun3gueG8XKXU6nUrqmpR+vV59h7erApEbngt7glV1URfzYubJqWsHz48SYvCe3T+dNX9Hpg2BJRCCNWACuQdRM1Vbh4eHMRqN3rMsWG8Gz9bZr+gybeZJ8fIf3KS5x4jdRs5cg5XknqaRWq1WXF1dxd3dXWWLVawa7cITYZVhSIBjpIwZ11arlbhXrJIFmz7jRTiPiT44OEgU53a7jdvb2/jLv/zLaDYfXuIORMuX4POKYLbFYvzYZhbvdHNzk8aCQiV45uVymdLRnU4n2u12WnLmykHm1QrqVR8YxTwBw7mPFmIsIJSHOUjchPdAICsH9sVV4qZIXbJFEp1yJE3VFBbD7w1hIthX2MqGdXQUj6Wg3SgcQsCkAGu2222aaKwgOBpKD2vHBPB/RCRc22g0KgswXVjE+QR9LAplwgwlbm5u4vr6umJMzMhwPzKbBH9AOLAstQ4IrdkfxyN4kBzmOfYhiOY59N0UIOPMUiiUAyGF0THWZw6ZM+TP1vpRQtzv9xOO9APhGK1ZL168iD/5kz9JWZ6iKBIl49rb4+PjGI/HcXFxEb/73e9iNBolaufs7CxpPFbz5OQkbUzC8xFuwx2SEM6OkTLOuV/XP7h00tuuItRYPgKhu7u7SnE6k+Pic5dKIhSbzSZlxKbTacLqTtfSR5icvKbCWJXP6M9kMonLy8s4PDxMLh3s7z3USEj1er14+vRpnJ2dxenpabo3L4yBXjs+Pk5lBWBkK7HbgSUeDAYxn8/j5uYmbm9v05h4cxuXDhjWsXjYAfUXsRPsoo6J52CScXnL5TJ++9vfxh//8R/HarWKs7OzilaPRqPKwG82m2Qdrq+vE5SozvYd6AAAIABJREFU1+spKMBtMWnGrTyTKB4lY1UBrARBCK4ox58IbERU9gnjcywJArTdbuPi4qISGJoXNwOCQHIuzzV/TtAGb40bRgBGo1Hc399X3rzJYcoxYrfXw2AwiLOzs2g0GnF7e5vorohIbhrlmM1m8fr16zg4eNi8m+so9GG5EXQe19qocKC4lBwQTPf7/cS4sKcdQRsp/IjqC3KsHLkCf7YQmxJisOyOsYS4CNdU4J4IzGhoTsOBmagpjogKbjWFh5CYanMEbKaAAcWq8xkQh6CQ7xBenk278DwoUR6QAZ08Vi60Mf20Wq3SZoYRkQIv+NeiKFIGzYyIaxEQMrza8fFxDIfDhG3JOkKHkWDCiNAPGBQEirgGK8s4YbzMQnmOgQ+MI2PBfZ255DwLpxNE3Iv7uyLu0QVARPIueGm1WvHkyZNEH5FGfPr0aXz//ffRbDbjH/yDf5Cs0Zs3b+Lw8DAmk0mFBqOBdBpc59d5ITwRu+yhax4czaJELnDBDYKfzXDg7j2IWCBwMoNPoMUeDMbuKDVWxQLIsiFDLywv+NkKRaTvSef5fEZ2i6VEYOHj4+MEeVqtVgyHw/QWqfF4HK3Ww3uo+/1+KtX0S3W88zxC4+Ir10Gbt3dSyjGM0/GDwaCSUDHMMTQFQqCszkfkSaOfLMRwhwgdguOGguE2m02MRqOo1Wppc5KDg4MEF+CK6dzt7W0KfsiAnZycVGgeXDWTZ2UC6zqr5MCOAScQjNhRhlBdXOfyQOMvCoOwIPzYojh5gtU1BUc78VjukzH0/f193NzcJGXAFa/X62R5+ZxNVMDteUaOKjzeuUeb6R9jCWuCsvEqBy/d4jzYEywz42JvVhRFqqTDUzF3sEXMA9k4e+EcHpmue7QQs+bL7hHmAHxDupHySiJKOvPy5ct48eJFwtBYCpYlkbVh13anOQmoPOAWHKe2scAWKqea7dq8js9crgM7LKqf7wDXNSS1Wq0y4JwPpgdCsT1A7iKpPXn79m1cX19X9lvzzj+MH9QW/eU7SiIR0qOjo3jy5ElERIofvAqGOaUtVmyPP+PD3h6GNAg0noZ5N8Si7JW+I08Ui+HR8u0KzCp97PjkNlZ22Qya3QX4hkorCmeY2Nvb20T3MHmAe4QEzpEKL7J+ZOXoNAEFgRtuptvtJkxJMTuLGV3EhKViJxxXa2Et+AGDmzNGYSPe30TchTlWJpdxYmn8vVkXxoj285s3n0IBNpvNVJtNW6gyY+sv7gv+9V7TtAvhQRGBVggWSo/7B7LQHpdJ0i8LtmXECS3DSBQTmTAth9x9kRBHvL9zS84URDy4BzanJnqFOiqKIhXG09GISJbLARX0kwE+gRN1sXDFBFxYJBIIEbu3dHa73dR2rPZkMkmF6K1WK05PT9Og231SKESdMClnt592IUhYEQet/CDkZLvAzAjey5cv47e//W2CasCJs7OzdA3jhedzgRX9XCwWyVpzfVmWKaGC52EfOpZQ4algWiJ2WTTeaTIcDis8t4WWANoBHrQnAmxYgMUHNrqWAyHGgqPcjxJiV0AhrLgNawrWkCzUYvHwLmRSrAQSrncFzCMsg8Egjo6OEkzB+jlxQufBUs7C8Tk42BZoNpvFdDqNq6ur+P3vfx+Xl5dxe3sbh4eH8etf/zrOz8/fw13UJmCp6TdCy/PBt6bmCNKYWISdSXYKmjc/vXz5Ml69ehX1ej3FBqzgILDcbrfpf4wF8YV5a4JStpWywNEfMmzcJy9OQvFJVdN3NnyEE0ZYKRlF8IlbMEZmacDWBLK26nlWD+PwaHYiv9CCa3ix3W5jOBzGDz/8kBrPy1JcMGK8SXYGi9nv9xO57qIYhJRKK+7D9VgcByxYD2ovqPC6vLyMH3/8Ma6urmI2m6WgBwXAxeFewZkIANaX/20hzGpAaWG5EXRgA0wN0Tnvtq7X62lfB7KSy+UyRqNRmkgHmhiX9XpXK40QMi8Ef3gTrB0QCkuNQnE+96UiDrzPfPEyc+INrKZZBgsuMmMYxdjXarXkRb0wgfHHaz9KiMF3TFQeoXMOfOd3332XBIgsEokNUrcIJuQ8TAbnMLhMIFQYuBkLyGSB/7DofM47O3gN2Hq9jtFoFC9evIjxeFwZPNYHttvtNJEoEdYNHE6hN6/edRDCgRfBOpsanM1m6eU3XjYE/rZXAScS8EREqhJrNBrJUoMtyYhh5RaLRTImCBdtaDQaaQUJVCNr+xhDlJnx5r2At7e3CWYBlyaTSaVuhVwC8mOGx6lmaEdz9MgXtB4C/SghttBG7Fa/QpcwWTT017/+dRwdHcXr169jNpvF9fV16hRkvPlfghYmGVxrjMwzCPbspqGNaBPWYj6fx9u3b+Pm5iaurq7i3bt3KTBstVrx9OnTuLi4iF/96lfx/fffx2Aw2BvgWchYVIliYaGm02lyqXmmiXGDRqIy79WrV6l+AP708PAwfvnLX8ZgMEi8L4EOuNVLv+C/iQ8sHAgxuB+rzLNILUdEojZZ+u/vgD1QlPf393F1dZWCdxI1QEiEEGrSNcU+MD5OcrgkgADY7NSj4YQnw2ljuyOngofDYcJj19fXqTgFi+WIlHu7VpQAwJytAb0LSDabTcJ8dBaNv729jVevXsWLFy8qQdx6/bA1AEkGrBVJjKIo0nIo4AADyDugcaNe3YCAeKBdkE/9CC4b98xiyUbjYZUzPzc3NzEcDmM8HqflW4YD/X4/nj59msaN+yMMVny/vixit1ORi6EMd1x0Q3+wqkAGYgwzESyAIBbJk0E2OMQMCCzPsdX2dV+UsQNjOr2MtsBHUntKMMc7LYAT7XY7vTwcF00EzfKisnwo2/T/fpYtrflqBGm9XifBKIqHwqM3b96k12WBHxuNRpyfn8fp6Wk8ffo0nj17Fp1OJ1lVBJqiejwPxUYoAskZ3vWM1WOfBZSbSfJETKfTZIWBUUwoyv+b3/wmFUpBoxHFs/MkQkrhjetOyIohkGQpLczgXYTt8PAwLf13UgnohzV3NtIVZ4eHh9Hr9SqGjfS1IUEOm8C7eDHkDWuMl/7Y8dkbCpr6cAVWs/mwkhfgvtlsKsEYNbgMnrNZ4LDhcBjPnj2rvC2TASerZN4WzIUQEw2zR8P5+XkqraStZ2dn8fz58zg9PU1vrI+IFABijRwPYEHyGg3XKpDRYvL9rujxeByvX7+O6+vrlKk0c4Ii8AakP/uzP4uzs7OUFgarNpvNyqoOPJI9grGnK+tgGrDO4OWTk5MYDAYVSHJzcxPb7TbNgT1SxIPlpSQh4iEpBlsBBef8AhQeuJ722yPSP6fajcnx3J8txGglwoQ1wRoZYjjNSzUZGg62AxuBqVEGGAk6jSAhMBSTMIBOupDepT21Wi29h65WqyV2BAv/i1/8Ip49e1ZZkHl9fZ1cJJsHGo9jJY6OjlLWjdW6nINAmS9lAxgUEEppOBwmLEo6nsWUjBm1DOBSBB4qiudzGIpxjekqr+j2KgwCRCc3SByhICivrbNpP7wPeB34Y6YDnO6MKnUiBPtmrkiHo4j2Ip8lxGiOKY+IXa0BdQ6uOCKypto/YrdsCSyU359qt8vLy4iIJFDG0XTQ+NkMiS00UIW0La6/LMv4/vvv49mzZ6n0kZpXK5qDJ57nveEIkHDvpGJd6eWyx6urqwRtCIT6/X70er2UAUTRXFuBxeczxwquHYHHJvWOQOD1GGvjTCwyY4zxwRoSQKPQttSMB89DEMkEeq9qBNlcMQpvmIUsULvBdg0IPEbus4X4+vo6Ca8jyPPz8/R2HII6CjoM6Ov1eiosuby8TK7+7OwsLStHGMFFUFdwrMazJsMRIohyAiSEmHcfDwaDGA6HiRr77rvvUqF3URSJbsIlmk50wMYgszkLLAa/vbwHjA3MefnyZUwmkzg8fHhzE1E8UMNF617ljcDyA5RD6GEmsN4IBVgXC875WGuEfjqdxrt37xIMACLQPuAHCoTw5+wLAovFdN0DkM9emN+cw/jas7EAAWF/dGDndKm10BVJaGvErmCIpSYRUdnrISJShO8iaSafDJ87hGUngCmKIglhvV5PQoG15ZnU6Jp7drEM/cith1fq4lLpF+MBDveyIReucy6FUl564/3n4F156TjCOhwOUy0KY4yioLwoKviU77FkrJomy8f8MZ9mmeDKoewIqg8ODlKRFlaXOWGM8CzUqWDIMExYbGcqWfpkrI2hdCyC0uCRHiXEsA9MOA9CcxA+HsJneaUUkSgRLoyAg5+ISBrPbkFMdrfbjc1mt48EeBe4AI6FqTB5jmAxGEAG2tTtduPi4iIajUbK8oH5jC8RDpIt3o4rIipuFgW9ubmpvMaA9vFzfX2dFp6u1+tU7ffmzZtUD0HghQUl4PKckGwgIF0ulynR5HLSvJKv0Xh4+TosA3CGXS3NEgCnEOaI6ovogVBWbFLbZN0ioiI/1MlgFPG0WGi8IsbjUUIM5kUjsGAG+06zeqEiXKqjTjeQQYvYZaFWq1WyBoPBID2LvxlksLYrp3ChrL7lmRDzROss+WEtGtwrxUK4UYSYlb9lWcbV1VWKB5gQR9SMxWQyievr63j58mXMZrO4vLxMVg+Lu16v03oylP7y8rJSjghOddWeC4zwLKZAsbAsBCBIRDFt1eHNUdaLi4s0dlhVEjwoKgKXtwNjVRRFUjqXHODVMFaGGBgkYBFza3j0aEucVw7xP42nYeA3a4uDrvw6fjMxBFGr1SrtS4bSFEWRFqyyjRR1rQRFEdU9DPLnYq0QanhqimQo+XSWyTQb7VgsFqlGwvs+MAlY4Lu7u8SRHhwcJCqu1+tFr9dLUAQY4+ibz9jS6uTkJEXotnwoEQaB+l68n1dt57GF+XZcPtbS36PUPIu5Nn2GUQLfI8jQgmB5Dqw08sD/CLALgein13d+thA7Y0LnEJ48q+PMHYJKjt8LPukIg437QhPhGb1KGZYA/OziIIQd6wIuBDKgMByGPwgAfcAqO4NECSjC6gk1tcbY0F5WbkPHuT4EOqvT6SRe3EurCNocILsPuGNbX34DASwgKDE/JBjMvuDemXMUxBaQ++TzHRFpPR+wkr0siGfwPJADFmRkARztsbQlf5QQ5ybclIhBuAtjHKkazJuIR/Nd7EERe6/XS6yBC4YM9i1gDAAu2AkULLcF1S8XtOegvSgKfeccLBtCywBT12BiniAvYldhRjCGVUfIKOLx6mnz7UyiAx8rmpMIzqjRJrA4rpt6ZNgYSjUtJEADngOtaJhlBbN1pT3gf8YUCjIikrW2ovBMB6EORh/NTlhwgA62DDSCqJ4BYTAJsIx/rHmujQBrmQ8FDyGUaKs1GWuIwMBJw25ERKVeYjabpc+BFLSJgTWVB87FwjBhBI1WAAQIiwebwLZe9gKTySSxFh4rCzjWC8vLuDkdy/NgX5gzBNgBtqEF3stehHGnLTzHiQ1jdsMN6DgU3OUCznQC6Zhzc+GMvZM4H7PAP0mILcAINYKIZvD3PjKbzI8FzwGAVzADORAYW3Q6bIHx92grnC0C7ewPwgcv7KIUW2DDJCwJVhYXj7KAI1kvCL7H6tAn+lur1VKUTk1xDhMYd4TRfHVEpOciOMyBV4AjwHzmMTSccOEQ3tABlbOjGDEUE6Xh2txDtVqtBCs4cs9ur4Hy5vNsOXuUEDPxOdlssM/nptvokNOwuCysGBSXkwa21vztAMQdNW/NeVgDis03m03abp8s4vX1dcL37IzuyYIishATCIKN+W48HsdoNKq8rgDGY71ex83NTaK7zMtCcXmJli17RCRO3C7Y0Mh1yhHVN1phEdfrdQqU6LN5fc63IEXsjBcZVoJNAi8MBm2AOiVT6vc8w2WbCIiISiYPY8TzLFcuvX2UEGNJECI0kkbgNiMiaSkCbbrKOBohpUOwFBTgkAmMqC5UBWsansAEsJO8S/uwUmQT6QdCXJZl5fUL9Au8h7XGiqLI/EZoEW6Eg2ch/ETtTtMT7Jh1sMWkr4yT09hmTxBmvme8aaPhV455LTwIMTjYqyxcckuwTmmthQ+rzrYMJJucqLAsWE6sOOaKkRXLzmcLMQKKm+LGdq+2ZNYYuwinjemgBwlBhV3YbDZp0IyPjTmxfrh/Bp/fCDgllvDFf/EXfxGr1cNWWxcXFzEcDqMoihSMsSkiEwB19fz587i5uYnnz59Hs9mMH3/8MVWfkTHDVRs6GPfB6kDFkVEriiJRb/DqeDv6zP03m03amwIhJQhG8aD3TCHiHVBcMpHe087jmQuXA0WUh/mkr1YKzwmKhlGxR2Cu8YTuN8LvzWM+W4i9mR3aygMYQC/08yAg7EAF84Vom7ll7mMcHbF75S4UV07D0HlTcA4OuC9CwGsZaBcJCLMrKACCwUJWYEmr1YrXr1+nyjAgAXCGdjvLBlzwJo3OJHItVBwwAAsMNCPriAXcbreV2gsHZjAiCBxJEMaP6kGXQrqQytk6DJXT4w7IYDBgiKx8xu4oBv/jsV1jzBwj0BHVLdU+S4hxzcYyETsrSwNc35sLExpnd+CKJPBhTrAzQeCxnKc17WbY4zQ5ltSbjTBQh4eH6UUnnU4nZQXpt1cAY7kc8FgZzbYgXAhvo7FbIczyKPrgsaCSDyuNQGNxTUtB61nwHIQC0RynoDQIFmO02WxSDQvvqSNwY+58HfdzDsFeAqXyNr8+cibH0MHUreULRX+UEJta4cDKefJwD9ZAGsX1dktOnADccYPUEaCNQBbaA0WDdTD/GLELOoEp3W43zs7OEs3V6/WS5UHQjO+oQLu+vo53796lgObZs2dJ8FA2W3hn8KgV5trFYpFWukwmkzg4OIjpdJoKb4pityM72TbGlj6hnPX6w9uWwOQOwpz+5fUQFg7mD/fdbO5e/kMKHIvqQikH+M64OgB2ANZoNNIOn6vVKsUANnCuJ+Zv8/4oL8+1Vf4sIYbHzLXDmomgGnKYdLcLYfCMkSOqFJStPpNnK2QNtlVBmRx8MVFs+V+v1+OHH36I4XAYvV4vLi4uKskS7sOC1qJ42OyEyrGzs7OEr4EmTPxsNkvFSCcnJ2lCPHmNRiMmk0l0u90E1ZyeRWFdlJ5jT/pljwL/DezAc2CxGf+IneV3sgh+H2F0DQwGhWch+NyD33yHlaYe2O/SQ05oPyt9UFbkirlk/EwxfrYQk5ZEMAy0jWtwZQy+D19v7ArmIogyfouo5uWBGkyqkyi0wbCFzrOsnKAKC8rav8FgkFK+1EPQTveXAcbF8jnMBC4eQeU3GBSBAU70er1UWESRDIoPD4w1BK9j6Zhg9q0gkASDe6zzQvLcoyJ0OSecJ0RM2zn4drDmHf2BkbAz+VpN7u09MXKZQYg9x48SYlef5TRNRPXNQvzvLB+aR0edTfJA4FJorBUCTSaNaavG+XnyBZeMsDsJURS7NXitVistU3dU7TQ6VBFLeLxvWK32sEJjPp9XFohiUYnk4Ykp1CFQ9DKhHIq5n+6vt7RCaW2xPUf8n7NLQELGhNUvbIwCdHGixasxmFvkAmtNm7kfzI4VwnJkJTLFZgrQePlRQsz6ODqOQFqwzTBgoU0RuRMohV14WT4sh8dawUFG7Fytrb6hCLylFcJBJOc5WqcIh5113rx5E69evaqsscPiUU2GEJ6cnMTLly+Tmy3Lh3cjv337Nv78z/88vRdjMBjE6elp6hdb3q7X67Qz+3Q6TUKJZc25d68WoWQTy0zsYBgCp4zAuYab9hIM1mq1VK7ptDqJIithRCR+mxXfyITZGFvZu7u7GI1GldUyJgHwvCgIbUc5gDXIWG5Af7IQW9ssuKZMrC1gV7smAoQc2Jtd4H8mhXvTMZP2PM8Dw/kIJxOJoHj7KbAbk0cgROE6SoM7Pj4+jjdv3kS/349+v5/O6/f7CfNRrIOVNlW32Wwq72IryzIlaMCPBJjAAf4uiiIVD0XsIE29Xk/ZMBsE+s3feC4HTQRTeVZyH5/vuMeW8EP5AwtaXnOM0eFz4CRtRg5QCOcZzIx9thA7b25ayalghN3FMabKwIImwjkHBoHJtLuztTUnbPxNB23dvTkIbs48JwNnJmG73Sar6cAzIlLi5OTkJM7OzpL1xytdXV1Vona4VASa/hKgkhy5ubmJsiyT23W1GhOJV8uDYjwKYwy7AGRxhI93JLHE+YwFhUgoOIksKx2KA9PgNDqewwVH/M3mObTT3tx1GA7ubPyYQ8biUULs6DRPVKAlxk0ROw7Z+MxCYZZjH8g3ZrJmmuZxe4yFcyvN97TTRUO+tyP2XIj9TNpvBTY3jmKQMYvYuVBvk+VgBRhEypYXt6DYriu2METsEjlmZjAcbquTGKaxInYMlI2Tx5qxMtuDd0BJ83oMX5//9lhyD/5mPN1fj/ujhRhLZ+rKjTF5nQvQPnqI62kwQpxTMAhgDug570Pfu8M+j98W9pyDZCCNwe0Sc97cE0vK2/AGj2Ee2sGm6S+EDEjGeY4v3I88kONvrBhjmAuoI3/ggsfJ4+P+5wF0fhhSWLFzQoBnOzFjYsD9zI3Rh46PCnFOj/GZI0gGCtxpzpcKNTJyNN6FKmAjNxJ8y+Tti7z5HAFwBEyJIzsGcS5/O7jBC+ST4XaA27gWKMRmJjmP7SwZ8ANYQP0EY4eVhqclwKJ9TvGWZZkykK45YQx92GgggLnQUINC+pySUmquna1jWRfeAs/DfckwAn32/TAejLvbbcOA4cwTZY8S4oj9ZtyWiofTKQuohZkJsjBbYGz1ubc5YibGGk6nc+thfAzWzq/z534+/TP+tBs2Q+Ml6giiFcSumMnie28wAoaEVwUHIrwEfBGRKDD330kmB2PuRz539kKmBvEmniPiCEPK/DAdmMsB7fRvnp97bzNelq9HC/E+s26NsTtvNpuVtXD59Tm2cUBm95hjJFsT3zMn7nPMvA9LWcu5jsnjwCvkWD9il4Y3BYTggxOZYCYeCtBctS02DAr/AyUoO3UZJONjGEPwlgfAHnfTWvSNfphTZ5dRr+j2XHHv3EjtgzQoK2OSMw1WOkNUFwF5/h4txH4AD7YWOkgybiMYtHBhmXxvOo4Q8L8tClxlzngwyPChnkSCIoIooA7RrlkNcLjvwUG/sOiuD7CrazQaqbyUc7bbbeXNnUT/sBSu+nNxzWazSaujj46O4vT0NNFiZblbJIvQs9M8dRmUZcKSYBVJPFgISX1z7zzBwnnmmekvwgos8HggJ7lXttc0Hrdy8Jn5bRuezxZiCrwrFzR2e47lLEPeSTTK6UuExArA5DUajUqqG77Ue/0yyEwsMMXlinSaNhqnO3hjgG2dGFTcOoUxZVmmMs6I3do+8OTJyUms1w+70fM+EvaTI0O4Xq/Ta2YdxbNs33Sc3bILlcCTtA1Lj5C53gTrT79sAMy91mq7HXycWGD8cmzbbrfT9YZktvzeVNIeyrDCEA7ZoW+GMR+rm/ikEDsXz0FQ4wIXF1t7mQ2DhUDkGp9rJYLB4DCQhi25Rd/n1ryS2dgSa+r2cQ/6w+DxG4XKj5wmov7XgS+VaUVRVLZ56nQ6CQKwaXatVksF/Agyrp3aCdOFsCUU+9hr5oGwvZRXRlMNSBIFhSCwZLwJPr0/3r6x8DyhMGaoCORor7E2ws/YW1np24eOTy5PQkjs/s0V564Q4WSgjN+Mg6mGMnA3hePkAQNkyGLu0oNpgbYLMyY0oW5hNk5j5xtbaDahtmDDTgyHw7Q05/b2tpI+jtjVKLM7PM9j0tj1iFQzgsxYEPhhgdnnAbiCK/ceHxZqF9AjPCgAFGGv10tLqByE8zcxQB5IW3ApcGI/OV6IA0tDH1zoZHlzsorx+SJL7FUWOQ+4L4Ayr4ng+FoOC5HdmgMpNBD3l9NFWApbdbuqvM05m2GmIY/g1+vdu4e3292+ZdQFIxC0j74zCa6B5v7UDntjbhQC/IsHcdDlcQEDsy6QH65zPyyEeVLBY+Fr9x1WYmfn+M4BcK1Wfe+za1kcR/h+QCTu5/mzvOUy5OOTOwAhsAbgpkFM5Vgg7FrNKNgtIMhlWVbKJu02uY7J4KDztuzG2XmQlrfHg8pzuV8ejDoF6zbzXFsQ2uFFAmBkW3zuj/smaMOl+mBxJuvyOB9lY88PaEXabcxpGGIBw+KbFbDg5kLHWHJP/nZmklf5Et8ADXJB5cjhof+2AfqgnH7sSzppIXYnzRBEREWjbfH2NcKcLIPvjlpoTBEZqnBvY1uYkVyT/WPMmysW1+QKhDvFeiEk4FbTSLaYEZFqNXLF4ztiAay8lYT9I3C/1D/nWB3lsnHhMDVn644xwhMwzhw2CJ5jFypZFni+eXUbCmcuHVMkYcww9E89ftIrwPx3biWt8baiprHM8XIeWmx4Yozl9K6tgp8PlZZ7AcML94HJc9DoaBgBJeCBAkOogADAILA+HCtZq5yG9AQywVg/xswUnK/1u0v4zN7P1xO8OnYwK5MLFIKDJ3EVXu7VHDzakFhWDCmh9zwWubFADvJYJYesnzo+mbGzsFqo7J6NC425XPmfk90RUbGkdvF8h4W2hcpxLhYyx4P8tkXNsbCxoRXF9cIWFqypVyTkSsEk8j/MBdvHklDwXmVFUVQKyV2pZ5jDWMIoWMnoI4LG9Yz9er1+r/YaRUJ5YC8YD+qL8QBQerTLQmzsjQy4LTZQjFNuJA31mKt9huizhNiRohuN2/FSfAYLgTTpb3oorw829fWhhjIAzrPvc3u5wNqaIAA5/rI1y72ArZ7bb0Xit105fXPpozOSpq3YtA+L2G6309o4Wz57P9przJqPQW4UgFH2OuBh1unhmfA6zKFXPe+zwhyGbGYXaIdrULhH3t583D32Hzp+8q6YTLgHzxbNDcAlm07xoNpVYKl8f1vIvIPmPiN2aeKcMjN29yCYh6TtVqg8eqeNDmboD7DFXojrUTIvaXIauSzL5Mbhoz0WtA1az5k+j7/xr/uAQNq65SyRfn4fAAAgAElEQVQABsSBnVkM4Jq5dvfZXpX75gE1Y7bPmuZBnA0K7fP/Hzp+0oaCbiATk1tik9a4fSYvD/ZySge8hvvz5NhVGnbk3K4t5qes1L7+ecCx+ngXrzzwBFrYPAlOruDN+JuXKWKlvETLu8jn6Vin2rFowAoHU/ww/u4f3pR+YH0dj3hcGQ/jYrMaNjIRUZlDxzQe73ysc6O1zwN+sRDbNXiSI3bLVBggBBMNZaIR+n0Dimvlf9wN5+bFIDmO/pAQ5lbcVBzP9wEGNEcL1uflKiy1ZwJJk2PJNpuHugcydVhOnmVsT/aKonRDkn3siq28x9i0Gs/hvmVZppT7drtbj8gYwUrkC0RRInPTjI1xrwOzer2e7nNxcZFenMP1NiT0NZ83Y388rD3Jh46PCrE1wsKRdyTHxC5bpKOmZRCifBUAHXGDLYAOyuBjHUnbEuSBhC2aNdwuyy7fwucNDg0vDg4O0to7ljexcTUTzkSCkf1a281mk4IqqDrDJZTOcIIf1/rmGN3zBx63i/Y4GvtasfNMLO3JvY7/tsfIjUQO0WxoeF5eW0wb/NlnC7F3orGLplF0EiFl0FghvV6vK7W2DICDODBjo9FIZL4xH28KajQe3nxJANRoNCo0mC25LTxuG29Alg1rhZv0C2ra7XY8efIkjo+PUxFLRMT3339f2Y6VdtEnXPjp6WlE7N7HPBqNEgY2nGo2m2lPYCylM5UolksxzcgYv/J82ATeNX1+fl7JjDJ/hoMRu5c10i8LEYXwbC1QFLuVJzZKzC/XwOY4mESYGQML9D7WKL/us4WYw67ALsEDAwxwRGx+0ZbbEMXMRw7k87S1A4R9kTgD4Kj2vQ433t8wLx+gfYyAr43YWWVbHBQMrEmNsTlins25tqZYYlxpxG5/OVLVq9XDG6ao01iv15XNyh20Mh4eIwwIgo3w5PiTeTOjk7MrVi7a6Lc92asYnjgN7hjGxsjp6o8JcMRPSDsblzAoBCO5ZjnwQbAd1LlKyQOOkDYajfRmJH+HmzEudNDDQOdpUtprGOPA0yWjzkCS9bOSGJvS1lzJ8CpMIrv7GDrZ1QJd8oAWAXMbsJzdbrdSr4wQM1abzSYpGsU4zWazUp3GXFAu6RfQ2EDsc+UOWl3lyGeuBqQ9/G0v4JIGe07+p/85/PlsIfZW+G4Ya9isOWx0AhmPy4vYZcZceG6NRBAtdEy2axWc2jQdllM9TMa+VLk5YY59QZ4tsLE+k4D1oy9AAIp/Go1Gep1ZvV6vjMU+q4cnAxrYUzGJCGen06nsl4YF92tkrfB+HmOJ5aQgKWL3ZinmltQ5UMJbHzg2wDDk98uhJ23w65NpGzQjcuY6jS8SYvbXyoXYNax87iDPOMmgn0nnPAdQrnkwTYWS+CevMc1BP/djoo3dvI8bA4VVccDk/nIeQZUZAVt/Pt9sNimIM2dr628Fy4PbnN2hTygR+2VgsQk8OWBGnH6297PXsrVkjwrHNmToHGs4IM6pUlOuhiPEHfbIZlscONJfG7NHY2JWT+QYE9djl283TwoTwTXPyP2sdQRGpr8c5VqA7WKYIISUwfXkcw3n0hZTOLQTqo+2uP0IO233Yk3DLSwmls60o8sYGQuUAYyO8mGdGWuPHfdn/L2DEEGs656daaOtWEE2c0HIGFtnJk2DMk7uH+3w8/bVAXu8PGael5xW8/w9Sog5cnBtQh1cVK/XK5sqG2OZPjH25F50jInhervDiOqLFLFqhhwedH4j9AwutQN+TzPYFLyPMOQUUk5TGSdjDZnIfGVEs9l8L/i1JXQUb+WOeH+HdbwXVh+LTN+soDYCjDdMSV7wQ9CHIqNMeAYwOS+XYZ81+tDv96Pb7UZZPizNctDq4N9ePIcJjr38+2PHTxJi3zAPcrAkaCq4jIHgYCAcdTKoVgQ6ZUrOAQaT4WDPnsKu2UEdAsygE+j4/dX2EBbgPAjhBwHxmNgDeQMU+msuNMfEjgssfC4uzzFyjkvdf/5GkDzutI90s2scaA+FTi6wMgPTbrfTPLu4nmupwGPOSBQ5gLNBy9mgiJ1B2sc0/SQh3re1FINswd6HXXmwr8thhSuZLHT5NXY7xnRYcbfHgYlxdq7ZppA8eBZcFNOWPW9vHjjt66+V3pG//7bFy+ktv3rB8If+0gaXpXLYyqNgEbvsoQ2HIYD5ft7MZHbF40M/HRdhNLwEDW7bpbv5vNtCG0J+7PjJtROe4Hq9nt42hBu1O3LwYytDQgHLx2phqLh8K086leNa7pnjMaJ6KxGWoyiK1C4ssxUqYgdhYFE4CELsWWzRaJNZEFt+cCzPMi3oZyLUpsu8egMM3el0KkFoxC5+qdfraWHpZrOJ8Xhc2XLXQkyQDgthDpksIoaCYqWTk5P0HfNI3124TxKK+bHs2LrmUIn+Y+BQnkdbYruCiJ0ViIikpTQSjImwEJiV5e6F5gge9QPWYtNFJso9EHkNql05SmSMDF+bB6QOODab6nvuYEyMW21xwaNmZhBY42HThjzXjIgpJBc5MYYIr4WYZ1L0AxRgl07ce6PxsM/zaDSK8XicFofS9vv7+7i7u0uln8Co4XCYcG9RFGnZE5u/oCCbzaayjYJhE8YKL2gGgv4y7lhshJifDwXjjxJiBJWbMUA0jsaAMSN2QR/Ra0SkoIalNg6kjJH///bObbeNo+miNbL0yRJJSZYCw1e5CAIkucr7v0GeIkCAIHASnyVStC2R/4Wxmmu2W9QhN78BNSDQJufQXV2HXburZ9g54U5nqOc3PJVDUiZ2CUUs1IwyZkTwcr6GFytSJsbN7h+h1LDEk+rr4qmMSQ0pEhLZESB3jL1qs7XMkYbjOS5X48C6vLSGBRs4XVgJaD3mw2WojImtWXYYXrFl/IzHrJXhKcc9WInt+nv0ljti/JeVaLZKJtQcZmaqeG0LwcuWiWMdbkyfuR8cY4GjYFkFZkGCvVerVXt5IkbI+Xi0qhptiU86bRiGtnw8nU5H9Rv8tlp9WUxysgv7YOrQ40UJ7GjoP5Vp9JXx8cnLckwb2iAsayIREI3rMBaW2ReLRb1//77Oz8/bE+6RoyOGDdMrlj240VvQurMS56oYSmCPTGf89nXOx2M6pGL1KBshicWB3EVAwysyuc7SnekzaT7Pmb5LCwnJ9mqERPrupDGT18+fP7cCJ4zEEcPn8btxoJMkJtL3tRKS4DlBdhLrPhH17M2t+P4eBUemyXgYI1OF58Io+oASX15etlc/+FVgyJdX/iZsyLyHBLBq7NEfpMTOeHPp1/UHJECEHheI5IIEyYexpmtfey290LYw42wdYaVwTDult6bSDQ9iz26jcH1Jhr+st/an+XFkRmWed3CgTMaQ6RgYl2VkRU2Gyf1nbtjsiuEgF2S4t7fXlJilZcZQVaNCJDss7slOn6rNOwZzEaYX6RnLg+GEOUc3MFviY9M/PaopEzF3HqX3eVYCZ+6czzXs0Y1TTdV4cr1sbUOwMmZiZyxJgmbY0duSb+Nxculk0w6C6zx9+nQU1XxsYnpDKRsu7IETzFQKKzewiD7QvHKJo0l+mmtyLY41BMRxVdXIQD3XzkswXObAz5DOdut77OgkN8yVp/yzoOA4oc7AWHt7e6N3xtHMkzLZXs5GiahrxZigluir6xPsfaDJEAzKCC51VZl5VbAloXRnZ/NuDMbE04IQNrgR5QJaMW68HFgaZcQoiFTIkryCnAIFJWpAwTH2nZ2dOj8/Hz2MkLGg2NzHkAoFpD9WbgzEvLO9J9cmEcS7Hx8f16dPn9rGATYNGNIkD15Vo7ziPymxFYDJYIUmsZYJdCbMOI5kCOVziLMlMvEMyJmrl7xtpXzPRLkg3jQd10bBoJzsrQ03zCo4mXGy6hUrrmPsS8OoLUvLyQaHEmUU4xzuR/Siv5a/6UYrh1mR6XRa0+m0bS3yIgZzigd18o5xZTLseaJ/+/v7o/d3T6fTEcPEeJgPxuOyXXP091JiMKqVGG9gL4FSclMmjxBrisjshBmG3KHhhIeWWMsT7LCPMaGg0H+ZxRu7Mhl+A5BpKsMZ411oJSY5IQBez/SYvVfiQCdflqUVGccAo8B1URgMC86X3MT0GPOLB/YCBrLjmiRiZo3cmBfDHCfeVZvnSDu6IicTBza0jMIPUmJ7SsMKTyLEO6HMyRSTR2fMADhDRxGYqOROua89i6mvvHf23ZjSioPRsRjDMZzj+5keTK+OEeMBHZ0cNl1szzU9XpeJsoiBF2JVbRiGlo84HJtpWK/XowcPmou3wcADHx0d1Ww2q9ls1sbL2MycMG+Gj76Wmau8j5NXEkf0x97d0Yx5sB7cW4k9UQ5RTE4mFPzRgcyYnRS5Is2T76QowxeDZHD8ZkzsUOsE0Mmkf6uqljWDEz1u+u2wRtShLzZIPp0fcG0ngxwHnLA3d+ENkYRiGiKTw7Hny7LnN6IJ84QSTafTOjk5qdPT0zo6Omq7aqo28M65hpXJ8kfRe2sARDT6YzLADJAjvg0mZdxrty4705IJcKNT1JeagUApbLXGQBYO+MisgA3HIdZJkPEeno1EwMZm6IOy4NkQOscnM4Pn8Zs7e8pixTd70VNsxm3vbrhGwkafc3eIV+O4nnG0wzBzNJlMGrdN6SQwAu/ue2S/7UTSOfG9i4jMBFkm9vjG/8bYvaS/17YqMTuNq8ZPbme7CsJZrTabHB2m7floVih7JbyxhU/45T6c7+M4196aY5IL5js/byxhi69lqq1qU+WFh0QextRWzExGe/fI7B4lHoYvfC2JILKyUSac8Pi94GHZmMpMViIxLYqahpGR0fLyb8xTOhvTsJaJcxWOdbS/qd0ZTrjTWX/rApAMqXTU4dLnJtax8lWNX+iYGDeVJPudid96va7FYtHewUGfneUjOPeBQnkwKors0NlThORCU1ktH3u+9HAee2b0GBPXMvORnD1zBT1Hwge7QyjPZJVP5Ol7ZBRkXszooNwwVE7WOb437oSZN7VbEzt3ihu4YN00CspqISeTwGD5s0UiUPODmUQi0PRm3MPKZwVC+VzszcQ6dGUClMLzci5960Eb/sytmr1gbMgTyFC1qbWwEqAACXk8NisSkZKl4GEYajabtSfzVFV99913X723LhUKr26vnzAp590e3IqJoXhOmL80nIysCcXctirxhw8fvlIG01wIzV4TtoJGuOJYIEJvSZUSQRf6XF1dtUeeeqmVJILrrVarxkUakyEk7oOyMMnmllE6TyKeb71et/dkAEdQMGM6vI7vZ8M3s8C5FAVResm1nMRxHZTIEMJe0UpOlKG24/T0tH744YcGf05PT9t2Il+P+6JMZhDM0tjQ7SC8EOL9fqz4sWJpQ0QmjA8dw0Af7Inn8/noolXVSiz9HYL0Ux6ratRpsCiTQlaLgl9dXbVXspqW4p5OYNILuQ985+TQlJA9sj02And49HXoP0kdkcMhPL03Y/ACTNX4GR38H6XnvokPubbP8TGWDY4FuOHoMJlMRsbDOCw/e2PDFH7zODORt8zwvglvnP/08DS6YXk8mJ0g7BgbU5xiJfagPWB+SyVy2MDKvbJnr5ZJHM2ZbtXXhe54cUeMrIVwUmOPTDNcqKrWv7x/hsoehvU1Gbc9jA2MZfUnT560JNIh3StqNkTfn8UOHEUmcO6j5zDn0+wQ/fRnr9m7WnHtfDxmXw99yX492BObAqF5ot0B02aeKIRAGMc7eHHBK37Z8UxsbChYdArFlpv4Cp60qkYGmUQ957kIhbHlPbOZjnQ+Ydkkc7Jer5vCWolRdntEe9ZMiDxPrqPIfliB7KQSFqUD6SmUYYbHmX3M6xiGOcr02n9K7HKSLEh/Z2hgHAlPiKfNSeIcwnt6Jntjrs3vNB+boSf5TgpzwIxZ/9vzRtzj6mqz+THDOM0Kn2WTGXo9Jnv8hBVWtr29vfbgFIr0M6HlWPoAg2E+nE8n7PTLEQtZJIvUC/GeqzQCywoIY1jBfWg3RYlee9BzJ/COeUxPsTz5fOZbK6s2uzlu8vK57Jm7JlzfwLlJz9iD95TUY+tlzNm3nGj6kYspKHFv97T7hZL7WXdV1eRCH4gmXmpOI/CmUO7V89jc33Iwv+uImGM1BOhdz46lZ/BJvd4FqvTara/F7X3Hdh7fHG9Mx1MAXk7sDYZBp7KatE+lR5jcw+v8XCsNDu9PxtyDJDTDGeNNww4roBX5+vp6VHhEvyim4V7Gxd7Hhgwsy+yfkzLuw/d8+pnGZo16hm2j4tPRuKdwZmMssx6WtYE4emxT9Lu0Wz1xLwHAyumIvaAHnwU/eAhXgJkJ8NYbQwRDAAwGxiK9gRMqahXMR+MRoQJRUqCGx+wwmljYNSJOglgI8cog3jghGJ8ukEG+yIR7+5FiYHlkmvAk5cTmT3ZkcG8rNePOxRE7CfriiOcIYZmt1+OHsSc/zB8JuPtcNYZi3qbUa3cqxbTX9cQ7nKbQzW3iufyMCg8qK71Qhuvr61FBNv/3ahPCRmmcydOH3EmBJ3bFHeMwf43yMR4nVVZ+T4AVMKED98zE1QbhsVeNGZFh2Gzl96INSuIa6uVyWfv7+62whxej+2n0ZiwyYlmhbBxEBtOXGXF9jpXW8209Mb62PLdBFrdb4YQxItblCaTReZTJvLDDlT2xz2dHBX+Q5qaWeFM9Xqm3hGlsmVmvkxbu7xW0YRhG3tNKXFVtJwaF4js7m8UWwxvDK4dorse4zcb4OHY9c13wL+fYkRgLw/TQDg4OajabtSKfZ8+etZ3NyMmLKJ5HwzuvBqLEqfTefGB+nH4ZmngsXJdmiNRbfOq1O9UT35SxWxHw0gkrLKxeMuVJR2Aud/S2HAaVIc19SV4ZAbr22MKn7wgYJcarOEzb08DG0Ac/fYjj7FFQ1Hw+XcI1y97jSlzqJJJ7eowoCo7DHtectou1zO96Od1JWq6COi/xjhicjGXhOWIMHqO/uw8+vpUnzqRlG9Xix1UR1gwpzG96RcbeECX2lqGdnS8PKrT1W5ndv6qNNzYWTiopl24xtKyrMGWEQZlKZEK5Xi8B8wQROYxjvSjj+9IveyfkmokRyRtzBOyazWZ1dHQ02s2cjsj/R152CIZ4rqPmuJ7TImo7H2DMrhFJQ0gGKJPVrp7e+IuaB96zECuDgbo9ohcU1ut1q5m157UieaHEnDKTiiJ5JcgsiKk4493Eqmn5y+WyedhkYBxKOabH6To5soKYCrR3wjCAVO6Tax+ymUZLxXRCDARj8cQK0ctrjFMNERwBLUPu3av18Njx1i7o4hoZqW1AhmC9tlWJPUFYVNXGQ5v8d3bOIzwdim+CJHTO1V68U9jYEiEkRUPCRH+TtnHj+j3vyu/L5bJVi1mZuZ6pKCeiPWiDMXt1kjHs7m5eBcz39tI0T+RqtWpblBITOznkPCub+2zcnDBlWyRx8ma8zzwxDitdQj/nSt6BY8aKa65Wm0eBPViJDRX8ndf1ScIcKig6QYgoG7iRxI8s2zSRLd4GkDW8cLCGJlaoXmJlo+R4K9h6vW41w54ch02MCY9p+WQW74nn31ZAR43co+dP/6XicI4L74dhQ8Px/GXOAw70kqWMqEmd0u/eE5Psdc3TW2b85qhLQwcse49/W7t1e1KyEz0LNXhH0Paaps8QpL1LepBcQiXsWInNBFRVHR4ejrBsXtMsh99/58IhTx6TZAhjj4yHxJtioMAcL6a4L4yBfplD9aRiVFmsj9zX63VTTv7PeFgVJbIgLz+VyV7aGNhQw57Xya8NkejE8cw5HjQN2FHPdSrG/dav25iJqjvACQTkxKmn1HQks3Ymy5PUCzFc3+S4J9Xhn3CEAJk495P+wZ3aEzMxngxPgneouK/28lyPsbnYxoqW5zBphjxOnjNsosDmXxkHFKixPmPGiJywZajmeukBua8ViZZz6mv4mJ7Ses4xOjs838tzc1MuRrvXs9isuD3siSdw7UDSWZ58Y1jzz+DFXtLk+6HUKHBm9ExwwggnjuBJJtzXdxLj8/nkLxNFK5qptjR4T7rxeRq4Zc1DyVer1WgVkwTQymM6zcrAfXpwIpU32YJURH9v2GOMbD3xMY7EaUz+PhHAvZQ4M3cuyNt23CnCwefPn9sjnahYYwtM1biCnyQQTPfkyZcn0DsjxSObIkMJjFf9om8mwk+Gd8JBn7m2saXDsrE148dA7Y158IgnEwVGscCCVWMMCca0InEsk9fbAm+FNy3G9XgdMZ9WYkckxmGczHz28Kznwk4j/+8k1YsaVlTLnmN9H9N129qdlNg379Ee+X1SXO5kdphjuBdwwRNlJex5cLw2ONBJhnFsJnOsHhJ+V6tV2+HNcbAfVmQmBtaE6EOS6WVtr+yZfnQ+wXXNZsBCgH2RFYkzW/n9O8qXdCTnurkPXvDxb/xuaJd9TzbD8NPe2PfxsakT/s1RbFu7FU70Ou7HJTmrZ0ECr5blgTATSVP54Xz2NNfXm7eQosRgYCbPhPrHjx9Hm1idoKFA1DJXfdlEuVwu68mTzWP8Ly4u2lh4hcHh4WF73dXh4eFoB6+V0NECJfbmThiZJnyxCYwTJWVH9mq1as/EM2bvKZqr4LwtKHlW5GznZAzqWhN791RUnIhhnqMcUczeH1mlvGhWbCv4g5X48vLyqwTOOMrhnNBZtVm5Q3l62MsPkPP//WBo0zz+5B5c34sm9gxmSjiOBz6v1+s6OjpqnpjJXi6XbVs/Cs7YbYC8xwKlmU6nX0EcP/AEpcH72/irxs+tS7bHuNdYN2VihcZ40hPbO/eoP5yPsashDo7E1/T8VG0MmqjCOIieXkCx97Uh2RAM0e6txH6mgRU3l3L9bwvFypRYMWGJCXCOy+TDMMWeFSUFF1uwJKdw2hcXF6NHt65Wq7ZShieez+e1WCza8xjAtYvFonZ3d9vbgaqqvd/CRfrGp2lE3vNmT2Uai4hVVSNv7gkGUiQmtiLz6RCPx04vjdG5/2lEHmP+xrwhd2RmViWZnmzuvyPdbZDiXokdigU+TMyTn1zDA+Z3lBhFRhldhmhlrdq8WoBrcJ5xqROP9Ny8nsqshgXINcxcoDSc41fIehEBXtSFM14U4BpV402mWejv0I4SUPOAEvsxWvagyAqFynnivslNp4L05hNZ3sQmOHKaduxBEORL5DQe51ygpHOam9qtSpz8HZ1zKK36Eg556J0TMoTgCeYZEnjHYRjq+Pi4rq+v6+XLl/X+/fsWTuyhOGdvb6+R+Ht7m8fwg1lPT09rNpuNitzxsOfn5/XmzZs6Pz+vYRhqPp83hXHxEV4OfMsjToEC79+/H8EGDM1wgLfJE15hYCaTSUtIbcyMjWIdMwYo7jAMdXl52fpPv1FuO57JZNLgCwYOY2HFt9OwFzZ/7whs47CHtrGjzLkz3jKCtTKkNAQyPNzWbq0nThBuy3Pns3SPAnVProVAuZ/f4u6lUz7Pzs5af5bLZXtB4NOnT9vjSKnSWq1W7d3DrnKjIByMa6jw999/tz7M5/N6/fp17e7u1qtXr2q5XDYMjbFeXFw0PHp9fV3z+bw9oNrlolBvflC5HzsFpOIZHt5xcX5+3h4iwzE0FISkFmVwlGNunJBldHO0wAGYm2fOgWRAvvl8PhoDfaoaJ2hcx0po+GJ9Ml5GrkQaP3znpnZrFZvDgZs9tMMOCggFhdBRUDyUt80sl8t69epV/e9//6uTk5Pa399vj1v68ccfW9KDAoFjKVD3uySm02kdHBw0tgLKjP6CCXd2dkbHLZfL9s62g4OD2t3drYuLi2aQGPS///7bPNPu7m5Np9NmrCgiE0PtAt7IDwjEY7rvLnbJZAlFwqty7tHRUR0fH9fbt2/b7mccymKxaN8RURizK+BMb3qRhnt6EcVcPMbKnKC8QAVzvKb8vL0NWQE98PzWt21Q4lYl9o5aKysW4yTKoN7JAlQPCuxVQJKWYRjq9PS0vevh06dPLRROp9NGq4F31+t1o5PMifaEZ6rGe/WYWD/1x7TY/v5+zefzqqqmzEwYUaOqRo8iQMlcWbZardreNhQMwzBuBbMjG/D2MAzNCBhfVTUj9PsxmBNkYEyZZZn8Rk5B30yz5VK6WRPm3YwDcA8ZeL6dP1iH0BMvrtgh9upP7qXEdvm+iItpDOIPDw/bRHojJx4QZSN8OatdLpd1cHBQp6enDbtVVT1//rwdRxIELWYOmX64hsIGZhjjvnkF0ErOkrkFDw5HmVFUlG21+rIsTBkpofzw8LC9DyOrv1z1x3XBw4wJNgF4YUXHCOzJuQ4ypJ/eg4d8XADl8dj7Muf0FRoUqpFrejFnGIbG+hhmoqCM3zAIZ+MFm6wBubcSbzvZ5DXewitYeAAs3hmykxFPBueDAx1GzH3i1emHJxaFsJd3RPCigt/Ig+DxVjaQyWTSFiAMFzAmY7nLy8u6uLhoivL58+eaTqctusxms5EC8ew5Jn82mzW4lXjx48ePDS9XjVdSvVMcx4Ai2CM652hKIJoPmRlqeAHKMicZxVERlZhXPDPXTqfQi5jWKXTlP8EJlCpBuIvFzR4gQIRI6EN4CN0enA7jJbByOu53CCdNxXd4ZQwHq+4R5/bQvX5UbZ6twf+9XR6vA7uQj3pdLBY1n89b4rheb17QjtLzeq98zYEn2DjT277m83lbGQVKOJHm02xCTzl8L9eQVI13ZmBg/KW8DDfsgQ3f+PQ9yXO4X86NWZbbGIpbN4raQhJsexC5dwyh+zyHExQSBSZLh94i3Nv6vSnS/XAIMoRhshJeeMWoR6rbC1RVe9JnLug4jHJtMwG8HZQx7u7utl0rhGbTYtBhMAKGOp5UQyiwNC+Z+fRp8+ZOQxc3R6+qTQ2Ei4qqNspsGo5+uj/mtM2cAGHS89sQPF/oU+ZV1p1eu9NihyfYwDs9njuSHCLXYeBYfmI/v/M5rTC9ANfDEzMRTIZX5fzJeRiEFdxCc7iDxY4ggJMAAAVISURBVEBRwJs+lklfLpet77AW9OXk5KR5WZTNhmOP7YQUOZHwkoPg4Vi9NMYG13M9R0/GxNg9h/4eT4zsMS6U2qWe9NF1Nb2VQ0cIXwtZ8OnIu61tVWK/CdS0B51zQgN0AG7s7Ox06wlsDCa14WMNQTjGRT6peAzUfCb42krmlpCm97sjAd7zn3/+ae8rwWvOZrOaTCb17NmzRuexbP3u3btG2Z2dnY0YA+7x4cOHUeESeNyUJHNAWIY9OTw8bP3gLZ0wD9CXjAev6ATPdRsunLKzsoxMy3ll0/AER+SqP88RBmL5O3KiX/TxLoq8VYmduFi58CxYETXEKBB0CkQ/AwAr4i0QKEvOwzC0+lc8AvXIJIA0h0L6gXBsHOYmGRMJ2HK5HC1SmMbDc87n83bfP//8s05OTtrq3Ww2q19++aW+//77+vnnn+vly5f122+/1dXVVf3111/1+vXrxm2v1+s6Ozurn376qSnkH3/8Ua9fv66rq6s6OjpqykSfkCMTCbODkgLZ3r1799Weuul0WsMw1IcPH0Z7IIF4GBlQ6dmzZ6NHYxER/B48KvqM9avGCxssQk0mk7q+vq537961ykHm38VNGOp6vW6PHWCuiVZeWLu3EmO5xkkuXjGGQSmc4S4WixbmOIakw7QbysVCCN6mqkbZshMBV1IRjs1B40Vt1abSWPXz0if0GZ7LTAjKhcefTqc1mUzaH6uCJycndXZ2VtfX180T7u3t1dnZWZ2dnbV3Q+/u7tbJyUm9ePGiqqpms1k9f/68sRNEBxI1FBZKywtJTmzNBVsRzReb2uSP/3unOQqaFJmV1rDA/2eeHMH9u6Of59XViWZGtjEUw7Yff//997XxonGoQwTLs+fn56MBoxTGqCiWV6DgN/M5Yev1umXgsAGmiwiZvEQQpgIF59EBeBjqg+kz/cRLEy0Wi0UL80523rx507wwYZPHRM1ms+YV3759+9VYMZjnz5+331hRw0hcY+BXjPHJuDF0lA4mhChiigwWBONkXqD/MEq+Y645t2rjYNxPr7B54Qa874QwWRbTd04+PR/JiFVV/frrr93sbqsnPj4+/oqesTJ6daiqRpygJ8TYJi2QRQHOMTNBnUPV5gmOeB0zDE5cEDie3AkGQkK4fmYy/UNpJ5NJC+so8YsXL0aFT1YWrvXkyZen7uTYDZ0Yi6MA0cV5A/IBshkzc22zG5a5jRTPhqwcwk01Wk7ptDIHcni3tzW37S1W1qFM7DjP5/Pv/8xOJJXlTjhc4928wGFrNcaigy6iQQkygTHW9XO9UAJ4ZZQHK2YJ1kvmTEwv609qjYnhfl5STfbCcMuZN8d4vGA+PI35cGSTmDTv61W7pDjNOvDJG5nwgBgxv/fYGC8tIw8nyi6htEK7DMEKnDyym5WWfvTQwTbEcGti58YNsDKEjYBIyOh08qfusHGOhZYYq2q8Td2TkEIwdnZ5JPeoGr8Q0QsmOWlcCw/POX6eQk5k8s4YMcbjl1hyfffH43MdgheUSOASg5oec+llj4UxNOG6ngszUqYZDQsw0GQcOA5jc65ib9vjr1PBe8f02q1PAPLFPWH2FMm9WnmrNt4DgZnKQWCuADPF5clAqfA69AUFtXLiZVEkrgfWZKEhx2djoS9OPo2R3c+UWdJ8rpuw92aCzayw5EwJZlaXVW22cVlOPtb5Awp5E5VIv70szW+GFsyvz/cagv9sKIzPBs49b/K6vf7e1LYmdo/tsX0L7W7++rE9tv/H7VGJH9s33x6V+LF98+1RiR/bN98elfixffPtUYkf2zff/g/2F/FtnXtNSwAAAABJRU5ErkJggg==\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": [
    "U, S, VT = np.linalg.svd(X,full_matrices=False)\n",
    "S = np.diag(S)\n",
    "\n",
    "j = 0\n",
    "for r in (5, 20, 100):\n",
    "    # Construct approximate image\n",
    "    Xapprox = U[:,:r] @ S[0:r,:r] @ VT[:r,:]\n",
    "    plt.figure(j+1)\n",
    "    j += 1\n",
    "    img = plt.imshow(Xapprox)\n",
    "    img.set_cmap('gray')\n",
    "    plt.axis('off')\n",
    "    plt.title('r = ' + str(r))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "## f_ch01_ex02_2\n",
    "\n",
    "plt.figure(1)\n",
    "plt.semilogy(np.diag(S))\n",
    "plt.title('Singular Values')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(np.cumsum(np.diag(S))/np.sum(np.diag(S)))\n",
    "plt.title('Singular Values: Cumulative Sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: CH01/CH01_SEC03_Rotation.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "plt.rcParams['figure.figsize'] = [16, 8]\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "theta = np.array([np.pi/15, -np.pi/9, -np.pi/20])\n",
    "Sigma = np.diag([3, 1, 0.5]) # scale x, then y, then z\n",
    "\n",
    "# Rotation about x axis\n",
    "Rx = np.array([[1, 0, 0],\n",
    "               [0, np.cos(theta[0]), -np.sin(theta[0])],\n",
    "               [0, np.sin(theta[0]), np.cos(theta[0])]])\n",
    "\n",
    "# Rotation about y axis\n",
    "Ry = np.array([[np.cos(theta[1]), 0, np.sin(theta[1])],\n",
    "               [0, 1, 0],\n",
    "               [-np.sin(theta[1]), 0, np.cos(theta[1])]])\n",
    "\n",
    "# Rotation about z axis\n",
    "Rz = np.array([[np.cos(theta[2]), -np.sin(theta[2]), 0],\n",
    "               [np.sin(theta[2]), np.cos(theta[2]), 0],\n",
    "               [0, 0, 1]])\n",
    "\n",
    "# Rotate and scale\n",
    "X = Rz @ Ry @ Rx @ Sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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

Download .txt

gitextract_1f08lisc/

├── .gitignore
├── CH01/
│   ├── CH01_SEC02.ipynb
│   ├── CH01_SEC03_Rotation.ipynb
│   ├── CH01_SEC04_1_Linear.ipynb
│   ├── CH01_SEC04_2_Cement.ipynb
│   ├── CH01_SEC04_3_Housing.ipynb
│   ├── CH01_SEC05_1_PCAGaussian.ipynb
│   ├── CH01_SEC05_2_OvarianCancer.ipynb
│   ├── CH01_SEC06_1.ipynb
│   ├── CH01_SEC06_2_3_4.ipynb
│   ├── CH01_SEC07_1.ipynb
│   ├── CH01_SEC07_2.ipynb
│   ├── CH01_SEC07_3.ipynb
│   ├── CH01_SEC08_RSVD.ipynb
│   └── CH01_SEC09_Tensor.ipynb
├── CH02/
│   ├── CH02_SEC01_0_InnerProduct.ipynb
│   ├── CH02_SEC01_1_FourierSines.ipynb
│   ├── CH02_SEC01_2_Gibbs.ipynb
│   ├── CH02_SEC01_2_Gibbs_Movie.ipynb
│   ├── CH02_SEC02_1_DFT.ipynb
│   ├── CH02_SEC02_2_Denoise.ipynb
│   ├── CH02_SEC02_3_SpectralDerivative.ipynb
│   ├── CH02_SEC03_1_FFTHeat.ipynb
│   ├── CH02_SEC03_2_FFTWave.ipynb
│   ├── CH02_SEC03_3_FFTBurgers.ipynb
│   ├── CH02_SEC04_1_SpectrogramChirp.ipynb
│   ├── CH02_SEC04_2_SpectrogramBeethoven.ipynb
│   ├── CH02_SEC05_HAAR.ipynb
│   ├── CH02_SEC06_1_2DFFT.ipynb
│   ├── CH02_SEC06_2_Compress.ipynb
│   ├── CH02_SEC06_3_Denoise.ipynb
│   ├── CH02_SEC06_4_Wavelet.ipynb
│   └── CH02_SEC06_5_WaveletCompress.ipynb
├── CH03/
│   ├── CH03_SEC01_Compress.ipynb
│   ├── CH03_SEC03_1_Underdetermined.ipynb
│   ├── CH03_SEC03_2_AudioCS.ipynb
│   ├── CH03_SEC04_Matrices.ipynb
│   ├── CH03_SEC05_1_RobustRegression.ipynb
│   ├── CH03_SEC05_2_LASSO.ipynb
│   ├── CH03_SEC06_SparseRepresentation.ipynb
│   └── CH03_SEC07_RPCA.ipynb
├── CH04/
│   ├── CH04_SEC01_LinearRegression.ipynb
│   ├── CH04_SEC02_1_GradientDescent.ipynb
│   ├── CH04_SEC03_1_OverUnderDetermined.ipynb
│   ├── CH04_SEC04_1_CompareRegression.ipynb
│   ├── CH04_SEC05_0_Fig4p16_Pareto.ipynb
│   ├── CH04_SEC05_1_CrossValidate.ipynb
│   ├── CH04_SEC06_1_kFoldValidation.ipynb
│   ├── CH04_SEC07_1_ModelValidation.ipynb
│   └── CH04_SEC07_2_RegressAIC_BIC.ipynb
├── CH05/
│   ├── CH05_SEC01_1_FischerExtraction.ipynb
│   ├── CH05_SEC02_1_Fig5p7_Fig5p8.ipynb
│   ├── CH05_SEC03_1_Kmeans.ipynb
│   ├── CH05_SEC04_1_Dendrogram.ipynb
│   ├── CH05_SEC05_1_GaussianMixtureModels.ipynb
│   ├── CH05_SEC06_1_LDA_Classify.ipynb
│   ├── CH05_SEC07_1_SVM.ipynb
│   └── CH05_SEC08_1_Trees.ipynb
├── CH06/
│   ├── CH06_SEC01_1_NN.ipynb
│   ├── CH06_SEC04_1_StochasticGradientDescent.ipynb
│   ├── CH06_SEC05_1_DeepCNN.ipynb
│   └── CH06_SEC06_1_NNLorenz.ipynb
├── CH07/
│   ├── CH07_SEC01_SimulateLogistic.ipynb
│   ├── CH07_SEC01_SimulateLorenz.ipynb
│   ├── CH07_SEC02_DMD_Cylinder.ipynb
│   ├── CH07_SEC03_SINDY_Lorenz.ipynb
│   ├── CH07_SEC04_Koopman.ipynb
│   └── CH07_SEC05_HAVOK_Lorenz.ipynb
├── CH08/
│   ├── CH08_SEC01_CruiseControl.ipynb
│   ├── CH08_SEC07_1_LQR.ipynb
│   ├── CH08_SEC07_2_KalmanFilter.ipynb
│   ├── CH08_SEC07_2b_Obsv.ipynb
│   ├── CH08_SEC08_1_TransferFunction.ipynb
│   ├── CH08_SEC08_2_SandT.ipynb
│   └── CH08_SEC08_3_PlantInversion.ipynb
├── CH09/
│   ├── CH09_SEC02_1_GramianPlot.ipynb
│   ├── CH09_SEC02_2_BalancedTruncation.ipynb
│   ├── CH09_SEC03_ERA_OKID.ipynb
│   └── CH09_SEC03_Fig9p5.ipynb
├── CH10/
│   ├── CH10_SEC03_ESCfixed.ipynb
│   └── CH10_SEC03_ESCsinusoidal.ipynb
├── CH11/
│   ├── CH11_SEC01_1_Fig11p1.ipynb
│   ├── CH11_SEC02_1_HarmonicOscillator.ipynb
│   ├── CH11_SEC03_1_NonlinearSchrodinger.ipynb
│   └── CH11_SEC05_1_Invariance.ipynb
├── CH12/
│   ├── CH12_SEC01_1_GAPPY.ipynb
│   ├── CH12_SEC02_1_GAPPY.ipynb
│   ├── CH12_SEC03_1_GAPPY_ConditionNumber.ipynb
│   ├── CH12_SEC04_1_GAPPY_Variance.ipynb
│   ├── CH12_SEC06_1_DEIM.ipynb
│   └── CH12_SEC06_2_DEIM.ipynb
├── DATA/
│   ├── CC2.mat
│   ├── VORTALL.mat
│   ├── allFaces.mat
│   ├── beethoven_40sec.mat
│   ├── burgers.mat
│   ├── catData.mat
│   ├── catData_w.mat
│   ├── census1994.csv
│   ├── colors2.mat
│   ├── coolcolors.mat
│   ├── coolcolorsBW.mat
│   ├── dogData.mat
│   ├── dogData_w.mat
│   ├── fisheriris.mat
│   ├── hald_heat.csv
│   ├── hald_ingredients.csv
│   ├── housing.data
│   ├── lettersTestSet.mat
│   ├── lettersTrainSet.mat
│   ├── ovariancancer_grp.csv
│   ├── ovariancancer_obs.csv
│   ├── testSys.mat
│   ├── testSys_ABCD.mat
│   └── testSys_Fig9p5_ABCD.mat
└── README.md

Copy disabled (too large) Download .json

Condensed preview — 116 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (47,615K chars).

[
  {
    "path": ".gitignore",
    "chars": 835,
    "preview": "*.doc\n*.zip\n*.rtf\n*.eps\n*.pyc\n*.mdl\n*.slx\n*.m\n*.doc#\n\nPython_Codebase_Notes.txt\nUTILS/*\nauxiliary book code files/*\n*/.i"
  },
  {
    "path": "CH01/CH01_SEC02.ipynb",
    "chars": 174336,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH01/CH01_SEC03_Rotation.ipynb",
    "chars": 185137,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH01/CH01_SEC04_1_Linear.ipynb",
    "chars": 43908,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH01/CH01_SEC04_2_Cement.ipynb",
    "chars": 50445,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH01/CH01_SEC04_3_Housing.ipynb",
    "chars": 143606,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH01/CH01_SEC05_1_PCAGaussian.ipynb",
    "chars": 76948,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH01/CH01_SEC05_2_OvarianCancer.ipynb",
    "chars": 139298,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH01/CH01_SEC06_2_3_4.ipynb",
    "chars": 495994,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outp"
  },
  {
    "path": "CH01/CH01_SEC07_1.ipynb",
    "chars": 1372359,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH01/CH01_SEC07_2.ipynb",
    "chars": 51245,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outp"
  },
  {
    "path": "CH01/CH01_SEC07_3.ipynb",
    "chars": 55640,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH01/CH01_SEC08_RSVD.ipynb",
    "chars": 292138,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH01/CH01_SEC09_Tensor.ipynb",
    "chars": 7165264,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH02/CH02_SEC01_0_InnerProduct.ipynb",
    "chars": 42249,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC01_1_FourierSines.ipynb",
    "chars": 104400,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC01_2_Gibbs.ipynb",
    "chars": 24981,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH02/CH02_SEC01_2_Gibbs_Movie.ipynb",
    "chars": 3742915,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC02_1_DFT.ipynb",
    "chars": 633138,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH02/CH02_SEC02_2_Denoise.ipynb",
    "chars": 252122,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH02/CH02_SEC02_3_SpectralDerivative.ipynb",
    "chars": 80605,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH02/CH02_SEC03_1_FFTHeat.ipynb",
    "chars": 214711,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 174,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data"
  },
  {
    "path": "CH02/CH02_SEC03_2_FFTWave.ipynb",
    "chars": 255117,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH02/CH02_SEC03_3_FFTBurgers.ipynb",
    "chars": 230719,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\":"
  },
  {
    "path": "CH02/CH02_SEC04_1_SpectrogramChirp.ipynb",
    "chars": 59459,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC04_2_SpectrogramBeethoven.ipynb",
    "chars": 2298,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"5d998359\",\n   \"metadata\": {},\n   \"output"
  },
  {
    "path": "CH02/CH02_SEC05_HAAR.ipynb",
    "chars": 52322,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC06_1_2DFFT.ipynb",
    "chars": 269402,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC06_2_Compress.ipynb",
    "chars": 825921,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH02/CH02_SEC06_3_Denoise.ipynb",
    "chars": 824286,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH02/CH02_SEC06_4_Wavelet.ipynb",
    "chars": 992686,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH02/CH02_SEC06_5_WaveletCompress.ipynb",
    "chars": 612431,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH03/CH03_SEC01_Compress.ipynb",
    "chars": 795786,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\""
  },
  {
    "path": "CH03/CH03_SEC03_1_Underdetermined.ipynb",
    "chars": 18669,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH03/CH03_SEC03_2_AudioCS.ipynb",
    "chars": 34451,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH03/CH03_SEC04_Matrices.ipynb",
    "chars": 65758,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 148,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH03/CH03_SEC05_1_RobustRegression.ipynb",
    "chars": 21869,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH03/CH03_SEC05_2_LASSO.ipynb",
    "chars": 36142,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH03/CH03_SEC06_SparseRepresentation.ipynb",
    "chars": 766157,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH03/CH03_SEC07_RPCA.ipynb",
    "chars": 1094412,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC01_LinearRegression.ipynb",
    "chars": 103739,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC02_1_GradientDescent.ipynb",
    "chars": 1253128,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH04/CH04_SEC03_1_OverUnderDetermined.ipynb",
    "chars": 94218,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC04_1_CompareRegression.ipynb",
    "chars": 199672,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC05_0_Fig4p16_Pareto.ipynb",
    "chars": 40849,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC05_1_CrossValidate.ipynb",
    "chars": 60432,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH04/CH04_SEC06_1_kFoldValidation.ipynb",
    "chars": 103186,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC07_1_ModelValidation.ipynb",
    "chars": 97543,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH04/CH04_SEC07_2_RegressAIC_BIC.ipynb",
    "chars": 2382,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC01_1_FischerExtraction.ipynb",
    "chars": 721494,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC02_1_Fig5p7_Fig5p8.ipynb",
    "chars": 336295,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH05/CH05_SEC03_1_Kmeans.ipynb",
    "chars": 179507,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC04_1_Dendrogram.ipynb",
    "chars": 64582,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC05_1_GaussianMixtureModels.ipynb",
    "chars": 779098,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC06_1_LDA_Classify.ipynb",
    "chars": 186803,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH05/CH05_SEC07_1_SVM.ipynb",
    "chars": 451971,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH05/CH05_SEC08_1_Trees.ipynb",
    "chars": 332841,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH06/CH06_SEC01_1_NN.ipynb",
    "chars": 57617,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH06/CH06_SEC04_1_StochasticGradientDescent.ipynb",
    "chars": 364055,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH06/CH06_SEC05_1_DeepCNN.ipynb",
    "chars": 38991,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outp"
  },
  {
    "path": "CH06/CH06_SEC06_1_NNLorenz.ipynb",
    "chars": 283952,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH07/CH07_SEC01_SimulateLogistic.ipynb",
    "chars": 651027,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH07/CH07_SEC01_SimulateLorenz.ipynb",
    "chars": 150867,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH07/CH07_SEC02_DMD_Cylinder.ipynb",
    "chars": 203762,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH07/CH07_SEC03_SINDY_Lorenz.ipynb",
    "chars": 5219,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH07/CH07_SEC04_Koopman.ipynb",
    "chars": 226176,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH07/CH07_SEC05_HAVOK_Lorenz.ipynb",
    "chars": 3182,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH08/CH08_SEC01_CruiseControl.ipynb",
    "chars": 33572,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH08/CH08_SEC07_1_LQR.ipynb",
    "chars": 2187902,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH08/CH08_SEC07_2_KalmanFilter.ipynb",
    "chars": 157865,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH08/CH08_SEC07_2b_Obsv.ipynb",
    "chars": 3045,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH08/CH08_SEC08_1_TransferFunction.ipynb",
    "chars": 71715,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH08/CH08_SEC08_2_SandT.ipynb",
    "chars": 50826,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH08/CH08_SEC08_3_PlantInversion.ipynb",
    "chars": 40313,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH09/CH09_SEC02_1_GramianPlot.ipynb",
    "chars": 56532,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH09/CH09_SEC02_2_BalancedTruncation.ipynb",
    "chars": 24479,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 149,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH09/CH09_SEC03_ERA_OKID.ipynb",
    "chars": 84177,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH09/CH09_SEC03_Fig9p5.ipynb",
    "chars": 30355,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH10/CH10_SEC03_ESCfixed.ipynb",
    "chars": 53242,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH10/CH10_SEC03_ESCsinusoidal.ipynb",
    "chars": 52122,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "CH11/CH11_SEC01_1_Fig11p1.ipynb",
    "chars": 161206,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH11/CH11_SEC02_1_HarmonicOscillator.ipynb",
    "chars": 564623,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 190,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH11/CH11_SEC03_1_NonlinearSchrodinger.ipynb",
    "chars": 384140,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH11/CH11_SEC05_1_Invariance.ipynb",
    "chars": 539540,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH12/CH12_SEC01_1_GAPPY.ipynb",
    "chars": 71453,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH12/CH12_SEC02_1_GAPPY.ipynb",
    "chars": 110679,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH12/CH12_SEC03_1_GAPPY_ConditionNumber.ipynb",
    "chars": 238912,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH12/CH12_SEC04_1_GAPPY_Variance.ipynb",
    "chars": 376498,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "CH12/CH12_SEC06_1_DEIM.ipynb",
    "chars": 420899,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": ["
  },
  {
    "path": "CH12/CH12_SEC06_2_DEIM.ipynb",
    "chars": 508327,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "DATA/census1994.csv",
    "chars": 3561226,
    "preview": "age,workClass,fnlwgt,education,education_num,marital_status,occupation,relationship,race,sex,capital_gain,capital_loss,h"
  },
  {
    "path": "DATA/hald_heat.csv",
    "chars": 71,
    "preview": "78.5\n74.3\n104.3\n87.6\n95.9\n109.2\n102.7\n72.5\n93.1\n115.9\n83.8\n113.3\n109.4\n"
  },
  {
    "path": "DATA/hald_ingredients.csv",
    "chars": 140,
    "preview": "7,26,6,60\n1,29,15,52\n11,56,8,20\n11,31,8,47\n7,52,6,33\n11,55,9,22\n3,71,17,6\n1,31,22,44\n2,54,18,22\n21,47,4,26\n1,40,23,34\n11"
  },
  {
    "path": "DATA/housing.data",
    "chars": 49082,
    "preview": " 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n 0.02731   0.00   7.070"
  },
  {
    "path": "DATA/ovariancancer_grp.csv",
    "chars": 1512,
    "preview": "Cancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nCancer\nC"
  },
  {
    "path": "DATA/ovariancancer_obs.csv",
    "chars": 7549028,
    "preview": "0.063915,0.033242,0.018484,0.0086177,0.035629,0.037925,0.028865,0.061731,0.0631,0.024787,0.034473,0.033539,0.01043,0.018"
  },
  {
    "path": "README.md",
    "chars": 823,
    "preview": "# Data_Driven_Science_Python_Demos\n\n<img src=\"http://www.databookuw.com/files/stacks-image-5bffc53-882x1200.png\" alt=\"Da"
  }
]

// ... and 19 more files (download for full content)

About this extraction

This page contains the full source code of the dylewsky/Data_Driven_Science_Python_Demos GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 116 files (211.9 MB), approximately 11.7M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

Extract another repo