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mne-tools/mne-tools.github.io | 0.15/_downloads/plot_eeg_erp.ipynb | 1 | 12882 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n\nEEG processing and Event Related Potentials (ERPs)\n==================================================\n\nFor a generic introduction to the computation of ERP and ERF\nsee `tut_epoching_and_averaging`. Here we cover the specifics\nof EEG, namely:\n\n - setting the reference\n - using standard montages :func:`mne.channels.Montage`\n - Evoked arithmetic (e.g. differences)\n\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"import mne\nfrom mne.datasets import sample"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Setup for reading the raw data\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.set_eeg_reference('average', projection=True) # set EEG average reference"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Let's restrict the data to the EEG channels\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.pick_types(meg=False, eeg=True, eog=True)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"By looking at the measurement info you will see that we have now\n59 EEG channels and 1 EOG channel\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"print(raw.info)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"In practice it's quite common to have some EEG channels that are actually\nEOG channels. To change a channel type you can use the\n:func:`mne.io.Raw.set_channel_types` method. For example\nto treat an EOG channel as EEG you can change its type using\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.set_channel_types(mapping={'EOG 061': 'eeg'})\nprint(raw.info)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"And to change the nameo of the EOG channel\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.rename_channels(mapping={'EOG 061': 'EOG'})"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Let's reset the EOG channel back to EOG type.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.set_channel_types(mapping={'EOG': 'eog'})"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"The EEG channels in the sample dataset already have locations.\nThese locations are available in the 'loc' of each channel description.\nFor the first channel we get\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"print(raw.info['chs'][0]['loc'])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"And it's actually possible to plot the channel locations using\n:func:`mne.io.Raw.plot_sensors`.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.plot_sensors()\nraw.plot_sensors('3d') # in 3D"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Setting EEG montage\n-------------------\n\nIn the case where your data don't have locations you can set them\nusing a :class:`mne.channels.Montage`. MNE comes with a set of default\nmontages. To read one of them do:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"montage = mne.channels.read_montage('standard_1020')\nprint(montage)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"To apply a montage on your data use the ``set_montage`` method.\nfunction. Here don't actually call this function as our demo dataset\nalready contains good EEG channel locations.\n\nNext we'll explore the definition of the reference.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"Setting EEG reference\n---------------------\n\nLet's first remove the reference from our Raw object.\n\nThis explicitly prevents MNE from adding a default EEG average reference\nrequired for source localization.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_no_ref, _ = mne.set_eeg_reference(raw, [])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"We next define Epochs and compute an ERP for the left auditory condition.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"reject = dict(eeg=180e-6, eog=150e-6)\nevent_id, tmin, tmax = {'left/auditory': 1}, -0.2, 0.5\nevents = mne.read_events(event_fname)\nepochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,\n reject=reject)\n\nevoked_no_ref = mne.Epochs(raw_no_ref, **epochs_params).average()\ndel raw_no_ref # save memory\n\ntitle = 'EEG Original reference'\nevoked_no_ref.plot(titles=dict(eeg=title))\nevoked_no_ref.plot_topomap(times=[0.1], size=3., title=title)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"**Average reference**: This is normally added by default, but can also\nbe added explicitly.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_car, _ = mne.set_eeg_reference(raw, 'average', projection=True)\nevoked_car = mne.Epochs(raw_car, **epochs_params).average()\ndel raw_car # save memory\n\ntitle = 'EEG Average reference'\nevoked_car.plot(titles=dict(eeg=title))\nevoked_car.plot_topomap(times=[0.1], size=3., title=title)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"**Custom reference**: Use the mean of channels EEG 001 and EEG 002 as\na reference\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_custom, _ = mne.set_eeg_reference(raw, ['EEG 001', 'EEG 002'])\nevoked_custom = mne.Epochs(raw_custom, **epochs_params).average()\ndel raw_custom # save memory\n\ntitle = 'EEG Custom reference'\nevoked_custom.plot(titles=dict(eeg=title))\nevoked_custom.plot_topomap(times=[0.1], size=3., title=title)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Evoked arithmetics\n------------------\n\nTrial subsets from Epochs can be selected using 'tags' separated by '/'.\nEvoked objects support basic arithmetic.\nFirst, we create an Epochs object containing 4 conditions.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"event_id = {'left/auditory': 1, 'right/auditory': 2,\n 'left/visual': 3, 'right/visual': 4}\nepochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,\n reject=reject)\nepochs = mne.Epochs(raw, **epochs_params)\n\nprint(epochs)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Next, we create averages of stimulation-left vs stimulation-right trials.\nWe can use basic arithmetic to, for example, construct and plot\ndifference ERPs.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"left, right = epochs[\"left\"].average(), epochs[\"right\"].average()\n\n# create and plot difference ERP\nmne.combine_evoked([left, -right], weights='equal').plot_joint()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"This is an equal-weighting difference. If you have imbalanced trial numbers,\nyou could also consider either equalizing the number of events per\ncondition (using\n:meth:`epochs.equalize_event_counts <mne.Epochs.equalize_event_counts>`).\nAs an example, first, we create individual ERPs for each condition.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"aud_l = epochs[\"auditory\", \"left\"].average()\naud_r = epochs[\"auditory\", \"right\"].average()\nvis_l = epochs[\"visual\", \"left\"].average()\nvis_r = epochs[\"visual\", \"right\"].average()\n\nall_evokeds = [aud_l, aud_r, vis_l, vis_r]\nprint(all_evokeds)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"This can be simplified with a Python list comprehension:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"all_evokeds = [epochs[cond].average() for cond in sorted(event_id.keys())]\nprint(all_evokeds)\n\n# Then, we construct and plot an unweighted average of left vs. right trials\n# this way, too:\nmne.combine_evoked(all_evokeds,\n weights=(0.25, -0.25, 0.25, -0.25)).plot_joint()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Often, it makes sense to store Evoked objects in a dictionary or a list -\neither different conditions, or different subjects.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# If they are stored in a list, they can be easily averaged, for example,\n# for a grand average across subjects (or conditions).\ngrand_average = mne.grand_average(all_evokeds)\nmne.write_evokeds('/tmp/tmp-ave.fif', all_evokeds)\n\n# If Evokeds objects are stored in a dictionary, they can be retrieved by name.\nall_evokeds = dict((cond, epochs[cond].average()) for cond in event_id)\nprint(all_evokeds['left/auditory'])\n\n# Besides for explicit access, this can be used for example to set titles.\nfor cond in all_evokeds:\n all_evokeds[cond].plot_joint(title=cond)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
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"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
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"name": "python",
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} | bsd-3-clause |
quantopian/research_public | notebooks/lectures/Random_Variables/answers/notebook.ipynb | 2 | 191504 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exercises: Random Variables - Answer Key\n",
"By Christopher van Hoecke, Max Margenot, and Delaney Mackenzie\n",
"\n",
"## Lecture Link : \n",
"https://www.quantopian.com/lectures/random-variables\n",
"\n",
"### IMPORTANT NOTE: \n",
"This lecture corresponds to the Random Variables lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.\n",
"\n",
"Part of the Quantopian Lecture Series:\n",
"\n",
"* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
"* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Key Concepts"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Useful Functions\n",
"class DiscreteRandomVariable:\n",
" def __init__(self, a=0, b=1):\n",
" self.variableType = \"\"\n",
" self.low = a\n",
" self.high = b\n",
" return\n",
" def draw(self, numberOfSamples):\n",
" samples = np.random.randint(self.low, self.high, numberOfSamples)\n",
" return samples\n",
" \n",
"class BinomialRandomVariable(DiscreteRandomVariable):\n",
" def __init__(self, numberOfTrials = 10, probabilityOfSuccess = 0.5):\n",
" self.variableType = \"Binomial\"\n",
" self.numberOfTrials = numberOfTrials\n",
" self.probabilityOfSuccess = probabilityOfSuccess\n",
" return\n",
" def draw(self, numberOfSamples):\n",
" samples = np.random.binomial(self.numberOfTrials, self.probabilityOfSuccess, numberOfSamples)\n",
" return samples\n",
" \n",
"def factorial(n):return reduce(lambda x,y:x*y,[1]+range(1,n+1))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Useful Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.stats as stats\n",
"from statsmodels.stats import stattools\n",
"from __future__ import division"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Exercise 1 : Uniform Distribution\n",
"- Plot the histogram of 10 tosses with a fair coin (let 1 be heads and 2 be tails). \n",
"- Plot the histogram of 1000000 tosses of a fair coin"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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9enVlQwEAALSlVoXPWWedlZtvvjmvvPJKRo8enSlTpuSMM86oeDQAAIC20arwefLJJzNy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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20c0f9b210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Histograms with 10 tosses. \n",
"cointoss = DiscreteRandomVariable(1, 3)\n",
"plt.hist(cointoss.draw(10), align = 'mid')\n",
"\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Occurences')\n",
"plt.legend(['Coin Tosses']);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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AUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDCh\nBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAA\nUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDChBQAAUDCh\nBQAAULBO7T1A0WbMmJEVK1akVCpl6tSpOeaYY9p7JAAAYC+zR4XWY489ljVr1mTevHlZtWpVLrvs\nssybN6+9xwIAAPYye9Spg8uWLcuoUaOSJH369Mkrr7ySzZs3t/NUAADA3maPekWrubk5H/3oR1sv\n77///mlubk7Xrl3bcSre77Zu/L/p1nWf9h6jXb20vibPPfdce4/RrtasWZPu3bu39xi8D1gLJNYB\n2+3t6+A3v/lNXn25qb3HaFd/zvHvUaH1n5XL5Xfdp6GhYTdMwvvZDVd/ub1HeF/4wx/+0N4jtKtD\nDz10r38M2M5aILEO2G5vXwcHHnhgZk89rb3H6LD2qNCqq6tLc3Nz6+WmpqYcdNBB77j/oEGDdsdY\nAADAXmaPeo/W0KFDs3DhwiTJM888k549e2bfffdt56kAAIC9zR71itaAAQPSr1+/TJgwIdXV1Zk2\nbVp7jwQAAOyFSuW2vJEJAACANtujTh0EAAB4PxBaAAAABRNaAAAABdsrQmvGjBmZMGFC/v7v/z5P\nPfXUDtfNnTs3EyZMSH19fWbMmNFOE7K7PPfcczn55JMzd+7cna5bunRpxo0blwkTJmT27NntMB27\ny67Wwa9//euceeaZmThxYi677LJ2mI7dZVfr4C3XX399Jk2atBunYnfb1Tp48cUXM3HixIwfPz5X\nXnnl7h+O3WpXa8Hzxb3HtddemwkTJmTcuHH5+c9/vsN1f+pzxT0+tB577LGsWbMm8+bNy9VXX51v\nfvObrddt2rQpN910U3784x9n7ty5ef755/Pkk0+247RU0pYtW3L11VdnyJAhb3v9N7/5zXzve9/L\nj3/84yxZsiSrVq3azROyO7zbOrjiiivy3e9+N3fccUc2bdqUX/3qV7t5QnaHd1sHSbJq1ao8/vjj\nKZVKu3Eydqd3WwczZ87Mueeem/nz56e6ujovvvjibp6Q3WVXa8Hzxb3HI488klWrVmXevHn50Y9+\nlGuuuWaH6//U54p7fGgtW7Yso0aNSpL06dMnr7zySjZv3pwk6dKlS7p06ZJNmzalpaUlr732Wnr0\n6NGe41JBNTU1mTNnTurq6na67ne/+13222+/9OzZM6VSKcOHD8+vf/3rdpiSStvVOkiSn/70p63X\n1dbWZuPGjbtzPHaTd1sHyfYn2RdeeOFunIrdbVfroFwup6GhISNHjkySXH755fngBz+4u0dkN9nV\nWvB8ce9xwgkn5IYbbkiSfOADH8iWLVvy1ge0v5fnint8aDU3N6e2trb18v7775/m5uYk239xvvjF\nL2bUqFH567/+6/Tv3z+HHnpoe41KhVVVVaVLly5ve91/Xie1tbVpamraXaOxG+1qHSRJ165dkyRN\nTU1ZunRphg8fvrtGYzd6t3XwL//yLxk8eHB69eq1G6did9vVOnjppZey77775pvf/GYmTpyYb3/7\n27t5OnanXa0Fzxf3HqVSKfvss0+S5J//+Z8zfPjw1rMa3stzxT0+tP6zP/7asE2bNuUHP/hBHnzw\nwfzyl7/MihUr8m//9m/tOB3vF75ebu+2YcOGfOELX8iVV17p/7XcC7388sv56U9/ms985jMpl8v+\nHuylyuVympqa8ulPfzq33357nn322SxevLi9x6IdeL649/nFL36Rn/70p7n88svfcZ+2/G/DHh9a\ndXV1ra9gJdv/X+qDDjooSbJ69ep86EMfSo8ePdKpU6ccf/zxeeaZZ9prVNpRXV1d1q9f33q5sbFx\nl6cUsefatGlT/vt//++58MILd/n+HfZcv/71r/P73/8+9fX1ueCCC/Kv//qvmTlzZnuPxW62//77\np3fv3jnkkENSVVWVIUOG5Pnnn2/vsWgHni/uXR5++OH88Ic/zJw5c9KtW7fW7e/lueIeH1pDhw7N\nwoULkyTPPPNMevbsmX333TdJ0rt376xevTpbt25Nkjz99NNeCt5L9e7dO5s3b866devS0tKShx56\nKH/1V3/V3mPRDmbOnJnPfOYzGTp0aHuPQjs55ZRTct9992XevHn53ve+l7/8y7/MJZdc0t5jsZtV\nV1fnkEMOyW9/+9sk259DHH744e08Fe3B88W9x6ZNm/IP//APufHGG9O9e/cdrnsvzxVL5b3gnIhv\nf/vbefTRR1NdXZ1p06bl2WefTffu3TNq1KjMnz8/CxYsSKdOnTJgwIBcdNFF7T0uFfLMM89k5syZ\nWbduXTp16pSePXtm5MiROeSQQzJq1Kg8/vjjue6665Ikn/jEJ/LpT3+6fQemIna1Dv7qr/4qJ5xw\nQo477riUy+WUSqWcdtppGTduXHuPTcHe7e/BW1544YVceumlufXWW9txWirl3dbBb3/721xyySUp\nl8v5yEc+kunTp7f3yFTIu60Fzxf3DvPnz8/3vve9HHbYYa3PAz7+8Y/nIx/5yHt6rrhXhBYAAMDu\ntMefOggAALC7CS0AAICCCS0AAICCCS0AAICCCS0AAICCCS0AAICCCS0A9jhnnXVWFi1atMO2119/\nPSeccEIaGxvf9jaTJk3KsmXLdsd4AOwFhBYAe5yxY8fmX/7lX3bY9vOf/zzHHXdcevbs2U5TAbA3\nEVoA7HE+8YlPpKGhIS+//HLrtrvuuitjx47NL37xi0yYMCH/7b/9t5x11llZt27dDrd99NFHM3Hi\nxNbLl156ae68884kyc9+9rPU19envr4+F1xwwQ73DwB/TGgBsMfZZ599cvLJJ+e+++5LkjQ1NWXl\nypUZOXJkXnnllfzP//k/c8stt+Skk07K7bffvtPtS6XSTttefPHF/OAHP8g//dM/Ze7cufnYxz6W\nG2+8seLHAkDH1Km9BwCASvjUpz6Vq666KvX19bn33ntz2mmnpVOnTjnggAMyZcqUlMvlNDc357jj\njmvT/S1fvjzr16/Pueeem3K5nG3btuWQQw6p8FEA0FEJLQD2SP3798/WrVuzatWq3H333Zk1a1Za\nWlry1a9+NXfffXc+9KEPZe7cuXn66ad3uN1/fjVr69atSZIuXbqkf//+XsUCoE2cOgjAHmvs2LGZ\nPXt29t133/Tp0yebN29OdXV1evXqlddffz2//OUvW0PqLd26dWv9ZMItW7bkySefTJIcc8wxeeqp\np9Lc3JwkeeCBB3b6ZEMAeItXtADYY5122mm57rrrMm3atCRJjx498jd/8zf51Kc+ld69e+ezn/1s\npkyZkoULF7a+knXUUUelb9+++eQnP5kPf/jDGThwYJKkrq4ul112Wf7H//gf2XfffbPPPvvkW9/6\nVrsdGwDvb6VyuVxu7yEAAAD2JE4dBAAAKJjQAgAAKJjQAgAAKJjQAgAAKJjQAgAAKJjQAgAAKJjQ\nAgAAKNj/Bwk0j948AfNcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20c11bb0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Histograms with 1000000 tosses. \n",
"cointoss = DiscreteRandomVariable(1, 3)\n",
"plt.hist(cointoss.draw(1000000), align = 'mid')\n",
"\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Occurences')\n",
"plt.legend(['Coin Tosses']);"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exercise 2 : Binomial Distributions.\n",
"- Graph the histogram of 1000000 samples from a binomial distribution of probability 0.25 and $n = 20$\n",
"- Find the value that occurs the most often\n",
"- Calculate the probability of the value that occurs the most often occurring. *Use the factorial(x) function to find factorials*"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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MIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAM\nIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAA\nAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBAC\nCwAAAAAM8fL0AABwIY6qKmVlZXl6jAsKDg6WzWbz9BgAAOASQmABuGSVFh/V9Lfy1dg/09Oj1FBS\nlKdlyXEKCQnx9CgAAOASQmABuKQ19rfLr3mQp8cAAABwCddgAQAAAIAhBBYAAAAAGEJgAQAAAIAh\nBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAA\nAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJg\nAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAAGEJgAQAAAIAhBBYAAAAA\nGEJgAQAAAIAhBBYAAAAAGOL2wNq/f7/uuusuLV++XJI0adIkDRw4UImJiUpMTNTWrVslSWvXrtWg\nQYM0ZMgQffzxx5KkiooKjR8/XnFxcUpISFBOTo4kad++fRo6dKji4uI0a9Ys5/davHixBg8erCFD\nhji3CwAAAAD1xcudGy8tLdVzzz2nnj17Vls+fvx49enTp9p6Cxcu1Jo1a+Tl5aVBgwYpJiZGW7Zs\nkb+/v+bNm6ft27dr/vz5eumllzR79mxNmzZNnTt3VlJSkrZt26YbbrhBGzZs0IcffqiioiLFx8cr\nMjJSFovFnbsIAAAAAE5uPYLl4+OjxYsXy26317re3r171bVrV/n6+srHx0dhYWFKS0vTzp07FR0d\nLUm64447lJ6ervLycuXk5Khz586SpKioKO3YsUO7du1SZGSkbDabAgICFBQUpO+//96duwcAAAAA\n1bg1sKxWq7y9vWss/+CDD/Tggw8qKSlJP//8s/Lz8xUQEOD8fEBAgI4ePVptucVikcViUX5+vpo1\na1Zt3by8PBUUFJx3GwAAAABQX9x6iuD5/Pa3v1WzZs1000036e2339aCBQsUGhpabR2Hw3Her3U4\nHLJYLBf8vCvbOFdaWppL6wH/iezsbE+PADfIyMhQcXGxp8eAG/CzATiN1wJQd/UeWD169HB+HBUV\npZkzZ6qlk9p7AAAep0lEQVR///76/PPPnctzc3MVGhoqu92u/Px8dezYURUVFXI4HGrVqpUKCwur\nrRsYGCi73a4ffvih2vKLnZooSeHh4Yb2DLiwJk2aSOuOeHoMGNalSxeFhIR4egwYlpaWxs8GQLwW\ngDPq+oeGer9N+xNPPKGDBw9Kknbt2qWQkBB17dpVGRkZOn78uE6cOKH09HSFh4erV69eSklJkSRt\n2bJFERERstlsat++vfbs2SNJ2rhxo3r37q2IiAht3bpVFRUVys3NVV5enm688cb63j0AAAAAVzC3\nHsH65ptvNGfOHB0+fFheXl5KTU1VQkKCxo0bp6uvvlq+vr6aPXu2fHx8lJSUpOHDh8tqterxxx+X\nn5+fYmNjtX37dsXFxcnHx0dz5syRJE2ePFnTp0+Xw+FQt27dnHcpvP/++xUfHy+LxVLt9u0AAAAA\nUB/cGlidO3fWsmXLaiy/6667aiyLiYlRTExMtWVWq1XJyck11g0ODna+r9bZ4uPjFR8f/x9MDAAA\nAAC/XL2fIggAAAAAlysCCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAA\nwBACCwAAAAAMIbAAAAAAwBACCwAAAAAMIbAAAAAAwJBfFFhVVVWm5wAAAACABs+lwPrkk0+0fPly\nVVRUaNiwYfr1r3+tFStWuHs2AAAAAGhQXAqs1atXa/Dgwdq0aZM6dOigzZs3a8OGDe6eDQAAAAAa\nFJcCy8fHR97e3tq6dasGDBggq5VLtwAAAADgXC6X0qxZs7Rnzx51795d6enpKisrc+dcAAAAANDg\nuBRY8+bNU7t27bRo0SLZbDYdOnRIs2bNcvdsAAAAANCguBRYdrtd7dq10/bt2yVJXbt2VceOHd06\nGAAAAAA0NC4F1osvvqg1a9bok08+kSR99tlneu6559w6GAAAAAA0NC4F1pdffqkFCxbI19dXkvTo\no4/qm2++cetgAAAAANDQuHwXQUmyWCySpMrKSlVWVrpvKgAAAABogLxcWSksLEwTJ05UXl6e3nvv\nPaWmpqp79+7ung0AAAAAGhSXAmvcuHFKSUnR1VdfrSNHjmj48OGKiYlx92wAAAAA0KC4FFglJSWq\nqqrSjBkzJEkrV67UiRMnnNdkAZeCyspKZWZmenqM88rKyvL0CAAAAKgHLgXWhAkTdPvttzsfnzx5\nUk8//bRef/11tw0G1FVmZqYSJq1QY3+7p0epoSDnO7Vo08nTYwAAAMDNXAqswsJCJSYmOh8//PDD\n2rJli9uGAn6pxv52+TUP8vQYNZQU5Xp6BAAAANQDl+4iWF5eXu3Uq4yMDJWXl7ttKAAAAABoiFw6\ngjVp0iSNGTNGxcXFqqysVEBAgF544QV3zwYAAAAADYpLgdWtWzelpqbq559/lsViUbNmzdw9FwAA\nAAA0OC4F1j//+U999NFHKioqksPhcC6fO3eu2wYDAAAAgIbGpcAaO3asBgwYoE6duAsaAAAAAFyI\nS4HVsmVLPfbYY+6eBQAAAAAaNJfuIhgZGakvvvhCZWVlqqqqcv4PAAAAAPBvLh3BeuONN3T8+HFJ\nksVikcPhkMVi0XfffefW4QAAAACgIXEpsL766it3zwEAAAAADZ5LpwgWFRXphRde0FNPPSVJ2rJl\ni3766Se3DgYAAAAADY1LgTV16lRde+21OnjwoCSprKxMEyZMcOtgAAAAANDQuBRYP/30kxITE9Wo\nUSNJUv/+/XXy5Em3DgYAAAAADY1LgSVJ5eXlslgskqT8/HyVlJS4bSgAAAAAaIhcuslFfHy8Bg0a\npKNHj2r06NH6+9//rilTprh7NgAAAABoUFwKrNjYWIWFhSk9PV3e3t565plnZLfb3T0bAAAAADQo\nLgXW2LFj9fLLL2vAgAHungcAAAAAGiyXAqtNmzb6+OOPFRoaKm9vb+fytm3bum0wAAAAAGhoXAqs\n9evX11hmsVi0efNm4wMBAAAAQEPlUmCtXLlSgYGB7p4FAAAAABo0l27T/tRTT7l7DgAAAABo8Fw6\ngnX99dfr6aefVmhoqPPNhiVp0KBBbhsMAAAAABoalwKrvLxcNptNX3/9dbXlBBYAAAAA/JtLgZWc\nnOzuOQAAAACgwXMpsPr06SOLxVJj+f/+7/+angcAAAAAGiyXAmvFihXOj8vLy7Vz506dPHnSbUMB\nAAAAQEPkUmAFBQVVe3z99ddrxIgRevjhh90yFAAAAAA0RC4F1s6dO6s9PnLkiH788Ue3DAQAAAAA\nDZVLgbVw4ULnxxaLRX5+fpo1a5bbhgIAAACAhsilwFq2bJmKi4vVpEkTSVJ+fr5atmzp1sEAAAAA\noKGxurLS8uXLNWHCBOfjP/7xj/rggw/cNhQAAAAANEQuBdbatWv16quvOh+/++67WrdunduGAgAA\nAICGyKXAqqyslJfXv88mtFgscjgcbhsKAAAAABoil67BioqK0tChQxUeHq6qqir99a9/VUxMjLtn\nAwAAAIAGxaXAGjNmjLp3766vv/5aFotFM2bM0K233uru2QAAAACgQXEpsPLy8vTtt99q+PDhkqSX\nXnpJ1157rQIDA906HABcqhxVVcrKyvL0GBcUHBwsm83m6TEAALjiuBRYkyZN0n333ed83LFjR02e\nPFnvvPOO2wYDgEtZafFRTX8rX439Mz09Sg0lRXlalhynkJAQT48CAMAVx6XAKisrU2xsrPNxbGys\nVq1a5bahAKAhaOxvl1/zIE+PAQAALiEu3UVQkv7v//5PJ0+eVElJiVJTU905EwAAAAA0SC4dwXru\nuec0Y8YMjR07VhaLRaGhoXr22WfdPRsAAAAANCgXDaydO3fqtdde07fffiuLxaJbbrlFjz76qNq1\na1cf8wEAAABAg1FrYK1fv14LFy7UH//4R+dt2f/+979r1qxZeuKJJxQVFVUvQwIAAABAQ1BrYC1Z\nskRvv/22rr32WueyPn36qFOnTnryyScJLAAAAAA4S603ubBYLNXi6gy73S6Hw+G2oQAAAACgIao1\nsE6ePHnBz5WUlBgfBgAAAAAasloDq1OnTlq2bFmN5YsXL1ZYWJjbhgIAAACAhqjWa7CefvppjRkz\nRuvWrdMtt9wih8Oh9PR0+fn56c0336yvGQEAAACgQag1sAICArRq1Spt375d3377rRo3bqwBAwbo\ntttuq6/5AAAAAKDBcOmNhnv16qVevXq5exYAAAAAaNBqvQYLAAAAAOA6AgsAAAAADCGwAAAAAMAQ\nAgsAAAAADCGwAAAAAMAQAgsAAAAADHF7YO3fv1933XWXli9fLkk6cuSIEhIS9MADD2jcuHEqLy+X\nJK1du1aDBg3SkCFD9PHHH0uSKioqNH78eMXFxSkhIUE5OTmSpH379mno0KGKi4vTrFmznN9r8eLF\nGjx4sIYMGaKtW7e6e9cAAAAAoBq3BlZpaamee+459ezZ07nslVdeUUJCgj744ANdd911WrNmjUpL\nS7Vw4UK9//77Wrp0qd5//30dO3ZM69atk7+/v1asWKHRo0dr/vz5kqTZs2dr2rRpWrFihY4dO6Zt\n27YpJydHGzZs0KpVq/TGG29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1drtdJ06c0OrVq+VyuRQVFaVz584ZHQuYVT09PTp69KgO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\nnzsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20b8132e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Binomial distribution with p=0.25 and n=20\n",
"binomialdistribution = BinomialRandomVariable(20, 0.25)\n",
"bins = np.arange(0,21,1)\n",
"n, bins, patches = plt.hist(binomialdistribution.draw(1000000), bins=bins)\n",
"\n",
"plt.title('Binomial Distribution with p=0.25 and n=20')\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Occurrences')\n",
"plt.legend(['Die Rolls']);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum occurance for x = 5\n"
]
}
],
"source": [
"# Finding x which occurs most often\n",
"elem = np.argmax(n)\n",
"print 'Maximum occurance for x =', elem"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"proabability of x = 5 0.0147857666016\n"
]
}
],
"source": [
"# Calculating the probability of finding x. \n",
"n = 20\n",
"p = 0.5\n",
"x = elem\n",
"n_factorial = factorial(n)\n",
"x_factorial = factorial(x)\n",
"n_x_factorial = factorial(n-x)\n",
"fact = n_factorial / (n_x_factorial * x_factorial)\n",
"probability = fact * (p**x) * ((1-p)**(n-x))\n",
"print 'proabability of x = %d' % x, probability"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exercise 3 : Normal Distributions\n",
"## a. Graphing\n",
"Graph a normal distribution using the Probability Density Function bellow, with a mean of 0 and standard deviation of 5. \n",
"\n",
"$$f(x) = \\frac{1}{\\sigma\\sqrt{2\\pi}}e^{-\\frac{(x - \\mu)^2}{2\\sigma^2}}$$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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RQUGBli5dqtTUVL355pt68cUXz/kaAADQPna7Xddee622b98uSe6t6wEAF+fRpXdr165V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\n6Ppap7P7cd3P0SG7w6n29g61O7re2jtkd3TI3rPty/2tdodaWtvV0uZ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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20a2f90d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Graphing a normal distribution pdf. \n",
"mu = 0\n",
"sigma = 5\n",
"x = np.linspace(-30, 30, 200)\n",
"y = (1/(sigma * np.sqrt(2 * 3.14159))) * np.exp(-(x - mu)*(x - mu) / (2 * sigma * sigma))\n",
"plt.plot(x, y)\n",
"plt.title('Graph of PDF with mu = 0 and sigma = 5')\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Probability');"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## b. Confidence Intervals. \n",
"- Calculate the first, second, and third confidence intervals. \n",
"- Plot the PDF and the first, second, and third confidence intervals. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1-sigma -> mu +/- 5\n",
"2-sigma -> mu +/- 10\n",
"3-sigma -> mu +/- 15\n"
]
}
],
"source": [
"# finding the 1st, 2nd, and third confidence intervals. \n",
"first_ci = (-sigma, sigma)\n",
"second_ci = (-2*sigma, 2*sigma)\n",
"third_ci = (-3*sigma, 3*sigma)\n",
"\n",
"print '1-sigma -> mu +/-', sigma\n",
"print '2-sigma -> mu +/-', second_ci[1]\n",
"print '3-sigma -> mu +/-', third_ci[1]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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2UC3kmiD3BiEqQZIdIYQQz+zW3UxCo+/SycYMxw7NtB3OEw192QYzUwN+On6T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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20b8184d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.axvline(first_ci[0], linestyle='dashdot', label='68% of observations', color = 'blue')\n",
"plt.axvline(first_ci[1], linestyle='dashdot', label='68% of observations', color = 'blue')\n",
"plt.axvline(second_ci[0], linestyle='dashdot', label='95% of observations', color = 'red')\n",
"plt.axvline(second_ci[1],linestyle='dashdot', color = 'red')\n",
"plt.axvline(third_ci[0], linestyle='dashdot', label='99% of observations', color = 'green')\n",
"plt.axvline(third_ci[1], linestyle='dashdot', color = 'green')\n",
"plt.plot(x,y)\n",
"plt.title('Graph of PDF with 3 confidence intervals.')\n",
"\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exercise 4: Financial Applications: \n",
"Fit the returns of SPY from 2016-01-01 to 2016-05-01 to a normal distribution. \n",
"- Fit the returns to a normal distribution by clacluating the values of $\\mu$ and $\\sigma$\n",
"- Plot the returns and the distribution, along with 3 confidence intervals. \n",
"- Use the Jarque-Bera test to check for normality. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Collect prices and returns. \n",
"prices = get_pricing('SPY', start_date = '2016-01-01', end_date='2016-05-01', \n",
" fields = 'price')\n",
"returns = prices.pct_change()[1:]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Calculating the mean and standard deviation. \n",
"sample_mean = np.mean(returns)\n",
"sample_std_dev = np.std(returns)\n",
"\n",
"x = np.linspace(-(sample_mean + 4 * sample_std_dev), (sample_mean + 4 * sample_std_dev), len(returns))\n",
"sample_distribution = ((1/(sample_std_dev * 2 * np.pi)) * \n",
" np.exp(-(x - sample_mean)*(x - sample_mean) / (2 * sample_std_dev * sample_std_dev)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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hbd26Vddff7327dunu+66S1VVVdq8ebMkae/evXrssce0cuXKZs9RXFzcsVUD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\nIiIiIiIiRU1Ji4iIiIiIFLX/D3hFGlI8qkdtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20a2ad7610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting histograms and confidence intervals. \n",
"plt.hist(returns, range=(returns.min(), returns.max()), normed = True);\n",
"plt.plot(x, sample_distribution)\n",
"\n",
"plt.axvline(sample_std_dev, linestyle='dashed', color='red', label='1st Confidence Interval')\n",
"plt.axvline(-sample_std_dev, linestyle='dashed', color='red')\n",
"plt.axvline(2*sample_std_dev, linestyle='dashed', color='k', label='2st Confidence Interval')\n",
"plt.axvline(-2*sample_std_dev, linestyle='dashed', color='k')\n",
"plt.axvline(3*sample_std_dev, linestyle='dashed', color='green', label='3st Confidence Interval')\n",
"plt.axvline(-3*sample_std_dev, linestyle='dashed', color='green')\n",
"\n",
"plt.legend();"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The JB test p-value is: 0.923015693884\n",
"We reject the hypothesis that the data are normally distributed False\n",
"The skewness of the returns is: -0.102081900914\n",
"The kurtosis of the returns is: 3.07608657316\n"
]
}
],
"source": [
"# Run the JB test for normality. \n",
"cutoff = 0.01\n",
"_, p_value, skewness, kurtosis = stattools.jarque_bera(returns)\n",
"print \"The JB test p-value is: \", p_value\n",
"print \"We reject the hypothesis that the data are normally distributed \", p_value < cutoff\n",
"print \"The skewness of the returns is: \", skewness\n",
"print \"The kurtosis of the returns is: \", kurtosis"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"\n",
"Congratulations on completing the Random Variables answer key!\n",
"\n",
"As you learn more about writing trading models and the Quantopian platform, enter a daily [Quantopian Contest](https://www.quantopian.com/contest). Your strategy will be evaluated for a cash prize every day.\n",
"\n",
"Start by going through the [Writing a Contest Algorithm](https://www.quantopian.com/tutorials/contest) tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
heyfaraday/CMB_test | outdated/0.2.3/4.12.16.ipynb | 1 | 966804 | {
"cells": [
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from math import sin, cos, tan, pi, sqrt, factorial, fabs, acos\n",
"\n",
"from numpy.fft import fft\n",
"from numpy import complex128, float64\n",
"import time\n",
"import pyfftw\n",
"from pyfftw.pyfftw import FFTW"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Lmax = 6"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a_coef = np.random.normal(size = (Lmax+1, Lmax+1))\n",
"b_coef = np.random.normal(size = (Lmax+1, Lmax+1))\n",
"\n",
"#a_coef = np.ones((Lmax+1, Lmax+1))\n",
"#b_coef = np.ones((Lmax+1, Lmax+1))\n",
"\n",
"a_coef[0][0] = 0.0\n",
"\n",
"for m in xrange(0, Lmax+1):\n",
" for l in xrange(0, m):\n",
" a_coef[m][l] = 0.0 \n",
" \n",
"for m in xrange(0, Lmax+1):\n",
" for l in xrange(0, m):\n",
" b_coef[m][l] = 0.0\n",
" \n",
"for l in xrange(0, Lmax+1):\n",
" b_coef[0][l] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N = 512\n",
"\n",
"field = np.zeros((N, N/2))\n",
"x = np.zeros((N, N/2))\n",
"y = np.zeros((N, N/2))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" P_ = np.zeros((Lmax+1, Lmax+1))\n",
" \n",
" P_[0][0] = 1/sqrt(4*pi)\n",
" \n",
" for m in xrange(1, Lmax+1):\n",
" P_[m][m] = P_[m-1][m-1]*(-sin(teta))*sqrt(2*m+3)/sqrt(2*m+2)\n",
" \n",
" for m in xrange(0, Lmax):\n",
" P_[m][m+1] = P_[m][m]*cos(teta)*sqrt(2*m+3)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m+2, Lmax+1):\n",
" P_[m][l] = sqrt((2*l+1)*(l-1-m))/sqrt(l**2-m**2)*(cos(teta)*sqrt(2*l-1)/sqrt(l-1-m)*P_[m][l-1] - sqrt(l+m-1)/sqrt(2*l-3)*P_[m][l-2])\n",
" \n",
" F = np.zeros((N+1))\n",
" F_ = np.zeros((N+1)) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" T = np.real(fft(F)) + np.imag(fft(F_))\n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field[i][j] = T[i]\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.9770330000000023"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time1-time0"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(512, 256)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"field.shape"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kQnAncai/89akJNIQIQO/5Fkrq2NR7bJU2tI0J4rTtSY2U6GXbTxAjgNsYR+J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/zm37/2y07ZZJ/Pc/2ZHea9P/Gt00//p2fJfMPjtEgBgX+1T0kiq6N5L0JbKgZJrKTKheUdtY5X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\nSR0MTEmSOhiYkiR1MDAlSerwAM5v8YyyGyJWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109a7a1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field, 100, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4nMfGZcwb0JX1oJan8WneduwKvxuXuNVOK48QNP/UYtFUH1XU0p7KrFk0mho/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\nAF5uat3hYmXCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1051afdd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,4))\n",
"ax = plt.pcolormesh(x, y, field)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"FFTW test"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pyfftw.interfaces.cache.enable()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" P_ = np.zeros((Lmax+1, Lmax+1))\n",
" \n",
" P_[0][0] = 1/sqrt(4*pi)\n",
" \n",
" for m in xrange(1, Lmax+1):\n",
" P_[m][m] = P_[m-1][m-1]*(-sin(teta))*sqrt(2*m+3)/sqrt(2*m+2)\n",
" \n",
" for m in xrange(0, Lmax):\n",
" P_[m][m+1] = P_[m][m]*cos(teta)*sqrt(2*m+3)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m+2, Lmax+1):\n",
" P_[m][l] = sqrt((2*l+1)*(l-1-m))/sqrt(l**2-m**2)*(cos(teta)*sqrt(2*l-1)/sqrt(l-1-m)*P_[m][l-1] - sqrt(l+m-1)/sqrt(2*l-3)*P_[m][l-2])\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" T = np.real(pyfftw.interfaces.numpy_fft.fft(F)) + np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field[i][j] = T[i]\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.7380320000000005"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time1-time0"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(512, 256)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"field.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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R0auSRhZJDQVMIcRmcPDLsAI4D9i9olUHhqYWX5HhafpktQVISH1klqvgHZeQzLOIQZZA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\nYAYFBQUFBSkoADMoKCgoKEhB/x/wr8xLh8u4KgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107e6dc10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field, 100, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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6MwA/A+ArAPw+gFfGGD+ktDVZwv4c/PbOyaWR92pZJvD57SWunyWD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\n3dDQ0NDQMAE0wm5oaGhoaJgA/n/3ZenWIlW1PQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109b91110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,4))\n",
"ax = plt.pcolormesh(x, y, field)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"level = 0.0\n",
"f = field"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.502577181673\n"
]
}
],
"source": [
"# S\n",
"area = 0.0\n",
"narea = 0.0\n",
"\n",
"\n",
"for i in xrange(0, N-1):\n",
" for j in xrange(1, N/2-1):\n",
" if ((f[i][j] + f[i+1][j+1] + f[i+1][j] + f[i][j+1])/4.0 > level):\n",
" area = area + fabs(sin(y[i][j]))\n",
" \n",
"for i in xrange(0, N-1):\n",
" for j in xrange(1, N/2-1):\n",
" narea = narea + fabs(sin(y[i][j])) \n",
"\n",
"area = area/narea\n",
"print area"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.9958344617\n"
]
}
],
"source": [
"# l\n",
"\n",
"l = 0.0\n",
"n = 0.0\n",
"nl = 0.0\n",
"\n",
"f = field - level\n",
"\n",
"teta = y\n",
"phi = x\n",
"\n",
"for i in xrange(0, N-1):\n",
" for j in xrange(0, N/2-1):\n",
" \n",
" \n",
" h_teta = y[N/2+1][N/4+1]\n",
" h_phi = fabs(x[i][0] - x[i+1][0])\n",
" \n",
" sql = 0.0\n",
" \n",
" phi1 = 0.0\n",
" phi2 = 0.0\n",
" teta1 = 0.0\n",
" teta2 = 0.0\n",
" \n",
" if (f[i][j]*f[i][j+1] < 0.0):\n",
" \n",
" if (f[i][j]*f[i+1][j] < 0.0):\n",
" \n",
" phi1 = phi[i][j]\n",
" teta1 = teta[i][j] + h_teta*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i][j+1]))\n",
" \n",
" teta2 = teta[i][j]\n",
" phi2 = phi[i][j] + h_phi*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i+1][j]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql\n",
" \n",
" if (f[i+1][j]*f[i+1][j+1] < 0.0):\n",
" \n",
" phi1 = phi[i][j]\n",
" teta1 = teta[i][j] + h_teta*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i][j+1]))\n",
" \n",
" phi2 = phi[i+1][j]\n",
" teta2 = teta[i+1][j] + h_teta*fabs(f[i+1][j])/(fabs(f[i+1][j]) + fabs(f[i+1][j+1]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql\n",
" \n",
" \n",
" if (f[i][j+1]*f[i+1][j+1] < 0.0):\n",
" \n",
" phi1 = phi[i][j]\n",
" teta1 = teta[i][j] + h_teta*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i][j+1]))\n",
" \n",
" teta2 = teta[i][j+1]\n",
" phi2 = phi[i][j+1] + h_phi*fabs(f[i][j+1])/(fabs(f[i][j+1]) + fabs(f[i+1][j+1]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql\n",
" \n",
" \n",
" \n",
" if (f[i][j]*f[i+1][j] < 0.0):\n",
" \n",
" if (f[i+1][j]*f[i+1][j+1] < 0.0):\n",
" \n",
" teta1 = teta[i][j]\n",
" phi1 = phi[i][j] + h_phi*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i+1][j]))\n",
" \n",
" phi2 = phi[i+1][j]\n",
" teta2 = teta[i+1][j] + h_teta*fabs(f[i+1][j])/(fabs(f[i+1][j]) + fabs(f[i+1][j+1]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql\n",
" \n",
" \n",
" if (f[i][j+1]*f[i+1][j+1] < 0.0):\n",
" \n",
" teta1 = teta[i][j]\n",
" phi1 = phi[i][j] + h_phi*fabs(f[i][j])/(fabs(f[i][j]) + fabs(f[i+1][j]))\n",
" \n",
" teta2 = teta[i][j+1]\n",
" phi2 = phi[i][j+1] + h_phi*fabs(f[i][j+1])/(fabs(f[i][j+1]) + fabs(f[i+1][j+1]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql\n",
" \n",
" if (f[i+1][j]*f[i+1][j+1] < 0.0):\n",
" \n",
" if (f[i][j+1]*f[i+1][j+1] < 0.0):\n",
" \n",
" phi1 = phi[i+1][j]\n",
" teta1 = teta[i+1][j] + h_teta*fabs(f[i+1][j])/(fabs(f[i+1][j]) + fabs(f[i+1][j+1]))\n",
" \n",
" teta2 = teta[i][j+1]\n",
" phi2 = phi[i][j+1] + h_phi*fabs(f[i][j+1])/(fabs(f[i][j+1]) + fabs(f[i+1][j+1]))\n",
" \n",
" #sq = [cos(teta1)*sin(phi1)*sin(teta2) - sin(teta1)*cos(teta2)*sin(phi2), sin(teta1)*cos(teta2)*cos(phi2) - cos(teta1)*cos(phi1)*sin(teta2), cos(teta1)*cos(phi1)*cos(teta2)*sin(phi2) - cos(teta1)*sin(phi1)*cos(teta2)*cos(phi2)]\n",
" #sql = sqrt(sq[0]**2 + sq[1]**2 + sq[2]**2) \n",
" \n",
" sql = acos(sin(teta1)*sin(teta2) + cos(teta1)*cos(teta2)*cos(phi1 - phi2))\n",
" \n",
" l = l + sql \n",
" \n",
"print l/pi"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# spin-weighted spherical harmonics\n",
"\n",
"#----------------------------------------------------------\n",
"#\n",
"# This module computes spin-weighted spherical harmonics.\n",
"#\n",
"# Released under the MIT License.\n",
"# (C) Christian Reisswig 2009-2011\n",
"#\n",
"#----------------------------------------------------------\n",
"\n",
"#def fac(n):\n",
"# result = 1\n",
"#\n",
"# for i in range(2, n+1):\n",
"# result *= i\n",
"#\n",
"# return result\n",
"\n",
"# coefficient function\n",
"def Cslm(s, l, m):\n",
" return sqrt( l*l * (4.0*l*l - 1.0) / ( (l*l - m*m) * (l*l - s*s) ) )\n",
"\n",
"# recursion function\n",
"def s_lambda_lm(s, l, m, x):\n",
"\n",
" Pm = pow(-0.5, m)\n",
"\n",
" if (m != s): Pm = Pm * pow(1.0+x, (m-s)*1.0/2)\n",
" if (m != -s): Pm = Pm * pow(1.0-x, (m+s)*1.0/2)\n",
" \n",
" Pm = Pm * sqrt( factorial(2*m + 1) * 1.0 / ( 4.0*pi * factorial(m+s) * factorial(m-s) ) )\n",
" \n",
" if (l == m):\n",
" return Pm\n",
" \n",
" Pm1 = (x + s*1.0/(m+1) ) * Cslm(s, m+1, m) * Pm\n",
" \n",
" if (l == m+1):\n",
" return Pm1\n",
" else:\n",
" for n in range (m+2, l+1):\n",
" \n",
" Pn = (x + s*m * 1.0 / ( n * (n-1.0) ) ) * Cslm(s, n, m) * Pm1 - Cslm(s, n, m) * 1.0 / Cslm(s, n-1, m) * Pm\n",
" Pm = Pm1\n",
" Pm1 = Pn\n",
" \n",
" \n",
" return Pn\n",
"\n",
"def sYlm(ss, ll, mm, theta, phi):\n",
" \n",
" Pm = 1.0\n",
"\n",
" l = ll\n",
" m = mm\n",
" s = ss\n",
"\n",
" if (l < 0):\n",
" return 0\n",
" if (abs(m) > l or l < abs(s)):\n",
" return 0\n",
"\n",
" if (abs(mm) < abs(ss)):\n",
" s=mm\n",
" m=ss\n",
" if ((m+s) % 2):\n",
" Pm = -Pm\n",
"\n",
" \n",
" if (m < 0):\n",
" s=-s\n",
" m=-m\n",
" if ((m+s) % 2):\n",
" Pm = -Pm\n",
"\n",
" result = Pm * s_lambda_lm(s, l, m, cos(theta))\n",
"\n",
" return complex(result * cos(mm*phi), result * sin(mm*phi))\n",
"\n",
"def sYlm_fix(ss, ll, mm, theta):\n",
" \n",
" Pm = 1.0\n",
"\n",
" l = ll\n",
" m = mm\n",
" s = ss\n",
"\n",
" if (l < 0):\n",
" return 0\n",
" if (abs(m) > l or l < abs(s)):\n",
" return 0\n",
"\n",
" if (abs(mm) < abs(ss)):\n",
" s=mm\n",
" m=ss\n",
" if ((m+s) % 2):\n",
" Pm = -Pm\n",
"\n",
" \n",
" if (m < 0):\n",
" s=-s\n",
" m=-m\n",
" if ((m+s) % 2):\n",
" Pm = -Pm\n",
"\n",
" result = Pm * s_lambda_lm(s, l, m, cos(theta))\n",
"\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сохранение P_ в памяти"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"N = 512"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"P_ = np.zeros((N/2+1, Lmax+4, Lmax+4))\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" P_[j][0][0] = 1/sqrt(4*pi)\n",
" for m in xrange(1, Lmax+1):\n",
" P_[j][m][m] = P_[j][m-1][m-1]*(-sin(teta))*sqrt(2*m+3)/sqrt(2*m+2)\n",
" \n",
" for m in xrange(0, Lmax):\n",
" P_[j][m][m+1] = P_[j][m][m]*cos(teta)*sqrt(2*m+3)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m+2, Lmax+1):\n",
" P_[j][m][l] = sqrt((2*l+1)*(l-1-m))/sqrt(l**2-m**2)*(cos(teta)*sqrt(2*l-1)/sqrt(l-1-m)*P_[j][m][l-1] - sqrt(l+m-1)/sqrt(2*l-3)*P_[j][m][l-2])"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"field = np.zeros((N, N/2))\n",
"field1dx = np.zeros((N, N/2))\n",
"field1dy = np.zeros((N, N/2))\n",
"\n",
"x = np.zeros((N, N/2))\n",
"y = np.zeros((N, N/2))"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[j][m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" T = np.real(pyfftw.interfaces.numpy_fft.fft(F)) + np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field[i][j] = T[i]\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GnPy4tRifcFP9fQ97/YVyYEk9mqqMkCHKsuG/FqLBks+T6tAHTMB1vWZsXLphh4VlcZHs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\n08PDw8PDYwP4f6Fb9Ao01y91AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116689c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"P_1d = np.zeros((N/2+1, Lmax+1, Lmax+1))\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m, Lmax-1):\n",
" P_1d[j][m][l] = -(1+l)*P_[j][m][l] + (1+l-m)*sqrt(float((2*l+1)*(l+1+m))/float((2*l+3)*(l+1-m)))*P_[j][m][l+1]"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"P_1d = np.zeros((N/2+1, Lmax+1, Lmax+1))\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m, Lmax-1):\n",
" P_1d[j][m][l] = P_[j][m][l] + (1+l-m)*sqrt(float((2*l+1)*(l+1+m))/float((2*l+3)*(l+1-m)))*P_[j][m][l+1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1d x derivative"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(1, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[j][m][l]\n",
" \n",
" F[m] = func1*m\n",
" F_[m] = func2*m\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" T = -np.imag(pyfftw.interfaces.numpy_fft.fft(F)) + np.real(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field1dx[i][j] = T[i]/sin(teta)\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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8z7c0ysRIShkB5pCilDrsqS9dds30Uo35boZPXLoJqzZMBK+XjE6CWAgUuTrV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\nlVKrNj506vYDN3Twqo9sBKKxEix9ncf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fFSc9zE6kRfsmteywfYYcXnLlK72/zAFPDpQS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\n8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ybZzDjX+y9FUvXJg9lUYJaPI+HnpFiiZQmZYpWpchzDCStful/U54+bBd78lN9EXdc/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\nK6usssoqq+wc9v8BUf5KFGhST44AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116966b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field1dx, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1d y derivative"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_1d[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_1d[j][m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" T = np.real(pyfftw.interfaces.numpy_fft.fft(F)) + np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field1dy[i][j] = T[i]\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NEq8pFpWPegW2trliQzMqprojLTPfdb1DyuGodWrXy0zLpwrUYOlK0wDKTbgy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\nEARBEDIgwhQEQRCEDIgwBUEQBCED/wcUqzMQ4BhD7gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10aa2c610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field1dy, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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XdcOZJAdNOqeJ6kXvCQaSJXYdc09Wns8Y9miXxQb3kPbLBZahNnDgITP4r+/s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vzTr/Yv10EqsTLaUejd09c8Bs1xytIHasQmCSuEeuEKJbK0bTukZpvtYLemJv3jCu0jBM5wr0Rb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\nwabAnNrUpja1qU2tg/1fJDbLKLcwUkoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1097e8cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field1dy*field1dx, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.figure(figsize=(15,7))\n",
"ax = plt.pcolormesh(x, y, field)\n",
"\n",
"N = field.shape[0]\n",
"\n",
"z_x = np.zeros((N, N/2)) # indicators for d/dx\n",
"z_y = np.zeros((N, N/2)) # indicators for d/dy\n",
"\n",
"f_x = field1dx\n",
"f_y = field1dy\n",
"\n",
"for i in xrange(0, N - 1):\n",
" for j in xrange(0, N / 2 - 1):\n",
"\n",
" if (f_x[i][j] * f_x[i][j + 1] < 0.0):\n",
"\n",
" if (f_x[i][j] * f_x[i + 1][j] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j] * f_x[i + 1][j] < 0.0):\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
"\n",
"for i in xrange(0, N - 1):\n",
" for j in xrange(0, N / 2 - 1):\n",
"\n",
" if (f_y[i][j] * f_y[i][j + 1] < 0.0):\n",
"\n",
" if (f_y[i][j] * f_y[i + 1][j] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j] * f_y[i + 1][j] < 0.0):\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
"for i in xrange(0, N - 1):\n",
" for j in xrange(0, N / 2 - 1):\n",
" if ((z_x * z_y)[i][j] != 0):\n",
" plt.plot(x[i][j], y[i][j], 'kx', ms = 5)"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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b5RQXyrR+7VRcJzUyJ5Gk4Xn5fshFDy8hZYB832mxES7C/XPZEUTuEkJ6uUp7xPGOJae2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RAAAg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\npAcDOK7TMQ5rAtbZ1mSpLE1kkgNuUj4YPpqR08DtAEh2SQu4AQiSlGhQx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hjhbH0AZgma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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109b874d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"P_1d = np.zeros((N/2+1, Lmax+1, Lmax+1))\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m, Lmax-1):\n",
" P_1d[j][m][l] = -(1+l)*P_[j][m][l] + (1+l-m)*sqrt(float((2*l+1)*(l+1+m))/float((2*l+3)*(l+1-m)))*P_[j][m][l+1]"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"P_2d = np.zeros((N/2+1, Lmax+1, Lmax+1))\n",
"\n",
"for j in xrange(0, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" for m in xrange(0, Lmax-1):\n",
" for l in xrange(m, Lmax-1):\n",
" P_2d[j][m][l] = (1+l)*(1+(2+l)*cos(teta)**2)*P_[j][m][l] + (1+l-m)*(-(5+2*l)*P_[j][m][l+1]*sqrt(float((2*l+1)*(l+1+m)*(l+2+m))/float((2*l+3)*(l+1-m))) + (2+l-m)*P_[j][m][l+2]*sqrt(float((2*l+1)*(l+1+m))/float((2*l+5)*(l+1-m)*(l+2-m)))) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2d xx derivative"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"field2dxx = np.zeros((N, N/2))\n",
"\n",
"x = np.zeros((N, N/2))\n",
"y = np.zeros((N, N/2))"
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(1, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[j][m][l]\n",
" \n",
" F[m] = func1*m**2\n",
" F_[m] = func2*m**2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" Tphiphi = -np.real(pyfftw.interfaces.numpy_fft.fft(F)) + -np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_1d[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_1d[j][m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" Tteta = np.real(pyfftw.interfaces.numpy_fft.fft(F)) + np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field2dxx[i][j] = Tphiphi[i]/sin(teta)**2 - Tteta[i]*(cos(teta)/sin(teta))\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2d yy derivative"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"field2dyy = np.zeros((N, N/2))\n",
"\n",
"x = np.zeros((N, N/2))\n",
"y = np.zeros((N, N/2))"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(1, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_2d[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_2d[j][m][l]\n",
" \n",
" F[m] = func1\n",
" F_[m] = func2\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" Ttetateta = np.real(pyfftw.interfaces.numpy_fft.fft(F)) + np.imag(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field2dyy[i][j] = Ttetateta[i]\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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OmOs0pdTPPf8YfH9jR3cLufQ3gCMOgi35NAElY5K0nPUr/6QNlD1wNunzWXKQ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\n3bLmWVA4SHJm1NTS9/XFzBGUEbB5FlRJCJr8lS6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ybfHUJ34BbTsJLFOMch2w\njAEkYLNIYBpIyn4pkLQayLFerGYnMWPf5VheNh4YLvtJz1YCfEbB0noR+eUT4NRsMyXJWsAZM2v8\nJTe0mtGw9MesUl5WOVat8ki6XGP/uwTKArsLueR5hG36ZylBQO5S/SfWpgCnHE9L8Zwe5Kd3oJdm\nF4h/A5Yz+wv3tlemKelyHGnE5Dz6rwZQzoCdqo9g7geil1U3bnbel7VmmNvY7N/B8GsV4XvK7hVJ\nE7PeUV3OY1NHCjiKH5NlWQ2Wgf8y1RmRPyuqFAhHgIgEXsBPWyjv4ibGlR8g/uytzqss+R2UNAa0\nmFRrAegCBvhRfm4L+Nq0H1RNUCOxCQB6NSNgmgDKa5+M9qcfvDjwd69/LIA/iZREYNmAOZvNqtm1\nromrnfG6AQgyaE2RXMuyMUFTg2LsXEA+SMq2IXimJVo5VgokYz7KmPyj/ZFO3mvMBnMMMBkk3e/Q\nbyk6vp/ODnGfZQwsNXBa+8bYJhC+sGJW472kde7dMgBK5KAGTo4khEov6Tg7al0vSzd7jQdNZSWS\nH0/2IFdOAk3Z14Ovl+FZmgXCoLjw2jKjZoGhNFsiDYZjxyppeQRp1lmr7d3wn9Vi3rOE1WJOw382\ne+Y5ZJWbCAFz/L3tiyvyfqamOByLotWBdqIIDKJjdedRA0TMV8nTyJUIp4YVH73uyMozsjquY4AJ\njHdkrWetAdRinRpEeV8r3QJPMZrBbbeLzgYYLEPQBBxhK//LL2DjdW95ymw2e27btrvmsfk0YxnE\nDv3dX+3svORvUdzipoNt6/olgTQ4AnGA5PWpIOnX4y/WWBCQteRrWdcENN16GRw7BZ6aXQIYvqzS\nuxW/vwV8KbCcwjBT7FI/8tjLKQAqfjANggyc1nE0GMpxxSzQBPop33JBU09ikAuaGhTlubHv2h2n\nGIAmW88yuyNlg6bsLD4vubfcKlyr9dJYynFzJNudGVB24FlWgUS+2lytNeWhO7Xdqc6xnFm0rIlB\nxBUy/Li9cRIpL+68AEPQFOYp+Rks9Xsn4AlMexfFUoAZk2XHfseC9TSzjI2vto7bl42/kbFx+Qya\nxd3ugiM3vsGp+OSn7wXgnckd1SmTtnzV61A9+hG52dc26yvuFki69f2VXMeCgOzlNP+INgHFvlHE\nFBk27O1y2oBdso+LX6AcH4gFlnr7GMMEhg2smCUPyovBDa8GRy3LyLo0GECcSSKxHeg6F2HkHYBB\ngBkzwvB3Pmj6fGH92TfQtNhMzGrKk1IFYvtaoCnHkw5Qrr+TJp7YXlZIzRkMwARRVxppF8qYpT7d\nJun9mZWvebk9J7CkDxVoY+amlRAuQy5b+aB2SnrV7x6fe4qLhK9TAyanp0AzxihT+cfAsgTMrx1l\n2mw2w/w/PwLz8y54HPYLMGez2Umzq14Fh17x/MkXNGYWQALTQVK2rcsmp4Atn19fn/UbyANB3dha\n6WmW2b3QnRQEIJSCmF3Wxm/9bUHxacbA0goKsl5Uq2fLxn4ZbhAEJEHpgc9xxGRfBkNuOMQHBAyv\nsUYwkwh6f0jpz78RPisemznVhG3GZHxgTdAU5qIzssmxmN3kgGMsncG2NJZj4AkYDaUHz37OYCAK\nogAGU1uGy/yObkyilaX1VaUhsyzCd8GfPDR2Q/B7Cvh3S8p3AdtXmeqs6vOnppPV18igLvkkbWzM\nNFS6xShjeWLHAIJ6AMQ/npGyAz/zUKye9acPm81mVdu2y1TeLMA89LK/uGT1hrdg46pXyck+auuC\nJJBmk5zu1/N8k1MnJ9DXqK9ziq0T6MOykJUOgAIN6GXll8r6vWNsk+UUsNRAyUWVarAL0FAEhOxS\njD8zJRKtdTyu+ixRMXDKlzdiDXw3DEIPimZXwhjL1KYb6HqwvwdOlugDywXNcsJMQCzNAjbLhEqz\nGlz60lMSOGWZYikWkPYACg+iQC/hAsiOlATi7ZFl1vhx/YUfAAZYZsrkAh7yjokdQvieyrtWYcgs\ndeePtwP2M7UsxSx5Oz+fMYCU9EVGnhRQUlQsMARKAPTMVTutVKKN618Xx0+7wxbe+8H7AvhXXQxs\nWYC5euNbceDBP5qTNWo5IKl/5wzfWFd21b5JfYwxkNyLFGvJsHudW5T3Hw6SVr7LcMdhr5TzMgNt\nEmmxXu6YHCu/S2N9zATs5LoHYEd59ctYq3VucAq6DpJmQS9jP6ZrIw6M2mL1hNN150mrCgELzQHN\nRdH5ZNcwXUbaNFg2xnqh8lqNrV6OASjU774hdywUJfzn3xIfgReLTZ9pmR4MzwAJGB9Stz6Jx5aq\n6wIqsRm1NGu0gJLTEfltmb4uzS6BoSQrS2vdAkgrvwWSfPwRRgmEz28MLPtbedADsHXWZx6JvQLm\nbDarZle7Kg6+4I/GsoYXlui15YCku7j9BcoUw9R5+bpy5NhYoznGFhk07e2ugUwBqvZfikXnrWQ2\nGdtmsUt5NBZwjoHlGCsRizXOljGwgZa64eAP7xa0LucSmauMHS9kmTkyT47PzPJZSz6esCIJyCcC\nNFmalYON5Y/l1Z0WzptiLLLU69p3nWqACwAHZi5B2CgqoITrLjOYAoj5wHQQifkB6KCDavgrrTLU\nwVZsulMnxxjzS8bOl/tO6WuwfmvmKUsNdrJMPdfRfcIOD7B/IMkqw+wh98XxP/zzh8xms1mbmJwg\npy9/t42b3xgb1z45mSlH1vh2BMpx/2ec5Vr3lLp3OaMVBRnfrx4FXTYzYtlig7qnaoFjCiT5xc0F\ny5zB0my6gY1ts4BStsvMQCzxynqtjsMgqouxLoAgVH2dFsiuL5bPOg6w8+FBN4Bi3qDphmk5iXA+\nBE2ZYH/MdGAUYLN2d7HeUiqCTmNA0MOJgPGGN8ZCx/IG5yAwBRw7Zet+7vYOukyLlYPuyPG16PNq\n1UWWC5Wm18cmCYlV2xgS6OfBeWPAaS2t5zrIa3dgTgRAatu41c2wtbU1P3r06K0AfDaWbxQwDz7r\nN97dHj4ySednG2sgvl2AcsxnaTHJdXyWLLFp+Y2NQbKAi8PLYZsA+sCDfTMdhj7WMPK2XLDkbez/\nitVQOS6zTAZKncZTafE6g6Q+F8uyaltTl9EXMNXZ0nmm2hy6btToo6SJbc4XcJMu1I2b4anLjcWa\noBkDO719St1g043vUqWvw170JBWx/Po8YrHgl6mm7/UAbNCMMc5ctrheH862GLvU61Z5JYFURbQq\nUAT2BoxjwxQtE2w7/MB7HZy94rX3xl4Ac/WuD2Dr1x837QIijcGJAEpO1z7KdYAyBZLrTLGlAzm0\nBBsDP+27Wsf6oIP4BYasMNXYjTGGqSaiR0wpTIElm7DI2DEGTJHSUiA5YrkMc8rkFmPBQn4fkWqL\nYR0xJNr5YumCUMrSfcptkZhtRswqO96WSk/J77Fb0+dKMZpc1qLH3qZkw9RSr1vlEquDMYvlTx2H\nXQz7ZbHzpQBT/7aGdUTGRa4LhtHO6ZpEzrID974HNv/57T8B4K9jeZJNw2w225hd7ao4cPc7J0+U\nesFTkaRTJdAYqMWmw9oLUI75oNZlCID3W1rSmx4AfyIB1bQpIGgxjVQe7kFbQGk1ZmM2pQhyQXiP\nxnVSfltLa799sZRfcwH0UyRas8mIWeWkWaZY7LJzZ3qyzr1Uv1NMxgLS/fKlpZa8LmUQq48WMKXq\norXNel659ZlmwomOVzTStf9Wf4N4LGAqB/hyA0JPtC3ucUfs1PXdUnnGivtmG1e/Cg6cfDVMcT7Z\nMux0oOQ8U8ZRxqJe9wKUU+U0ZpIxv6UFeiXtI795ftITApL+5EMZk7cJ6+BtOp8+njx2bjB08aX8\nVqnb1bVXz/gzpZc8wYKecaR+ucNbKsVQrcg6J6kTXDdkhhnZ0s84s1GgmBe9X1Nk2d6vKXPPyjCG\nbXVCCxBTMqE2CyxTzNPal81inLKeO9xB8pxIENWdMo4STkmWg/XIYHz125rVRoMaMA5sLm1c4hyb\nWCaV5rftp3acttg3Z/12Iii3vAlWq9XVZrPZSW3bftPKnzzaSa95/tnH/u5NWRc2BpLuZOnG40TI\nr7Exl1Ze61zW/Y09cD2Ojtnk2MTafL59AceyQfQxl/SX+twTT9l13Mgfk3X0qesur+TnRoSvSZa6\nkWNJrVTbNEjyduv4Ob/Z72KM6Yo1HrFOm1ufJuvHzKof7hwqKEjY5mI5DAbiCds3EYKmNe9ujvGr\nYYGlFZSS035yYJBYTkdr6pjB1DrXPe37ttSLEsMxxLwtWMajQQGYA/HDdcobqaMpd4D7bbfXsX1y\nXAf7ZSng4/cgRkAspW4Qgb4B3OY2t9n++Mc/ficA77DOlQTM+nNfwoHb3CyLUbGlCn6q/Crbc1ml\nxSDT+8aBMgWSVmWKDUC3JNhgYm0yCyTd8XxDyKxzcJ0bDYqyRlEW2Chq7E4N/mEwlKUwyxoheO4H\n2Y0B1hhY5oAkb7dAFpTPYhuy3jVAvtGqw0bJYJd+W55qEbOcjpPL4zpjcwCaba425v09DYKB9gKa\nue1hDCwt5sn5LLM6QFaAEOfV9SXGMnOibeXa+dur+npS0v/gmMMB+Boc9exFACbNYDTWnqVmLYul\npdLXsVgd9+mraN7h5C2l+m2reZZ9/q63vBo+/vFbYR3AbD73JSzu9wNB4eqLifUiLJ9frvwqefbC\nKsf29dvGx11a9zN2z3ocXeyh6bQC9tdKJF3nF/Asu0bTrBTcYJQjv9nfyHmAoZ9mLNCBJdnYdjbt\np9KMwQLPhdrG96TToNZ1Pr4mBarS27fk2PB3fodMr4+ZViqCCNl+uwdK6WTNgUCiXXbybB8MBISg\nWcMGSl7P9Qfr589gqYGUt8csJucDQ9k29vWbGIDyPqnxniXCMtFlwfU+1kkrkZzeD/AdMwbHVPto\npfMyTMsDzL2yyCmSqPXbmpZQ5yuCPJ6UMIHRqp9lNQocuNWNsLW1dfvY9aYZ5pe+jOpmPxWkpQoo\nFhgTe0jD7fvrq0yxypgMzNc51vOyTPuXNGjq41oPTzPImGX1mmKSrADFUv3m7EtKcyd0xvNdxi8u\n9H0JOwXyfVSxRi0FlhokK7VvbNYYXgagOpRjw0Ys/gHjscZNto2Z1CHZX3e+NHCKaba5QgVsANVi\nqfyaS+yWjYuglQkOYkAZm8QeSI+Z1RHYY6xTr6eM66tmmYB/5haASro1F2pD+WqE9YaBMyW5WukC\nlmtMJp8iBX59ets2BErDnzmhcycWk0LD30VQd+Nz965o3eezgFS/I6E7LA6g1c2uh2Jz83ax+0kz\nzK9ejPK615pcUCkJYB1WKfulImBjvsocVjkFKGPAyb18+a1BU7bnSAN8PmGWOQFABRqUpRvEXh5o\nsGrKrtGfhYDIPWANUPKbhu8BsIGS577UM+bIvtY5Y2bJahYj0B+ejQGn3l83eNaUXQET0Y1axzSN\nxsgyq2Mn6e4Uee+W9cLL8Szg1LKsg88lJCAI5Xzo11zA5ZMJDsZAk59jbBiKSPqWjUm0OUNSCpXP\n6nhxh4+fLafXlKbVDElLWc6wkgywtL77Gf8dIw72FKDuEmJqWroTNwUDxkCS2y1rPfZ1GL3Nj0le\nBdtjX4Aaa3sLNJhf95o43rbXjeWJNl2z2Ww2q+bYPPVqaKmwLEkx9jtHT0/1iPZDgo319q0GzALK\nXGapeytaPuO0+L4lpcUjYgt1zKKDVGufjaLGbtkhhQUaPBE5s0wgbAS2EUZUWkDpLzAE4lgDaNlY\nAIcGxxjjXNA1ahBNAWiQFmeXwzqWbsxiQGn17mOmx11yT3m4TRqMAkuEbHOJCvONVS/RuvsrsNym\nYScoPWjypPU7/QmGIMrGHaYcpshgGZNoYz5PrisMgtb8tRo8pQ5roLSAM2X8aTidN5B2XfQrg+V8\nsQzq1RzLniDE6lWFFbiOAQjyu1tMKx3hMu1+yqmjVnuV+tILr+tPpfE2aeH0MXS6B8VmcEzrfqzr\nra57TXxzubxW7B5T1eAgig1sbMkn7v2JxiwFlLJuNSAxYIuDYrxRymGg+vx8nbnMEhjOymODomcF\nMYuBZEyijZ6LAn/6r2ywLCqMz5IkBUS5ARCTgBD+pBDbwe7YOwhnM9HnSVkuw+RrtYCT0ytK10yS\njzEA1SG7lEbNXWo+WKYD3PK0x5BZFkGHzIOnZ5ZiFVYI2abvia825r1ECyAMBkI5BCepGxIgFPs4\nd86zBsI6pCNsJW0sKCjFMBn8+EPkGiRjDDPHdD72aRaUR+pUtRyA5XzDAWCFZQCahQLO2PoU1mkB\nqbvE/DYvZro9soBOlmNgGUu3tnGdD8FzKMOmWOb85ENYLpeHYttTVeIqxVW2zJdZMyH7wHG2Fuvx\nxIAt1185hYFa59fXq9djFmOXYw8n57h6fwFVBlEJ+OHAn1CWbTomWcpBQsBkiS32QWUxi1FqNiky\nLH/TD8j/YK0+pm7ANLhVkXXNOEu1jAEoyWYMlmUZ1ilZnwKWOQ2V2zb0r0jeoczvGx8LOLVMK/sI\n8xxKtAiDgXQd0KCp65G7cG/8Dccx0wA6NTCI5VM99lEDogbJHNMtptyX1ZIOOqRtUKcYLJlVetB0\ngMhsUtYlj/yFv/NA1C1tZU2v51iKObq0IXvkTtzw7RqyyWHeIVByvS/6lth2kWk7sCixu7tbzGaz\nsm3bQY1NAeahjUObQWHqYRPhgVLSbNx/o3tBnB5nkOlGKddfmRNNpk3OkxOUM2b+WOMMNX4MHWUr\nHs9iOLxELllYpgZKYAiWstQsVMuyQOjDBIaNXc77p/2lMZDktE1j3fptgaUJpA4s54tl719KSbFj\nYGn5k3IbKf6tO2S6AZgDWGHep8eAE0BfS1aYu/d6A0OJFvDBQJgNAcACQAs8Y7M35Qb1TB27yQCm\n2aVc2xSQ1McGHUvOpX34UPlIsYiB5SaO9e2XlmWrQdoQJD2wDre5SwhBFNibi0Abs0ZXNHFfpM0S\nh2mrLtKb6y+Dp3QE3T52vY8FxZmscwZUVXV8e3v7KgAu1feYavXLjQMjnxZSFnvZcx5OqmGxHvKV\nBZYxBm0BZwzs2I9ppeWAZNE/es9chW36KkRwudFVFs0yd8pQ3rQkWFnXQAiEDUNNvxtKk33k47Z8\nLCDd0PK6BZ6WdMq/tY8zJc2ayzZo2IqyQbVYDsDSlsPGf7vbGPb09bplzDr9/mFjsdn3nm3gXKLq\nrj1km+4YXUDQAqFECwBlARwph1Ks1JkDAI4gBEp5flY0tdQXy8aUiHUiabVN6e8KC2VWqlUWYKiQ\nBO9YqFgwWAoYCuBxmgZSAUMtzYYS7hK63ZxeF/MLNhalCgzlVs0sNavUeecSqNaBp67P+li6gxjz\n7aesKIpdRGrISLVpzZc0ebIR4EnJArk9ohMFlkPAnxbyfyJMP2CRYguE4zJDWdZtL4ktbBS1k9jK\nAihndsPHDWCNkF2y1LoDD5QVXFg+A6UGSS5G2cey2NASYMgu9XARC0gtUIytC1guVtgoalSbq4Hf\nMh2Mkffb3Yr9DuQad9Z858n/L7vz6Z72ChUqLLPZJoBeol3tVMAhhB9EBsK6cwgeLFmy5efob2Jo\nU4KDrPzMItcFUm0ctMQdRUkfyK4I77Wvi45VzheroANWBgC5CsBwC9uBPMvbOL9mlWN+TndbMYk2\nHzSt4SF6mfJPxhijlbbEvK/renvK/WDlkfscA07LUi19i93dLJDUFgNK3hYHxu8csMw1K8w61Pbz\nHlwKPIVxSrQsADdWsASaskG1ufJRkOjG21mgqWXUmv40cEo6B/hwuhynom1jYzfFUpKs/E4BZYxV\n6nQlw2qw5DB/qwFKgWdMKXFpcT/m2DsndUBLstyguHV7WEkoVzmgbLrrXnUPq0EJzNFLtAD5NVHa\nwKV93rHgoAbDWYUsxga1vTt1UL94m163ZFNhi2Mm+zYIO3JsSX0OVL9qUixcB0zAbhPHBmCpl5qF\nhn7OkJlqGdfuvNntLBC2e5ZvnS0WAatZJKfngGUfkNbVSyYGsn2FagCK5rdi4d8Trby4+x0CZ+L7\n0clHfmz32M5awGEVeqqh2CtYahBeR4aNXf+JNEtXt6xAKOmKFMvrzDgZSLEBP63WgQY14IaZLAoP\nmswEZV1e+B2EoMmNDxB+PFofI/UdTctSUqxeapAErVtDT3Q0beDbHPosmQXEmOUYWFp1MVUf9XrM\nJA+rPpa/hqFT0mOylvTK5fjiOxKJtihrrHaqoV8TiEe68m9LzWAgSo1jrBECGBDWwZjxsBINmrzd\nagVj30/VZtU/qWPELgMpNgDFIVimQNQCT9vHGfo0x+qlrLvl3uVYDZo5TJLr5grzoC7relvCuRek\n3uqhU/o8Yvp7spYtl8sDAI5a21KAeUVzxfZoLyNmscYg5sfMaVw4TY5h9Zr4GLI/p1vX5devHLDc\nq3HPSNZ9+YUfphZpdt6xSzezS9cj40Hq2lepAxqYPfJ2UB7xW0oajPWUTfFlWg2VMBzNKC1fJ0mw\nU8fE5YJlMajTeWPh3G3Z754OvuNoQPfbCrFPAWeDJebwbMA3Y0vMA4m2KBusduber4kyfGb6OTPr\ntHyfYinlwfKLct1khSNlVjCQvmZ9L1YLaaVFhyx5dlktlt3wkTrKKBkst3AsAEb5zX7LLWxHANMe\nohJtb5tuvaZ2o9mNF2Wx0a83ZVdviqHkKr9T4OhVEQ+QFZb9nTPD5P3cI/J1WIPqEkMb+vVD8Nw9\nXqNpmhmG3++JPnqxK3aPbGOjWWFW2IgsL6X1YluAlJIBpoBljsTgzz2UvGLXuY6lHN78MPbq59S+\nS4CZhg/OYpYpJtIswKCpBqmnJFf9p8ETxjqM9SnDSgCbWcqS163ZelKS7cJPeM2sUoaOyJg4CwDn\niMlc+VPkxYDSkmWt+mnNnamlWR1KPwac7lguXeQuOb/05qMS7Q7Vbauaj8m3JTyYxky2HUccNGX7\nuharfweM7bEpFiPsck6KRaX8kRosPWg6QBTQdIBpscx4wJDZuWsaFHWNstmFfBClZHTJ6NxWnTjv\n7n2FtgCa0v0BDlCbskRT2MxSfOmrjiUuUfU1UzpvRaSe6oA1DmZjWVaPP9ZsM3zUnZ//8DFUVbWz\nvb1t6rLRVrxt23p+7aujufhSVKdcY7CdGY27cO0DTPsxp4IlH8tqjPj4+vwnSorNAcGpvspUHpFf\n5X48w6z737Jtrv7HvljRN3p14dimZpXsM9LgqGVZYBw0dVpK6tLbGfzkt8UyLZAsAf4yhLDK+WIV\nTHLNAGiNf7PAMncMHDBe98VSHTneJnVApCYdOejyF4OGg4FzhaoHRZa7pNfe+8RRY7VR2RItKlfG\nPL+slDv31a16ISCnZ5PSlgJUOV5K2uW6VKh0/dodUPm0OyAqv2LALueLlZJivbwqgT0aLAUcZeny\nbGf7OQN1pFkFAFlKkB6/q5YqpH/rd5V+zwqgFLGhBKpyF22x6kG0LjawqqoBeApQzbHqr56Bk+sg\n+9el3mu2KfkFRMeMWS0A1Bd+A4vFwvwWplUEgS1OvTqOf/VibJ1yldETu5PbgTMMZNbchlq2ku1j\n/kk+T6xBOlGWEyFm5ZUKo/flB58yqUyyzufQ5a9B0/piBQD0Eq18CmyHGKfLkPZVxpbAtDGY7qac\npSJmY0xTR9L2jNL5KAFkAWWKKaa3rz/riqy75bDeaoUkJsu69XCSgrFoQgZIlma5Ry/nKOHKazV3\n9SqQaNmvWcBP7B9euDeLDerZpPQE73qIEh83Jg1bMj9gz+bD4BkDSZ3fmq940WKjWnZcLzg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yQ5r7axgB7OMwaUU4+hz5tjYwA1BRhT5T3W2dH7WddmXV+ujdW7VCT3On5hn2aXbw4T1GBp+StT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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107474f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field2dyy, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2dxy derivative "
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"field2dyx = np.zeros((N, N/2))\n",
"\n",
"x = np.zeros((N, N/2))\n",
"y = np.zeros((N, N/2))"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time0 = time.clock()\n",
"\n",
"for j in xrange(1, N/2):\n",
" \n",
" teta = 2*pi*j/float(N)\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_1d[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_1d[j][m][l]\n",
" \n",
" F[m] = func1*m\n",
" F_[m] = func2*m\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" Ttetaphi= -np.imag(pyfftw.interfaces.numpy_fft.fft(F)) + np.real(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" F = complex128(np.zeros((N+1)))\n",
" F_ = complex128(np.zeros((N+1))) \n",
" \n",
" for m in xrange(0, Lmax+1):\n",
" for l in xrange(m, Lmax+1):\n",
" func1 = func1 + a_coef[m][l]*P_[j][m][l]\n",
" func2 = func2 + b_coef[m][l]*P_[j][m][l]\n",
" \n",
" F[m] = func1*m\n",
" F_[m] = func2*m\n",
" \n",
" func1 = 0.0\n",
" func2 = 0.0\n",
" \n",
" Tphi= -np.imag(pyfftw.interfaces.numpy_fft.fft(F)) + np.real(pyfftw.interfaces.numpy_fft.fft(F_))\n",
" \n",
" \n",
" for i in xrange(0, N):\n",
" phi = pi*i*2/float(N)\n",
" \n",
" field2dyx[i][j] = Ttetaphi[i] + Tphi[i]*cos(teta)/sin(teta)**2\n",
" \n",
" x[i][j] = (i-N/2)*2/float(N)*pi\n",
" y[i][j] = teta - pi/2*(N/4)*4/float(N)\n",
" \n",
"time1 = time.clock()"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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k/0uVoslTP2Qs2C5LMFUPTcg+R4vo/5e8p50NXdh033Z88KctyCvLQePm5hvj8fj/+VuT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1078ef290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.basemap import Basemap\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"RAD = 180/np.pi\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"m = Basemap(projection='moll',lon_0=0,resolution='c')\n",
"#m.contour(X*RAD, Y*RAD, Z, 10, colors='k',latlon=True)\n",
"m.contourf(x*RAD, y*RAD, field2dyx, 512, cmap=plt.cm.jet,latlon=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(15,7))\n",
"ax = plt.pcolormesh(x, y, field)\n",
"\n",
"N = field.shape[0]\n",
"\n",
"z_x = np.zeros((N, N/2)) # indicators for d/dx\n",
"z_y = np.zeros((N, N/2)) # indicators for d/dy\n",
"\n",
"f_x = field1dx\n",
"f_y = field1dy\n",
"\n",
"for i in xrange(0, N - 1):\n",
" for j in xrange(0, N / 2 - 1):\n",
"\n",
" if (f_x[i][j] * f_x[i][j + 1] < 0.0):\n",
"\n",
" if (f_x[i][j] * f_x[i + 1][j] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j] * f_x[i + 1][j] < 0.0):\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
" if (f_x[i + 1][j] * f_x[i + 1][j + 1] < 0.0):\n",
"\n",
" if (f_x[i][j + 1] * f_x[i + 1][j + 1] < 0.0):\n",
" z_x[i][j] = 1\n",
"\n",
"\n",
"for i in xrange(0, N - 1):\n",
" for j in xrange(0, N / 2 - 1):\n",
"\n",
" if (f_y[i][j] * f_y[i][j + 1] < 0.0):\n",
"\n",
" if (f_y[i][j] * f_y[i + 1][j] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j] * f_y[i + 1][j] < 0.0):\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
" if (f_y[i + 1][j] * f_y[i + 1][j + 1] < 0.0):\n",
"\n",
" if (f_y[i][j + 1] * f_y[i + 1][j + 1] < 0.0):\n",
" z_y[i][j] = 1\n",
"\n",
"for i in xrange(1, N - 1):\n",
" for j in xrange(1, N / 2 - 1):\n",
" if ((z_x * z_y)[i][j] != 0 and (field2dxx[i][j]*field2dyy[i][j] - field2dyx[i][j]*field2dyx[i][j]) < 0.0) :\n",
" plt.plot(x[i][j], y[i][j], 'kx', ms = 5)"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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te+c+fu7WD+BNL3wjAOBnb/0vuPklv75H8t54+/fgV17+aXzy81/Qq/O81oRp\n9Vr1za3EjXVpnKKEzeVGqa+vn09l89vNe4+c9zgsICOIXGCPnIQ+N6lL6ZxIet0utciZnihbJweF\nzEkm4gbiGnGbQubA0rV1cFJazY6p66WHzNHzGllaGDbcRZOSPU7m6DWSbKTvkQv0AsFOuq3HkLsc\nrMdHOye5LUrHlpujdV4iaRD+SucoWaSE0MrPbTQCxm1ydS2Avdi0O/ZGpFw73CZzZG9NyJFnHR2t\nj+fR1rSlOjzulXNHqaTlUkxZDyeVV4rq5tP4tNtfi3/29u8AALzxhW9CdfMGNsRFcvXKA/zSn/6r\ne0L3c7e+F09/yVvwGbe/ek/aPv321+DjL38QT9x8ejefscWTN28MSNwUjCEzY0jMnPUcq/7cOas+\nT73eOrw2JXb9PMcN/BI4XwSRC4jI+dFra8NKtjSg6XbQlK6u8yZnR1nft/+K8rq5PaEbW76HpNF0\nT5pGwKQ8moJlkT2urHFYZI6fl8igZqPZWd81tROQrx3FIW8lq2xLfbOInJZPIl3URiNeGllLabwc\nTXHL2WikjZeTjmkQE4kA7vJYChwgE6e0Fi6nyEnELilhlo1F0jRFzrNmziqPHie71CaaLtl06TZZ\nk4jaHEFL/t2L/wBPPfsWLHZka3PnLl59+QP4hOffOiir2a1G2WIB3LmLj9z6HrzxhW8CAHz4d/8p\nvPF//iZUN58GAHza7a/Fx1/+qYHL5Cc9/4W97zTXQL1ECZqLOB1LbZubnFp15erzlu+1GWPb2k8j\na+EG+vggiFxAheclMyYyUqlS1x336/K4X85FvDT1b25om4lrCl3FTTyky8JY90utLI3MATZh0ogQ\nzecJ/uLBXOWcGqzbVfquGoHzqHUaUUvnSgkcTZPK0shVLeSzbEpVuyv5iJQS2dJIGT3WlLNUzooo\nZZqNRtKsyJaWkmfl4zbcjv4dux+cRtTmDGBy7dnPxkdu/SW8/vbXAQB+8dZf3n/et+POXfz8rffg\nM174QwCAn7/1HrzuSz4br7/99ahu3kCNDZ75X/8oPv7yT+HKGz4FALC4eQOf+PxvhOwf3aKEZHnJ\nWwmxOSRpm9NFc3yglMO4YXptxtlOV9PmJmkR5OS08bgNXQLnCE9YYiBP6qR0zf2Su1Fa69MSqfMq\nex4XzVJ3ULlNSvm75Hq73Q0U07Vt18+ltXNARp0rUdumQFKmAn1wFfLQ9YxJ96h1GrnLqWkwzlsK\nnHRs2VgDiS3kAAAgAElEQVT5NEVuyY4NBQ5oVThNgVvvSZq+/k1ytbQImLZGzuM2ycleOpfKtlwp\nUz7adsmG2uUiT5a6UvL8XmiTexSLm0/h9be/Dh95+58FALzhhT+Mxc2nelavvPxBfNrt379X7T7t\n9u/Hqy9/YH/clpOIGynbSXC8JGuq6jaFDM6h7M1J1Ka6QE5xv/TadLbjOvxDq2ZB0B4PRNTKQKAA\nnoGEhyR6ynHZxFRMH57rMZeNZ0wZv08f+WCGrVvjsWwCPawdP9BcNuftJl+Cm89/0YC0cdfLY8Az\n8D7DKmszl+tgoA8PYfNc+/h9AiWIYUZAxNhOIudqCYxX58a6W2rumzQPXcvncaPUbLiKN2xzV5+u\nHDIbxc1SjGpZ6iboWe9mpeXqKln/dkxIKtKY9Wv17h9/XHKun4dQ5nLtlc5b6hs/5u6T9LOk0Elu\nlJIKJ9nyjbapvWQjKWmajdf98gr2KhzQd6VsjxdY17ZKthIChyQVK6l2HiWNRomUXCJp3R7VLp3j\n5azJlgCaegegpyYmSEFTuA0tS8oj2efSSzFoz51X8dFb78EbXvjDAIBf3LlZLm4+NakePhj3KFo5\nFcvrNimROd6edDdZZaXPUxVDrQwrXavDqie/Lm7aeatNUru0dnrJ3BztDTz+CCJ3CXGsh98TIpmS\nqH7e4fo8nqaROsslMhGrMS6RNTaq/ZQ1dG7iWFObzJo5L0HTIJG0McTt2Mh9R0qwOAFL56iNZgdm\nw8lpysfbJrXnUJBuqWO5VmoukPSc5kZpkT3JJTLnbslJoVaOYLNZdqq3tLE3X7fGiRR1h+TujdLm\n35yAcRuJEFqulTmXTakcTjBTPvqXn7dsKKTgJzy/hLlInPTue+3ln8Svvf0HUO2I2+tvfx3uvvwB\n3Hz+i8x8HiIzJGplxC1XBs/vcZOc4obprWNMuVrZVvlznJ9LRaO2U8dYQdACJQgiFzgovGQOGHaW\nkkJnqXZj189xEsgVM060kq1VHrejbedKogTJZl/untBtsNi2ZC4NNtOZvTo3Vm0rwbFVtUS0Up00\n+AklWZSASQqZRdo4McsRNYnoUVueLqFEnSv5vTxKnGTnUdv4Z021k5Q3ni+XJilpNJ3WZaltS3LM\nSeLOhkajlLYSALp1axa5okRPW0dnKXKetXaewCo5RS5H7nhenp/apPzcTjvW0jg8rpi5ATftgxOe\nfv6L+2k3n1JJnIfcSIqWlt8ibVPyeo49Zer5/OvWDhNxcty6uVx7vPWPtR3mPd1tAg4enTswG+KX\nuoTwdB5zPcQlHZVG6Nr2+BU6i9DJpKpPAi13S0vJG7pR9skYJ4e0HGubBonQUTIHAIvto956uZ6r\npUTcwNI1te3QJI0TKZpO6y99V+aInWQD5ZgTNukaSY+L99p5lE4NnvKlcj3kLkfaUh6PYsfLkohd\nzpXSo755VDvFJVPaD269tPdt4xtjS4qcRLi0bQRoWlLz2mP/XnMespc+S9+L5uuOh4FPUl5qw897\nSBwvZwo0t30Kn4vjPARMy1dSn+VaOWe77bRpETOtc3nSVr4lgb9s34tlDOG6aOH9+TN4yiQz0EcQ\nuUuIBbbZF+d5PsSWu2WCpdBZhI6TRUrSUh4e2ZKutZPsUpu7dB+Z085L38smc20LTDLXFt7HWHVu\n7miXCRqpo6CKGf1eOZdIjbRpJE1S7+gxTdMeJe81Kn3UvOVaj7imvknncoqcdt5S6nKEjKZJyppU\nHrfhaWwj70TeAIxa/0Y36C5d/6Zt7N0nieP2msuRPVonLSelp3zUnp+XbPhn6XjKdgISrPeU9B4p\nIXEWCZqSx0Pa9PLnVunmjWKZOzeWlE13rfR1siXk63Fxf0zP6EUjnoEOEbXyEiMe3PODx5VornUh\nl2q6xhOp0HM95oqu6CnnMsFzPW7kTVzX/nreZB2RLXuYKyLligRN0RCuW+VYOiJSnmF9tHI8ETIv\nE3zX7Hg2jwvZDNiInvSSIpE4i8ydZ3hoTZVL8Lpa9o+HCpoU2VJS26Q1c1QpowpeThn0rJnTyqBt\n73233T5zac1cm7ZT5aQ1ZN1FyafNqcB5VDdqQxU42hZtjRwdY0quldQ+p77RsjTXSYsISOVIOATZ\n034vS22Tzmv2moInqWU0/To51hQ4oCNz3EWSHnuiVl6Xbagb5frKMJCJJ0rk2EAm1n5w6dnWlD5p\nvVuJasfLSuXQdBptUtsPTopIySeeJDI3jFA5rWOx1mCPdUn0uENKCpzHbTLZe9bUSfn44F2rK9lZ\nit0Sq+x1OcM6q65RMjdmPVzuHK3Lwpxr38YSoBobxxTGYckcfw69auZsE8eBoyOI3CWE9mDzBznX\nMZ73PkBTyRxQRtJya+ZSubysBFom33YhS9R65ZBtFtIf76bhY6NZ8nwS2Su9HTykjsNyrUz1LxWb\n1M5SMpPycSyNc/Q8UL7GrxS5ay99J+91sMidRdq0cjiZo+cs4ialSa6Wio3kRskDmXiiRK72AUhk\ncpVzm7QiXXY2w7V32nq3HNlL56Q8PB9vT2fTd7Hkn2ldtD4JYwaMJa56ZQFEfITMsuuf95G2HGGz\nvoOHpPE8Vp1SWVJ5Wt5culVHri5v2V6bErvOvuwlNcWNc44xFa2/LCr3+MjbgfNFELlLCIn8SOmS\nDYXW+Z43weOQlDGts7VImrXfXLKVbHjZyS5hSPj6hJCWz9u/t6lbMpfQrvXp9poD2oFsb80c0PUA\nW3acW/81J7xqG7WXbIAhccuRNInYlZK2dG4sWbPe+2N6aO0385A5i9xpKpymqmnp6Vg6b6UtMCyH\nq2310C6pb9I2AhIpa49ldYuvW/OqbYng8TReN1fkLEI4Vn2z1r9p0SYlIqdtLcDtjomxSpeHlM1j\nMy8htPLxengZkr2edj5r27w2rV0Z4RqztGSsUnfo8rXnjX9HT2yEcHm+eIhf7BLC21loREI6TzFV\nyZu6ds9DTHl7rOAqSUmTgqX07WWbHJGUVENeh/Rb8HbT4Cf1Zkf0yBNec5LTFuJX2jQXxEOO2STS\nRtN5e2hbpO+SU6GATtHT4B0veO3GuFZ6em7JxkPyPGTOInJSeVy542SPq3Dps6bGScRtZ2MRN8Dn\ngkgJkKTSAX0ipRFCaiMFQLHa44lsSevKlZ2gEb50TvqbyqYYuknKN5eH1E1RSTwKnESMUt4xpEwj\nVxZpm+KimfseWj75+HARKH15/YTrkMFHppCyYweD82xXNMwzfO74xLKEdM1PbUI+oCOI3CWEb/uB\nodtiSu/K8ZM7ivMKssJVNkC+Ftp3t8vTr5emygECGRPKkcrgbZfybeon9DVzwJCQtYXm3SY9a+bG\nrKuT3CbBjucmbZ7vwcGJl8e18pDIfQcvodPUOUvV1IidZKO5SdJjTXGj9rwMp9tk6Xqz3GbflOjl\nbCRCOPf6N0nZo+m0nJSe0uhf+tlyl5T6+rFKnNZ/lihMHgVMI0kauePneX6J4Fmq4BgCqLV7WL5f\nfdPeb3r6OEXOKnMu+zZPObGaaxwyp0o35+Q4vya5dfjD/OFqeVEQUSsDJg7tShDQ4ZkR89hscwP9\nxwme985c0SY9No7IiS6bxwUzXdfGE5GyzocduIdrWRtPBMbHBZ6B26kN7i7TO+oa7jls7s9UTt4m\nIlv2MZer6FwRKT1k9TL9Po8rLtMQL7ADl85zs1ke1YnbaudPFZbLZAms9XdjoG1irtoI7pXATo3b\nHSb1YqDMtYV1yhjIeUuVk9a2ecHVt33jU8NZG3mAE7DzCwAPhHZwxY6SOUtR8wQ0yRGPQ5O5Qyty\nY10tJTfKGyRNc5sE2mvK09jf5jrE/d+SAgegtxecpmTdwzVTSQPkKJbWPnKSkma5Vmr1j1Htkj0t\nJ0Hb/Jvn5+VQO55nDpSqcZIy5nWR1M73y7LdL302uoo3xtUTAK4ykqapbNdw37x+rY1M1Prl3BPT\nKSjp0N59FlmY+s4dS+QPMQGwxGqvvLd1pLXsshpmtckic3wsYI27pAjlfAL4DKtBGwMXB/HLXULw\nh5+/jDeoBx1yS1CGLn+0HJ7O65MwdiBwEWdhLddOfi09hNBjI7pXam6L9FgibXOtiZNcOnPtSaAE\nj/deyU5S3KzLlCNr1EbCnLfimGs7hsh5yN1cRC59Tsc3YLtZLoQ86K99o8QNwOj1bx4CprlNWtsP\nHDKQSS5YiUbeEvh57kYp1cFtNWgeAqWDdcu9sH/e50ZZem7oErmFRQqlvBax0wiYRCz550TmpkWt\nHB9BUirbgznf2XNNlh5imYe0R9/UevhzJS8J0cmiNiaj6dLauAh+cjEQv9AlxBwdqkTs2vS8ajd3\nW7xlXkTyVwJ+7beLharKAbsolpLipZEpvj5tK5znipmHmHBiJymCYDZSmlSXpDrmcNx17EOMWVtY\nWo6HnJXYamviaJpHtWPr3ehfHrQEsNek5daJjSVgh1jbxqNdUjurnFwgk5IgJlbUSa3/nhIQwSIa\n7bGtwPE8iQQNSZhNwDTFLEf6LMJH2y0RujnWyFmqHoX03rPehYcKRjIWh3xvn+qYYMxkuBWh0lIG\ncyRvge2k5zxweMxC5Kqqeg+A3w7gY03TfLZi8+cAfDmA1wB8TdM075uj7kA5SjpqrZPo23S30VjV\nbi54O+bcbOalgubeCHSkyiJp9LzWo2iRJ602pXy8PbQMq5wxAVeA4wUpkTAXidPK0sr3EjmN1JUS\nueQeKZC2pLa1x/1ok8BQMcq5D3rInqWIlapkhyJ7nu8pXR/prxWxsksf3lxjBnSeiIgSKbGjRtpE\njJ6ziF33y8jEjefhZI+2mf7S0veoWX7NZsz14LYJpdEoPedLcKpEieLYESgtdJ469nWjz6ZnzCWN\n4/jSGovkSQpe4HQwlyL3PQC+A8D3SSerqvpyAG9smuYzq6r6jQC+C8BbZ6o7UIgppIV3CJobZoKX\n2Ek2HnheFKUvkykvH+ulYLm25F7IcyINmmupCkt901Q7fh4YT6K09kjpEiQ3zGNhru/rQe67eUmc\npbZJ5yWSxu0ElS65RSZQla097lwkNbdATnLS+TZtSKQsMuMlexZpPDTZK/1eHgInlc3PS8djZ+Qt\nIpFTlCS1i9r7SZqlyGkkLU/2tLo9ZI/no9+9lMx5j2k5Gi71hOYOxyKf0jhHu/4510ppzCWRPZ5G\nj0N5u7iYJWpl0zR/D8C/M0y+AjuS1zTNjwN4uqqqT5mj7sB4zEWCovMPnAw8ESk9NnMFKDmmzdMO\nm3PGvevzRJL02Kwd0SY9ESl95eRlXE855425ZtzneifMFRXRU44nSuN5gwc7kfAUXs3aeCJbXnVc\nj7lsfL9z3ua84bkXPWMqz3edKyKlbxx4OqplYIhjrZH7VAC/QI5/aZf2sSPVHyA4w9qljAH6WjiO\nqQodxVwzYlo5XrdKbUZ0I1w7va7HmORy1Q4Yuk1K+79NdR+0LqnXLfIYZG7hsOHlWNfGY6ORuTFq\nHLdRPjckH1/XBvRVt3v1VeA6RHfJ9rjNmIia5k7IbbQ1YWucqYpYghaRkiprVjmpbrrvnFUO/x4e\nV0+pLOnaSQocLVe6RtSOp09xpfSqRzn3wjSotdwoz7ASXRupwnWGtanaAS2Zq41ytLI1l0orvf89\n7H3u0t9E0iSFjtpSMqe9g64pQVMo5iZzVuCMi0bmpOvW/h75CJBcrZWQG6dtUYvPGneZpBEpuTKX\nVDl6j2kqXduLBKE7RRyLyFVCWqMZv/Od79x/fu655/Dcc8/N36JLjLEPIyd1tANIx9KC2xJil8oZ\nC6tz1DteX34tT8l5Xr5Vfz4KWcY9cyP/zqJLZQ7JfbJtmB5Z0loHN6e7pYWxa9xK2jbnWraSMj1r\n3Dx5HGkNK1MiacBwTRuAQUASoBtg3KuvDsiERiKsQB5t2qKXR3JBpOm0XIskesiVVo6WJxchM52T\nvqtEQq2AJhoZO+R6uJK1cN5AJuWujD6XSNqO5Z7gWSRNJm5SuSVtpmV1xzax5WSOX0ft2nvPjYXv\nvS0TsbmU4FPAYveEj8EYF8o23Xaj5G6TNBqlRO748eP0+5w6XnrpJbz00ktFeaqmUflUWUFV9QYA\nf0sKdlJV1XcB+NGmaX5od/whAF/aNM1AkauqqpmrTQEZP4ovFl/u3fFwUAUMOxltUMDLkfJK5RwK\nYwic9WKl50tewOmztiaCn89FahPXfmzJ8Y7Ipe0HEoGjRG4ftZJeIu0zfzfxyyoRRM/7bM5JvjG3\n0hwky1PvmHVtE85xIgYMSZiUTolZe471DwJJ2+dl5ILacLJD7VOenJKkET9LldL2TrNUOnqeE67c\nmr1cIBMrj0Us+TU5ZERKL4mbK5CJn/DkiRPtJ5dYsTSZkCU7Xj+1sepKIec9dZX2//QaWteWXl8J\ncy+B8N4jJe/3y0AYSr+jZq9df+tZ5+namI7b8XK+BD9utDgwJ6qqQtM0khi2x5wj6Aqy8gYAfxPA\n2wH8UFVVbwVwRyJxgeNgjtk4SWlLkGZ2JKWua48+6zQGpS+zMbPGw8+2qwv/nCOSU9W4fTlkD7mD\nQwpM4nGLPBT58pSdO6/VMzPpAmTiBejkyzrHyVhrOzSmpAzID+q9L3ovmdCIi2TDiQ+1k8rRSKFE\n3jTSyPNJZMwihBJJ89TF6/N+d40g8/P8M4cW+MDrbSD1jTS/Rt40G69KllwsaX5Kyqh7ZapLzyep\ndrqyJxFJiRDSayIROX49pOs+eB9sh7+L5pExFakfkVZ88v6kRafCeUjMGHfei7TXmTX+kq5PTnnj\n6dwdsrMZXiOutmnPvaTyBU4Hs9z9VVX9IIDnAHxSVVU/D+Bb0T7nTdM03900zd+uquq3VVX1M2i3\nH/h9c9QbGIcxRG4LeX84Tug87pfA8MVkEbupKJ015m2wBy822ePHVgQ1ft5S4qR2SWpc+xni56wa\nNwbclZJj6ntgCjGT6tbsC9JLCJhFyiTy1ebRM0kDJ+8MrvRiz6k1/FgiadzOIheSHbf3kDbpvMf1\ncMyaNE6qJMKokUVvvtLvKl03fu1K4VFwSibB+udlUkPPWWvZOgK2NsmV5DaZ0tLxYl/OMI0ecwVO\nI20WkfN8d0Duy+mknOQeP8pl3olev7UaukdK/drY/iw3PpH6t7nVxkPCIqq5SW2NUHFCxo+lejTX\nysDFw1xRK39X0zS/tmmaZdM0n9Y0zfc0TfMXmqb5bmLzjU3TvKlpmv+4aZp/PEe9gWnwEDoPqZqr\nEz21cg6JOVTROcs5OYwdf3reRY5AJ41jnd3KEcTEFaXx2tW8Te2wcURyvO+KCDlPXePrnytqpSeS\nZN7GM8DxRL+cK7T3eQ+4PH2OjwTm3y2e4BbLmSJSemw8ESFdNtu8jYTlK3mbai6b1/I2i3xQRCxX\nea8Qj3ooqY4DmxGTD2Ntxubz3K9jJlHG2xxvjBc4DGZbIzcXYo3c4fEP8LnmTHhC6SJ5a8ZfG3x4\n1YMS5DodrxqnzSQnldHjeplT2zq73LqJjGq3e8nRF+Ji+0hU4yr6dbV1biXr3bwzwdxuDFnLBfu4\nAuCBYcvTnAobn3H2zkBrs8+yC1L5eiWPquZNk+qxlDZ67Hn2t6hxD9dwDfdM9Uhz/5PWjKXPngAo\nVjmWuyOvp3TdmqfsEjUu990orHXMYwihNDDkfROgR6zkylqyzbspShEohwrYcK3bMI0f04iXbdkr\ntVyuti33qp2myPVVxH0666/d65hfQReZVup3x6hyY70cMsGRAL9XgqbetfYFa+yUfvVYqLfb0W2w\nnkdP3yyV4/WooOV5xnhfgJ9U2xqYF8deIxe4IPDMwGxRq77W1IZK+OlFxfNI7pZdW2RZf84ZoNzg\ng7ZVqp+/vKm9tSBdcjPidVskLudSSQcEgE7ixEEBUOZSKf0cpYMGb28z1T1SUtyIjRaNUTsuCf6x\nTxtBkqR8Wpp1bmxdnrK8UQ+ll7+0bQC3twYWJTYSeaJ18bJywUg0G4+74xiSJtWtXTMJHpcsrU+W\nzlvpYye0LBdELS3ti0WJHg80wm34+jfqjknXvnFS1j+2A6KoWxtstyJZG0yq8Qk0KchUSqNq2lzu\nlNakF1fdasEGuwAJrJyFcAtxF9DtAqg3jwZpXRlDBUsjfpqyV0IGp8KjHIr5oJNA6fnl463ONtwm\nLxuCyF1CTAmNy8lZInMJiQBKeWg+ag/Ig4axnU/OncBL4vhghNvn1n7wsmWSpkeo1FS4/fk5IlTy\nY68Sd8gBBKC7SC6Uz8Kxl7CNjdJ4iDVnUrla2Vr+sWV6ytII6hjlzhP8xGPnVa68BJDXnStHIoBT\no3Hy7+L57lNQ2me2aeWTWb5ok3LgEE1Z05S0dEzXv6U8FtnjAVF0stf/DmfbNerNZkDYRLKWPmtE\nbsyEW65PLgnCtECfwPHbjJM8flyzdJKv2vSPKdlLfTZ9b0kKXr15ZK435ighg/my8q6iY8uuNxuT\ndEpkj06mzw0+XgucJoLIXVJwtY0+sFRpo2mlCh2vh+bjM0YpP8VcHYg1CKHt1PJIylqpK2W/HJ8K\nZwUMyLnm0M8uFY7/nOdB3gCf4raQ06WNqfmxRdr4JtUJYyIA5hS5uRSyKWWVKHDecj3Xih6XuFZK\n+aw8HkUuR5wsYknLKCFgHvI7dT0d72e79PH9rXfCy6O+0fzUTZKWIaltnFwN1bYhIaMEkJK71D5a\nVmoXD6Iikjumtp092PW1nLBJZC2nxPGfwpp8K4GmvHlIG7en6RtmK32PGv12L3bHVNXj5aAlepLb\nZnq/lRA6ap/emf/n3wG+6K3AzZtt+p07wN//B8CX/1a7HDu4zPho0YvtupwIHlhwk8ZrgdNBrJG7\nhHg/ngFgDyw8s79j9ijyDGi1vF5YnY1EQK31HDyfHXEyr7Kl87lyzDVzgrsOMCRxoxS4QxI4QB5I\nlJA38tlS3CTiZm1WnZC7p6Xnguaz8uby8bzeMrS0Q5HHOdbN8Xwl19VL2rS+i+bJ/c4eG03tS+dK\niGXppEHufpLK8sDqJ/U+cti/WcSO2vcjSW6FtKGNpK5p6htV0vrEbj0ou8vHyB7pd5erR8M+doOW\n4HDCJpG1nBLnJW5Sf517bVp9a62kSzbSea+dZeM5JtCiBlNYZO/OHeBb3gW861va4/Q5EbtjozSy\n8Zh9PttzeTd5rS9/M/6F3sjArPCskZslamXgYiLnYijZyOXMOdLP1TXPjNAhozDl9vwZXY64V9Co\nood4TCbahkpcnryshMiFaxaFkB9L+aQIiJ5ohp7IiXNFkpS+B4d0Pca0R/pe91k+qRxevxTpkl9X\nqS7+XaXfwjNZ5LO5eM4tcn9veyq0Nvn+jactMQxvyNPOHDYSruG+w2aeiJTXP+5QWhwRIUfb8LS7\nDhsp+iRPeyDY8EvvsZHgsfG8xxw2leM9xt+ZN2+2xO2b3tH+e9e3AJ90o7wcKe2QNtzFU1of6Bu/\n5W0iauVp4+K9fQKTQd0f6UMsbfJN3St5uuWeSfN6XC1be90NyJOuQSOaHiWO5k8zyel7nYoa514P\nJy2kBzoSp7lgauBuMnMhN9ML2Y1yu0gzlo9UJU5TURJ5oPdqIgIpbY2zgWLH862xFIjiWS/Pvl29\nuob5+LFEeniZEpnj5dDvoZWzKmyP9t3W7PpIJDGVQ8vg9Scy1//Nznp5eF1t2lkvH80juUaWrOGb\n6uY51uOAQupfeX2tnfxAa8SN9lGWEsdVNCtqI13TRtOoitZf1za0oa6W1I3yGu4Latuqp7Rdw729\n0qaV8xRe7dqwW1eV1r2d7YjM9Y8/akkNV9aSIge0ZEpT5LbMhqZRu2RD0yVSZpG5dAu/Ro5XJC0d\n1xhG+6W2qU0PyPnU5qRCLogdd5tMdTBXyj2426UG4Z3DyVsluGLmiFG9BZ5ous+SjQRuU22HgmgJ\nUWsDv1g2ffLWjgE2xIaN3+rh8hgOaVkN/Zz6Fz52C5wOwrXyEuKn8CYAuouOtdCfIjf4sfLwujim\nzHBbCmFOLcuth6NETrK3SZq9Oaxq41wPJ66F87pRapdszrUYUjr/+SXiRj6nF7THjVJyMckFoaB/\nqb03MAe19bis8Ly8rlw53jKOWY7HXdXKV7KNSe63oPm9BEuz1eqZErTFanvpfTAHMUzQ+0Xd3Vxb\nB8fdKHmaFf5fWssG9N0fOSmT3ChTOdwdk66P2+fbbrFcrff96yIRlQfoEzfuRsnTtiQdLK+1Jm6u\n9XCA7qKYc6HkLpKSjZQulcv/Wjb8PD8nHWtpAiQ3zDuvALe+Dbj937bH6fPNp4e2OcXvxR8Bnv3C\nLu+dV4CX/yHw/G/xtUXC2C0cpPXf+2Oj/8u9EwHgTfgFR8sDc8DjWhlE7hLiQ/j0waBizABl6oBN\nymvhvS/+Wzzz7Cfi+s0nAQCv3XmIf/7yv8HnPf/vmflK3YM80djSZysAikTypJlsqc4aW5XAtWkZ\nBU4jcDnyNreXbO4lnHupC+vhqAKXsKmfGLy4SicnqEo39vmgnz3rEyQ7i5TwvLn8WjmHirA59nt5\n843pc6bk8fyGnu/pbV8JkdPSPOdzXg3WBJdnskpS6qg9V9ek42Hgki5qJD3ma+T6xG4tqnScIJ6t\nVvv+dR+0JClUiZxRxU1bD8fJHiVtwJDc0TQKa9JNg/RTawSMp+WIm3Q+l6cmn626tHZqbdWOtbQM\nSshXDndeAW79SeBLvgi4d68t591/oj33wz8K3LieLzdH8Eq2y/GsnxvjhRBE7ngIIhcQIRE5+hcY\nN1gdOzjT8lP8xIv/Gnc+tsKHf+wOvvrdb8ZrrzzEd/2BD+A3f+3r8exX/loxz5j1HVNInIecdXkz\nNsSFEoC4ubdbgSud7R3rDm+9gEpe1AaRy5E4HnlScnvTyFuys5Qb76zlWBXGyiMd5/JreUrJY2m7\npqiMJWUdgixa5Wjf4xht1vKXngd0Imetm/ZOVNmKXEfsKJnqH+uBTCTSliVpRG0bEMLVqgtckkha\nIiq0VGIAACAASURBVGOJyKXPnMhxG4nsaSRO65MtD4ocNKLjIU68H9bSaN6apWn2tZDGyylts3Ts\nPXdg3HkF+Pr/BvjHP9kSxHf9UeBdfwZABbz7nbLSZ2HsRuseQjdmAh8APgMfzTc8MAtiQ/CACr5p\n5BZ932h/OeN8qKW6+P5zFG9+9hPwve/4EB6uHuE7vub9+PkPvIpnvugT8Dlv+1U9O2tRrmcdHC3D\nXhOSdytK54cE0CCBRIXLKnC59W9cpeOfuZ0EazDBew9ajv8WykKLTpleVNIaOE+oeMtmTOh6aq8N\n1ksVHZ6Ht8eTf6qSJqXP3aa53bNPqT05N3Heh9IypDXEGgnb7vPkZ2IkGyvQiad/86yTK9mkm0eX\nlElbX7Xrk7ZuvR11pRyobyv0lbQH6Ktt9DxNS+6WIOcoSdOOx/TRHnASlNalSec5keTneBoUe2+b\nUj6aVlLORcHu9z17EvgNbwFe+TjwpV8BfMHnAH/x24Gb19H/fR3fX1vzx4mbtR1Dbn+6timxBu6i\nIqJWBkzMFZHSM7jgoAOJ6zefxFe/+824//GHeP+P/DK26wa/89s+E0/fzN/CY+rWyuHXwyKEejlD\nGx6RkitwgOKf73HBGWPj+dnndsOkyLiPSPAqT8OB9nAgzgffUgREXo4naqVHYZFeprxsKfqkJ0qj\nJ9Imb49Uztj28LKk9niIkodIegit59qfGnx9jO15IJWz2E1BUCR1K2FJiFNn0w9LSNekdfmGx9xG\nij55lUWblKJPXmX5JJtr277N0hMR0mMjBR/xlPNxRzk87TUljWKlpFE8wDACJbeR3D2l6JOeVyu3\nOeJ7QwInRd61ah7ceQ249aeAF/4k8O4/Dnzgp4FH47eU20N6988WsZqXK41PZhpDBQ6DcK28hPgQ\nPh1A3u1rzELYOdy8OFKZ//oj9/DO3/wPsVk/wn/wuU/jdb/qDF/77s/Ejd2aOQnyAGa4Hi4hp8bl\n1bZxahwlckmNK3KltNwoc0RurheC9BL1us3w88SmqTEIbiIFNkkkLc0sSm6Slislve+tNXKaYuex\noed5Hq9KNoaQjHkOp7p8SuWtsdwP/I+hvHnbOtalcUwbvWVp5eXKtpCL4HuGNdY46/V5NK+0Pk7y\nTuBuk7wPpYFLPJt0U5UutZNHvEzH1IZHoFyu1p0KB/TdKDXXyqS+cZVuIxxTAqQpciBpQL//zfXF\nrwG4LqSXulHyNO4uSV0lNZdKLR89lvLxer3viJxrpfNR0IibZ+uCHNJ6O2yAd/x3wHoNPPfFwI/9\no/b8u/+44FpZ8Ah73Cz5soMu3e9e2abrY75wrTweYo1cQMRFI3IAcPfOQ3z7f/l+PP0pZ/jd3/ZG\n/NV3/Rwerh7hC377J+M3feWvEfN4ApokeNfEJZJgkTh9LYlsM4rEaQQut0aO21hpXkgvIp6mDSqk\ncwaRo26V0ro4K6hJOp/bjFnLy22k85YN/avZcZuL7JIolXkssqaleesYU+4cZXvOWXVpGONy7lkD\nLBO7rp9LBIz2e2csTSJp6Vha75byDAOg9NfILbHC2Xa9J28ACWJCSZvmWknXumnr5jhJkwKggNnk\nXCs5LBdED3HjNhYB04iYdHyF1aPlKSVynu8E43iHOZW2UpL34o8Ad18D3vZlLXG78wrww//XLtjJ\nf0oMC11Lg8hdPgSRC4jwELlcsAZ6fAwi949e/De4f3eDz33bJ+PGzSdx985DvO+HfxlXbizw+c93\n6+RyClx7PI7ESbbSjLWkwtF6+Zo4KahJdj2cNrMrDRLoeUAeNIwhcjkClyNzMxI5TWmj96gW+MTK\nl8uboL0AczbJLvd56pq0kvza83jI9njKKEmzSI6lYo0lV1OVM08fWGKbc4Xy9okWedMmqrStBJKN\npcClPJYCl8rRFDgeyGSRiBqgkzZK7vj6tzkVObA0ml6KUvWNk6z0WTpeAHuvZw9JKyFyHqWOkzvp\nuzGURnuUMMZdcQ4lLwdvwBOerhE5KXolMHxHSWM8IIjcMRHBTgIqPJGJ7PzleaaAkjUAuHHzSfwn\nihJXAk9gE26bznlJXM6VEkBvb7j0MlG3FJAGB/yzZxG99NLaAi/+feDZtwA3n2qT7rwKvPwB4Pkv\n2n+xYf4FS1sgv5idn3cufuf75fSbL5Moel4id/ScROJyCpz2PJWEdubHY7Yr4PlK85aWV9oejzrl\nIZJa26x2esobY+MBJ0q8bXOsQfGW4ZnISp9LXMWlqJWdIte5P6ZjaZuA7ngFaWuBviKnK3AAC2RC\nSZrmRslVOkrSpOAm2lYDuWAnIGlAGZGjt40UzGSLfv+c0uixtx6tXXOOGj0kTiJy0INgTcEYsndI\nV02t7NKolRo8HiKB00f8YoEBPGqcla/LU5Z/KsaocZ6yaB6tvJyql2xyrpQ9EpdbB2cpcJL6llsr\nt8Pd+8A7vhN49ze0x+/4TuC3fL6RryZlU0K3YOlbxTaVR28RbsObnonA1TW13hU3DGDS2QzJn5bP\nUvGkOqU6uN1U8jZFbTs2aRtDJLW2WfV4yptqS2FF3JUwJYjUmLy5rVi41wDP0/cs6KtztiK3BlXx\nqDuktkauW/vW3zeOKnLps6rAAbLaxt0mKWnTbCzXyg075v12ziU+ITfo57eURG5S3QrxccGapAM5\np93ikvomqX3cBiwNLA/yxM1Dwii5oVGhvZCiRdJzFHO6diZ4VDhgSOL4MgTAfk/R855JtsD5Iojc\nJQQdVKaXqfcBlfKkNWO0/H7Y7HHuSocEDbWrrRHh9hR8trpLGxK9VFevnl04YDU6ZXrpU2LEZ1s5\nieNqGDAkRJw08Rf2BnjbbwB+5B8CX//tABrg6ettmkjOeJm8zfwz/Q7cxkoTsK3rgbKZzbP7lWro\noZYTUevf022e/ktteJ+n35gSwHarj3pQDn9+6ARKKlcjPlKeVBe1057vHAGj25Pw787LTnkX+7ps\nIsnL4teVf7dU9lDF2vRINC+HttH67lK+KaBbCPB+g6cdyqbGFiuc9aJFpu94xtKo6pXKWhIXxjXO\nBsFLVlj21r+tseypbSkfTaPHiaitd+UkMrfCskfazrDCfVzruVEmm70it1phvVz23SivoA0MQonc\ndZLGjxMJ42k3dseU8F1HG4lyQ45fQ5/IpbTUPdxgx9QmIa05e8DS0nG6/Zfok8wrpC2pj13u8vFj\nvq6Nk7ItOZfA+2Opb+YELNml9FRGStuyY7C0Rdd+uodoIlKJuNQbYLXsk5bF9lHP9X65WmO17EfL\nrTebXtpytR6Qn1QOPU7t6Mrpt4umcZv02QNeJs8r1bWpn+i1OX3/tHTDS+KkMR5NC5wegshdcowZ\nwHhcnC6CPG8FADhGXXRtnIrSmVspj7ecnc3NG8C3fBXwpX+kPf5//oc2TS1bKudIPz99ORXlc9z3\nuW0K2jRbsfLajHl+xq4z82COKJdyuT4Fjpc155q6MTZjID3zPO2QNjzkv8djoSVsZ0IaL3vFbIax\n6Xk+qRyej5cLDLcgkGzOViyNh9cH8qH7pbS7RyxHarO2TcCC2fDugrtd8mPgqP30oB7pkeNpV4Ym\nHhWOEzJO4tp8i4ENfx/zctrjvornac9Yl88xZUvulF7vlV6ekX1p4Hxw+qPtwEEwxRVMy0PzSXms\ntFOEprjR9lsuS9p+cXyzb0BxqUzQXHS8rpS59Rgs7c5d4F3fD3zBZ7bH7/p+4N1fT8gcMFxDkWZ+\nNdBZWVrvSDVuLlgLuvn5BM+aOXrO+ly6RQHPY+U7VDnednuPpbZI5Wp5x9icAg4xw62Vaa2L4/lo\nSH8xyi45p23+zV0p+8d8s299PVxRREprPVwiRK+xY+pWKblkAm2/Jq2b42vekqultrZZWrcsHSfw\n2z/lT3XTPph7OkhIHhO8ji07zrWDpnOlrmafqQ1Ns2zQV+HoXwADxSlh7KSelF/z9GiVsuEPtqll\nxY7Ccss025XJo7lRAsNrIvXhucBblht/4DQQRO4SQht4TgnGQPOPIW/eDsJS0eaS/60gJ9J5LS3l\ntdqUgpsMFkbzQQLQHxhogwVpzZxn4EDq/+H3AngE/MVvbo/f8ZfatK/8MqUMzd1ScqGk+Wpyjqc5\nkF62U1/gFNp9WqpITVGkrWdlbCCTEhI35pnntiWunFpZUr5c+nlgap8zJX9uvZzVd3nWyEnBTzip\nk4idFNwE6K+Hk4Kb8GO+3cA+QiUhcb094RJRS8eJoPGolRbZo8FNaDl0Hd2G2aQ+mffbkgs8IPfB\nHNI7QXO1t8Bd4HOTaRIoAeOfU5mcpNF/yfYKy8PIYIpQzIlbKTkZg54rPSV17IfQXPo1krdvGyF7\npTADfAmKm3aNSicOeb6L4Gl1GRG/yiVE7qH2PNBSPp5HOk4Y2+HydTPnBVFty5E2RY3rYYPhyx9G\nmnYsDRicbpo3rgDv/rpOgXv31wEvf1Cw50SMEzo+0ztSgau2fLzyCMBmlMuIB9a9Ofa+Pfbs5niX\nyukq3BQiKOX3nuPQru+UfsMfGdJfR0nwklzbfQGfhut4edlWVF5O7ighkxW57b4Ouq3AcI+4Tm3j\n5G6/bo5v7C0RN07IEsED/ESOkjYa6GRL0jixk4gcyLHVF0vgahedwPM+BtLkGV8XJ9VF61goNkuS\nlj5Lx9Y2Bovh9jIScZuzD6Gg9zIthz6T0phDI3opvVTRy8F610kTmdrEmaffzk3yB04P8etcQswh\nqWvqm9dtSoIUzMCC2MGiZp3z8RfpegZmNEqlGqZYm72l7pea+450jp+Xygbw/G/on7t5ZZcmqW5A\nn9AthM/SuQTp1pDcfx5D8IAec+bhLsBzB/Sg8KpmYwdgc6n53N5L6HL9R/58mWo2R905F0tNqfO5\nVOqulVyBk9wou03BO3LH94OTyB3dWmBJA5BoahsPdgL0VTqwMqyold7tB3jfLPS9DzOvhyel/rXE\n5Zx7QliPBy+Tq2UpjbpNXiHnKSlbKseUyJE8VH2j7pKUuJV4C2lpFvTnpBbtJLIn9Sd7QieUz4mX\nFbDL423iGXN5xmveJTaB08NjOkQKWBiz/q1kBn7K3k5eWz44olHbUnvkiHVtRD5rMDcX+Sua+ZdI\nFz+fI2FeEmcNJKRzkopG0/mAI52TZoKt8qjtY4ZEpKwosSkKI7eR1mV2EVe76JJSPq0cTghp3dpL\newzxLMExSFwJSklSe274AGn9wNwkzVs/J2Q0n63Kaa6VXV4akTKlScd9csc3+96YrpQAfATMcpuU\nyJ9ms0XfvRLks+RGSdIoaZOEmIe7y/tk3bejAsyTw2wyUhmSCyWFRN64S6SUZpG0dJwClNTo3Civ\ndPmo+rZaPpElbnQZSKkq54H2vPB+R1K1hxNlOsmjZXOMXRrg2Y6llLhp+YPInTbyOwYGAoFAIBB4\n7BEhxk8c5V55gSNijWGEzEDg0AhF7hKCBzvx+FOXrHvpbAy/7okuEHzfrNQOa+8vbZCSU+CkfZzG\nttsFSX3j5/lna02cpNRpx7n6gKGLJFXmJDdLfo6Xe2KTfZbyJO0/SFUuoL1fJHWN34tbDPdL0xS2\nXF3tOV2Zk1Q6S5WT2ky/P61TU/Es9dEDK99UJW6OtWZtmk+B85c3Xe3zrZPrBzWhNjyISbLna+RK\nolbSQCZ0LzmgvwF4e7zW18RRd8fk8qgpaZJqJ7lf8vVuktInrZGT3CiJCpcUuIfk0mvLo1J6UuIe\nbvsq3ZNpzbHl2bAV0vi6Nr7ejSpw9JinLdEpa1SNS4rcFagKHY1AmVQ4AHsljkYB3pK7CCh3rUyQ\n+gf5me+IV7qP17CfF14W7//WOIPHNVMq1wNtXJVb5uJZ71zqmRU4DcSvcgnBiVwpcaN5eD4rj9Ue\nwB7EaK4O3J1huIlwfxCcXCCo64NE/mjZUmfLN4O22n2QWe5c1XOROKtsbT1czhYs7USgEQ++5rJN\n27B7b+i2yEmZdC9yUJLU2i1G3T9aPutZy7U55ZdcOy3yl1Dqmjn3ur5DkbcxUW3nJoWlbdXa41sj\n13eltNfIbfYBTgD0SFw/SEpH9iiJS2vi9iROiiypEbAN7DVxyWaTsaEkjqcxN8rNRiduubVxFItF\nvxyArJ3bF47+JFjN/u4LE87z9W/cbZITtysYkjTJJtVxBWiWHXkDsHejXO3I07Z3B3WkgW/5kshe\nd6wv87DAdzEse174eGjolkztNfd2Xu8c/ZvHJb0k2Emskbt4OLGhVOAYoB1pgkbcjrlPnCefNdjm\nxCuRNK5MSLZSxztUAeXO2PoOkyJrlhItr71k52mmpqil9XBp1jjZelS5XLt2qAZNeNTPUPtVGu9v\n19oOCVvbVE64+iRNU66kOi+KOxslaZL6CPBnbKgIavm4nZVGy0llWRgTTKSUwOWIWskMv17/OPKX\nI220HG/kSnpeInY8kmV/q4E1kg4DdHvEUfJXY4t6sxnur5maQ0kbVcU42Uv5tIAo1hYFW2azYWnk\nmJK4vRKXlDkUgBhPDsrL17qlNE7cqA0lcVRt4+obt2HEbrMncWf7NWArnPVI2hpnA+LGFbqU5vEg\n8sJ+Ps56x1ydk4MFdXm2g7L9a2bl9mBXbv47jo0oXqLOBU4TQeQuIaQZLwrP7I10zPPPDWsRcn/Q\n2JE0HgRlqIws9mXzchK4DU+X2jMZ9GtOXReRy+9tMiVpWj2WMied4wFRJsBLmq2XEv/9LMJGValT\ng0aAuBvmoaCpj5JqSdujkTmal2NOt+dSlyo9nzxjz8vIlSOV4QmeUkIkJZVOU9/65/pulJS0SSod\n0He1TGldkBSyZ9xqheXqERY5JY2TO3pMFTlK9rbkeCuUkz4/YDY0bXd8f3ec1LPNZkjgHiKPFNDE\n6kIH4AQt/dXcJiUCJhEyapdsNIWOkLtmCawTkaufwHq53JM3oCVuW9RY7TJ10wAdcZvLtVJDja2p\nzOUV7LN9GXmCN1Ty6Phk2DY5QIoE77KVHOkdu+1U4LQQRO4SIi3ILXFX8MyCHdt/mhO7Ma5YvGO1\nyCL/zqWkrYtOtUGrKKVyOsVpjzm5geTWOAVcWZNGHzn1Tct3ZAzJ+ZDIb4R7bC7CnlOlpGPaTu9a\nOV6OtdbNypcDJ74UmmqZrgNtB82jffexKCVwHuI1Jn+pSifnGU9GLeLG6++G19uBDVXWpHV0/DjZ\ncNVu367tFovto1aNE9ag7Y+52kbJHdBX5LbMxlLkLGLHXCkpgQN2yhw6cBKXjt2RKDXwtW5JVbPc\nJqmNpK4BsiK3BHAdMpEjCtxq2ZI3oFPf2i3dOw+gpMKl4y3qQVr7t6/Q0XP0fClKnw0PYWvPdwSP\n5vc80105Zd/JugZedc4a58153QOHRUStDAQCgcBRMJtiHTgIJrmCBw6PmHo/aUTUysB5ILqFS4jV\nfqqthTY7M3YD3zE2uQGe5ZKglSWth+Pr5qSyJVXOyu/FbINYTWFbCGmnCB4A5UjQ9gykkFx1JUjq\nkgVLaaOqWntOD3YireX0lsdnVnkead2a9B2soCUeBU+7ptq6OdpmKb9Wn6dPGaaVKWnaDL/kgjlW\n9cvV6SlvTFuoktYd5/eRo2nJhZK7UdI1coPj3dq4/bo4oO8CCfRVM0mlozYboxx+bNWVzqFbE0eR\n1DhNhZPSipS5dJtzhS2laevfJPVNC2RynRxfZzbsmLpSrpZnWNdn+7FFUuKSCge04w7qNknXzFEX\nPr5mLqUnlAQ7KXnGfS6TedfK9H2TUkfbIUXElNpVAs+4zKO88bJijdzFQRC5Swi+eNjjNy2VUZLu\naZM18JrTnc0DD5mjcLuV7otMnfajXvLAxVICrZoHE6Hp2nuBE8Ga5c3h2H06+R5Vr/pHfYNMu+Tf\nc/g7cvLA3Sy9L1xra4FUd8510CJ5qb20rf3v1n8Re6NN0jq92xF4wYlnjgxLpE4j317kBneA7X7I\nj0vXm1G7oU2O0E13vyxztey3fRjsRCZ2GtnjbpQpbYEt6u2O7CW3SgprbRu1oS6RUj6eh9dB8yQI\ndUmBTaRiPevjKKS3yGJBIlZq699osBG+/m3BjnngkuvK8XWSxo65K+U9XBPdKFe7zSTatHpP5vix\nRuSSnWdphwXP2lbP86zbnO36tCG5k9rA625xJqTlIb3XpLQxhE2LlxA4LcSvcwlBFyAnlMzgUMy5\nCJZvHXBI8IXF1iB6DkVxFpQEC6Hrz1LTOLmjxzSv9lW0y3DIXmTmdXTamjh6rjTkv0XQ+H3Foy1K\nxEwmGtrESb88Xlb3ObWHvrSHeekgig9MKLHjM+ZDwjku2EpO3cxtq9CvK6fI+QZa+sDPp75Zdp7y\nc+2z0sZG2pQIXEqn5zRix7/Hgh3zcsTfyiJdgEzmQI6ln18idlv0ydtG+YuyLQQsPKl8BlrStlh0\n+8g9Sde+UVJWuv6NErKUJpE2Ja3Zlbu+Aty7dnW3+x9V4JY99Y2qctTGInJ0TVx7PFTnEsZEq/UQ\nt6EKLT/LXGXTyF2CpuJ52m3BG69AI2wetTMUudNGELlLiDXpTBM8naRnVqb0gdcGW9I+bdaeb6XI\nuc4dFL1LtEE28Ak1Fcsg5zUyB7QDFYvMWWVL4LcDPbbKOIdex6PmUBJh7aVG0/X6ZBdBSYGj4OGr\nE7RBS78O/kzLRK07T9tS7/NoA6lE7HiQFDoQozb0e44ZCHBSJwV0oXXkUBqq3yJlNG/prH5JHsue\nY0pkS40ASoqEFqGPljVUMLq/0vUAgHonbw3cKoGhi6Q0BuZ5aN45uvmZXhUSiUt3MyVxC07S6D5t\nSYHzuE1Sl8jrLM1D5G50ChwA3F9ewwpL3MdV5kpJidx+N8AMketvSdA52eajVJa4VFqTJZ4tN/h5\nSsosckfLSeBumhyW+2VCrj/NjedCkXu8EL/OJQSNEpVQuv5trhkaa2ArkS17k255/6puEK1v4i2V\nO5bspbKW+9BoAsaSOZpFI3O0/JyiJZ3PzTqL/j+ZNG+emeBR3tr0oSvlGDWYl8Hr8WxVYCmBZ3sb\n/qOvXS/ts33eda8cKVocV+04sasJcUuulTzaHFXzNJXO65YpqX5Tt3zwrknzKGulKp1E3HJ5ePkl\n38WT5nW3tAa+/JzWvqLzU8nTTAoagLZPNcpLc2KJnD2EvgaOpi/QuU5SEvck3xKgRn5LAM1NErvP\nOSJ3AwPVbnW9U+AA4D6u7YnaPVwD0K2JSwodJXIpjRK3lEdaEyepcukchUeFt54fS32TyFlnU0bu\nWnSkjUe2lNo2BaVr5kKRu/iIqJWBQAGiQzttnIvCGnAjfp/TRkStPHEs8yaB88N9XD3vJgQuIUKR\nu4RYOVwrgbwP+hS5XZp90tbIWcqYpdBp4Mqc5DonBTRJeZPSRtWLLZmpS2WtsOzN3tVEM9yrdfuv\nPAx+4gp8UgpNyaPnvT8rt5PKzblb5upaOGwGzdDWZ/XdGyU3ynzZm4G95q6Zc6McYqiuSci5PXfq\n27BcSV2jNpJbpaTQ9RW8vvrGgxVQd8ukOo5V5zSMWS+T2iZ9pse5WXxqUxo4pKRu6XuMUd+6NLls\nvu5Ns0+/o9T3au2nQU+OggVgOUaoebbsePc1niR90cMtcX9Eu36OeqpbESmpKyXQldNT45KRpcZJ\n6htd/+ZV5G70bZolcO/60JUSaAObdIpcm8bVt/Q5uVO2Nq0CRxU67krJFbkpahxPs9yF+fm+K/HQ\n1dJS6e7jqlC253keF+wkwTOGK4mNoKl2gdNDELlLiDlcK0vsZAKmr1Xi7aGBSWgAEu7O1g4OFwP7\nHBEcBpyQ196ldE7QxiC93GpssKi3qHebhW+3W9T1Bks82l+hAaFLi9rncDtKlaSyxvYIdO0GhM/0\nvEYArbod7aq3W7LpOq9CDkBiuVl6MWXdVg7Ss3Umnl8LaS3ooIi6aEqBTCgJ1NbL1SQvH3Clc153\ny1JIzz1QpiRZAz6vm2T67HO3LCF7ZcRtLGkrLVtzNePl8e8+GzyErFbsKLvSyqE21BaQ1xajJV1p\nQ/BExBKZs0DJIF8P1wtskghZInHamjig7yKpBTK5jr7rZEq7jsF6uHvXz3CvbklacqW8v3OjTBEq\nE6EDsHezzK+R6461NXKcQHi3G8jd955AJvwe1oL98AkM6d6X67YDoEh5PfCO3zzr4XiZ4YV0+ggi\ndwnhVeSs9BJYZeRUjXSus+sUOC34SX/gKKtvw0F9V660tcBU4ubG7jKssMFisVPnNkBdA1V6Wq0+\nnhMz6TwlbXOtIdECn9QZG35rSGkETeHtyEnW8P6Qt5KwBqPaPXsstzRP4JP2uLWj6+O0QVKy0VS5\n9nNH0Lpr2J2jZXKyx1U6oK/CSb/PoeElLvy8NYM/LMdL9vJr1DzHUllSufkyZDUyl88L2qdvMJw4\na1J/R8kVnQTakjSJuME4X2NYjhQAiq83Jo9X0f5vtGiivNG0Pbmj0SiXLI2vibuuHHOSlsq5gSFx\nSwrc7jhFpLyHaz0FjpI0icjRdXPA/IpcOpeDRaTSeYm80XMaSetsNFWOP+v9dXGSeq2r8tM2Fi9R\n53JjwVDjTh+zELmqqn4rgD+Lds3de5qm+e/Z+a8G8O0AfnGX9OebpvnLc9QdKIfkxz02SuUY0M4s\n507Z2g8jB1IFjpZpq3YLkwByOz6YoqRwQVqQ6u9m5Ybulq1NLaQNy1nWSV0ZulsCTKHjkc14ZEsv\nWfOQRE9+WjdPXyi2UrpmC2DrvC25ux6/jzipLyViB1EeHJBe8Ymo9Y9baIOivvrWuV72B1V9skUJ\nH7WnxI4rcppKN5y80Tcjl7+vpOSXDvY4OclHm5warnxYZp40au3lbeZt1+BR8uYKvkCxxaLXP9N6\ntjuWs92uh/vIpX4s3R4rdi79TcQsHa8wJG7UdXJDjnn/wy8RuzUlMkdVOukc0HfH7ClwQOdGSYkc\nVd/4HnG5iJQCadsTul3a5no/ImVL3K72FDh6vNq5VHLXSk2RKw12okWt9KCEyHncJCm543msTK0g\n3AAAIABJREFUCR2rH6B1SYrc1OfuEMFOtOPAaWDySL2qqicA/HkAvxnARwG8t6qqv9E0zYeY6V9p\nmuabp9YXmI7V/m3Q4pBqXAInYdI5yZ0SGLpIpjx8UN6ly6odzcMVOJpfU+l4h9sv20fSOAGUIluu\nANHdUlToeJ9PCZQ0o0wHQxtml2zH8BNJWZOOueLGXS1riHm5Euclc12xQ5KgTRDMpax5y5lKCMe4\nyGjKWzsJse7ZSLPlEkmj5XhnlLuBz5DYlUAjdp66u+NyEucty1OOTTB19U5qj5ZXw1yTEtpvSMsf\n7t2ZeuCdQlc/gXrxCAtKrnjzUj/BiduG5VmgT8p4n0fLSOrWA6E+EPtksyDK2rYlZQ83faLG8aTW\n/3FFbsnS+KbcEpFLBI2vieOulSRtcx149XVX99En7+PqXoFLE76JuHFiJ0WtpGQvt48cdavkRC7B\nS+hyCnKfeMlETlLbLGLHCRyPWtl/7s9E4qZtS8C/lwXtungm5y2ippG9wOlhjqiVXwjgnzdN85Gm\naR4C+CsAvkKwO0jshkDgmFhPdHkIBC4zlkQ1DAQChYiolSeNGB8EzgNz+M59KoBfIMe/iJbccfzn\nVVX9JgD/DMAfaZrmFwWbwBGQOptDRqVMoC6PErjS1uaR1DfZhTKn0mnqG7VJSlpCOi+5W1IlQpot\nS+XR+qjqwBdsWJEt9zN+O3Vuu93Z7NS5pMwBRJ2jM83ctTKlbckxj1IpRa3MeXpIt4nmGskVN02N\nU/JTJW5T9+ehtEAn/erle4Yel8LKM/ZcZyNffOvZ1GZZ+Qwrn+2WFDhprZuW1ubp1Dmelmbfl1iD\nu1pq3106z/eRk9Y0SrPU9m/hd2/MBTax1teU1JVzuZqiwJXYaeD9q9Um/ntwL4gN6r0HAtC5V/bW\nBV9BH0lJs9wml+QzyPkFK+MKWpUN8I2KrqDfL+760idzXRD3QEh9Mg9sQtMkt0oetCSpc5IrpeBa\nudqlvXrtxl5hA9q1brJrpazQ9dfILQfr6OiauORmSfujpNDl+iYPuNIlPafSkon09wwr0W3Sp9p1\n6e01OBuUA0jPfH5/uTGQrlnOTVLb/DvcKU8fcxA5SWlr2PHfBPCDTdM8rKrqDwL4XrSumCLe+c53\n7j8/99xzeO6556a3MrCHFOyEY46Hlw+SaXpXT5+gAV2HMlxLMQxk0pXZuVFye4m00To6l7IuaIPk\nTpHKouRLWw+noX2RcbcMmfwNyq4Tkattd8v2InZEjbj/iK6U6Rwgr5Ur6SW0tXL0HCdv0nli09R9\nArdd9Enc1vJlEkDvB3q8L4/ds5abm1V+Py0/CC97gevKlvWCTuf4WrdkZ61XoU5FXdTVoWtljf6A\ngBLCZLc2pAX++9Dvxa+RROpoGRY8gQcsl0aJdFkhznNumVZ5Uj6pHR7MQeBSOfw38bpgUbs1znb3\n4zY1cO9emawqYO/OuGuErE7xySzuNqmBlq2BTjRt2HFKs5Bs+YQVPU4uldJ6OC1KZToW1r9xItdc\nb7cVuL9sSdqreEokcm16P42StI7ILXc2V3tErnOtXPbWzW2x6E0kD1wrH9XYbGpsN4xMbIRxxIL1\nywvyfC22qJ/IEzn6XK4J+Ur39BKr3rG2Rm6YNuwP/K6V455RaazlCYzlJXiB4+Cll17CSy+9VJSn\nahrOucpQVdVbAbyzaZrfujv+owAaHvCE2D8B4FeaprmpnG+mtilg4/fiL4oPfcLUB9ciM/IgN58m\nzWhxO2nw4xkweWbQ5YHZ0E8+V16NDZZYD9Kkl4trwXVS6TYbLLaP9kEC0t8qDWQg/AX6s9cQPlNo\nP6t2u2jr5CTyRj+zNXEaiaMELqlxll//lC02ErQXbG7APbift+Qe3ChrnraPitqWMFAqUxCJWp/5\npuQtfU7nzYEX6t7EUCJ/dOadlsPTJNLI2yLtKZUbbOS8Cby/V46AWX3F0EYmeby+0jaWoJz02YFU\ntL4013edYd2zPSN94hIrnG3XqDcbLFftM7BYoSVbyaEhfaZpmk1Kl2xWrCyg7efoOrkHaPtE3pfS\nY8BH5HgfyNfDpWONyEkKXLJh699wHcDT6K2HS9sK3MVTADq17dXdRnKdIjeMWilFsby3V+2u7oOb\nAOhFrKTkbosa60e7/mRH2Labek/UNg9rPOq7XWQuKgBC6J7YvfgWT257RK9ebFEvNj2Cx9/X6b3M\n3738XvW8+zmx6+orWxvbpes3l9bXeVS5sfvMfR++Xm1PYF5UVYWmacylaXMocu8F8Kaqqt4A4F8C\n+K8A/E7WkF/dNM2/2h1+BYAPzlBvYCRS58sxVYXrZtKHt5XmYul3rRy68cjKWj/vFv2BkeRaWfeO\nFz27Nl8Nqpil+moMVbN0rmu3pL7x6Jfty4K2UVPpaHs0lQ4AsMSe2O09lpaM2IF81sgVtfP0FpYi\nx88LCl0icJy87ZvhJHFeTBnYjiFrnJzx6Hz8uCpoXrp2Z7sop/vrtlrvj7mSSa+dFoFyH1Vwd47v\nBVVjux/ASYpcjRor9DcqT2ml0Nwo02/RuXWOi/zmG1TlCZU2EWXV4yVxUxQ16RmxyhsGCNoM+vBx\nONvfJ9jdHakPXGEJ1O39stpfh0fTBiupmOSV4LHl/ST1bkieDdR1c4nhZJfU/9GAJ+l4SY6lzb49\ne8RRVS6lPd25UaZtBV7FjZ7bJFffNJVOUujuob+3HCVy+y0IdsRt9WCJ7abG6n57/KjtkHb/qv51\nppAe5QU72B0/Wuy2Olg0PYKHxRZP1Bssntzi7MruXcuI3RpL1NjuVTgA+75tuX8/28qepvZ17+/h\nlgQJ1ruldDuCqYFO+HGocqeNyUSuaZptVVXfCODvott+4KerqvoTAN7bNM3/AeCbq6r6HQAeAvgV\nAF8ztd7AeEgLcqc8qNwFkoOTq5TW5pFdK2lHJhE0Su60crT1cJJdv0Pt7JItjU7ZltXvuFObEnmj\n+aQ8PK0b4OZn9zRXjromxA7bjtjtBgQDYgdlbzrpxVl6exSsm5PUN20tHFWXKKYqNByeyIC9eyZD\n2rhSCggkTRpA8nMGKvYVex6ni1bVOKu7Nm0X6/113tRP7ImdppqlNS/0WaSkDujWw6XPrc1yvy6O\nRsxdYj2ZzPG+gRO6HOaKUDqWOOYwV/sseCdArIk0WhYnhjxyMH/7tG62ieoDZ1j3yByQCN1EMgd0\nJMq68Wr2d4HO9ZI+o0tybDWMT5JxZc5D5Dhx44rcdQxdKW8Ar72uc6OkpC0RuUTaXt0pdClqpeRa\nyY+lTcLpnnGr9RnWD5YdcVstW9L2gJG2hxh6hpQ8TtxNFQCerNoOcH9ugUeLM6wXW6wftG3UiN36\nibPBxKoUwTrd593aOmnyNfWdsiLXf1amrZfzKHBSeqkqFzg9zKHIoWmavwPgGZb2reTzHwPwx+ao\nKxA4T9DZOQ1rMrscOC74JEDgtLDEarD9CccCWzcRC8wLiYiNsQkcCNcBvGabNE8D1StHaU2A4R6u\n4hrun3czApcMsxC5wMWCdx85D/gs7fD8cNZWizaplcldKGmbuYuSR6Vr0+iC/cXARlPo6Mx/jbo3\nc0Zn69Y71yEtsMmoYCek7Z4961CjjXiZ2i4odMBOJdqlpc+qWlQC4baw9oOj6lDfZuhGCeizitYG\npzkUrQliKpxLgZOUT2nNIrfJpbUN1Y93l6Qis9eLVafandWP9goddV9d12fEXbG965Iy1zW5u59b\n96S+a2Xb5G7dHCVzy707Hb0EeTJOg5pw1b39eqdHBk99gmG7/91k98u+y3rdu+5aWVJ0X9o3tjbJ\ndVd4znZVthEtV8DObXj2gQtVc1a743RTJpfKzBq5F38ZePZp4OZup/A7D4GXXwGe/2RWh6XIXSHH\nKY1HqBT2g+NqXPN0G9Tk1eVTgvqmu1a+iqd6AU+A/t5ybZ4uYmVPkXt0htVO7Vo/aNU4PDjrFLgH\naNU3el3pP5D0hIeQQXdjp4oc/0yv816laxMfXekrdGdXVru1dFtsr+zu3yfaO52OP1pVedUbi1Av\nmnu4qnrRUNVN8w6SYLs/y/3cHO6Vp9aHBmQEkbuE8G4/kIO0vo1Cc6ksJWn0OLdGjubjbaCkjddZ\nQx6Y9m0SSVsOOmGJpHXp5b70tmtlnzTyNFpurz2M2K3RkpGasI0UBXN//dKppZCWgbRpN0+zSBtg\nu1BOdZ/k8K5/owFmAEaIyV+RuFmBZjYsHSw9B+4uJhA50Q0JLcFLY5x6uVtjt12jrjf73ySRujRR\n0RaTorgOyXNqztn+/9U+WiVX5jQ3S2kTd35e6meODWnSiIP3OePrGvZzpXk98LwfOInmE2NJIZfd\ny/t9b3/CZLMvE2jd19rbJd0lM7hZtpV2rpM0LZG59ku0x3yNXDq3w7NPALc+DNze+Sbd+hfA7V+P\njnRoRO4KOU4ELudaKa2Hu94FNAGAV+uncB9X94SMkraOpHVr4trjq4M1cpTcteV0ESvvP9oFQLl7\nDav7Z637JAA8WLTX9D76wWc25Fonl0qLyFngrvo910rIxO4KsXlQAVc6Yrd+cAZcWeOJerMPvpKC\npGzP+ksf6H2f3r1tsBRt6QWNiMnfM2f7cvi6ubHwuFmOCQZ23v1sQEYQuUuINIA6lBInBTaZQtJS\nfknFk9Q3idzROjlpo23u2irPnA3XxNWqDSdSVGmTlLzuOC241hQ5XdnTFMKURl8Ukmq3z5/UpiWG\nURWZoieBEzSKErK2L89J2nL3tOclKZE4aw1cvVGUNz44kYjdlh1DsAGz84BfBm3Qk45T2xZAiuKd\nSN12u1svUm+wXnaTGG22bj1dQrfaiR+fIY3q1lgOyBy/7u0dvRmk9b9mf6+5KevlaPm8f9FsPOVM\ngUX+pvTfpXuIegJY8QBR7d8+sdO8Eto6ut+rXVO5wJLR++1y1ycu1lgu1jiricrMSVL6XAtp6fgB\ny5vS6Hq4FTnmzyO5PW8ugdufC7z9n7THL3wucJPGFZOIXFLfKJFLJC49GinICVfgrgB43e54tzfc\nvWuUuF3dE7X2uP3cErOOpN3r2bQRLbkCd5eQvXu4hvuPrmL1YIl7r7blPHrtWkuM0vV8wP6lNHoN\n+THQKXDWY0PJWgL/XSVF7ir620ykvQNTAJlFBWyWeLQ4w/3djOMT9QbLq+v9dgj1YoPl2brXN6R3\ntRTETJ5o7YgbX4vffZ15+o2EMSTNo+gFTgPxq1xCpNn0MQMBTsBy5zjRatN8JK1/3J/xtjYET+Ak\nTRoUecgdVe04UaLfgbs3pjLy0a2GrpX9Gb+yTUm7Ng83JZWCs4jRs+rue6HuXjwUY1cBTnEFsfKX\n1u0ZZHMb6kJJFbieKyp1F9qytK4hOtmjx2A2gD3I6RregSoMK3KeD3x42rIldYl318tHWGzvY7U8\n25fPN/9OsMmcPIEynJTRycsYkibBo5LxSSIeiIm3VVqnyd23ZSVrGD23pJ3W99O+0zC9nPR2x91v\nqnk8aAGfEnlLtpzEbXa91X7tcQ1gCWzqDZY7L4KFNHiXiFwt2KyYDVWOKInTVPT0mdb/OvRJmlQ/\n3TcuHXNFTiJyO3Wuebo9THvDaaQM6LtWaopccrt8tUfcru5VOQC4d+8a7r16tVXf7u6+yKvob+FA\n1ThO5ChZk1wrNXfKBKpwgnyWFLkr5Pxqd91S/vR7bYjNBntCB2BP6tK2BonULa+s9tEu03PrmXwd\nTKSCPtPzbA5eqsZ5+oJQ4k4bQeQuIWiocC+07QOAodrW5ZHdHb0kjdqXuln2beX6aJ0WudM7X1u1\ns9S3nEuk7Vppb0ra/w7DcsDaQ79/zb6X9UIZE6nPuue0l8WcLxF+H7rI3HY7UCWpCjdQ4Oisvbam\nhg8MaVrXQF8Ut1SGdpkk1yM+eE0uXrxOMhBdIH3ndbemsk7PSH/NG0A5Y713nUzPVFLj+s9del77\nhIfCmkjiKCF5Ut+RI1dSv6S1n/c73BWc1ifZW/1vDt7JEG8ax7BvtifF+ICWKhrJtlXk6v33TQpd\nqmOLGpu6xrJeo8jd0lLoBgP63bE2MUP/ArhzD7j1E8AL/1l7fOvHgNtfDNxM5Is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vAAAg\nAElEQVTfuAkTRYJjZTcMgFvuq0iEjKsnaTCLWJakLm+Tkjt/Kba5IUFT4aQCpz1TvI313EkFTqYm\noeNp0Aie1kfNRZ+UBJjXH6LaWccnWH3CnKUAmVdaZpMUtZLImm+TBj/halxC3CZyR/vtptQHRKY6\n9Nh1I7rdFHxlgDe/XKrI+QvLzCuHDpg+q55Eujbct2tcTOfXBZJGpE6mG7BSDeR542IUS8oP1085\n4gKJ44FMqHwp6krpB3ZI88olBM6KLsLtKqWN5SNlmRM6JrFR/yjNJmW/KYOZNIhEtBaULJzIPdVx\nMgd+HpNCd9P6QCjNOPvISCsnjprxDCdtkpzVkLXbcaUFLxvWX+whYolD7kTw5MttdUauGdXOgjom\nInXBnGCaWXJnLzY/0yFkcV5xajPiUiJnvE2NSaTu75aSNI0wWmaZ2rFkO+3atWuQ22rbSViqJi/P\npYKIA179Wjm8CVdUSojA6T5ZcT+DO4NrbtO5D05uSnUlyLaaSZdsN6cELjkPmZKAthPqW3aOlWkp\n+X0tK0TcTFJX30itoKecTPA4sVtqGqitmyNb/PmhNvn7k7fh++EqW8lvMPrj6X2VRbKWkLZc/dQV\nPXkMrRzP2yJzel9FESs9oaOolW0wrwQQ/N6I0AGpAufLUY3TiFu8F02u/rldjHQ59nDDgGa8TVKP\nuCEq0Rr20+2gT+fYeEV/bBqM08QQkTa6BjIXJXNKgHzkUj85InE8+uSVIHe0/joQwgvdlJKTNF6W\nUSq1hOAy/UDSzzwTlc+gEzmtc+LPOIWlJEJHhJDK51Glu2ZNtCiUfNclEicPxftATuD46Vt9NIBo\nD5qaW57C6olQo7LNkra5seEazOVeYo1auWLFAtREbFujVr441ETdq4k8uuJuUDNZ8yIndB46riqc\niWrarLgb1Hxb1t/nBaLK5H3t31acFqsi9xAxbJTKys5FKHRnbjDtqEvqHDe3bJoxs/m2FLo5H5Ja\nHLr9rm+ZTV6a+JaDR62MbfhMfKo01fmtlU0ja1U6a73dxjIRLS/zc+WwzK2s4BiWeSW/dm5euUMb\nTCu5XxI3t9yhC3UyV1WfRDqIM/9ANLEM5pWYEgzzU+S+ZOmJz/vNQdlOrpO+OqcA933jpqLyC7FA\ngePQ1KJcNZo3o+zRZuaWMsoiN6/kdfxYtZDqm99f/q7yNlqiX6nQafuRx8tRnqSoyQ+nmVbKdVc4\nn1XmeBt5LA1av2CbhQ/o0E/vaTR3JD85OpZ/X1NzS2maq+eWa8J+o3lmzFEn/WJ3roNzQ1DsAMCN\nYxLJthlvITE4P09OgZJGF8+vZ4oyqXIAmFnleagrqXFXoo5InG+f+syF6JSXTZ7cm9S3p8hNKaXP\nHPeP4wnA90Cqsl0DuEKqwA3QlThuWilDVF4jqnK0/lzs99zXl/zfqF+2lDg6BFfdNMhAKryOb58p\ngpMq14zwkS09gqB3hDp3sMmk3M4imeqYccV9wUrkHiK0Aab2ojb79MWeOiBe5p0DkTrqjHiEy3Q5\nbsNJXeLkqxA74DgCN+cgzFFKmr7r9QFVlophQkycXmtqaScS59tJcmeRuFJglZKPXX5e8pznyZbc\nRi7rZXtfkrTxNuSfBOR+c0TY0mt28IQtmmalZA4AdtOQrplKQOf6YF4JTHMbnKTx0+cmOTWml4da\n2WjvNF8n/fno+LyNY39LqDzH3PSuSZa1ZM5WHe2PBsHUhkgcD4cv/eZKgU/mr+Ew00o+USOjqurm\n1PG5levK56ebNy4xrdTuWc12ktzVoMaUnHzjHMZg7iyDnfCgJdzcUgtsoqckIEJIKQxiG3r/iTTy\n9Aih73MDGqf3Y/I30VJjcFPgfjL4pPOjiJVE0ng5Bjs5T8wor3AefOg44bvCBa6uJtPKywtP4rhP\nHBE3LbAJN7ckvziwdTcQpOiaVTxjZctHDsh942g/kszJTow7xCn+dHukJpHPEOfmeFM+cXUzleV2\nfEKL+y0L38es39cujZlYEojQlSbFa3CQ2WRG5GYI26kmDlecFCuRW2GbqPOXukTqmErHOyNO6GKE\npkjofJlFbBKdSonYHYKD8qWENvPHD4Mwi9AxB2Z+bZ7o5QpdjVJmk7R5hY7vd4406n/LhCu59iNI\nnwwkow2INeLGid0In5PK+8ek5z8q580Hevz7307tx6bBOMZgCIkqR6ROi2bpWJtB1HEyNYe5NnKg\ngelcnCgf8gXg+2jEX3gFQvrDjeyfP3QkD7HsSdqg1AFRreAD4VhOfeZKipxG5ErRcOOzl07EaGq1\ng1P9OEkN9udcfp71Y+coEQbZZk6Js0jbHLHTzmMOc5NFDnHSpU8mWaIfG6lxRNbS65DESU9JQNdE\nJI63kWqg7E9ln8mvp0Su6R7yiQfLR46IHE89kJE0FsWS/nGV7qo/x9XTifBcdjlxI3VNkjTehkep\nBFuX9EFE2q5EmTuuWSROm/mibTjb4mxqruN6lB6a714jbrTuWrShZS7+yTonyvKSMtC4KqpzwPGE\n7iDFTSNuK1l76bASuYcI60WVs/i8jl74ZgpFNbhA4DihIzIHwFTn9HIeqUkjXpbqVVLQCBphswia\nVb+kg83NJCay1owm6ePJQqOSlw8c5tIG1EakrFX+aB3flrfhf2U937fcdm47SxHUBtR8sKwFOyFl\njUgaT1hN6NisP4HIm9+vH4w554LpVAh8wqNWamaKI/Ixy4h80KCpZxI1be4CCnFLSRxCIAc+oNbN\n3tLybiJlRBZIqZADc2mKNqfI8ePQfvj5cZRy3+mKGiWft94hfT3fDyeItUocx5xKZkXwnMu9N7ed\nPLZWJsy947K/IvNo3ewZQY3jwU60wCallAS+7MkhJ3d0rEjM8oi3si8F8n5NCyDDVWWq000rZbCT\nc0Wlk+aXPuJlIHe357i+vMDt5ya/OiJsl4jqlFTfiKRxBW4n2iQk7r3p7zVSRY6UOBnsRNokarIV\nV9h4R8o7vXPkLO2RqHuUWkk08OSNT6YRcZOH4uSOK3ScxMkxklTuSpcEIDG1BDJCp+HMDfNRJfnY\n5RjithK6lwYrkXuIkB2QrOfrSoSOkzkG6T/HCQ1X52RZmlyqp34gYaNjzdVpRK3YcRbOp99BvT9A\nns4h3qM2ED1+n3jiUMCreCUSpg049HWacqCRNF2ly5d1sifbyfNI6/N9aLP4NaamVKZw5tq5ykiB\npciaCYKv2A7AbXydaEBgkTtedqKOq3i8Pj3BvK6mJ+fHamAra41o75Q2YjuK0De4s0DGNPWNBp40\ncO4ho092SNWK3OzMG8aVFbmUJOpmgzVIn+EaRS6SO6uNth//t0wM5s69lCOupLbVqHTJb8msI3h/\nXGPZAKSTVUDel/F3N5pNpiaS9OwQEYvXIQmqZmZb9qOTPnK0rJ1juCbkaVUkqebnwklj/oxHVQ6w\n88jtWIoCIn6kygE+2Xd/08Uk1Ty3myRuPG3A5xDJG7W5Zm3CYyqJm1aWUSprTCvl+kfQVTgyreTk\njYLBDH47SoFAZOwZcnKnqW014t/A/mqqHN/umVE/CFNLxX2Fo0p10+okgdM+cbV1K+4d1qiVK1Ys\nwc18VETcrPMjLwo1UUWv16huLww1Zng1kUdX3A2qoiJerlF5XxSi764Ny4d7xXPAKcziV6xYiHXE\n+ZBRGyBBK4d6YRqgKFCHRGMqJcW02uv1rlguOghr9uXDxiZzfNbrRpEvmpAhFbc0WJ3uV0+mLlOZ\nK3b+3rVBoYt/dZUO0JWrZet0/xWtjdUub7Pc1LJ0PlL1aNgMvoweSLPqpMxd43zyi4mmloCfPe+y\nufUyXDOmUSy1/EVcpeNliDr+l5Yl97HqJPgrYfEnw0RSbUMKHVfpprqYK6sJSppmRslN5LjZ5G5S\n2WK5DW2i/1BU4yzTSm66piki1IZQIpZSZbFMjC0zXyR1ZQWOB0jRjm1BUxlrcsZpfmOAN8fDGVPt\nJvWNlDfqZ4nM8f50qdl5anEwZmbl5CcHxCAlMtqkpcCl16orcHSvpILbYASZU9I28nfNrSH075u8\n97ppZTmPXHzm28zcsmf342pS6QBP5nbXbfSLA6L6JhN3c/WNK3bXok0SyfEa3qyyRpGrCXaimVNa\nkCaVFMFS2kQ2cb+aufszxGiTtAnVW9ZKA/IAKLR/nnuOHwew++sGZSunJakKlqpwqwL3vsFK5B4i\neC5NjsyMEvYTMmwSYnIMpLllrM9NLQ8JTFIib2Zkp1KnOGzKHZ7pg6hFhGrEPW6AZu+JHid57P6c\nuaFI7oDcbAmYJ0NyXVqvm1vGs7bMJHWzS2sbjexZg2LNFMthnFI/SNNKPygc4YJ5liRtS0kc7bt3\n7WRmSX43wGZA6v9mJacF8veuhlzRNmRmWXotuGmkRsp4WZpfNqIst2uAfceInMvNKGlwysvRlDIO\nRPusXRvMKX2bDpppJSeNROBUHzDDJHAOpcBEpQiq1Mb/1UyT0/3w9kugEdMSiS2ZTQKezA2Dwzg0\noe8cBxf6zdBn1vSVEtM3o+f9fTOyPo1I3Yhd06Jrp4BC8MGK6B6mESCjWSL3hzvHVZG40f3hhI8f\ni/oC7iPXsv6hZG7O76dmWimfV34NROJ4sBOa1Ijl6EsH+HdlhxbXt+fenBLA7a6LJAxICRkRN8m/\nyMxSbneNaKaYRKQsETlKPSCJHFhZW26QbiNZkdZedlZkjimiWHLi1olT4ikE+KU2og0dnpM76Usn\n98MfDWuCnMZUBdPKBKU+bAmJK41l1MibK+4bViL3UGGlbSlhbqLsDnFoZElrxthU3sLgxOgIa2a1\nLJTaJpe3mcoNW9eEQdAtWj8QMsgdEAdDfjmd6eYDyFoCN0faNMJnbVfaxvIn4tuU/OMkSeUR7dw0\nEKNBVByouelIC5U4fj0OwWfODT0aSdyk/5vmR8fbzkEOGGqeLenvJssOfqDC6zpRpvVdrBsbYNdx\n1awNChuQBi2RvkGS7O0ESZPqG/kGcQVO+tWRQmepSZyczIEHJqJyXB6SCRMgJXe+HNMRaM8xR03U\nSk1BLAVyKfppKfdHEreEtMm+0VKQi2B9W3guG9w27O3b9qE/65spuMl2h75p0Z7FSZg8lUAekXLE\ntUncaBvpI9dNgVS4zxxFu+WqHUUo9W3slzZXCCOx5JFYgejrJlU6/l7JOoAmNDrsbjqvxAHeKoT7\nttEyjz+yY//8Sfo2xNUwlZPflUgabySJHCdxXEGTZG4JrBmuQfwD0vQEoimBJto6ZR0ne3J7JvQV\nMTdeqhlPLVLkDvCD09qs5O2lw0rkVuQ4MWGbM6u0wvUfCxqUVJlPJmROUdy0QYvW4R16KVp/LVPp\nUNjiMBA6nNz5sj4QTf/epWllPUmsjbapKXC0D4p0FwMmNEmZIGfwLWRBUabf0HUDkuAnQB3hl0lo\n+TPBiZt0qqf1JVNpJ8pbUdchJXO8TrbZxrp9B+y6M69KIpKv3OTNqeROltNEyOeJ6sAHuJLs8UiX\n460LihLg+wFVUQLKAyXRL5G5swxMVBOUCJh/p0oRMzVIUpeSFd28kt8bSWhV4sZJm3zuqP/jp1rz\nnHMVmPBoE+tvOtw2HfrtEH4DstoYttM5t35iJiduLntnl5pW+j5hl0zwkCoXJ3/s35JDix4qFTlp\nhqwFQOkz4ubraLsdWvS3LcbBeSUOyE0iibiMyPkX/W5E2naIv2vol2gjehB4jrhn0M0oZdRKoO4h\nkZAd3COkH0iNuHGJjEWwlOqa3ITX137Pydxds2riZa1OmlkeYul0ChK3EriXFiuRW1GPZEJs6myE\nicwc8nD79jbSR642Klpppj0zC6LlxHRS/OXLvLObG8As+V5pl8b3nwx6+DYKuQNA5pk2uQNqB6L+\n8DlxSk+/zkySt9XaWSSxlA+PZsc1n7hI7CKJk4qcTDitmVfWELtwft0INZJlDSSZ8xeY18tBAA0k\nNEhTSSCSOKasoYMfxFAdLcs21A5AvwX6Tpp9efJFQWV4zqs0D1YkbdEHqAuR+WQY9h06XONciWzZ\nob+dyoNDf9PlxI0rSkDdwKdJf+vbZhpEK2aBoWj4flnvVKgz3pUSNMJCf6UZqUZqM0JbIm4U7U/2\niYvUOAb+LMqyw2QZF/uzfvIdCuRz632ox1ZOGOT3pHZiRm4nTbAd279Md2KljpBmr1KRIxKX+nw6\n4S/aQZpX5lFfGwyD82oc+WfL35BHraRTHRFVOCDlXlSXmFQCUY2T9oVcbXum1GkfUQvyd9JML2XH\nJusMRU4qcHczj5wf18F2a0naLiRzc0m86fil8oqXGmvUyhUrlqCm09cG4yueC64rou7VRH5bcTeo\niUi5/j4vEJfzTararLgTaKa1GdZB+ooVDwqrIrcix5FPRR5xsU6Jm4tSydfXqnOqX1ytGqeZEVkz\n0PLUNTJnXZ7l4yQvkco7pLPXgFDpgOCL0rCp7maP28YlETJrzcUAqKqCPwU9mbEVlc9SImwzzjQv\nF7WVEeQchkACqI77z/AgBt4vJTWt5L4y0sdoMTqAVDl/TQsgJ5/lcyZNLudMoaUKR3XcjJKWmdom\n1TdeHiautevaxORL9+eJalufKXcxL5aM1KcFfVDzbvVtCPAwTopc5tM1px5JkUCbNadgRUH19v+R\nUgcgKOA1Kl1Yr75XdSHktQAu3GzSlyvUSbo/RNKkAifVnUvYfWQNHsFW5LjAEiIBenWuZ2ag3Xnv\nr3Uyt5TT0tKMUa6jv5rfXBrUhPvFjaGOWwDQeg0y4EzP1DZS42TwnhFNqOPnyJVFrtr1aDEODfvG\nIZpJakEjeU44/juSQsetJAG2Id+IN5I54ga5A2U/GuiHt9bRvmsd+08AruIdgxG21cQhsJS4Uj+n\n3frVpPKlx0rkVtjQfIu55E8DEGG6F1ZXkrglaQb4NrVkTkUWaU0sl3xBSqaXh5hYWiSuNKBvxF8i\nd6YJJqaBKA8y4EJ0TI3cAUiiYwbHfhFEBUgHowBCUBUa+GhBAZaYZ+aRNvOky7TOR60cQ9ACaqMF\nMeADuFLESj7II+Q+SsKsq3PwZkhAMLOcI1zcjJKWOQG7gR9U8GeRoq+V9kt/ZbCTkmmlLL+CQOKu\nXpkGnk5GkdT8edI0AkTSuKklD7vOiRsndz68epuYUu5uOvQ3bSQqu4nE3WzsMaU0EQRrJ++Zdh+z\nyRPm3zWZN2vkjpCaN8sAKsvsvCRhA5TJK43UAvn9oUNzkmZZzgG6qWUtpGklmVPydXyCIuSF9hW3\ng8P12KDdxtQh2ALjmVPf1XjKy0fS0Vw7D6bkWP8yZ7aZhmSKPnLSn5QCuaTEr0muK6u79Saz/ree\nDki/Ge8riKRBtLG2SRryDeXMpiR2vI6bVNZ8DO/ZsPRUp7Pk0eMpCUrrV6zAvXtjVjxXyEktjbhZ\nKJA4a5DCyyXyJkkBADVcuCRzbvKhoGOMg89ftyS/kTnDXOsfUmuLPqcAyO3kbzMY9bxMt7FKtaN1\nzkx9oBE8AIHkzRF0GVhFKnQy2l+8rFTNKwU/oe2JtPH9cU86f/QeNYEPeEAFgpzltwJPEAkamx4X\n6NPXiogVJ2lA/L120/IO8fd2SJ9FasvJHcQ6OhY/uBXIhPvEEYl7JZaHV9LgJpbv2zUjZOQvJ0ka\n9/nh61MfOR785CJT4HbXbSRvQCRw10gHsKV3l9eXCJwsawQZmNSmisBEYX/5+7UEqq9bKBtqJCdu\ng/jH6/gk1tx9rD19ef9omdfLUPDDVBf4wwYYOvRMbRwHB7yKkzuMcEVO6qR8osgKUGP5yaXlNBiL\nFcRlEPui/QyDQuT8xvq3SQppEG1UMic3Lv3g7xOZZynvt8ZNVF8rJJ4otdOKh4GVyD1UlEicVS87\nF3OA7yFznMV6fYBfgmxDxG5JfrmDoQU1qVXlaiNblgbhtB+ecBTICZ0ciMoBkyR2gKHaISN3AHDb\nxA37MT1hjcTzPHcAZsleSvTyXF1hG6QRKmmmPK2LSXwBTOHDm0SlIzNLOWBKB1aR6BFkXYnkhXUO\nwCtA10y55hywkUSOBrM7ViallZs+aYOz0iskVThaXmhaSSTuuuMk7TwhZJygURtO0mQAFC0gCo9a\nSekGAJ/fjBQ4InK4aVP1jQgcD5luERkJy/TJ6ivl7walzCdReB7J5P2aVKZG0ASrXyzmuFT+ahNS\nGmkrETftmeNCC9//HLSJJflsUl8m83ZlkQabyXg5vjYU2bJknVoTFZRAptj+CNQv5aaVfOKI1xFk\nCgiqy8u6+i+Vu2z/csJSm7AoErQlOGzSYR6n+o4bjEn7Pmrfw6WoGT+tWHFHWB+zh4g5EqfOmIoo\nlQUSp5kKzZE37nNlgfuD0Pac0I1DE1Q5OjY3PTpzQ54AXMOc2ZVWZw2cgPzDWfMN5G0kEQPyCFg0\n8NGInVTvpGpH+wIU1Q46uUNUGIBI8riJJodF9jnZ40Sv5ENE+ZukvxyfFZcJfCl8OB9s0cCI+9HJ\nnFJzJI7qAD5QS9U8wuAcxotpwOZ26BphbimJHf/96HaQSkeqmWUmyMEHLEuJHPOHu3qlRe9aRTWz\nIlTmpE1T6WIbIm5+sBz85iYzyqvLi6jAUWQ+yotFjwgROE7kuPpE9/VQRU6aAwKpv1fpr2VKGNpt\nRHmmrypNHFlm4Jr6xv3htHZQ2lrH18oc/H7wCQtpWkkpLmhfWj6v8OxPRJjtEpj6jUmdkwSrlLPP\nv+NjEnQnJXM5qH/hScv9aer9h2YFkJabZBu5H2klECYyNZM77feQl79YQJv7jlJCbm27uQ5LG6DI\nl0VjXDIlgdiXnECgXcllh/zy+LNJ66022r5r6sN6NmH+E58CPvY68IHHvvzeE+Bvvwv8rjf133oO\n/NtCoPN+n4ioDxFr1MoVK1a8b3ClhZwW6EtT9SvuFETcSniK157DmaxQsUatvNc4OPjSipcTH3sd\n+HNveQL33hO//LHXX/RZrbhnWBW5hwxrppmXZXCTgk+cZko57ydVD7nNeOsSZY6rckB0/qfzS0xP\n6BxLCYElaIa6EXV8uWRmZPmTfOZTwJe+DlxMs25XT4Cffxf4ijdjW+tN5eaSlkKnKTxQ1sv9ETSV\nDrBNxYBgjklIzDJnVLs5/zvKy8V97UiNo4EOqXGDKJMCR5Hj+Mw2bS995PhfzfQpb9uEv7l51LSu\ncxibHl3To5184zJTS02R24qyVEc01ChyDTJTyn3n88TtukkRc+fBb437xPHIknM+ctchSmXqMye3\neYrX/LH682BGeX15EU0pyZ+Q58ai+yAVOakuWfdrqSKnqW2W2aBsX9ov388cLLNvea3SYmBU2g9I\ncz4DtqnlpVgvz6UE6x7xvofMKOV94e8BoYkLXJVzzQhs/XeDT944jMFc15cHOLgsNUZU6hvswFU5\nwFLmNGjJ2VXz62w73e92MZnTnmm5i5MFgZQfoGdiHc/1Jk1FOKiOn5iltllKnHJRlqKurad1swqa\nWC59r7XtSm0/8Bj4jreB7/qjvvyJH4zqXA00Bc4C/TxzbVbcO6xE7iGixpQS8CTOSPjNB91W1LW5\nsPXAMl8FjhEO7mwM5pZNMyZkjp9LZl4J2ASupuOrNb3U1mntPvo68J/8a8BX/QHgy3438Ne/D/iG\n7wT+lx8GuleBr3xTN1/S3l6N2FnErZbsSfNLILecKZE8apC10ckeD7AC5GaZrnGZGSYROzeZRI0Y\nEzMnx8o5cfMnRv5z0kfO35Y0GMEIh/MpIiWvo7YjHC5wJequU7PLydRycH5Q2DW3ng/TIPVmWuYJ\nfCUp0Qby7JYHOFFHxE2Wk9QCZ+i76OvGo0haBEwLbFIqy/QDSSTLvsX15UX0h7vsoikljaOJxBEB\n4f5xkqjMkQ7rnbJ8aCySVjIblG35X7lcA61f0CaXNNNSi+iSqapGAIF88kAz7bTAyZs0Fad1W8To\nilxgJ3InkXQjDW7JVPtmmgy64P61I3Ys4i2F//f9Ak3AjOBky4V3Nu5HRqikvqQUvbIURXMueNLB\nWEri5EQPwMxXOXG6Rv4CyI+NzH49IGUL/OQs00la5seS5Ub8kxcjDmE10UylZbvGWO+UtnxZ9gEW\nlgQ5obbSxJJ/v81tjTYrUXspsRK5hwxzpln4w7FlS4WjZTt4xdQGxxE5Pgj3J2RcA5Coc4TMIbwh\nljJ1hrX+RqV21uDaWr54DPyh/xj4D3838N+9DfybPwJ86rv9uj/w/Ye7GhyKErGjY2v+ekAekIVv\nk9XJDxD7ARnJ04KspCHcY+47PnlAxM63iFEseboBqb5Jckcz6BeGTwuvi2Wb7JEycD6RvBHNlKbA\nR7Z03YBudxtvxwAkrjlyoH6sjxyVJwI3NlGBI384niPO8mOrDWxCZpNamx1av7+riRQ+Pff+cJfT\nSd4gV+A0RU7mz5KkBFj27pQUubnAHTDKVYq3cT4ln7RT+shZQVIsQlxzTy3FX/Yp8p5BKVPdTpRv\nKM2F/ybt+hau9TsnEtezYErjpNBxcueSPoDXR/9aCW4RQGXZVkatPBXCd7fZI3zLtPsnhS6L3Kg8\nUiNt1myJJHG1kEzpEasDq+OK2yOlTlz8lHkn6fMeiTLfjE98WZx6bkLGel6tskbiyJzyEz/oy3/u\nLa/QcVWu2c+TOfX7q9SteClxkt5ks9n8XgA/AD+s/qH9fv+9Yn0L4D8D8DEA/x+Ab97v93/3FMde\ncQDM2SY7R5yVWkAzeQvls6iGcOTlE/UmZ6m5JYAsPQHg+y6KdFZtWllD4GqhzoR9EPjirwJ+4aeA\nH/xG4J/4GuAP/UWgfaxboMg6q1xzLrxD13oEqVSUypLgEaSap51j8lHbpCsavqwnNie1DgDGidTR\nszCe+UEYETp/qjEACpW5KufrUoWOCJhVB3jlStaRGsdn4PsptTUNKlvXo3M79N1EPnc7NOMt2htg\nI6P3STIn76c2Y8zrtsDeeeIGePVtbBr0TiTbnsgV4IncNc4DoQPyRN5E0J7i1TsGhe8AACAASURB\nVOqUBKF8ex4CmgDA7ecuvBnl0+mcd/Ak7RI5cdOInCQlS0mHdR81Va4UuIO3k9sBMTKpPE4NLNNK\nXjen0vE2FvmV9VDW1UIqGjSXxu+rAzJ313Pkv+kNW99QmYiMw+7aT/KMbToRI99vx95NCnbC31VS\n2rgqx79jUb3jqt3hqpo2wcmDOcngToD/Nvd0H0KlKFvPr5zg4ZxoAFK17ZGxoSRc16hHjSJ3weq0\nY/PthGmlQ356koBpxI7q5WSY1gZKvYZs/KUQOBrD/Mz/DHznnwE+OPkNf+efAf7WFOxE7uNQMsdh\nTRqvuNc4+ifabDZnAH4QwNcB+PsAfnqz2fzV/X7/c6zZtwL4tf1+/6WbzeabAXwfgH/l2GOvOAKl\nzoT5wuVmbfUKHE/YzGElgp6D/6DGbf1APM56OowzCp1LyBzACF346BqRoOZO8ZjZrfeeAD/6FvAv\nfT/wX/4x4P/+CWBURkdzM2pWWSNZBCu42FJoZmnWR0OeB916jezJbQGoic2J2LHnlj+vrhnRbXeB\n0PnTI1NLmnl34Tmyosj55eusrkevqHbXyX56tCGKZod2InL+L4BA6qjNrmvRoceuG+GGaSJlvIUb\nAHotN5WmlfvpHhNxI+VtdHFAS0m5Y3JvSuwd0wHwZN7UZj6P3HlRuQMQSNz15YU3oQRy0nYDT+p2\nom5ATuQ0onKMPxegkzNqY63jM/qa35y2DZR1HNb7KsURXq8RtDmSpt1D2b50PhaIuBFKpmxzsYt4\n/3UD4WPqcNs0GAeHXT8pcK2muPtegJtSErnzbcggm7eR6lv9TZDfuxHRBJwrdTICb8+Ok5E4mlRt\nRgTLBnrOuLpkWSVSG8fa0ORRyOPNGQ+xak3q5eBkrtZHzlLkeJ2lyImXacOa0LMh74dmdl5DzPj7\nr7WV7771jGf7Zc/H1/3+dN0HHwNf/3sBjPkktGZqWbrVK2l73+AUUSt/G4Cf3+/3v7jf758B+C8A\nfJNo800A/vK0/F/Bk74VK14+1HR0Sydi/993gd/zncBf+27g1S8E/t2fBPpr4Ef+uA98sqIeN/MR\nKSmcfQk10RVXLMdTvDrfaI2c+OJQc19r4nwsEWNWnBbrYPzF4RBFbsWKI3GKR+qLAPw9Vv4leHKn\nttnv9+Nms3my2Ww+tN/vf+0Ex1+xFGHmSTGlZMupH5JuSgnMK3FzCtyhAU8sBD+6s1FV5ThC8BN2\nNsDGnq3SlCcgb+9QP+v/5W8C/8engC/7euC3fIP3mfvWHwZ+9q97kvflb+rb1Zo8SpVOBkQBbHPH\nJbDuDV8PpY2mJFk+eICRxLxh19p6hW4cE5NgUmS77Q79bYvxzCWRLcnMiiJcXuEimFDKNmQ+yevo\nuWuZ710wm5z0N20bUrza0Go3Xd6IHj2cG+HoOkg3nHwFSakDvFqX3FYX5+nGaZZeKnA8+Aqpb1yR\n82eTK3KpKWW9Ikc+ckGh6ycTzcsL9JcXMT/cJaIaR2qbVeaK3DN4oqGpUOHGsLYlaKqwZaammabJ\n7azgHnL7pShdm+YrZ6lt0lRRqnD8/HeYv48amSMFTTNPI4zIg5qcI+3L6Nxu2H4GCFVxAwwO4+CC\nif3YSrUtD2w0nvh7xCGVPFkXz24I50htSDsckvpJoTtjihz1hdKckAInNYh5+a5ZPeDvM5n6cjUp\nmFdSIxmJRpY5+IPCf0TNh64R6+gCzsXxNVlRqnTI1Td+P+T94QqdZUqpWZVCWTbVNvnXGIPVwIq8\nXWNmyc9hAPA/fQr4Z0W+uv/tXeB3vrkSz5cAp/iJNFs0afQr22yUNiueF6RNtvCHA+pIXC2B48Tt\nmGAn2kdwFmcoPuVZ8BOCtk3pVHn7Z6J+EMsauZJk7eIx8LFvLhzQgCRLc/5s0qxREjppmkF1xxI+\njhKxmyOiQG6ydrOBNy9qcNtMpKQZcdbtAqED/DM9bl14fskkkptQyYiU3NSS6shEcpxGrjQ45GaT\nFMFyENtw4tYzc0t/OX5PLXbhHaIBnXNjuAc1Zl154uE06EKPFhTBjwc30YncOTO3tBN9l1ILXOMi\nRKUE4EncZRNNJ4FI1Egl4hEqZdRKPsCXPnKWf1zhtu1/k/9kbX5xb5M3IDdNW0LsdqKtZsb5f33K\nR7U9nwZY10+Av/Mu8E+/Ga+NQyOtvKwFNpHpCG5EvdyPJIn8WBZkn8NjYdA7zckbRWzl/Q+ZTtI2\n0ppPmtAOAAaH4ZlDO+17vHUYzxpgei8HOLTsL8G/F31yCSNc1btGkN82/u3i38TgFgByHxhBppTU\nluJoAm2YaBrZRJGDd3Not7sQrRPnG/+M8Si4ROIkceNtOqT3dQQzr8S0EyJzDdvRNVIyR8yIR6kc\nMB8EhbMrzr7O2fpz0UbajMKPMuW3QQY74ZvIYFC8DX/uJFGzzDDlRI9crwWVk5DrNJ/+xjCzrI1m\n2QD46teB/+gt4N9+29d9clrOztk+1RUvDqf4WX4JwJew8hfD+8px/D0AvxHA399sNg7AB/b7/a9b\nO/z4xz8elt944w288cYbJzjNFQkKESkBVPnD1RI4/kEr+cs9/dRP4uL1r4B77GftxydPcfXuZ/Da\nm79dvYRqYkfChPG0D4Cuyi2BpTaNrI6TOEmQNMI0h1O8vdJfRYsYLY9VImKHEDxJ1LR12v3iAw3A\nGDBT4IMGt4MLhA5AIHXtdsoX1bjgQ5cTtxj4QCpwtMxJkde2UnJHdb5NJHJuIjjkkeamAUuHfiJy\nbUbkCOQ/U8IgroXORxI57icH5ESOBzrhfnOcpFFqgrnUAldXFz4q5ecmE9bLjR8Hfg6RuFFZi1BJ\nas8OMVy+v9g0fD7YX4uAFLD/yETofm4fB3VzqQUksaNtJGGz/OrA2v/jr3v/2a+fBlifnpa5TyDE\nskbceJuSIkewyJ51TH48Ce7HJvs/Drom7pvVsHVbxHvPgyZKniD6ituxySwxZCRJWSfLPGLlHKx2\nViCUxNctvOcuTPAQgSQCx8+Hl/umnXLnTRTwpktJ2xZRfZPE7Zq1IWWTtqPAM/Re7kvmG7IzpzQF\nQErqIJZlVEr+gJyLOiqfs/I5UnKHlJjRpMAjRDXSInbSb06qdnz/vA2UvxI13+waYpcRtwVkjsB/\nqg889sTt3/82X/5Tn4zq3EreniveeecdvPPOO4u2OcVP9NMA/qnNZvMRAL8CH8TkXxVtfhTAtwD4\nKQD/MoAfL+2QE7kVdwBDgQurRZAI/3ciZ0yFqyVwcuCZnApb99rrX45ffeuT+PDbfwQA8A/f+gv4\n8Nt/ZHagSsegjyO1l7l5SuocnVUa/GQhmeM7AmLnT6evKXIWoTsUdzWDVjKbPEads/a1VKWjv5b5\nGpG6idABUaXjCt2w9VFPx7OUpBGZ0YOU5MqaFsmSWvlt+rAvej+oDb0jtM7hPFfkxDvnLzF9T9Kk\nw830NzUpo+si00qeIoGIHCdpPHIlUCZyWfqBKaAJgBjUhJtJagrcJVLSpqUfGJDnkbMIDGFhpMX9\nl02E7v/cz5M0PubUJhm0dUA6lqV2m8fA73wb+G+mAdbv+6Sv04icRlRrFTkgJ7+193Cue5bK/5wg\nI4kZr9dInqa2hvNN+/BhcCGC5RI0SCdQCPybp02ySPAAXRpi8K4xvM+kvnEz8Hi8ePz2rMewdTHq\n67b11gkyL+UW8R4TcSOCzNU3HimX/70GUiLFITtmTt7kR0SzLdbq6ViccRFxo7KIUklqnEbaNFNK\n2QYzdXw/vI4g+4Fk+7JLSxVU4sa2p3X8WCVSR+UNW9a+92F/B4yNVlRBilef+MQnZrc5epg3+bz9\nUQD/A2L6gZ/dbDafAPDT+/3+xwD8EID/fLPZ/DyAf4Q1YuWLR6UZZckPrpbAaYNN9aP4+AJf9PYf\nxi992/cBAL74k9+B5vEFkHwc6UO3/GPsLxbFpz5V5+gYd9xpaSSplhyV3uAaUnfgbTwJSte4VKXT\nZvo5seMfVkWlAxBIXbvtMVAag9aB0hQAqW9bHvkuVb64mWSWamCqIdNJ3yYtX08z7lx1u0tFbs60\nUksIzolbWhZ+c/15ntybyBsQSRuPUil94sikkptWkvomzQFrTCsPwP63ToTuM3ubpEmV+xHyZxGw\niR1EO54Q/hqpU4Jl3mipkCUCNxjtYazj608JPviVuaO1d19es3JOUpGrgfaNKrkJxHp7cmUOLagf\niD5yDkMgcd00eTSiSawCqNw3Lbpz3+Z6bIBtl5M2+gv49+gZIuGh+zkCeEWcHF1KMLOsIXJSIrUk\noTki1yAlbtJnblpHn2oirPwdO4e/Tj7pIttIPzoJqbjzU7fIT7KtneJpMTTixtdZUS2BnNS99wT4\n828B3/VJX/7zbwHf/jbwoQ8edm4rnitOMl+/3+//GoDfLOq+iy3vAPzBUxxrxYoXCjlLfJdtViwH\nNxuy8BSYYm6YuL68wPmrVyc6qRUBTzrg8UzYw0tgNrhlTZs7xP4rmEJ3l7h5AvzkW8DXTQOsn3wL\n+O1vA9vH5e0OhSShGkrq/JI2Kxajxp3A+8l1xTZJgJMVzxcayRI4cwNux+f4Av2v73riRuaU3/62\nr/s9v7+83Yp7gbWrfYhoxsyUEoBqTlkKaFKrxGmzmNps5v7JZ/Grb/0Q/slP/jEAwN996y/gS97+\nVjSPX61W4HyCZ/2xpnXjmYOjBLHNiGFwyYfvaJ+5mkHMWGhz6CBINYVQ6rRbWeN/fgyWkFZp9maZ\nY2n+cnKSVzOJcfBk7hG8Orf1jUidG545NI9GXF9eYNw22DUtujbOhFOkSa6kSbNJ7ifn2+Q5465x\njpapdlGBS33k5LtWY8IlwdU4X6aomvOmlTyK5UGK3NUF+pvWBzR54q8MT5H6v1GQE1LotKiV0qyS\n1nPFjZctdSq9MR4HKtNBoSNCp5lUjchVu5JCR20GAL/wLvDb3vbmlIBf/oV3gY+y4Eian1qteSWt\np3tDEyElvzltn3zfdC2HTlTRO6qJOSX/PKuuEkveKZ6Ym8r0ns4F95L55/z3qmV1UXkjjGjQoceI\nETt06LBTrAIc+rMeA87Rbr11Qb9to/pCkUa38O/OFn4yhBQ4IDWtHJQ6wlMq8wAkDaKNMy3LH5GC\npJRQo8hJH7kJ0kySJvW4j1zDNtMiVMqgJ/J9Vs0l2T5Gto313SyocDQ242M0DpXgWaaWFmGUAe8C\nYZvqP/TBlMRl57tSh/uE9dd4gOAh2QlWVEoicIBtTnlIsBMt6MmvvfsZfPTtb0Hz2Pe+H337W/De\nu5/Bh978moyg0QeRm1paoZ15qOn8ZsC/BRTwYmjgGofd9RFkTpoBLcWhap31NluD1JL5yKkwdx21\nvkqldjLiJkVn42MIXuambDRgBAKp4+aWROrGieyNW4f+rA1+K343RO5yIqf5xAGAw4Ua3IT7yFmm\nldpfv5zebP6+yIAOh5pWUooCjbj5cpf5w+2uWx/U5HJ6f54gNaUEUn+4z0113JQSSM0qNZ84Ks+Z\nCcplwhHPPRE6wPCj4+chyR49l9KXDAA+/KbviOh83WPgN76Zkyy+f7D2c35wtI7XW1Er+V++T+34\nh4Cune5HaXKpYEa5/LA52UrLaYgjaqOVpS/dXOCTmLKE0ALYoUcX3vkRI7pgbn0eSFw7/aX9DHC4\nQPQBHLcNhleucDtMNpJE2ugvpr+vsDJXuef65kDmgCzsP64R/eN4HoqaWUoZ7ITKlNuTHg5h2sm5\nXodI1GgdEbQtq+OHeqTU0TaSvGn+b3wbCWlWGerT58Mib7KNSeaAlLyVzC9rcKjZ54rnipXIPVBo\nUSnjsk3iNJK2LNhJ7jdH2/yGN796Kk2znY/PsX3zq3HoCEHm3DGh+M115z3GIQ7mb9EBcH52s0SY\nNBI2R+xkMC/a15LLLl3mMYStpk3p21O69pJCcgjkQFhGy6O/cpkPsAeYKh2AkItu3LoQEIUID0WQ\nI1KnqXT0/JO/XOoj1yfvWEmRI1jvU3pb0ll7AIHE8YFgjSK3Q+tTByiRLKmN6Q/HA5lYgU20dAOa\nIkeXW/KRI8yRHsKJ/EUzPzpJ3Oj4nOxJxUm+d6X3Qv70mvrG11lK25wCB2W9BWudptTJoFBU5gFN\neKATC4U29H1rjIGp9GXT3jNZl05iRhJXsjqhehm1koyNWwCu8CDy99eXm1AmhS7kqtw6jIPzvnIA\nMHSRtBFh4z5xQKrG1fhC8ndzzwOOkCLH62jnMv8ch3Qc5X5w0pmNgfvAgS1LYid95Li/Gyd7krA1\nSht+bPm+WmpcIbhJDYnjbU3TS0uJqyF1FnFbFbl7jfXXeICwSBwPbGKZUkqzkToTy9gmnEPFrKWE\n/AByosYVu4MCosymKNjhtmliBzhMpE4z74Oom/P/hlIG5s0d5zD3dp/q7df2Y92LErTHYOmMO7+f\nfH+aQiI/tjRba6h0gA8gcOYGjIPDrpkiWW53GM9coly16Cf1yiJyMWIlmV9yBY7KdaaV5fdHRqr0\nt0kjcl0goQBCeRfO7yIjd1R3fetHTLubzqcV2HU+LxyQmk0+nU6KylpgE95GBjYh8zAetVGmHyBC\nMjcQleLACdQdjsyPjivHGqnjJsQ1wgVBU8msa7fuTame71s+anP3TKqRQN6vccLG9yu777ljVfaX\n/P2RZIzW87/WpKX1XpZcC/xlxHx09A7GICZkOpmaVvJtOdJ3W+SKPLvGMJE5AOhfdcDQpAT9lbBj\njxnf4QSSBJElJYBI4LhNJi1z8qYFTLFMK8UPTCK4Q07catQ2uVu+nl+X/E5oqQhoGy3yLKCrcROW\nEDhtu2pTS7m+BibZnPHBXPFcsRK5BwjpC+eXdX84OeMY2rMBplZPy9a2yflUEjne1iJpJR85In4+\nCiF9AGMdgOA/p4VjGFBhbskJixZxzZp512bil/bvp1LYrDY151MippLMaUrk3LFq74k2SJfma1oe\nL2l+SclxKdLl0OG2aXG960I+uv6mRbvt0U3muWR62U0mUkCqwPlyG4jbVeYTV1bkAF3lLoEP8Pzt\nSIkcN62kulrTyiSx900X0wqUzCapTC8akT3eRipyA1IyR2044ahV5AiWIHBCZH50tagJ1U/tCHNm\npKX7U6Pcyf1pZe29m8MS4lqCpYTMgN4GXubELi6nxE2SvJZ9OeyUBWmqnOT7IxQ3Cennqn3rolo3\nmVlO6tvwzOH21embNfcblqCZF9JyotDRCq6qSWe7OUWO1zFskJpIEomjzbbsnyxrdbwsyR0/Fa7Y\nSZPKOcykfToUs6aWwDKzSoXkneI8V9wdzuabrFixgkDJo4uo6djnzIRWHIaa703FT7hGdLsjXM43\nqbr3L+Hvs/+tm8SX7l7iVOO1ddx3J7jAfCTdi8QnzcALjPj6vsaJpBEev2DFijmsitwDhaXGhboK\ns0nLubtkWlITpGHRdcwqdHH2c4mpZdf2GCngxeBCEBTAk7nhmbNNLbmqw2e1uW39gDThqqbEHft2\nntq0ck4ttFQwWbZ+7pJfYO0MslQarPPZsWNJs0v6d4P0twz7yBOLD88c+pvJjHLbY9e06FvdlBLw\nicUdclPKWtNKQq2aLd+Bkmkl94mT5aDI9ZNP3E0Xg5kAPgEx93cDUt836SNHY06KYKn5w1nJv2md\nDMphKUkELoxJXvUcxk8hufj/M6PQkbnlHErmjtq9GES7uaAmUNZrbWQ9f99PpbbJ5dJ+GwDNHmdu\niFYoZ3bQEmk2yb95zfRu8iTdcj8tdsJU0/ZfTQN3+a8UENW0kp+cBbqKK1wkZC4EP3ntGleAD37C\n03cMWPbck1ql+Ypxk0QygU4ec5G4WzWtNCBNKRulrKlr/FytKJV0zrKNVN80wZD2JVW86flbCiJx\nGpkjX22J2XQFFSkPLHNLLcL5ivuFlcg9QNQm+5YfIM3UUiNxMiHqXHCGpUlTaRtpflLyobNAppj0\n4UzEGsVvzjVDCOIwAADZqROpI0JHDSQJIHNCflqaf8ipcFdvuEbISiRNG9Tx+1HCHIkrlTVTT7lM\nbcjsUrpo8AECbeMQf+emwe1Ng37rd9TfdD6XU9MFBbdvWvRnkciRmaX3kfNtNB8b8pWpMVPmy3LS\nQguSoPnM9WhN08oRDv1ti6vLC+yuPbm73XXATaOTNh5tUhI7mVqAm1rKKJV0WTzQyZxpJSAGkAbu\nOA1c8dC/aSJ0PzedROn9qYH23Gs+c7Revnclgif3f9eQRK1kYioH3cnAfJx8wqOZZCPMJqks/eY0\nfzj5bsaJmp26nfVto3ryl4v+3WkQEwmKfUuw2vl1/uoo+AkuwIKfMB+npb8rv7+cvFC0YCD269LV\ngN+Omndvg9gn899ZEqduKst0A1rwE2kOyvejHUtes1xX5a5QZ1Y5R5SaR2ORzAGGz5w4di20COcr\n7h9WIvdAQR+2UD7T7fm1PDnZvsTsJt82D8xgq3SngqXShTxycLBSElCuHs1vjnLO+Rw9XqEjR3Ii\ndZlKB6TEDshVIj5IkSTvWNyBz0+iMBK0n3DpwJSufYRNtjhq63i93JecUW7gCQT/gMvol/RvFG1u\npo22jVfntn0gPM2jEe22D+/ddXOOdiJ2lo9cgzFR5RAOlw8QtXdIPtv8mab1kbS14d0gBY6I3a6f\nFDlS34i8AVFVoxkQrsZpJG0ukIlV50+onDeO8ALJ2SEghQ4ANr/ITt56TjlqlGrN981aTyhNrtwF\nodOuT+u7tPd1QX9J75L8rklyJ4kdqXGS7JFPHLUppQrRzyf6agN8oiX9Pmr+dlrgE+43d66YYY6v\nTkGbQk1XnwKGIAkVX+b95oh0ok7zzQTKfqrasWQkSU2RIxKnBT/hZE+qb1qUSk72INbzc9ZI3ksI\nSTBXEnf/8RI/biuOhVTiCJbZZLIt+2xYppRz6+Qx7wpz5pcW+KyplUh8HKaZVCJ1bMYskLpASgSx\n8ztPsVXqDsWp327L5FNTGuXPWSJjfH+czB1ybjV1kkhrah3/y8+Fz9hq5A7wo6TzDXDT4XaKbNlv\nvZLLZziJ2NF7eH2WDw79IevMlS1oARSKitztRO5uOh+dU1PfuGrGSds1q5NpBDhJKyX71oKbUPkZ\n0mAnROJeMvJmYf+RSaX7RXFBS/sES22rXV+Lm08B7evA2ZSw/PYJ0L8LbFnC8rl+iJvqWZCETZr1\nURupsEz5UbnrgK625aaUGrFL6+I2nMRp7gcWZBoBB4cGPnIl7YcmVk6B8axJ/OOuAWCojEBI9/WS\nlUvq1oC0L5eTCPTuav7i/LfWglJxRW7LyrQvaX65RU7cNBNN2ZfL504SPHmuHNKsckYRuw+EaS6o\niTtA1Vtx91iJ3AOEVOOA9OOmbiM+SlZ7y0dgzhzsGD852o9X2mSOHj2KpQz/rEWxLBE/dzYGlQ5A\nIHWUZwwAxiE1g7htpvOQtuqa7fpwYFCEWpv8n/gU8LHXgQ9MA7D3ngB/+13gd71pbyPPSRI7SZA0\nYqcpYtogk98SSbZKsNY/U5Z5qHd+PnN/yQTTInKk6pG5D+AJUNOoxI4+4G4adLrGTyNTGhBL1faH\nqvuwygFj2OtE2vjzG8KV33T+2ZTqGyljVCcJmYxIafm7zRE5Sdq4GSWf5Z995C3J4S7k6tPAJHQl\nPE/TR0L7OvD0LeC1t32ZlmWXK7s4a4DM18k2kjhQW803CQD3j4tuA0OmtkkfuRa7jKTRd41MnVvm\nMUr71RR1y/Q51uWKHM8NV8JSl4QwcXOGlMwNGitWD5guP2V1mr+ZtKag95ZOu2bSUiNLUv3asjJX\n5DT1jeeak/uRPtLyeFDWHYBjIkBaZpV3Bf5tWnF/sUatfMCQStyKebSYD3lYE9mSQtcX8WpNeMUD\ncXUJfM+f8ATuvSd++aompOD7ADWPfU1UxAdyu06Kp/NNqu591Vhoqc3Y/cP+I5tA6u4N+ED27LEn\nbp/9Nv/vtbejOrfi5DiviEhZ1easIrLlihUrXgqsitwDhWVWCUQFrcbcsTQDyfeVtj9OXTgGLuh2\nBbUNY1DmAK9iRK1vDHVAvH/chw5A8KNrtwgKxzjE6xueuUjmuoKD8gnJHJ8J3H/D78D+b/73wJ/+\nN3zFax/A5ht+BzaveL8K9XxKSuKw0c0NqUyml/wntswtNcypeBq0cfzI/nIfN75NUNFQVuYaeDIn\nTX1ondzeUOh6UlGbEWjGxPxS5nyUAYoI2ntMahthYL+XVN+GZ87/5lyBGxB90IA0GXeNsiaDluwQ\nVTrpD6ftl0wruWmWLJt4Jv5abe6vKsdxkEJXwlyQobn1lkLeIJ0etnyJtH1piZk1szr6a5nBhXIM\ndMIVOGk1ogU7IVXOtxkmNS6vy3M+agGK9O8dkCrkfj19Z4aQg5Kf5xwo+qxG5rhlyhUuPJmblLnr\nkAnc+A5RX8fLDjHnI//NAK98XcMrYVxht/zkSpC/fSn4CdXJ4CfcjFL201LF08wmtWdW89nLLE7q\nxzTDM/dCzSvXXHEvL1Yit2LWpNL/ze3/a7bJ6/J1NYEbJEa42Q+bNLO0oljyesukMka3JJPMNCCK\nw5j40AFIzC5DHfOpS9FP648znag2gTi/wO2f/A703/iNAID2x34MZx++COcBHhlNnFPiAxgqRYAX\nACEdA5Ca2ViR9JKDTH+1cbY06ZTR9jBzDCtABO1P+sTJ85DmNdaAUg42JLEL7WL0y8T8UhA8wiHh\noMNvNorfjPttktkiDy5CBAuI5Er6sVk+c5LccUJoBTKhYwCRtMlykctw9lEamNB9kHa29xuB0P3y\nXidR1iVbxGtuW24are3j7Ik3p/z8T/ryZ98CPvg20D5O9yEHz7yO2mijEct0D6zM36nknYv+cdFs\nMjV/TIOdpOSOzCY5saM2FM+Vuxp0oo3lI8e/S9wPG8hN/al9H1KAtEmU2xK40SiVM0gzS7wWbyC/\nr1xJ52aV3G+O/xa8v+fPkOYnR+s0lPpfjUhR3blR5ikJtGAnJbNKKOs1OwQ4xgAAIABJREFUHDGq\n1shcrTllMf3AAbgPPnsr6rASuRUA6siTto0WpYvDUun4+rnj83Vx5jJtX0PstHOwiduIdJY0tmmx\nS1Q6QhYV8GzaXiF3YZsjidscpHJDuH3yWVz9wPeg+ef+GV/xA9+D7Z/90zh7/EH1nIiAAmAqY0o+\nQ249gkYUOHiIaiB+5PnHvjQWXzKQ1dqXynLQWiJ3NWqBRuwg2mQDlJTgEYjoAdMvMOcXqQXXkX/5\nbDknaZLY3Yg20meOEzS6Z7WBTGSeuFEpm5d6aPSOl4PASey/yIh0Se8AXZYMHsSfa37pnKjx9TxC\nrdwHAFy9C3zobcBNxO1DbwO7d4H2zbiddovlu8DLnLQ1or0khJr/0vQ+cP+4SNxkjrjoHyfJHZXJ\nR4784qgNJ3tE4ngeyJKPXP5NzH20qQ2ROH5OJ4Ukc80F0HS26bj8TaiuQxpxlp5F2b9rOR5r5lws\nf0lr4mxrlEt9suyDNXJXg0KfTITLUsAO8YM7NYnjSHMOr+TuPmIlcg8Q1uBei4i3FNbHRn64SlH3\nakndXUCaVcY6lxC6Bqki59vlxA4QuX7O0pnRdgpItjSiJj+3Q9pfffrTcJsRn/+ffjdGOLz3x/8D\n7D/9P6L95m8M50QY4YCWKXQsQEaoCwnTI7lLiB3PsVdD0g5BDYmbI3NafWYuM/2V5E6bNdaIG/2V\nAwkUtuFtskdlow+WJbfh5ytnxjUSbeVskyRNqnYUpISrdFLJswKZlEwpq60KayMovD+QqXT8ebWi\nycpnmoiaRuYg9kEYAVyI4Ejt40ji6Pjy+ZbmalKN46TNGlBbkyUNQOo1N6vsmLrGVTMyq/QkLQYu\n0clemkdOkj+ZfoAHQqFjWdDM+OU3SEavPOm3kMxhg6klkOSaA+J9JnPyS+T3XprHPkM0XdRShch+\nqATrOdIUuVJAFCvYiSR42rH5cazk4M8Jd0neVrxcWJ+EFSpO8ZGYU+PmjlljOiLbkgccLWsfz5LJ\nZWIqidTshW8XI2TGNjJCZjSP6bPznYtIdgiWEPDh1Uf4wPd/O9xj/1U7//5/Bzfv/gwuRO4hGbIe\nQCCjnIRKM9KYZ2+qJ1KnqXR8xrY2PsXcx39JovG5dlaeIzmw2Il67WNvKXH0tzTrDGW5VFcirSVF\njq/jJI3acPNHTUWzTCQtfzuwfXBTymqiX/PQyBv0cipxJZBKt/llhfFquR81aKrb0n1ZecBoG3qu\n5aCbkzaNGHDCx83ipELE/EqbRvq7pYSMCByvI7PJ1GeOVLld0oZHsZRJwjVfc+2bln6zuCKn9L13\nDabOJbnmiLgRtL5LU8R4P0CTBVrfQ5gzsQTK6Qi0OioTiTOjnBrXVXPrD/h5bsfmYL+0lcSt4Fij\nVq5YsQA1USu7ijYXFZHFOuandmq88ubXwj3+QCi7xx/AK29+7Z0d77mh5vtW8+2s4cRr4LflqInd\nczKl9mEOdvZfdESky5r5pRoOXHPr339c+s5R891oK74bcsJOw/mr821WrFjx4vEwv3QrqnBXZoya\nWeVSNU5rw2c2j0GurEVzS3/uO0SndDr/6OMAeDInVTc5q0of5ZJZJSdzdffkNKNgOtc2lJnZ5HS+\niVp3Nt0PljAdSBW6/qa1zS39DiO4SqeFo5+7FXO3oWY22DoG1TukZK6kRHBFgreVM75aGaJOe1xq\nTCuB3CdF+8uXb5R1sm6J+lbat5ytPzjZ95x8WWAQ9yzS/zEoRrosmVHOYa6tpmZYJm9cjTtn22lm\nb5ZfXXK8fVA5yD9OJvdOy4NSR/5v0R8uN7eUAVLyPHJcnaNtqC1H4lcNMuGPViI82EmNmeYVLtCi\nL1p9XOPCJnOTKnd1eYHzV698F9e0SF4Ouu9PRZn/3vSMDawN7194oJMlny3rmZLreP1WqdMUOcus\n0rKa0M5rAWpVucUK3JzvveJeI8+FAq/w3Lgr7idWIvcAMQzO9JPjOB0xOsyPK63TOztJuHydHhCl\n9thx+yE5RvStaBNzTN4mDYoiSVpfJG1Lz/OY9XOQ56mZhabX3IfhDqATO9cMgdABwtwS8GHvyZeB\nEyQewrpU508uHTwQtDotYpr2mGl12v7kLbfMJAF7YFAig3JZK2vQTCq1ZRkeXAYn4KaXMiCKVi6R\nNul/94ytW5z+jd+0Z0Z9BTaoU6ReMixKXbCE0FmQkw6a76gMRCEH3VvRxill2lfiRzeyJMZDiFLJ\nCZjlMyfbyITgWkqC1NdOpihIg6ZY5IvXc1opJwfJT45bhdT08wd9c0QAlJ0bcIuL6YQ3aTJwQCfV\nAyKh43UageN9kfX+a30jX9ZMKTViZxE5q411LktHz4MzCdTRWBo0jdovJGjHRtVecTdYidyKDHwm\n8D6g7FcX13HCFetSnzkiaktTF3CVrp1ISymMtDwnibmP67Hq27E5+UrXQcMTrtDlfh09dmgj2ZtS\nMxChA4D+psM4jDE0PpAqdP5E/KB/K8qAPujms7/pBdRcdPqXoA0stDrLQR7wqpSmqpXSGvB9yGs9\nZLCtBT+RZE4OtMDacB826Vun5YqS0Sc1wlealT/oET6AvAH67/E+Q0Lo5EQEH3AfA+05lc+1jB5I\nZc1HTip3vEzbJYP3mHuxa6PvGydgls+cRdJS1Y770cUoli20Y+lRnd2o+Mk57oedBtLSLCAOxYim\nfj+T4437wAjXRP3OR81tyikB6Pniz5nmI0d9AAUgGRH7+hIsnzlNmaM2mgLXVLY5FQwyd9B+TgFx\nPsf47a14cViJ3AoAMnDIaV/kUyl7c5AKmq/jCtth58DJXyS5+bG06xxPdN3Wudv1x/2GkoDy54Mr\nb9Q2zmvHQYcMDjPAoT/rgkrXNCN2N13IqTc+GtHfdNNHajq+ZgJJahyZYnH1TvZoc+qOvH1LHPDn\n9gWUCVgDm+DJ9lZPXduDW4FP5HVaKh2VS3VANKMstYFSfp7g5G2OSL8Pwf3nMlJHz59lFgfkAqhG\nfqViog2WEyUN6bOuBUDRBtgdr/Nmle02TREgk31bppYykqVU22SUShnFUkbEDHnlGHFzg94vU/3Y\n+LQDzrnw7dihm/zeWpScTLXJwbkJQ9IeZ3ERF/tHI3pcgKdEyX4fnlrmhrXRJoF4HZD3VRos6wbC\nnBkllHLN8bTbWdqHpnwtVcMOVds0aMcU58PTI3DzSgCrieU9xQP4bK2wQGHkcXY36pul7EVfs7uB\nVOkshW7ZPsdkW02J03wfDjuW/iWzInBa53sM5kwrNYVOziJzYrdDByfIcH/WwV2M2PXe1JJy1Q3P\nnFfngOkjozgucdPKc0Qyx3s0y0xsbqBQk+eolifPKYNzStuciY+2Dw0WYeVlS4krDbw0c8yaOnle\n2v2kGXz6+Rf7yk2Qqhtgh8V/HytyGooqnYS2XnsuS8ozJ3HcJ84ym6TttqwdxHqq2/bozvvgNsBN\nJKW5o2ZGydvk25R95DiJ47nm3DgGktaMvlfTBA+yrmvGHoMjKcz/4f7Wrupln/ap9OFzpM1S6xyG\nQOaCRUXTIUhpXEWl8g3S90yq8vx5shKDW8+jvAztOZN/NTJnmWOW+t1T9RHHqmqHbM+3kaTMUOe0\nJOUr7hfWqJUrijiVT9f7BTUEqYak1kS2rIlaWRNF80GhZmqqpk3Nd6tGUVojWy5HzUCpJijJOk1Z\njf1HpkiXp3p/aj4b6++zGDXfn5rvRs3353ztvFaseCmwdqUPEKR8lAKeaLnTlkJTlrxKxRWzuzW7\n1CNQuqysnYPm98ZNawA9sAqZYc4Ruq7CaV1GPuPnQGixW6TW1UD+5pYpJV9P9bT9AId+mrGl+0zK\nHODv7w4dXOvPs2+myGyNC3fGR7hkEdN4j8UTzdLgn592SQ1rjHqtvWb280z8LUEbD1mO+zuj3noF\nl8wOzyUI53+pvWyjmU3yv3OqHfnNzIEnsqZjSfJWInPcJI/2Z5n78fUP/IuYKHRc0ea548jsUoMT\ny9Z9PxdlMpMEUrNKqcCpCouXas/cECJV+sNHn7XUJFL3h8vb2OaXWkLwzNxyUuOkEqcpclQ3NoAb\nbjEyZW5sGsBFs8oenUnWqM/t0EP62HFc47xI5qivVslcC+BVb2YX1jYt0GxydYu/76TGSYVO9gk1\nPso1lguWKaWlzGuKHZRt5HE0DJvwXPryPfON4/uSZp+F81yDndxPPPDP1gpgMrEU2uwp/eQkcQIi\nMSACogUlids3R52PFn2Sn1ut6SXRF/75mwu2Untu2rHSdsvKpX3XYM5HruQnB6SmlEAcYHAivZvK\nO0pycIbE1BIAxmEMKXcBADebOAgkcOd47jgvBweOLddCIyr+gu22NajxpwPm/Y+WCrLWOVoRPC1i\nJ80mCZywWSRXOwc+6NN8sWpRimxXIm4a6XjAyCJdLv1drHuv+cidQzejpHq+D608DTybRz6Qkubr\nFk0i04k4qw0vU3uZ3Ju2l21CeSJxksBtCp+HtAsgA/MBLQDnXIhaqZG5EU1SpwVJGVgfzdvV+Mnx\na6fJN8LODT4ICvnN0e/DfYCJxJUmfbQJpS3yPmPOpFczrQTKfrGl7QnHmFUu8Y1bQpiGA3KmSJIJ\nxPNiZW5eCWA1sbynWD9bDxSkygG2MneKKFlA+rGLClauynF169Rkjh+zNmWBFSSlM9IIHOL3pxMw\nnaTJ619K9pagxkeOq2+UfmAnFLjgD4cO5B/HVbpA4hAVOlYVntM4PGl1Msf9Z/xJpX50DavnvV6N\nolbr13UIQZSQPfLOqOeofU1ryKckbnK7ErnT1lvHJcjgGhqWfKX4gI2g+c2UfLdWBARC98v7VEkp\nQVOStfvM6yRJk5Etiehp5cLAWAYg4YFOOGSbdB1X4lKfuHT7iSYlwU1SBW6j+YkyaO6cntBNfnaO\n9r0LfSrgg2pJhU1T5LR+PY9QXPkStOnYYYdIPYEm/kbSJ06WKSgKQetDrMmfUp31HNaodhyH9glS\nlQv1x/rGnSDZJd8HnaNG6BiZAxAI3Yr7hZXIPUDIBI/D4PyTcGJVTvsgROLEiCTyQCKS2MltjlXo\n5AykJD2nMvmc20fJbFJLnC7LS8ldLTTTSp4kXEaolAnSZfATTuLoHEmR42W6JmluSQjqnPYx42Y7\nW6PML0sGQyk9UpLwlcwtCUtvvVP2QVgyEyx79ZpXxYrSOXedFqm1roOTAeuaOLmzUBMkRg7k5gid\nNohbAQDYf9FE6H51XzewnVNGeZASMrXkJFyauTlRDuvTBOBNkwYpQdg81mmBTSQ4sZP7iOWc4AHI\ngpsQEhI3807ylIZuALD1ZC70hs6OYEn9MhE7Pd9pk63j28plEzRmmEwt+0fUb3fAzWRuSb/ZNdLf\nUJpaUp1MR8DXWZgjX1qKArmdpc7x7bV1d4la0rZ0OJTcc3JbUAgdI3MA1tQE9xTrZ+sBQto5UxSq\nsP5E09IaAatdp0WIlAqdrzusY9HUOe386vc33147V4t4pYMQnaxpAxWtfMg90kwr5UeeJwaX5pZU\n109DD4chkLidocjRHvrwFYcfKIi8Qj3gBwnbwkeOSJv8UGsf5GdGWYM1sKghN3MotV/yOFqv79w+\ntOPPXaNsw83vav2qLFhET+5HG8SVclzRviVRWE0rZ7H/MEtd8I+MMKLa71MyrdRyxEkip/1eQo0j\n/zjL182C9G3LL2feaoLayRxxbmCmlBaJk35kE0KwVkeqXlTmnKN+Nn4L/DW4JADWAKeaW9p/cxWv\nhPAtOhuTfto1oze33LH8EA18v8z7W1Ll+L2RxI5QYz1RyuVZMqG01pduQ01foSlfNW2L7eqaVe1j\njtAxMgecKHn5ipNjjVr5gFHjuFpjO79GtkxRQ5xOF1nsarZNzbHeN6j5ztSoW6d6pNcJzOWoSQjc\nzTfBKxVtXq1os0LF/vM32H++Mfi8b+/h+wRS5VPbVMz6tFURkU/TZoXAKUwjq4/1/A614sVhpdcP\nHAmZm54GivoV2sw8JnORLf36uX0MRZ88Ldqkdm5L1ac5ZU5rr+WHk8elcmk2txSRsgltdkVTISCS\nuZJZpXasEjQTGy24Ca2XppTeX65NVFUyr6Q6Wi9NKzOUVDkAIVoaqT43eXvfDnEGmNoDuXklj5hI\ndRBtOKz8a1Z7uZ3E0tx3h5hS1u7/GHNRHvGQqy/SnApK2SJzvIvokJtL0jK1ewW6msPP71W2zQPL\nI3cKEJnbfE4oDlJ1ozpZb+WIK5laMvWUAjBQoBMtaEkpIqWEpuLF9vUvl2qFVlLlCA0yZU76zI3j\nCDh+Xr5/HafvJC1LNa5FT558YdmvS5U4MoPX2nDEZOUIwaoAhIBVOwC3pJqSqSX5yHH/OH5fNIVf\nKnU1sPoYS5HjZc3HVmu/BJzALSFztY+c1a7kXyifOyBV5rgqB+AkkTdXnBwrkXuAIIdV8pPLlLkG\nQavVfMdeJKS5ZV4/TxiX1EtYs5015jeWuaPmC6f7d6Qkr9TGOq8aMmelHkg/9n3y0c+jVrYJ+aZy\njzY5/x6pD5yZeJdCil/E808CoJAhEpE4Igv0ER6RpivwO/fgJpWaeaX2SNUE9JgzC7J+iiW+Yda+\ngeNSE2io+YZrAwR5HvQ71IQV1/Ytl0uR6Ph6PpAr1a04CPtXJkI3KoSOL1v3npfpn0UAQ9sx8fd+\nX4BIi0LmfHoCwLk0+MmIMVPH/KRZjBxMpIynJqBlsv6QppZWnVZOMHXrrhnRTxNuPTARg2m7m028\nTmneLgnvnNk3ofT514icLJd862qPcwrMDUeOVdpU00pRR8FaeEqCNf3AvcRK5B4oePShSOjY48DI\nHIcWgfIuwBW6BrbipxG7ElnRffKW9YoWmatxjpfbS+KmETItQtpcG22fh8DP3EbyRnVW6gEZtXIX\nfOQ8aZM+cDI3U19jM3eRFmN6gplZTiJ3fFYfSnlJEBSO2sAg1jZLjlHTc/PjHfqqLiFvwLLz09po\nddrsuKXAUZuSAsdJAa/jCt/TmXNfYWLvJkK32Zd/Ax7sRMvnpZE78PI+Cb5AgU5OEaTqhWNGgWrG\nW0/qWN9JfuX0XSCSxttoxK0TPnX8r2/TJH9r4doxCVblmhG763bKDQpP6G4a3UfQInZ8ecnplFS4\nmsBJpfIpcQoCd6j1hrzXDVIyB6yK3D3FSuQeIGRuEEJG6Awy9zyQBjvRUw9oJp0lM0/rA18bDfNQ\n1a1GcdMUOovc8fWWuaVl2lk7yLFUOb8uVd9GjEFH48SaolJSmQc8oXORilwtxm16fiqZ4wMEPjCk\nOhpIjqwNLwN5aOwa5UprK2977QyzBjFbX2xnHf+uUPqizJHJmiS/khDQek4YrOiUc0SO72clckdj\nv98Az4DNdq/fZyvYCVfjtDpCQyaVtpXE4VF7GyAxS/Tl+K3QfcNG50LUSlLPToIhNbH0+x4wOj7Z\nmbsfUAAU3qbDLrRrp2VJ/lLVLlXr0tOyX+oBLjGLHyYyMA7+NxmeOZ+uYHDRnC9ThZCaZvN17N6Y\nWNIf1ZhQLq2vwbHkbckzJtsWFTlawSdIV0XuPmIlcg8RgwN3mZbvtmvGlMwBRxE6LZS/llpgqTI2\nMLMRC2mky3nVzjqP2pxtmmmjFQabt9fIW2mdbCPPsSb6ZS1GNEyVi5HStKiV0ZRST/7No1bSOdE5\n0l6qBmCW39ww7ZsUlhJIoZPmWpoCNIgyryNos8cStf5zJcxtUzqPpT3+Id/tWvNIrV4bXGmz6ZIE\n8PVzZpM1ZOJXCtewYhH2N9EvavOFe/v3ACvPPXf0eWIJiqVv96HQJrFaUR97qjhRRf6/46Q4NWM/\nqWYem7jDur6iADd4/kNRMkfnwhnE3HJNIG9ASux4n9syM3lO9KRSp5lulgJpEYEMbhotETl/8a5x\nkw9dExUfInX8/sh7xSPi+gMdBq2fqrUSWLK+hENJ2gknCBLISUIqh9/gOQZqWVGNNWrliiKGqsiW\np2nzfkFN1LAaUlWzn5o2NdEv3zfYVnzhaj68NY/rOg22HDXRJl+raPP4RG0+r6LNipNh/w832P8K\nBVOo2GB9xxbj0FQ4h+ynrfi21LRZIXAqorbEgmTFS4u1m3yIEPbOt2Nj9huuGdSE4aOY1bPMLrhW\nw3PVlD4kh6hzS2BFwDzk+HMBTHITyrS9pcQ1SFWq0raNsZ6fB49+aV03R/p79mEuN65vwowrN63k\nSusObTDPoXJ6rXU+cRZZHc+m82HKXDDX0bo28n0bkaoy56Idj1rZoGxeeYz5ovaILf3wliJcar37\nnH/JUvNHQu1MNr/XJV8UInOlQCZE1Erq22O2jaW+fR4r1xDNFUdj/0uTH92X7nOzVu0vN40+MUbR\nd/F6ucwDPEmjcFLoqH8b3BmA29y8kqselpn0wut0Ux/My1F9yyNb+nJcT2ob1bTY4RoXYT8y+iXV\n7dCaRI3Oh9qMaIAWGKdxBze1JDePoM6FMQq8CiSVuSXmlRYONZM81TN4iBpX2sb6dpS+KbIPlvdW\nBt5ZGcO9xPqzPFQkaQfGjMx580oxqhNkzoI0m5TrZP0QPjC5uaUvD2abpdBInJXWgBOzGjNO7Vj6\n35TAaQRN83/j5Ky0T+2Y1IajdD3S94HMK/2yC4MBaksDISJr5C9HPnA8cI2ZZmAhwjlOZpbj4DA8\nczjrdpHM0SWPsEPaUxRLTvIII/KPnfbxk6B93PWMKO1/KaHTMOf0r+1L27fmx8bbnrPlUiCTkhkl\nX2cFyrD833jZCnm/4rlg//MTofvKfXrva5KzVwRe0CYYibjJ9WkU3jhxRf5xRHJ4e2pD5ooDXEjW\n7U/Rm1f6NpN55RwpCRuny/sKK4FaH/Fofkn0k0/K+rqWBbeiOmBJ7rgWYCQvmKHSBFzTAtsdxqGB\na1JSFwKi0CQyH4tIfzp/4nndHO6rInwIKT30O8O3k24EgGJaeeBxVtwp1s/WQ4SMRDSB/ObKLj7T\ngLwZM1JH5EcSBE6K4gck/QBK4pQGO8nbnIrYEVx2XjH33RJ1UPOls4gaP64kaS5pk5M0qdzJY5UC\nrVhlDk7a/N+UuPk6uj/+bvEccT3arGyROE29tJCqguz5PQMGFgBlB+B2cMBWsemnn1MGPyECQOkL\nwMrxoLG9DJKiHYOTGb6tnOk8BseoZHPkTfou1WwnyRZvX5MmQK6XZE/7zTTSppE7IBLJUpsVzxX7\nn5kI3T+/T38D7VkC/PdrBlw104gblYm4gbXN/eFSnzgK2hT36yetWrC+0kVVjhDI3BKwaydSOGrv\ntgI6a7ouGQAlV+1yJY8TOr6NRgBzn7lI5np06LAL34D2rIdrHcZmZOqcJ3VJQJSxQZrLDJHgEYYZ\nc12N9B0D6/m7Kx+yQ5S4pdAmBU/5nVpxZ1iJ3EPEAGSRiFhHeYvK93ZS6A5V4Kw2RNhk+gE+21gi\ndjLAiXZeklByOLEffj58O4t42PU5yZX1nMRp5E5uUwp8oqtykszpvzQ3iaTrjdvkQU/I1HJMjpWW\n9XtdmXJA2UemGrZp+XpsECnpJppIWspc2BCxZ5QqkSR5c0hkbqTkT/tI8o9ozQe6lsRZBK6WuFnb\nWKTNaqORMoj1c0EwNAVOI2TnFW1kOPwKgrDi7rD/mxOh+3rld6hR6WAHK+FljbhxdW2EC/0STUBx\n80UHl01KUZAQqu+wQ991Pnk3EElOg2BuWTvs37syeTuVD3qkq5wA+m9Jja/bjt2zqMa1YS2PEB0s\nOc6cV+foHJohCYgSTC+nm5YRO3/AbGI6wfMa6cr+o0TsjiVIc9+H2rltedukSndqErzi5DjqZ9ls\nNp8H4IcBfATALwD4g/v9/rNKuxHAZ+D7rV/c7/f/wjHHXXECZJGI5tW5cXBZqOcBzqtzQFDouIpD\nREFT4Hx5yAjYEiKlETtOAC0sUdtK+z5EEdTUtpSQpWqbJHZ0/NJ+5HH4tR4SREVLPxCPHRU57UOt\n7SOe07IBiKnITedFY4ZxaNBud3F+eOhyXzi/w5RcNdDJEb8dFgmaAw+jTeqc5pNAWLL/peqblY9N\ntj81abO2lWSLr9dImzyORtos1W5L6/cI5nnNiDM3JFEQV7w47D89Ebp/UQ6M6W8+QB5vnfDjTuPg\n+rq0HGM4cnPLMdTzNju07JtEX7f4XfPLbVCwdpMCRe/Brmvh3IBmZDGjGakrQSpxgzvD2DQh/UC8\n5nJ/yomaP7xO2jTVTjPH5N8fBycCa6Wmlf4eRlpHZcCrcwC8QnfLJg0nUucJ3VT/KCV2ACN3hOed\nuNo6Hid2GqnTyNwpFLAl3Zj2bSM8w2E5VVc8VxwbtfLfA/A39vv9bwbw4wD+pNHuc/v9/qv2+/1X\nriTuHqHipbytsOGoiWxZyjdD2GXu4y8nTjU7WoM6IjrfpsbvYamP4ItAlf9GzfRVDYmaU/Zqj3Wq\nWc6XYbb01Yo2NdEmv+A0bV79gicVO1rxorD/rzf+31+xlY3Ml1trU9EnH5rT8nnCm2qWcUp17hRt\nThXZst3W+ua9QDzPhNlLJxKPwRr98l7j2E//NwH42mn5LwN4B57cSazJJ+4T1IhZmzh7NBMIhUAK\nHY9sOcIl+XzIUZqrWdL/jSt3u8mXKlfbciXPUug0Jc9fbm56aaHsNB5VRststPY4h6CkxuWK3JC0\noXrehmCRIH5vm2k2lqtt8b67xJhGmlbKGVpAJ/il/ETSJIovd9gFM6osYfhADnEALhEVm1IAFB78\nZMvqz+HNL0sBVOKJ1ZM5K/DBEtMWqWhxLPVjk21Kfm616tsrFdsBabRJS337gkKbR7JNrr515/4Z\ne/ULnsA1A5pmPFkushWnx/4vTSrdH47fqeGZSwJzcZVMy/3GFThf1zAFzvu5kbk39ZfcH26e7EXN\nqUU/mWT6F7hzk69ZA7hJTWrG26rE4VGR8x0FqXFpgBbeHx5O5qJq1yQKnlTfHIbwbSBTSjJH9ZEw\n/V3wffJu8pHrky+ATzw+hjZkahlyzk3qHClz7bYP446EwD+KkS+fqAYoAAAgAElEQVT9BswM8xSY\nmyzgBI6Ws2Bx9NyK4XCwjCrt32gjc+odC02de8b+Pk/yuKIaxz7lv2G/3/8DANjv97+62Wy+0GjX\nbTabvwX/GH7vfr//q0ced8Ux0ELKan5zDDWBULwJxBCeKk7qpLmlZqIoCRiBEzsHSdxyYqcRQL7f\nQyNQyo+jlnbhFGkTLP83zdRS862T++HXUOsjJ9vQRx3g9z76yBG64BjvP8498xfx+5IfxDbsx3aY\nj88PzdwScZPrB5z7QcRZHyJZAoiRLIfGJl/kQ6cRPPKLIx85/kGrUeYOhUburF77UOJGbeeIm2Y2\naa3T9kd/X0FO9jSTSNqPFlmy2GYibdOAqt3upgmn6V1hpI2/Y1qKjhX3D/u/OBG6P5GOYIfBEwAe\n2ESLQEl9E4AicdsFUpIHaSJ/ORf2w9fH6SzvWxZrG5f2wZ7EDWLu1H9tpfpGicY5iUuvK/X9o3tQ\nKvtrSc0o7QnM3ByT+m4/CRj7ciJsROboDhCZ8+eSfiO0YxGpG2+jC8cwuGmswe6jRu4AgJlKJ0Rv\nATKzzRLEJLi6zjK3lGMyIB9w1Zpdcn/sQzGi7D+34t5glshtNptPA/jHeBWAPYA/teA4XzIRvY8C\n+PHNZvO/7/f7v2M1/vjHPx6W33jjDbzxxhsLDrWiCloI5ITYlSNb9mODs2nWizpP6lijo/IQfOg4\noaM5UV/OI1paBKzcpi6ypVTKSqkPNJIjCd2pzQ1PNZC0febqUxDIdtEfZACf/SVSx2eF+2T4kkYs\ns9Q2CyNcphbKoCcyshrgBwHtNpK967EBtg7JhAV/D7gPHRE3IiZckeNlQg2ZO+TjWjv2qAlaUvKR\nk8SotE7u2/J142RPU840QsbrtoU2gbilpI2UNrIUAICmGX2EvNB35Aq29r6suN/Y/1n/0HWf8G75\n49D4HGXMV5v7yQEpcQN8vzZH3PTvwHw/Oor+Nto1DGgc65eZn5sbRzWoieYLJ1U4Xu+X/boatwaJ\n1Jojqm98HY9sKbcB+LeaH3+nELcYBIX7W/P9jmeRpLp2DLnogInAB1+6lNwl1xTGJ8vuR+3UrEr4\nNGKX1AlSJwmcRugkmTu1KsdBl/OcXQ4fMt555x288847i7bZ7PeHR+nabDY/C+CN/X7/DzabzYcB\n/MR+v/8tM9v8JQA/ut/vf8RYvz/mnFbMY/NTqBv4JXXTb6KYEBCho0ABvDPlAyoAYVaNiB2fEQ/b\nKKRtro3Wjg/S4qWV22j53ezE3WOyXakubuMTqVoDSD+Duysqci16dTt5bO06rHtWA0mepCmPHET0\naMM2NPM6IkZ169FiN9Xxco8WV7jI6gAfQOAa50ndFS6wQ4vr6a/frsMVLmKbqwv0Ny36mw64mUjm\n5cabSBLXu5n+WXX+wmKZbt2glAf4j6tWF29aXAdluRaWYldL3Gj9HHGzyN4cIeNtaAx3SJttJG3U\n55RIm3wPSc0tpfig8t/4t74RK14uXLz9a2i3O1y0/mWNPccOF0jrLnA1lf1yiz4o/Oe4Qoce51Ob\nLumF/ISR32aX9MkddlPfTKrUbva5A/TvkwUt6JRfjv0vVyR5nxz73zYhtlwVo3X+iluxXey3qczz\nhPasL+f7kceXKiJvI9NEaISVrwt1kwmm5a9Pk8t5fR1DmWtnqX2ZaafcT1YWZpfye1D6VpTI3Knm\np6bT3X/Nifa3YhabzQb7/b7onnasaeV/C+BfB/C9AL4FQGYyudlsHgO42u/3/Waz+QL8/+29e7Al\n213f912n9+x9ZnRHuhJKBEHmFUJhwA6PoMKI2De8JF4SGBKjOAW2A6RcaCIKZLAtiIQxZaAIMR7h\nFK+Ywg8EDjiAeAjJcCkjApaNBAIkEJUAUvFIgbjS3Dsze8/ep/NH96/7t379W6tX773POXvP+X6q\nZk736tWre7+6+7t+L+AT2/7kMvGyEWV9teV7NLxgWSudXNRE2Olsl+J+GdWjAyI3J89tErACpHfV\nlD4p61ql1nP16pr1oSWud+zczW1Svz6NV3dvH5Y+z5UyJ3xz2PephDniBAL6s5LtAKKZcJnptcHv\n/We6xtwU5523ab+11U5cL2W/hbLMreTmeepc/tboY+OAoaXtnmqTr4PEzel1Dy1K5DheJkt7PnYM\nj5xo08tTXCJ1O+Bb1jyxl7OkjVnbTvVYNXC6iiaKqtkG89NldN2QiY3m8L1oS7kdywN2btKFHB93\nX/EsAMCzv/1dTcMJ4MW22XqW9rpe+h3wPAVsfK+1ZOnvpbTFRcbLj23/dun8p4qgkcfA3pOlt9DJ\neurcy61xMjHZ38+tNc5a6VxX/5P2HOfqXq6yX2q3zOi1qUnmlNhr+k235q0fVN21SzhL9FVHanc2\ndfE8C50+XbmfCFbU7cPVEnsag+ydXbNWfjOATwsh/BaATwXwTQAQQvi4EMJ3tX3+PID/EEJ4M4B/\nC+Af1XX99h2PS/ZBiTm+RLsUXNxW98czgq3OxvuUZb8cr0tW0mfb46diDC6LfcTtNeOMX8VL+mxT\n/qCUkuOfLMYzpO0tI6VX8sBSEkBe8nUtOVZJn5JjlWSf9CjISHnyjKfGhzkZzzb5KMb7PLOgDzku\n/uRlz8WfvOy5+INbH9K1lVzvS7JWlmRWLuuz3f2n5Bz3df8puQbv655Q0qfEJb8ka/FiPt5HC7wp\n2PJMHlbcubCeJZnATha5uq7fjUbA2fb/CODL2uX/G8Bf3OU45BwRMecVfuxq9ugdnIQoppi4sEYz\nKyWWudX9eReQ7FnoZrNNJ+Z09jg9WyeWt6H1LbbQSbYwa6HTcXLSRyc/ycXlNe0VbOZFi42j8yxx\nNk7PtksWtX1hYxm85VSbtsZ5cYapvk3//rVJjNwmer/jmV4JoN+oZe2e1BxjFt3U9cy4uPiIm1T3\n3p4A61mFatZkuouSn1zrBopj4SSxiY7JAnqrnN6mrXCn6BOnrNFb6/TEiWyTv9fQn4fup5/J9LNf\nyjVa11OzVjrdx8a2yT7SZwHf/VHv8whiC5zt41nfHnXagMhtEmjEnHWbXMzFZbLp8+jJE916n0wn\n/r48ir5Plz0QsevlM6M+nHJ+mPidWx8FAPiI27/SFfW21rg+IdOqi6FLMccyKdTEGid95LqkPRHk\n+qjvP1Nc3eXY2mVdSFnjhta78sc+nb3S3qd6C1lssdOvYwN7v4jj4eSqrt8Xne3YHqPJctncG1IW\nunnbx2bPBtBZ7BbzVWSxA3qrHdDH3Vl0sfK4fePW2bXIiCfV2q99p+PodJZLa32zcXJe3ByQvpfw\nMvdQsatrJTlGNhh6SOofvBZ1Je6WcmGT5CjtxeisXdaJUUTY6QQpfcap9gKvhF1zusp1Irpx+TFz\nWjjZPsNYN79AuXdD7WO5Ftioh8ccXhYwr21YYDzuY91B9T77JCfstCDWN2d9zhorRlPuOLrQrOeq\nZIvRisukjk/pQ++rztVS4hGb/WaDmVg3+UkKnbWyOTF9kg3yGxKBsk60QfW37pSpEgQW26fEtTIn\n3FKxbal4uNNMHyvStCgEGjHXCjexjpa6Tcq6fK4pITfsE//2dZyp3o88nPzmrY8FAPyF2/8+ap8W\nI6xFUZwlUkSc7iPXpdz3TjJbpmLmPHoxN4xRlnPxzjGOT7N9y614cv33Qx1mXR+gn7xLocsP2NfU\nH0syhK7NMYbPAlrceecT3VNNqZFoW8JFE8i7aeqEK24WTY0pmZB2u1RjWFGXQr5CKXdL7c5Pjh4K\nuauKJ+aEEiudixNH14q5sy717lDYAX08HQBUs/aiHiVN6ZMZdG3GaufFwHnCzpY/qBJ9tNVIt8kF\nX9cI6mNzYprZXhvvNbxZDbJ0qT7eOjB080xbyeJYw20ZxjDGsYj2fKzQ1LO0fVuc+lr6aeuJnm3V\nMXRaQPaCrX+I0q9dHvQ3s6r7Xs1Pl20v+dQKBJ2Og7PWOz3poQWcbrMxDlMC1jVTslSWJjLJCTdZ\nP3X6WIucFW6ngGSX9IQbgChJiRVlet0+COfW5xkhl27jk83DzFtvPQ8A8F/d/oXRviWWKy2EbFyu\niLj+exhb5Jq2/loHyDVzXvQ91MLDE0Ib9a2WPlpoSn8r4lKirp9M66/H2mImr9Eiv98Sd1Jbo7R/\nv/rXIX20WPM8RazY1Pvo1y/Yfbt2x7IH9GIvF4snzy39JHVa3OkzKxJ1qezLqcE9UWdPhZe/o4RC\n7iqjf7TetSVlpRtjEKg7Q1RsXAs7YK9Wu+alxDN1Q2E3LG0wdK3srXm9lS5u83z2V5gnxBzQp+KP\n+9jaanbdJgnRN5Oh2+cQz1VS2nNukrtg3Uvj4/WvQ9ciskVomz6b6OFI1zDSJQdsm1jotNV0jaqr\nL6fpkp/MZr2Y0SJEowWcta551mt5a61Fzgo9JNY9Uta4lLVN+qSEGxBb42wfL4ukre02WO8LcJ8s\nlphd22B+ukqWBNAZ/mJR5gu73o0qzvDqC7l0hldP3JGHn/9w65MA9IJOhFiKTXdViq1b2uKvHQOl\nj56kK8lsvMLwep36TnqlB7y/noUuFetdIl61t4i9/+S8RBZYRiJNJzGx2SplHNmm7yd2IlH31a9j\n6A0yvA/a87UWPT1e0+64a5pauUAj8qzA8zJqamGnl5OiToWxJEVd/ILa/cy653pphwQo7I4ECjnS\nMEXUAdOEHRCLOy3sAFfcrTb9CdnyBkCZ1S7ljjmMS/BE2/gVbAmoB8EKWqjZtqa9t87pi/4ScSC3\nzVyWczvZpkaQduNMu0mmXUtz2FnSft/+RpiKrbD1iuJx4xlsHVunXStzlrzuoeukny2tZlVnJTpr\n350BVszJbyFlrfZEmrXInar+qd+RnUjxsJY2IO1CmRJutk9kSTN9Ts04Em93qsqTtJa2Slk+c8Kt\nGXrjCrehsIvLdwyFXL+e6qNFnBV35Gohgu4Tb/9s1K6FnRY/sXVLi5J118e7vjZ9xicQ7IRiw9xp\nG16PrXBr2qwFLl+iQJO7t9j7hXd+2yDxbvFko+9aaV9XfL/x76EaT7TaSVQZ2xN8cbiDIxyNJU/E\nXSoWT9wy4xwCVaELpj3vAu8SDwq7o2TXrJXkYWdfP9wS0Xd/fF5hdb8gI2VJny2zmNlAd28c28e7\nidixvQB6e6P2zqdkBrVEgJX02Rf7yobmjWPbvCxmtm1x3cliZrM7elksS4qCl2S/3FefkkySNwv6\nFGSWxDMLhnn6MCPko/gzczpPOn2eyK6fZx9yNfnFW5+Mn7v1GaP9pL7lZfW5ay5M/n3Dt9Tl2qYk\nQNGUXKdL+pRkpNxX1sqS+48tf1N6PiWlLOYn8X5a2HX7mTbvHjXIvuwlWtnGw4McHTsVBD8PWBD8\n/Am/uMVOuef8XPr0EtexQR+n+LhZ9610G/N3aKED4tn3VAHtVPzMBpU76+/VphLLgS0I67mP2X1i\n97GSOKB0YfHu/SmY/Z0yq+rN9Pbrw2QAUhBW2iSDpVd4ti8A3pfalT5ekXApIi7jL1U/AFER8agg\n+WreTQ4s781xtlw0EwqS3ESKg+vYuDXiAuDSpi1sYoHbmDbrjglMnyyxv8VUsW8vHs6zwMm69Eu5\nTertndVuDa9A9+J02T2w6O/z++BPcQePRBY4ILbQ6SQl3u9AW+SG65vot5jq4/1epC8A/NVbP5V4\n88lV4TNv/zAAvyD4Ql2ZgD5+03P79WI1ve+irOu/zXJ/0VhhkRUZuZpx1sVStnkWOn09967lqUQq\nvhVzOLYeZ+xcdbvXx75u2z+1v6XUapcbu8ImEte58+pes3LBFPdLnRlTx9R1rpfaQidFx7Xrpizn\nCozbyXVvsj0Rs82C4BfHRRQEJ8eI/N6nPECm+lYoT9AADH22pS1aD70LpuBlxMTQBTOOrYvdL7Ww\ns/F0Y/FwctvaqP46/kqnku4LXUtK60W0T5wAZR7NIso+S5VCPxdT57mCCJJdc5e4nxIXy03mhjr1\nWNa1U8fENePrR4Q+nkG7ZabcL+Vco3IHs033/Zhd2zTfp9PmyBF61RYAB3whZ90t7Xdeu1fqdo/c\nhIgWbIAv7HLCTdZzrpRAMmnJ/HTVFVzXmSatIJPP4CaeHLhW5iYwPGGnJzBSwk63lblWMj6O9Pzk\nrc8HALzg9o9C3CiHwiV2E7du6tqVPI6Ry383pX9D7FrpFdUW7LU5V55Atu9yzU5RaqnzCoDH72Hv\nVqnd86e6UpYIsrnT5hVoz429QVwQXu+ffB06adu8itwvxfXSJkuJkqaI62U7mdaVNVhXKjZOznmC\ny6VNkAJMe9YjFwaF3FVGX4+2daHcdj+dGRPIZ8ecQcXYOeIO6GLscrF1WtilrXVyeU1nrVyprGJx\n0LrExOkYuf6GPG8FWuxbH8/AzhGLOQCt+JtHAlEEo8hBLSD3gRcDock9MMh2O6tbfmw/Tk6fW/M5\nxAwTpwwF4GA2+wTD5Cf3F0D7XcFp+727127UiU5yyU6sBc4Gm8v3f4Fpv6GcNS5lkdPxcba/tb5V\niGvRzdBY3YAo9s1mmxzGuvUCTK97/Zq3YTUqAGMhZ63csWXali1IPTynLHKECK+79WIAjYUuFkBV\nJ6oki66egBOxIRYaXb8wlS3Vs87ZZennMSZYxixe2mI2Rk6s2Zgxb79YBG+687Jix0626vPXx4uP\nMV2QeePo8xSGgm81eF+98cZeR5fU5UQJ23k8GQ004k6XOcgJu85a5wk7/VyVQ3/Vct5X5NKgkLuK\nXEM+qHXKg6W9n9hv1NhYGwwfTh8gvmDoh+FoWV2ERODN+sHOZrHVTgu7MWudb5Hr20Rc6YuzWNvi\nPovowl0ZAZayvukEKLJ9mETFZsicqxvYuI+/ndXU6FnkFJ5rS8lDwNQELdb6lrLa6dnsvqpcbLXz\nxF4n4mdrzFth04nE+/NezDUn3//NJTuZOX30umeNs8dIXZ09a5wn5LSFLpXIRPfpLHKta/PpCifV\nuovPEJdJsbo1h/AFmnUfTlngPIucJ9yacXpXNeuS6a1bd2V/8oUWOVKGZ6HTGXhlXbuO6++vzpCc\nc63U39WmzVrogFQCFDkXj1SJAm+fMUudFlt9W3wunpiT0gF6H30fEn8KWzbAnveYUCsRaZ4FTouy\n3Hgpl8m5WR8Tn3Onr3tfPek/p2q+KRd27ST2+kFVkCRlywQp5NKhkLuqpApFAs1vO/X8Pvass82z\nkFcDRR9frjVa4HmWDhF3XdssstqdzRrXTF3qQOpZyWyWWOus+6Wf7dKLbUj3kXUZUyjdrh9Me/fM\ndIbMoWVxKFCBWNANXVbiunwa72amb4K5+IhdSYk2bYFzRZvZLyrqPm/ewWpWdRanFdB+fyQOoe3r\nuUraCYcH6FP3bzAUdd6yR6lbZa7+m7a4Ab246yxyvcukWLClXIAUUhfhpeN+rKtjLtbNCjAvxii2\n5A1jjNLZLtdmPXZRy7mzydgl7mCEiIXuBbd/VFnfZlhi3mXLBfpJJ/leyfZSt19B+msPBO+7mkqZ\nb/EsdPa6nUOLsfheMa2UjYzjuRt6Y+ZEUbO9f70pkQa3fXxMK9DUVF/XNuZyac8pJdxKxJ212llh\nBzRxdnFpA9XnQRW7YALtfYvC7hhh1sqrynlKeO/BtCQWqKTPfbN+r6DP/eEF6WwZxxks7zkZKldx\nW/PYafYz6yV9lq3NIt5vuG5jIbzYiLFjAcMbTEk2Tu9mZ49fIsr2JdxK8B7GvSxi1spo++jU+R2n\n5st4HWVZK232Sy+8xWab9LJPPq2gT8k4NmvlI0PL7c1H70Tr13HXGeZOdj3Vx2atfMT0eSaeGGSS\ntOM8iiecsZ8063eKznFwrM2wDyEer7v1YvzYrf9u0H7H/PBspkmgSb4U9xlmqLTX8ru4XnDfWIxe\np0sm1PYZNze4vjrZHqMJNfhZIwfZhgvGueFcu+z1zD8feyzvfOyx7uGGeSCx+13H3cHxvH3s2N5r\ntX1sNkwA3YSkMLivYY/ZL8mlwayVV5Dwa+2CFUrWMudNrp2HRU4Yu1jY7bl4IW8f7TYmFyuVcQ/o\nLXSViaNr3C0la2XvKqZnVu1sa8rta6zocc4S0ewzdDHzkj7oLJnNy7fWw9hC5fUpJeWaY2dXddyc\nZKn01uVhZIV5tL7EHBvMWue9PpOlbpd+NtultEkfGXeQNVNlstysq2ZZZirvz4bxb17WSiTaSn5j\nltT3HEjHxul2sdZFrpVtwW5V602+917iEqDP3qe/m2kLXW+R05Y2yVq5u2ul7zbpuVZ68XCuVU7F\n1z79K8bTmBOiedHtH3K/m7msxakYuZR75QqLTpz4Vrlc3JpncfJjncfcL3Mxdna/VHbJEmug13/K\n60mN640/ZdzUeS6whGRjtvvl3ltZTr1ntt/YdsmEuY4yXs4GGTC3zX5ZfxjIBcGslcTH+9RthqIH\n8F0sbdIGO0aKEoE3Fhs0yG5pzkvi7XQiFb3PGsDMJE2ZVZ3LJYDO7dLG0Ym75XpddX4WNiFKcxrD\nG7DnOmnj5kq255Kk2MQq4kLZZ8kEbAZLcVupoN1XGldKfaMoqbuTukl5N6ChS2Z+fQyRjF7Mhudq\n2c+Izrv2hua9EldCoEl8Mj9ddje8M6C5ua1D/H29h6EbpRcjp5OlTCE7OWHatXDTfdpSAUCf4XV+\nKvFvjQtlzm3RS0jiCTk7OWHTskvWyqlj68mKnGulPATbB2ih2mxQrdeYbfqokWoNVFM/E0IU2jr3\nots/BCkVIBNH8rsQF0sgHyMXx8b1Cbbu4oZ7T+j7lpNzJ5ziTbGNW7K4VMb3v3Ts2xQx1rT3/eeJ\ndu0iOdzmH1fcH+N9erdNue6lXSZjF09PlGlXTvsejLlfbjADTiSerm1rM2Fqt8tm4k4LOyf7ZYd9\nX+h2eUhQyF1Fxj51EXUi5oSpcXOlD0aeS2VOzAlW1M3Mdjlf/RqumX6z0Ig6kyRltdkMkqOIoFuu\n5sC8F0vNIdYQa52OPZtjZURZLLhk35Ltubg5LVikfYa+7p30qcyNwY8XUv73jkBKMebCY610w5lF\nneVy/CGif806CF7HxOn2uByB9K2wVjflJs2MFXNN6mex4i1xNpu1Yk7NVurvlMTHeZMOJRMg3tud\nEnKetU0sz8bqLNZmoLHA6ThQr0ZbKq4tJ7b6elux1Vna7VhWkFkLnN7Hs8h5lukZNphvVqjWbZ/N\nWSTSqjUQUjGLFHNkD4ioe9HtH+q+m2tU3bVWx9GVlcbwr/cl2Rm3Yez6q+Pb+n3SmSoFGwM3QyxC\n+ntnHzMnDOPUhHzsmzBVCMqYNhAhZRkssSZ698P4da2c+2J/zxQB5/Xpvwv9GN05WGGnyhvo0gbV\nrBpkv+xi6QBlraN0OCT4aVxFTtftD3LCrIq10EViqP07xeq2j36ehS4n7rTV7prp01nq0CZJqaKs\nl9ZKB8BNiCLLwNBq1iz3gq85fBUFyC/aNNbaj96KO9ku+8qx9LpO0CI3Ad/lrO/Xva7ofP1lTc4V\nxXPB0duGbiGzwXilQtKWLdBZK9ftK41fWzVYb47Xty3mqyalTGtJa2507Q1OrLoi6sT9xFqBBZ38\npDnQ2AvqSblTduu+cGvOeRNlnGyG9lx810nhBKSEnC++holM4oLKNtvlzlkrW7fIar3GYnnWizX9\nGei/dptdJmQPiKB7we0fjZKdDEsSpF0rY2HXZ6v0PDmG8b/7EXYewwyUvqjrvT2qaD3V1rTnz7s0\n87FNgOL3SY+Vc6ncpgi5FlfD5CfDcj1a3OnjD2sCrgbHqBBb/1LCzsuACUBlv9z0LpiVfegjhwA/\njatI98DXrnuizooga6HbmO1j7Gq1GxvXe3CW84tcKxE/yIkrps34tw4D90ttpQMwqEfXPyTbWdPt\nLHT9LFws7rSFTgvA4XpsgdNlFJrxtVtP/4BgLXb92znt5ppys7Q3I9/FxPYdbstRQWaMewtd097f\nLO3DUGeNa+MchMV8Fc1gSvxc985U695KB8SWOv19zJUc8Ei6U8aiDYAr3OS7KS6TWsRr4STvRYnb\npPRLuU32kYnDjJRapOXKEeTqyHVisxVui+UqcovsLG3arXXjrAPxZ0ERR84RnelSu1LqkgRA2rXS\nd7ccutbba3eJi/wuYm+KK6e1tpUcO3WtLxdl+VjXbax4KQFXIvrsfcyz0HljDgXcULjlrHZDd8xe\n2KUyYMZumLJcJqDJxcJkJ1eQxbvf25vMbWDr2tTMkn8igHSyhrEHorFl645m27z1XB/74Kvb9EO0\nbdPrIup0wojOatenaAfQuarNT5ftxW4dFUiWm7RYHjaYRckYbuBu524jD7piVdMPsdpl8wbudfJM\nP1iLC+VMrXt99MOwrAP9Q7OuHzfHCrr4OdBkAYtEjhI9WniuVNscqy5zWx8DIDX2qu49kCBxfYPT\nbXZ9iXm3nyRF0eKw6dOfh06IYhOs+OM3xdu7PiqAXALHl/fmWFxfdYHjZ8vF0A3lyVmTXER4MgCP\nmGucbZN1LdieXODkGU91373Vkzdw/dE7nWibzTa488RNPPqsJ7p34M7qETw6fyJySbyDm3gmnugs\nW/dwHY/iiUhI3cFNPIonuu/gHdzE++BPou9q0/an3XfnDm5ijhWejT/p3CafbMfRQu4ObuLZ+JPu\ne3kHj+DZ7Tj2+PKdvosbeHTzRGRxu/OMOZ7+7lWXbKZ+BhDejf6atQHwdADSBjTZP9+D+Nr1jLZN\nr/8LEHJufOrt10bXf7kfiH+E/H71fUQnS1lhMbiWA70wmpu2Ph4tPTFn3R51m7aqDa1nlbuvRl6n\noGunptq8Pndxo5v8AdDFIWru4UaUmdKu27YNZriH67huskfew/XIM0buYYuo7UZ0jmtU3dgiyu7i\nuvKcqaLXIX30seT+o1/rBrNuHC3g7uFG91l7+zRt/X6S4Et/Xno/2Wd1Nu8mpjdnVTeBKa75d248\nG+RiKEl2wvIDVx2banbmiOjzstuWxAvtq48tSeC1eX0Gx7jh64UAACAASURBVKoGpQvEQhO1Ybx0\ngE057ZUOsOmlvVTWYympvTZbbsA7vjdO2TmPj2P7TC0SntuvZIa5xAXJPkTIjS3qc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hZt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\nGp2xjIQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109407d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [new2]",
"language": "python",
"name": "Python [new2]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
chengsoonong/crowdastro | notebooks/24_simple_pipeline.ipynb | 1 | 17131 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Pipeline\n",
"\n",
"This notebook contains the training pipeline for the simple (one-host) data set."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load training data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Imports.\n",
"\n",
"import sys\n",
"\n",
"import h5py\n",
"import IPython.core.display\n",
"import keras.backend\n",
"import keras.models\n",
"import numpy\n",
"\n",
"sys.path.insert(1, '..')\n",
"import crowdastro.data\n",
"\n",
"## Constants.\n",
"\n",
"TRAINING_DATA_PATH = '../crowdastro-data/training_simple_flipped.h5'\n",
"COLUMNS = ['zooniverse_id', 'source', 'x', 'y', 'flux24', 'flux36', 'flux45', 'flux58', 'flux80', 'label']\n",
"N_DATA = 84298\n",
"N_FEATURES = 160 # 155 (convolutional with PCA) + 5 (astro) dimensional.\n",
"CNN_MODEL_PATH = '../crowdastro-data/cnn_model.json'\n",
"CNN_WEIGHTS_PATH = '../crowdastro-data/my_cnn_weights.h5'\n",
"PCA_PATH = '../crowdastro-data/pca.h5'\n",
"RADIUS = 40 # 0.4'\n",
"PADDING = 150 # (5' - 2') / 2\n",
"GATOR_CACHE = 'gator_cache'\n",
"DB_PATH = '../crowdastro-data/processed.db'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Load CNN and PCA.\n",
"\n",
"with open(CNN_MODEL_PATH) as f:\n",
" cnn = keras.models.model_from_json(f.read())\n",
"cnn.load_weights(CNN_WEIGHTS_PATH)\n",
"cnn.compile(optimizer='sgd', loss='mse')\n",
"get_convolutional_features_ = keras.backend.function([cnn.layers[0].input], [cnn.layers[4].output])\n",
"get_convolutional_features = lambda p: get_convolutional_features_([p])[0].reshape((1, -1))\n",
"\n",
"with h5py.File(PCA_PATH) as f:\n",
" pca = f['conv_2'][:]\n",
"\n",
"## Load training data.\n",
"\n",
"subjects = []\n",
"xs = numpy.zeros((N_DATA, N_FEATURES))\n",
"ts = numpy.zeros((N_DATA,))\n",
"radio_patches = numpy.zeros((100, 1, RADIUS * 2, RADIUS * 2)) # Buffer.\n",
"\n",
"last_zid = None\n",
"subject = None\n",
"radio = None\n",
"\n",
"with h5py.File(TRAINING_DATA_PATH) as f:\n",
" data = f['data']\n",
" \n",
" for index, datapoint in enumerate(data):\n",
" if index % 100 == 0 and index > 0:\n",
" IPython.core.display.clear_output()\n",
" print('{:.02%}'.format((index + 1) / N_DATA))\n",
" \n",
" # Batch the features.\n",
" convolutional_features = numpy.dot(get_convolutional_features(radio_patches).reshape((100, -1)), pca.T)\n",
" xs[index - 100 : index, :-5] = convolutional_features\n",
"\n",
" datapoint = tuple(datapoint)\n",
" zooniverse_id = datapoint[0].decode('ascii')\n",
" x = datapoint[2]\n",
" y = datapoint[3]\n",
" astro_features = datapoint[4:9]\n",
" label = datapoint[-1]\n",
" \n",
" # Fetch image patch.\n",
" if last_zid != zooniverse_id:\n",
" subject = crowdastro.data.db.radio_subjects.find_one({'zooniverse_id': zooniverse_id})\n",
" radio = crowdastro.data.get_radio(subject, size='5x5')\n",
" last_zid = zooniverse_id\n",
"\n",
" patch = radio[x - RADIUS + PADDING : x + RADIUS + PADDING, y - RADIUS + PADDING : y + RADIUS + PADDING]\n",
" \n",
" # Store patch so we can get CNN features later.\n",
" radio_patches[index % 100, 0] = patch\n",
" \n",
" # Store astro features.\n",
" xs[index, -5:] = astro_features\n",
" \n",
" # Store label.\n",
" ts[index] = label"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Can't just train_test_split - have to make sure we have training and testing *subjects*.\n",
"# Assume xs/ts ordered by subject.\n",
"# In future, should have separate test subjects!\n",
"xs_train = numpy.vstack([xs[:N_DATA//4], xs[-N_DATA//4:]])\n",
"xs_test = xs[N_DATA//4:-N_DATA//4]\n",
"ts_train = numpy.hstack([ts[:N_DATA//4], ts[-N_DATA//4:]])\n",
"ts_test = ts[N_DATA//4:-N_DATA//4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Normalise and scale.\n",
"import sklearn.preprocessing\n",
"\n",
"normaliser = sklearn.preprocessing.Normalizer()\n",
"scaler = sklearn.preprocessing.StandardScaler().fit(normaliser.transform(xs_train[:, -5:]))\n",
"xs_train = numpy.hstack([normaliser.transform(xs_train[:, :-5]),\n",
" scaler.transform(normaliser.transform(xs_train[:, -5:]))])\n",
"xs_test = numpy.hstack([normaliser.transform(xs_test[:, :-5]),\n",
" scaler.transform(normaliser.transform(xs_test[:, -5:]))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing framework"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from crowdastro.training_data import remove_nans\n",
"import crowdastro.config\n",
"\n",
"def test_subject(subject, conn, classifier, stellarity_cutoff=1):\n",
" radio = crowdastro.data.get_radio(subject, size='5x5')\n",
" potential_hosts = crowdastro.data.get_potential_hosts(subject, GATOR_CACHE)\n",
" \n",
" potential_hosts = {i:j for i, j in potential_hosts.items() if j['stell_36'] < 0.75}\n",
" \n",
" cur = conn.cursor()\n",
" consensus = list(cur.execute('SELECT source_x, source_y FROM consensuses_kde WHERE zooniverse_id = ?'\n",
" 'AND radio_agreement >= 0.5',\n",
" [subject['zooniverse_id']]))\n",
" if not consensus:\n",
" raise ValueError('Found null in data assumed sanitised.')\n",
" \n",
" ((cx, cy),) = consensus\n",
" if not cx or not cy:\n",
" raise ValueError('Found null in data assumed sanitised.')\n",
" # Consensuses are inverted vertically w.r.t. the potential hosts.\n",
" cy = crowdastro.config.get('fits_image_height') - cy\n",
"\n",
" \n",
" n = len(potential_hosts)\n",
" xs = numpy.zeros((n, N_FEATURES))\n",
" radio_patches = numpy.zeros((n, 1, RADIUS * 2, RADIUS * 2))\n",
" ts = numpy.zeros((n,))\n",
" \n",
" points = []\n",
" \n",
" for index, ((x, y), data) in enumerate(potential_hosts.items()):\n",
" patch = radio[x - RADIUS + PADDING : x + RADIUS + PADDING, y - RADIUS + PADDING : y + RADIUS + PADDING]\n",
" radio_patches[index, 0] = patch\n",
" xs[index, -5] = remove_nans(data['flux_ap2_24'])\n",
" xs[index, -4] = remove_nans(data['flux_ap2_36'])\n",
" xs[index, -3] = remove_nans(data['flux_ap2_45'])\n",
" xs[index, -2] = remove_nans(data['flux_ap2_58'])\n",
" xs[index, -1] = remove_nans(data['flux_ap2_80'])\n",
"\n",
" points.append((x, y))\n",
" \n",
" closest = min(enumerate(points), key=lambda z: numpy.hypot(z[1][0] - cx, z[1][1] - cy))\n",
" assert points[closest[0]] == closest[1]\n",
" \n",
" xs[:, :-5] = numpy.dot(get_convolutional_features(radio_patches).reshape((n, -1)), pca.T)\n",
" \n",
" xs = numpy.hstack([normaliser.transform(xs[:, :-5]),\n",
" scaler.transform(normaliser.transform(xs[:, -5:]))])\n",
" \n",
" probs = classifier.predict_proba(xs)[:, 1]\n",
" selection = probs.argmax()\n",
" return selection, closest[0], probs, points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train logistic regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sklearn.linear_model\n",
"\n",
"lr = sklearn.linear_model.LogisticRegression(class_weight='balanced')\n",
"lr.fit(xs_train, ts_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lr.score(xs_test, ts_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"import sqlite3\n",
"\n",
"import matplotlib.pyplot\n",
"%matplotlib inline\n",
"\n",
"import crowdastro.show\n",
"\n",
"def softmax(x):\n",
" exp = numpy.exp(x)\n",
" return exp / numpy.sum(exp, axis=0)\n",
"\n",
"with sqlite3.connect(DB_PATH) as conn:\n",
" cur = conn.cursor()\n",
" zooniverse_ids = list(cur.execute(\"\"\"SELECT zooniverse_id\n",
" FROM consensuses_kde\n",
" GROUP BY zooniverse_id\n",
" HAVING COUNT(zooniverse_id) = 1\"\"\"))\n",
" assert len(zooniverse_ids) in {1087, 1093}, len(zooniverse_ids)\n",
" n_correct = 0\n",
" n_total = 0\n",
" split_index = len(zooniverse_ids)//4\n",
" for index, (zooniverse_id,) in enumerate(zooniverse_ids[split_index:-split_index]):\n",
" subject = crowdastro.data.get_subject(zooniverse_id)\n",
" try:\n",
"# print(zooniverse_id)\n",
" guess, groundtruth, probs, points = test_subject(subject, conn, lr)\n",
"# print(points, guess, groundtruth)\n",
" n_correct += numpy.hypot(points[guess][0] - points[groundtruth][0],\n",
" points[guess][1] - points[groundtruth][1]) <= 5 # 5 px radius = 3''\n",
" n_total += 1\n",
"# print(sorted(points))\n",
" print('{:.02%} / {:.02%}'.format(index / (len(zooniverse_ids[split_index:-split_index])),\n",
" n_correct / n_total))\n",
" \n",
" if index < 50:\n",
" matplotlib.pyplot.figure(figsize=(20, 10))\n",
" print(zooniverse_id)\n",
" matplotlib.pyplot.subplot(1, 2, 1)\n",
" crowdastro.show.subject(subject)\n",
" matplotlib.pyplot.scatter(*zip(*points), c=softmax(probs), s=200, cmap='cool')\n",
" matplotlib.pyplot.axis('off')\n",
" matplotlib.pyplot.colorbar()\n",
" matplotlib.pyplot.scatter(points[guess][0], points[guess][1], c='green', s=200, marker='+')\n",
" # print(points[groundtruth][0], 200-points[groundtruth][1])\n",
" matplotlib.pyplot.scatter(points[groundtruth][0], points[groundtruth][1], c='red', s=200, marker='x')\n",
" matplotlib.pyplot.subplot(1, 2, 2)\n",
" matplotlib.pyplot.scatter(range(len(probs)), sorted(softmax(probs)), marker='o', c=sorted(softmax(probs)),\n",
" cmap='cool', s=200)\n",
" matplotlib.pyplot.xlabel('Index')\n",
" matplotlib.pyplot.ylabel('Probability of being the true host')\n",
" matplotlib.pyplot.show()\n",
" except ValueError:\n",
" continue\n",
"\n",
" print('{:.02%}'.format(n_correct / n_total))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train random forest"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sklearn.ensemble\n",
"\n",
"rf = sklearn.ensemble.RandomForestClassifier(class_weight='balanced')\n",
"rf.fit(xs_train, ts_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rf.score(xs_test, ts_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"with sqlite3.connect(DB_PATH) as conn:\n",
" cur = conn.cursor()\n",
" zooniverse_ids = list(cur.execute(\"\"\"SELECT zooniverse_id\n",
" FROM consensuses_kde\n",
" GROUP BY zooniverse_id\n",
" HAVING COUNT(zooniverse_id) = 1\"\"\"))\n",
" assert len(zooniverse_ids) in {1087, 1093}, len(zooniverse_ids)\n",
" n_correct = 0\n",
" n_total = 0\n",
" split_index = len(zooniverse_ids)//4\n",
" for index, (zooniverse_id,) in enumerate(zooniverse_ids[split_index:-split_index]):\n",
" subject = crowdastro.data.get_subject(zooniverse_id)\n",
" try:\n",
"# print(zooniverse_id)\n",
" guess, groundtruth, probs, points = test_subject(subject, conn, rf)\n",
" n_correct += numpy.hypot(points[guess][0] - points[groundtruth][0],\n",
" points[guess][1] - points[groundtruth][1]) <= 5 # 5 px radius = 3''\n",
" n_total += 1\n",
"# print(sorted(points))\n",
" print('{:.02%} / {:.02%}'.format(index / (len(zooniverse_ids[split_index:-split_index])),\n",
" n_correct / n_total))\n",
" \n",
" if index < 50:\n",
" matplotlib.pyplot.figure(figsize=(20, 10))\n",
" print(zooniverse_id)\n",
" matplotlib.pyplot.subplot(1, 2, 1)\n",
" crowdastro.show.subject(subject)\n",
" matplotlib.pyplot.scatter(*zip(*points), c=softmax(probs), s=200, cmap='cool')\n",
" matplotlib.pyplot.axis('off')\n",
" matplotlib.pyplot.colorbar()\n",
" matplotlib.pyplot.scatter(points[guess][0], points[guess][1], c='green', s=200, marker='+')\n",
" # print(points[groundtruth][0], 200-points[groundtruth][1])\n",
" matplotlib.pyplot.scatter(points[groundtruth][0], points[groundtruth][1], c='red', s=200, marker='x')\n",
" matplotlib.pyplot.subplot(1, 2, 2)\n",
" matplotlib.pyplot.scatter(range(len(probs)), sorted(softmax(probs)), marker='o', c=sorted(softmax(probs)),\n",
" cmap='cool', s=200)\n",
" matplotlib.pyplot.xlabel('Index')\n",
" matplotlib.pyplot.ylabel('Probability of being the true host')\n",
" matplotlib.pyplot.show()\n",
" except ValueError:\n",
" continue\n",
"\n",
" print('{:.02%}'.format(n_correct / n_total))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rf.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"matplotlib.pyplot.figure(figsize=(15, 15))\n",
"matplotlib.pyplot.xlabel('Feature index')\n",
"matplotlib.pyplot.ylabel('Importance')\n",
"matplotlib.pyplot.plot(numpy.abs(rf.feature_importances_ / numpy.max(rf.feature_importances_)), color='blue')\n",
"matplotlib.pyplot.plot(numpy.abs(-lr.coef_[0] / numpy.min(lr.coef_)), color='red')\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lr.coef_ / numpy.max(lr.coef_)"
]
},
{
"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
}
| mit |
rsignell-usgs/notebook | UGRID/FVCOM_hslice.ipynb | 1 | 83737 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FVCOM horizontal slice at fixed depth"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import numpy.ma as ma\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.tri as tri\n",
"\n",
"import cartopy.crs as ccrs\n",
"from cartopy.io import shapereader\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"\n",
"import pyugrid\n",
"import iris\n",
"import warnings\n",
"\n",
"from ciso import zslice"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#url = 'http://crow.marine.usf.edu:8080/thredds/dodsC/FVCOM-Nowcast-Agg.nc'\n",
"url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM3_FORECAST.nc'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" cubes = iris.load_raw(url)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"var = cubes.extract_strict('sea_water_potential_temperature')[-1, ...] # Last time step."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lon = var.coord(axis='X').points\n",
"lat = var.coord(axis='Y').points"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<iris 'Cube' of sea_water_potential_temperature / (degrees_C) (-- : 40; -- : 53087)>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# calculate the 3D z values using formula terms by specifying this derived vertical coordinate\n",
"# with a terrible name \n",
"z3d = var.coord('sea_surface_height_above_reference_ellipsoid').points"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read the 3D chuck of data\n",
"var3d = var.data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# specify depth for fixed z slice\n",
"z0 = -25\n",
"isoslice = zslice(var3d, z3d, z0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# For some reason I cannot tricontourf with NaNs.\n",
"isoslice = ma.masked_invalid(isoslice)\n",
"vmin, vmax = isoslice.min(), isoslice.max()\n",
"isoslice = isoslice.filled(fill_value=-999)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_map(projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(9, 13),\n",
" subplot_kw=dict(projection=projection))\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",
" ax.coastlines('50m')\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# use UGRID conventions to locate lon,lat and connectivity array\n",
"ugrid = pyugrid.UGrid.from_ncfile(url)\n",
"\n",
"lon = ugrid.nodes[:, 0]\n",
"lat = ugrid.nodes[:, 1]\n",
"triangles = ugrid.faces[:]\n",
"\n",
"triang = tri.Triangulation(lon, lat, triangles=triangles)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KlIlMmTJRrFgxPD09OX/+PG/fvn2v/sKFC2Nraxv6KFeuHBYWtyOUco5la99i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\nH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WyzzTa89957/OpXv+K2225jww035MYbbyx4XJuBinu5sgcnBhk0l8SW2vao2vcdJlD7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9975208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = make_map()\n",
"extent = [lon.min(), lon.max(),\n",
" lat.min(), lat.max()]\n",
"ax.set_extent(extent)\n",
"\n",
"levels = np.linspace(vmin, vmax, 20)\n",
"\n",
"kw = dict(cmap='jet', alpha=1.0, levels=levels)\n",
"cs = ax.tricontourf(triang, isoslice, **kw)\n",
"kw = dict(shrink=0.5, orientation='vertical')\n",
"cbar = fig.colorbar(cs, **kw)"
]
}
],
"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 |
nvictus/svgpath2mpl | examples/arcs.ipynb | 1 | 53320 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "chronic-individual",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bizarre-envelope",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import HTML, SVG\n",
"import numpy as np\n",
"from svgpath2mpl import parse_path"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "tight-smart",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x648 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rx, ry = 100, 50\n",
"xi, yi = 200, 200\n",
"xf, yf = 50, 50\n",
"SVG_TEMPLATE = \"\"\"\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"{d}\" />\n",
"</svg>\n",
"\"\"\"\n",
"\n",
"fig = plt.figure(figsize=(9, 9))\n",
"gs = plt.GridSpec(nrows=3, ncols=4)\n",
"\n",
"svgs = []\n",
"i = 0\n",
"for rotation in (0, 45, 270):\n",
" for large in (0, 1):\n",
" for sweep in (0, 1):\n",
" d = f'M {xi},{yi} a{rx},{ry} {rotation} {large},{sweep} {xf},{yf}'\n",
" svgs.append(SVG_TEMPLATE.format(d=d))\n",
" path = parse_path(d)\n",
" ax = plt.subplot(gs[i])\n",
" patch = mpl.patches.PathPatch(\n",
" path, \n",
" facecolor='r', \n",
" edgecolor='k', \n",
" linewidth=3)\n",
" patch.set_transform(ax.transData);\n",
" ax.add_patch(patch);\n",
" ax.set_aspect(1);\n",
" ax.set_xlim(0, 400);\n",
" ax.set_ylim(400, 0);\n",
" if i in range(4):\n",
" ax.set_title(f\"large: {large}; sweep: {sweep}\")\n",
" i += 1\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "damaged-consequence",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"display: grid; grid-template-columns: auto auto auto auto;\"><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 0 0,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 0 0,1 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 0 1,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 0 1,1 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 45 0,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 45 0,1 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 45 1,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 45 1,1 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 270 0,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 270 0,1 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 270 1,0 50,50\" />\n",
"</svg>\n",
"</div><div>\n",
"<svg height=\"400\" width=\"400\" stroke=\"black\" fill=\"red\" style=\"stroke-width:3;border:1px solid black;\">\n",
" <path d=\"M 200,200 a100,50 270 1,1 50,50\" />\n",
"</svg>\n",
"</div></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid = '<div style=\"display: grid; grid-template-columns: auto auto auto auto;\">'\n",
"for svg in svgs:\n",
" grid += f\"<div>{svg}</div>\"\n",
"grid += \"</div>\"\n",
"HTML(grid)"
]
},
{
"cell_type": "markdown",
"id": "helpful-burns",
"metadata": {},
"source": [
"### Ice cream"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "basic-cheese",
"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"
}
],
"source": [
"d = 'M368 160h-.94a144 144 0 1 0-286.12 0H80a48 48 0 0 0 0 96h288a48 48 0 0 0 0-96zM195.38 493.69a31.52 31.52 0 0 0 57.24 0L352 288H96z'\n",
"path = parse_path(d)\n",
"\n",
"fig, ax = plt.subplots(nrows=1, ncols=1)\n",
"patch = mpl.patches.PathPatch(\n",
" path, \n",
" facecolor='lightblue', \n",
" edgecolor='black', \n",
" linewidth=1)\n",
"patch.set_transform(ax.transData);\n",
"ax.add_patch(patch);\n",
"ax.set_aspect(1);\n",
"ax.set_xlim(0, 445);\n",
"ax.set_ylim(515, 0);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "boxed-richmond",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"515\" width=\"445\" stroke=\"black\" fill=\"lightblue\" style=\"stroke-width:1;border:1px solid black;\">\n",
" <path d=\"M368 160h-.94a144 144 0 1 0-286.12 0H80a48 48 0 0 0 0 96h288a48 48 0 0 0 0-96zM195.38 493.69a31.52 31.52 0 0 0 57.24 0L352 288H96z\"/>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVG(data=f\"\"\"\n",
"<svg height=\"515\" width=\"445\" stroke=\"black\" fill=\"lightblue\" style=\"stroke-width:1;border:1px solid black;\">\n",
" <path d=\"{d}\" />\n",
"</svg>\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "supreme-display",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "awful-devon",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "compliant-telling",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fatty-valley",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| bsd-3-clause |
wasit7/PythonDay | notebook/00 index.ipynb | 4 | 1584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcome to PythonDay Tutorial\n",
"====\n",
"\n",
"This tutorial will give you fundametal concepts in PYthon programming and also bring you to the Python world, where coding is fun :)\n",
"\n",
"Wasit\n",
"\n",
"<img src=\"files/python.png\" alt=\"python.png\" width=\"400\"><a href=\"https://xkcd.com/353/\">source</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"10:00-10:30 Web App Ideas\n",
"--\n",
"10:30-12:00 Intro Python\n",
"--\n",
"Installation, Package management, Using Modules\n",
"Datatype, Control flow\n",
"Functions and Class\n",
"\n",
"13:00-15:00 Workshop on Flask and Django\n",
"--\n",
"15:00-16:00 Google App engine and APIs\n",
"--\n",
"16:00-18:00 Making a simple web chat\n",
"--"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
ergosimulation/mpslib | scikit-mps/examples/ex_mpslib_mask.ipynb | 2 | 92995 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MPSlib: Using masks"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mpslib as mps\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import copy\n",
"from numpy import squeeze"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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T74O/If9K0pmYHbqG5SfCWBbjTPWv5CA/Wod3Fn1v2h45ZbiCkdMePJDCPfwrGZrnbe9vsISnqrwAB5xCPPmfQ3abemS+lsjJmjPJNeskGdKRvgpEw4UijzWkpbF1RhNi3/vXTOO2F+eHG8x4agDkRBenJHqVRshZWVbJ5GS9vvA+sqxoj/jBlWrpwzk+fIZN59YJrSk5LPiTM5y7G8al2AAiR96fqJszkxpTofp1Rds7QYvMciwsIWbOcDwvWFYqUoOF2bXxUbnR3escC/5sAoD667K4rCsdvh1N+UCSFyjpDCp5nVMk9D0/8G/Wd4gxbXv2uGOzmemBFO7qm0lE/D2Arc2/IUjjiVqoON3Xi0iqI/8HBXzCoCY0i3bwewnBM1P+YqTfeXIFe5ohi6bTXyXkT9se8rIL9vDy8eH8sXy+iZ7hIe49pGOnCEutRFSNvhxpNhedKDriJj9yE9EWhpZl4hvdTO0h6+9gOOjcVdOtIU3I8jF3iI5tv5oRvgXNf3mC/ZaUStOtwy2OW2XJNSO1tgX4168BhRzuqXJlc81fjWO8MAC3Wk2NO2So8PXu+5IspqoRzZmefpyqWfRK7J2yJxVRUzGjhhP2w3WkWOukaA+kcNefu0DkgGt8ubM5XwUZl5pnu88iMnsYkWlVkE6duc8zdC40z950OEv0ymtN6OP9JflZ6X5MrkTonJM2p/bLej3qNKUzeHHb2Uyo1QvD4eIx1z2EfdCfu0BYr8tMPRzNIJ8jJiKrbFli4d3CyxT3975iein39b5F39F52m1Vr2GEqasjMiUMRx3/LVXu7sgxxpyId19bRBcPywlaheGSPoUvb7YgrKflkEZn+MHOtJxv4qLPliU67BiI5sBpDKn3rmSmpnIl4p4tw6kBtq3A8uPE4BnUTBlOpaWZVonVHlg1TM7OYss3yvKVp/vM5PT7nqjcbWduc4m/Qd/zza13vI/w+sR82ZotSzZF9aQHyGZMdpMWPmt3mJi4fIOnTz9l2n7UVYXB3cWuMR7CSTBI/FXDi68S8u7/eH0Gy2LKm3+qBXFZSiNTzkYqJEz31MCZrFu9hGG/rjJSItgLlRqh0ZD5WDXWrV7CutVL7BbskmzghVO9OVHPuRq0JBvIlLPJlgt/ULRCzYZl35PRrJrxu6vuQUU3lZq4iT6cGGpZsGfK2aZPYb/ZkbEzOP5hoNX5PrDC3RJ2N5uB/1+2Cx395StsORlZgjMqGQw435qyPxyw3tFJkG4lcPxwJUXb1abFd4L1Pd8cvyOl6zYUQlQUQvwjhDguhDgmhBiV015GCLFBCHE65691EpH7DVlmSPQTdIlqQZXfhlrs1tkjjYXx/6L2tVw3uDB4bCrD4vh/+XXeNw5Pscbsl9F1dC6FNED0ohF0iWpBl0efKbLfyu+msjj+X7z+Lfmf88kjCRxsarkU4YRb0cY553xiNg8otN/JJ2ZRb1/RhelL11NlJ8r9dYGWx5S0mX5qdz4J+Z3AHd4E7vAmubs57eeDhrQg84Stt4L/ILV9rUJ6K6FLFKQYima3tBW622rSDHnmmWf6/YuqdgwIx4scHfi1BuXXlHz1KjuhB16VZbka0BgYIYSoBrwJbJRlORLYmLN937BncB2q7+gNQKjGhQYHJTQVQ8z6GdLSMKSmEjPpJq37DOTxIYMLHS9A7UGjf2/S4KDE5TebWjyvJrQiDQ5KNDgo8VXor5RVe9gdpptiyKDlgEG07jOQ8PkXiu1QTxjUhPYLtpq2H/lkOFGzLmNITUW6dIXWfQayL7PwPBMflRtl1R5MCl1JyM6SidpRubtT5wD08z5eqK+k+jfDad1nIFsGNcCQmmr6VPkwjdZ9BtJ0jPLFrBNaRpfdgUc1yw7mB9Lmngv9pctc/7cpUjWDwtFXSeNpslEf/nw9w/Wj8Ph1bw5XygMElRqhVtP0LXPy/uoubqSVU1slTQj5dAc1Q0cS3+lbKz2tI/TjvfzRqyzPed4FYHy5Y9z6fTdtP32dgBnWw7V8/3Tn6qMpiuiII6NnEF5xCJEj7a8RWVKQZfkqcDXn/2QhxAmgAvA0ebW2FgCbgHH3YYpG7D6C/6KGRGX1I/bxBUwIOEIHV8urUOlMPJoz8Wh1OqIWDAMBfTv8o3DYvV/O6Fy9/ZwHf1RqWOg4Kq9s1gSsztkqWhhe0KfQ5ofXzdqFBJXXG4vaFNcYc/2VplR+7iyj/c4hyQail4wg6vdL6M8ZI8FkvR7N3/t4Ye5o9J4yAXWus7XWL2bjVNJ4MilkA3VmjiKXz6TMITVlZxeP8kEdFcGJ18rwe8Bs1MLcZByxdChVf7uFdDzWbJ904jSaE+Dr7U3UI8PY2vtLAnL4oALUHlTQWDavPtDCHSA9uOhbo5aLKwsmfcWokc8j99eafvD8cI/VkdQ23aR9XHqyLBXTMx2qhypn6/nmZitmVCg+l0zsnEeY2WIRTVzvUBj3TWJ1GX+VuuiXlixTYb0KOuU1pVfNROh0dmtLcnYWE6b04bn/5dkLy6o9WP3mF/Q//grqTUXHUfst2EknzesseGcS1V3yvo9/WCJqb2+ku3ftms+9gBCiMvAIsAsIzBH8ANeAwPs1r1y4rdxNoGgEjxu3r7cMpHxGVpHONjkz08R3vkDVkthmAfhq05kanBciOKPCTrDxHl6V6s7yW/UL3Xc2qSxhb5YsH45fp8usjDQme+mRqPLeAfQZ5qvVihOMCkhS78b0fVnpZ+tadi+dPdLwUbkR/3Qeod2zNdpy80aeb8NryxmbKB8yOjXEoDGuaG9Hq4nvOINcQ8mcpGC23qkCgEFWUXX8CaQ7SUWOJ929S9ibO+hZvycVPO7Qxu+4iV7cEh5oswxAzOQEMmWlgH//ZnXq7evGiSwjz3mE1pPfo/6g2i8XyVxfGalFXVReXqb+laYc5PdU43JWK9QcfnUG11sHOzQfQ2oq2xfVdfDb5EHl6kq5oCTau1vOTF3SeToqV/s5dra1moLaz9eheQWvucToq8oHOUTjSUqIi3V7rSzj/90O+n4ylgv6PKfbnrrLODmtCioP51WhdwaEEJ7ACmC0LMuKN49srCxf6JpYCDFYCLFXCLE3m5KP31fpZeKzjddz3wczufRcJZuvZfgbO7je5C5neodyNjuFs9kpZMpF23JzEZ/Tf8zvfbne5G6hH8/2lkMXnQG1fxncNMb5ZssSZ7KtrwN8luw0m+fYVX25qjd3BP9SZQNbps82fZJaRaIu61/0p1w5Fk2fZDrm2Eil83Tad11M573Z9I5VwZ4fmjYXuN7kLp/+1M16X5tHfYCw5fUmlPv7IC8/PpK459U83WA/k4P2Gsmqyh8gbXEW9Xe+SGifMxgyMjCkpfHllO70fiffj+C4GdkpOPV5HU7VmU7Bknr5EaJJ527HWnguu3eMk/pzF9h7MxwKZKz+/dlU6oWPptKH1s0zZefs4PEGY4h/Ks9UdLr1dzToO4JyM0sH66EQQotRsC+RZTl3DX9dCBEky/JVIUQQcKOwY2VZngPMAfAWZZxfTLcAXFfvZsTOZ1h7aAMAh96YQcyjL1DpedsZNqVTZxge+hgAlXe7MTvE+u8wot7TSLcSqML9Yzx9b88GGrsan5H5dyuyIiYAsN/HFPHqTrpvGWuiLbCEbZNn2Thiydju7UGxNHchxDkhxBEhxEEhxN6ctvseUeD61hWjnW3jPqKG7uZ01wpszvd7u6tcONRkAV0PnMNnqz/qKmGoJOUz+PwrfyFKiHnSFrzado2CrKswhGg8udLOfotlgNqd4x9Wst7RAjze8+D7uwGKNneVi113U8znt9idmachqoWKV0atgIY1HZ6XsyCEEMBc4IQsy5Py7VoF9Mv5vx/w272em634u9FMqu61PckpPy49W4YOjz9LozfzCly3GPgSHR5/VvG5n6yLmvKBvHrmGPWc+Ih6rDts+m6b0kvGqNHipZcI+fZoiYxdEM74Bi1lWa4jy3LuWv2+RxSc3a4kS9Kfu8DYT4fx0sVHOZGVZmI5HOhzjWXhG6n5czx1Bii1nIG+B7n+Uj2Hzh+06TYTbkUr2mSdhNrPhvecECQMbEJbD2VCSe3dPQn/60Wz7q81+RN1VIRd81MLFV4B9sUiK7D7CF8s7moWP9y24x7U/mVsGkKKu0CP1S8rom/6e9/guQUbEQ3uu4B/FHgBaJWjuBwUQnQAPgPaCiFOA21ytksFDHfvUm/8MI5lpQMQpPGkqZdjyXz6S5eRTsdRduN56o0fRr3xw3DfEYt0Ok7xcTaFta0wNHuEtEWuPOGebVKAup5tw/wPOxdv3IwM03cb98EQhl92XqRdkiGduh8Ow337mWL7luI+b0LvZ/+22q8kXk9PY4wkIOdvlxI4R5Fo164AyZUs4//tDi61glcfeYr5dysqdn8eeJC5lbYq2sqqPbhTxzGKXsPhk2zvUUsh4I+0n4ZmpXU1QxNakZXvT1TwuBzOysBrkTd+W82PH+F7EX05L7P2kkalifuYm6TU/j8rv40bXaraNoBBImrsPmr/MEqRqDHY5wpVZphHDdxLyLK8VZZlIctyrRzFpY4sy2tlWU6QZbm1LMuRsiy3kWW51BCGy5mZlJ29g8t6b1NbuMsNro1p6lhyEsYckLKzd1B29g677MIliewn6nOmr4Z/qisXTQcvVMT7R+eZh3wX7WDzirpU295H8dmZYV/E3ecJkVTb3of6m4dRbvZOp9APV28cZ6EAjBLFFe4ysF4IsU8IkRs8a1NEgbOcTiIrm88THiHJkG5q89MWXjDakJqKlJjI8kFPEPVvP4uZa86AdDyWudvyPPKeKlf8ddZTnDMiylFGpUzC6v/ZGDxW7EKXJCsckfcTcmYms2cocwzcVS4seHcSWe0Kj5wwG0Ovp8p7B4j6Z6Ci/X+BG0kY2MRpc33QoKkQjKpWtOVPjWiLuQW/36nDpZx7pKFOy/7XpiHXr2ZX1nZphToqgqsvZSp8NQDbMgwYEpyfLV3hs+1U7HpU8Rlzqhs/JfsV+ilM8M/e1IqKXY8S3uugU1Y66mpRJjmSZEjntsGy6ba4DtXHZFm+LIQIADYIIRSvE1mWZSEK54Z1ltNJf+4CO+u48OuJUPp7G/1bWiEhNG4WCYHEtoNE7HOlzpiRjOu3zGpI0b2ENC7BaL/OwStXGhC07jJ6wGvpTjqGvMGBsdMUcf1neuqI3FbIYCWM4LVXGNK3icL5Vt3FjUxfDbY+aoaMDEK/V7Hv0Szq6YxHBWk8SQ+4zx7t+4EcgX18fDDxHb6z2C1TzqZLdCsMKQVe9LLMqfrZPLtmALsf+RkwmuD+/GUhLQa+hO6P0sGGaDdyrkvLXw6ytoySMEuSDYwZP4LIBffGEe/95Fnmk8/sm+8le2N4Tw7kCxOWZIOFeCoHIQSfrV1oKmQy/XYdbh21HMBQLOEuy/LlnL83hBC/Ag2xMaKgJDG2zGGWjRpN0FeWv7ghI4OQT7ezeNNTzHwvhbU1FplxsNxvpBgy2PhLA0LO5X2PkHUJGMbKihiaZ5rs4d64aJTQx53j0nMhDFmhFPA36wq8lto+jnbjfo5lBlNPl/eSzayZhsrVFUMh8cr/RQitC1PP/IOvCjzFLiji9agTWhYcX6doO5ntwcfhdSwe8+ucKTRa8mqJx5w7HY1rsehnI3Oiv8qNgsaGTm17UCZ2j1NlqK1Q+/ow5/AaXHIEvE5sJ38+SvMxw4n67YBT5qYJrcj8rT+ZEpjCN7xI1eGnijzGYbOMEMJDCOGV+z/wBHCU+xFRIMtMWPOsadNd5UJakMEmIiCx4xC+z1xmabIys+9EVhpR3zleFs9uqNTcXFWVLyKNGtctKZVHFo+h4hfK7FT53CUe2dXXrqF9tp2j/rvDFKarMu7pTokr11+8xN65dUiU8kxh3zw3D00FO/IEZJnPFinjdrc8Ng2VnTwnDyISXmpCjX0qau7KJkLjRoDaQ7Fys4QAtYfi00iXTY19KmrsU/Fd9UVm/f3U7kztOo8a+1SU2+5bAt/E+bg1pAltvttm+o6F0U2L1PR7Stkb/2Nt03Wu+U8SIRpP0/zy56M0e3kIPhtjnVKnIKNTQ+qvijMJdgBZr7LKZFkczT0Q+NUYNYYG+EGW5XVCiD3AMiHEQOA8YD3a3gnQJaj45FZV3i5rfJvt6/E1j194lcCp1uOuDRkZ/DKoDb7zV9HDy+jwSDLoUB05izknW8kg+fkG/Fz7S1MtzUcXvkb4/3aYvfUNqamkn/MyMp7YCP216wSuU5P9Qd632VRjJY+3GozranNqA3tRdvYOGoa9yskXpqMWKtq7Z7L45zQSnw1Ef+269QGAyisTmNgzgtdzlt1eKg1XukYQMM224x8UqL29OTkhr/hCjdrxJtrq4rjAdEKbb5zCy9S1d8+kvft+UgwZ1Jo6iuiP4kpVjVW1rw8nP8wLQqhT54zpfrCE4+8GUnW2L/Ie22P67YHh8Uc4+1zey3ZGg+9p725ZYJ/NTqHdsteJXHsAyQmrzjt9myB3v8X4cnn1HGrs7E3Qeuui22HhLstyHFC7kPYEoLWj4zqKkE+2s2VtPW6t3m8iMho7fBk/bGuHvM96oQux/RC/3KxLD6+NANTTQfz8cILm6nD5c6+VowuH51kNiVKaVXNPUp/GzJ8wSVEkOWxlisXlXPTEc6zrrDPdZJFu1zkR1dgmAv/8kJ0YKxXx/n6qhfXnVDNjLdAFoX/TtO0IfBfZJpylY6f4Ma6+6WH2VLkS0/MESSvKo7/qfMbAew1VrWjSKnmT4acmrqvlAg3jb1bjSqZ9K5Yq7jesCsH88FS5Etd1NvUPDyPwT51VXvASgRBkdqiPnM9mnVGm8GsjyQZGXmmKJAu6+e+mtVue4zK+w3dE3hpGcGAeD47rmj0OOy+FRkN6+7wM84vtBHHPFV2rFmBuUnl2J4dx7HYQEa/vKJ5SKAQZTzUAwP2FK4rIoNFX6xMwyw3teuuRQf+tDNWTcTTeMoIzLb4HwF+TQkaAO7bmOcQtiEL6YANqoUIr1Jx8bBGHG2bwwsEBVHhbwnD2vF3LrApT9rJ/mJfiZiyIjI4NmfrRN4oq95f0KaC3fHvor15j5PIXOd3X+CAM9b3MgcVXuNjm/vGzyJmZZKXkaThqoaLi4NMkm1sILMLje18O1sykTk7y2A9h/9Cs8RDcf31whbtQq1H7+BH7pitnWphnP6YYMrhtyDMrbBveELHtoF3nONmpNd2n5xW5KKtyMTPt3JBSyZBlXIUwLe/3fjiTR3TDCZh+74S70OlQubsjXHX8Onu6VRNUtiwRm51F3GMycmYGg2YO5N+nJqECU8nG031nQo6lMlPO5tn6ndBfv2k3UaDQaFBXCuHfOUVnqebO66qUZ+ac9s2zBMzYjgfFpFtQqdEEBbJ+9nQFe6QkG7gspXH6KX+012xTNh94bpn8MGRkoDuaJySfcs/gg2lzbQ7NC1x7nip/DOZsdl4UQi0XVw41/JEf1y8gs6V1it38kCWJ0Ye7m7bb+B03S9C5G6qhoU6ZSdhs/WhkK6XPIpYnK7JEPwvayK1nqxd5zL0yMYHxZjy8xT6efPdfdtFz/hhF25Vns8hu41gyWWlAlep3WX10o0nhKIhafw/npdBmpo+9gh2M9AP5x2h1uJdZn+4vjeal0GY8P3xMISPcO5z7X11WH93Iqr1rbfItfJMYydiwpialKmqY8bsOadq90P46oWXV3rVIzc2MClaR1rEuq7aYs0UWhlevNlZcc1tYUW2B9HhtVu3+3YwWeGcmvBTazGYzJ/zXNHfA7brMJX2K6a3ews3AoL4Skf9aj7zQX75C1KAr9Hjxdea+97Up5AiMnM9RHx7lUmxl9HHnbJuMQcJtpQ/kkMr19krgy0ZeBOREpMV93oRvnpurOGROUjBu8S5Wl5Xy3qPMjm9G/9orAKPDLKVjMn7fF95fun6DxivHKpaYdyI0VimDbYV4pDq/tpoOOeskAzIRPyTa/UIJ/zaO6T0qmmptnm09n8jLwwj/y0kTvccQCIt1Z2t9OZyYH+PQOyPTM98Y/oPT6eCtdHW5xh3BIMtmoXnfvTaZrnWHEzXQMdNjUUju0Zg3PlqsaIvU7kAtrN91tScOp8KftxAZWSCfU+6UZfRXrtGhTTc+WbPItNLLhVqoeGfu99wxGBW905mB/FXDcqKf/q9KjAz9m3Lq/VZrBNd/dxgBOxIQ6ZkgO7cOwaW3mjLzpRlmc+h6tg1pA31Ats/s+p8T7v5zd9C2whuKMlanWswl+oMRhNsYBlZm3g6GJ40ic0Aie+ouM7XPDtnBy0sbEd+1IvrzBQv92g8pKNPMOfPtF09Tcb5jWsAbNdaztE6bQgsey3o9rteV0UMvv7SSFV8HOiW5wuCuVTxkKgRne/sR9pZ94+ivXmPa0k6MGJL3+3V5YifH59jxUn0AUP2b4YSuvlYi/gT9pcsW93keukKtr4Zz+FXj9a2nc+HLZst4bf7zRA3YV6zzCp2OK0vDUeekttQvf6SQknuWBXt8dgrPfvEGABX+vIp0Jt7yyQwS0vFYBk0YjeQqyGiRzIlH82yALdwMgPHcKW63+OG3/haHmh+xIOfeVQrV8L9exGeX0jkd/Of5Iq+vo4j7rAn9OvxN8wK+8JhtL1BusRtusfYHPvznhDtAhS0ZkK9wiVqoiGh4wa54U48VuzBoGtPe/SnWRa8xtU+rsIvaU3pSvotjc0tqkEG5prW51tiDxhEnFPvWpenwiXc8dKq/9w2+D/ZAd9DhIZwGAzJhvzlWdDh4SyYMydueWP4AjadF4NPBSZO7j0iU0nh830BjgfL7QLylv3iJkHmpSGPzCtw853mXiBYz6D/SaLKpsPJCkU5Wtbc3V/rVMGs3aOFAg2lWtd/8aHuiE9eTjavs1GRXqkw3Kja2Wsv95xoVttRrjail7YkA9jZYrCDd81S5cqDBT0WMkqeUpBmyaLzXGMld6Uc1uj+UilZJBF3eGtKEEZ3+YLTfOcBoz6+/pw8y4P+TO26/7XJo3P+kcHc5cJbHDj+rqLbyVfhyhncaZVfon9fSnaj+KkPU9L4cazbfdMN4uTougI+2nsX6pmXMNJpsWeKT1/rhtqn4oYmWELIxlTX9XXnK3Wie8lenoAmp65RoiZSK5uF3qpQsp9n551ZbZPfvVxpxXq8mqMsJm4VXiUCSWJnqi1oYqK+7RojGkzo6HQffMmrz9TOGEbDNcoRXekVvU19zWBbsOzMkrknKSCDNq14EWfEv2QKPFbvwWGF0ii45GoSvOo2aLtcUEWjWkCilseBuNYK6nLDeuRjQVAhG9smb1/K3JxKWM88kQzq/poQS1PV0seP3/5PCXbqTxOWrVUirkWVy2lR3cSNxYDJBa6xULio4VsJtqoyAE/uyqeViFO6DK2/mhwZPOhRb665yUQj2bFlic4YLIxYPJ3zLyWI/9Oe7ykStLXzf5RYetHdLI/cBfM7zLm+NqEjYm8UU7io1j77hmHZRGOK6mSefVXdxIzVQjZtGc0+TVooL+b7kThYN6e5d5kSFA3BtZScONfxRsX/veMuhmnafKx8p3KvjhuP5c8H7pPiCPT9kvZ4fo40JdLGzenGmk6386zDqUnuuNyn5aLO4yf4KE1J+7veJtxqyp44aZ6wR/pPCHSDmtXhadhjFjA+nmDhLptZcymfaBsiZ9olQ6XYi3eeP5USOHbiv9y1mRnrgXUyqjkw5m+o/jaTq1xcJvbTdbsF+63AA2bUkxRJ0bKMN/E7h1MIVNqexYaBbkUkY9kJoXTg1tQ4rAqaSmzxzVZ9C1+N98U1OtVtzV9WK5vNWhXMXLHrnKzpVfZWI1x+cFPozx7yZfifPQVzaENzrPDVHDOfIGEuauONoe6IT2q55ioxX8r57+qqLHn2YTm/ZnnJzL5SGVkdSGek3l8LoJcL+GET06FNAslPO9Z8V7lLCbXwX7WDK8DamYtm1XVJwXe9L1mAvpFN2cF3LMmE/XGN697yH1H/webJ/sm8VUBBbM1yJ+iIO/XXH6HeiZlwipVemzZw41xu485hrEvkzGLN9JEQxtOG48fU42XkqOpE35vTbTfDscB69A9fmQscydPMsnF42xsWdCZ1/YuG3rexO2LpfkCWJX0e05Ydxd9hW6xeitILKu41OxVMf1EC39v6SeRnS0qj00wUePzHY1Nbh038Y53/a6rFNxw5Fm2L59e16Ix0p8f691AwZGVBKuIlUrq4Eb9Iw0Hcb7ipz2o+YOcOJ+TkBKdk5gh3+w8I9F7s2VocXjcLdT+3Oysg/aednHzcLgHQ6jhOpwZAj3AdX+JcZ6hrIVgRYYVmgb1+vxU+bmxL6h4TL9Xv3cJefsoPJL9ZRcEEf7DSFnl/1KjoywQLUURH0fupfs5jcvz97FC+DY9zashUyyB5eibz/QgCV3y154S6EUAN7gcuyLHcUQoQBPwH+wD7gBVmWrRIQqf/Zj1ShMfW6d2NfvWUmkrVagyvj4dPYqTzkjkB/8RKu+fwui6PbMrtSq0L7BkfdZFHMQlqvfI2qqw5jSCucXvshCkCrZVbFTWiFuWAPWzWY6F9uIx0rmgjMXpQK4Z5ZyR1K6AUftuIu0gCDwoNfbtIFEvvYH1p36JM6MN1oM2zjdoc33qlP6PuWwxaF1oWuY5UB2rHZqRzsEUXkqZJ5oCu6JKCu1gjpeCEFLwoJedSitsgNbg1pVcrwfrk8m6kkG6jyx2Ci1xxzyJGqDgxg0gBl3H/H2Cc5ebk8Z1rON7UNemY9m+bXvBehkaOAE0BuBYzPga9lWf5JCDELGAjYZKD2WbyTzNsNePvjWnwSeBiAww1/JPzui3j/aOXge4zgLy3f06ldGzFoaC8iR+206zfOblMPWWNbFI1u4yHk7HtI2ldCkJvUJtvHaH7Ru6lQ8a9if4ohg7evNSPmrdNOKeJREKVCuKtdDAitS4n8oKpzV2h59Dk21/zV1La48iZqdxpO+Snn7BrL7VreEs9d5UKWr5XbWyWo765MR5ZkgUgruaViF48UxvXyp/I7he8/n+6v2NYJDbFDA4h41b606TsvNCFiqLIazLHsLKp9cBW9g0tLodFQV3cbyNNukqZUgqYqU2lEgNfLnGVJx3YETj3n0HlsmosQIcBTwMfA2Jy6qq2A3PTPBcAH2CjcAXRr93DoUAhXd2wnKCfJTqUxoPLwsMrwV1rgsXwXLLetr9BoTEVCvpk7VVFdrCh0aNkVrhpNlbIkPTDXJj9UXl6ETT3FjAp5StxVvXKV83d6KKfqZwPOF+xQSugHot0TOPtRyaSYS4mJXDodYL1jScBgrinHuLhz/MNCi1PZDfluCiMv2hf8ffqDaopttVARVsf+pIw7VY3cL/nx/JIxJZLgEfnBYTZnKB1Q6Y+mlHQB88nAG+SxNvgDd2RZznVOXAIq2Duo/vIV+oc2Y1+mUZE50+J72u12/jUrDUjo24DVJzax+sQmmwU7wOq/l5mOa7XrweQVmnb0D4VgvyGlMiCiFf1Dm5k+C6MdL1JvC0qFcFchGNbxzxIZWx0TyaIOSuWqwTvDCFla8jZbVVQYvqp0s/ZfW8wg4aUmaEIrFnKU7ZASE9m3vpr1jvmgS8hgd2a2om1WlR9J79LQwhHmUNWpRlYF5SqrW1xrqsxy3Lam9i/D2a/9cwoyGNHhVAe8NsViSE3lle+GKPqvazwDuXaUw+crCkKIjsANWZYdStm0WkKygHlskM9Jep68UtIvqxKHyt2d3icvMeDUeQacOs/kd4wU0PYkNQGmY9RCxTDfY6bxBpw6z9WxTUto9s6BJrwyA06dp7JGGeTgr3LjxWOnUMdEGn//3E9JzqVER7cDWiEhdDqnkNvnh8HVhcb5npkkQzr+h5PtIuDJG8t68Y/8SI7yxVeVRcGwpzo6HXvHzyS64jDCPku4t06p3Ufo/s8w4tvnlXGL0HqS6a22mWcm9fN04mv+YNpOM2Rx64PKaC86nr6e2C6Kk4/NIr++cTnJh/KJVwAIXXmTT3rm8fWHaT05/xqE9ihexJIFPAp0FkJ0wBha5A1MAXyFEJoc7T0EKFTltqWE5KCJozHoQH48kcMNf+QFr2t8+WNbDAaB78+eeC29v05WWxH/SRM0UUYznFptoLfX1nzCvPi6o6fK1VRjAWBfnx2s9m1C6HulKxz2yutNkRrdxds9I2e+yu+uFiq6eSYxZZKexJQaiP3ehHziHLIxSyg1wn2obxyzXn2qxL9wnQ0vE7X/oEPHlp1wTrEdWu0qaj8/i86QK82F2Rs8PzYP+JLHooYR1uOwQ/NxFNXevcKJVmkKmuGbDQ34LhIOaRON9vSnwqZDjscwC8Gzb28osot04jQbb+QJd4BdTefwXPMRqDftt3ygA5Bl+S3gLePURAvgNVmWewshfga6YoyYKVaVsVwWwYvuTaGh8eE/2ngJADVUvcn0dbxAeOCmm/aF+toJlZcXN3oZ6QeGP52XNp+zt9BjWhztQlq2ttB9ltC54hFFZBcYqSj8uqSx4rIxmidg0aH7FrEjNBpuvtgABDR87jBzK221esy2nKz50WH12XGpCb4LS+4lVWqEu1aoSQvRFyvmujCkhyhtfTObLWZq9NOFR5MUAXW5ctTwUpodbtz1pFKWeXHt5B6NqT7mCGP855u0mIOZmQz6ZDRZT93hQIMlqIWKALUHixvNZeiqPpR7OtZuwSo0GjLL2a+1Gu4m829aJDEueYrnrPbzmKR5xKpT+/aAJnwbNZlcPo5sWSL1slexfjNNhWCq6pRhYLekVMp+U3T8vo/KjeRKOnwdPrPdGAf8JISYABwA5lrpbxXaVNiWYeBR1zyheLTxErsqbRVE9TLDqTzfsdWpLRBBAex7v2g/8omsNM7r85Lp3Hslo7uVYNd5lr3aipYjjiuuDcDbZU/x9vvG+6XtyQFo9sbeF6erytOD3e9PL9LsFJ+dwnXJjcYFVv2Tg/ay7YPdfLzzeQDkS1ed/pIqFTb3XOzvNBl1hSDnDSgE4f87obj4C683RSQVZKqzjksvRCq0iEw5m4wLXmY3lcrVFbnfTb6tuE2RCfrs76/g/+0OKg5L5Ea+eqMNdVpGR21EqO0z+SAEqsoV2df5a7u/iyE5mVmznrb7OKHT8cH/5ivYH2Ozs4h+33rCS1GIHVmJzh7KGzvZION6TEmLkC2ZX6NOr/1j1uZMyLK8SZbljjn/x8my3FCW5SqyLD8vy3KxbYjlJ2/nwx79rHe0A8dGziB+Wjmnjmkvus14ja+rxJg+kp2CHSDoq+2M7zOgyD4bfpxPwvP21VlwGmzwJbRe9SpvDx1S6L5HXVWs3bSCtZtWcPepmoX2KQ5KjeYOxrC8Ow2D8XQCna7QaDjzeX2WhXxNLs3ozgyJpK469FevFHv8xp+OouqP5lwwwlXHvJhFQJ7WuSldRczH54xsEYVo5929rvLp292o9KFtJilRvwZxXb34rtvMQrNT1dHJZLeph/avImzgBaZRXpOMoWG1IotFxL1flzZuW4G85XXX+a9S6XYxl5aFhNm3++F1wq4r7c5u73qS9kuWosiDqhRyt9iNvcfpUMuYJt9y0zm7SuZZwu7G33H9fNGrqZFt+xWZ6Zvdph7fzp1i1q4VW8nPh5KLW1Iq/Ro8i6zXU8FJVANi51HTtQE49XUlzraer+jz+0dfkjxeZn5ikxxelhKAEPzv7AEqqPMUQxWgFsrrsD5Ny+TGzU3bVdOOIGdlKb7Dpe8COFyAz2f5V1+R9qXx/wSDjvcim/y3iMPcVS74DL+IvLb4cb8XxjXkYPdJeOaLvuiz4mUirjnHUeV621AoZWv8qOqEapT248E/DzEJKlkycCrbmyBNXoy8TmgZ3n0N6+bVsBpKqKlciacX/c1gH8svqONNF7O5Lrw1bkghRE0546TJJBnSTRXba7m4crq3C1HbCh9TaDRkB2UpslG3ZRhwv+Z8r3+KIQP3q+b2f9XRs9TbMbAA6dJ/AAbJpNmuG9WCVd5tuFFfxakBjhN4eapc8bSiWFb54QJ3sr0t7g9z32mVVTH62+GUO2hUcYRBxu2ak1k7810bgKivAom6OIzY/nnXJkDtQYAaRpTZwZhtnbnTKtWpgRmasFD8f0ikiU5CKyxfj17xLbkyoQq6W+ZZ5/m/Q/CEIJpVHEJ6WZWJpC037wGgsmwgYItRaTs5qzp+CxxTnkqFcD+dkUcDujrqd5p1Go7XT8UTwpnR6Xiq8vhOFt4tS8SKVMcFkQ1JnJrwynR8eodCs/z+bgDhK1KQc84r3bzJ8PlDOT5cSdQ02PcMf3g0sHqOcz1DGOi9koIWtQ6nOmCQhYl7vrkrjPr4J+b9GlmoBuA/byevDW7DtxXzSfMivqOoVoWTT8wiv9b+Qd8XKbvN+VEdExPqEfiN+Q1tSEtDOuNpjGX5j0KzcR8aIOxcdcL8BlvtbwkxMZdYW9UCPWgOpgY7Rn0Rn51CqzVjAYhecRvD4ZNWjnAekqt4oY4s3KwapPFkSeW/iP5oBMKJNSWzfSXiQ38DCl8VRPw9AMNdLX5H1JT7w7oglvcexX0vePmXIewR42/8ddsfTGyxaqEy8WG16F2G+BjLzvWsqZafP6vCXQgxD8iN+62R01YGWApUBs4B3WRZTszJ4psCdADSgP6yLFsNZchKUhZWbjZuJweXOR7iJjepzR/Np5Gb6ZgpZ7NwWGfUOx2LqlD7+jBucOFMhfmRHu7PxPIrFW2LLjVGU4AaWDgYuaepGMLT3baaOXA+uVUVns/k6gtVITqvvaP7Td5/pzeVPijE3CPLGAoQufz25FTerN6vcI4LIdDku7nXpLmivZ2GVEytXRMWyqKu08j/4EiyyuaX8DC/Ayx75TUCp5ZslNW9hnzgGFHDHT8+oW8T3hlbtB339bK7TSs3e3Aq25+o4UYN/V7W5QW4/KREfBErN7VQcbqP8yiLi0K2LDH+Zh2i30t0iApDSrhtuo6vLn4e98aLecJdmYOyqcZKMK+LYkLDJTct7rPFofo90L5A25vARlmWI4GNOdsATwKROZ/B2Jia7XJXImrhMNP2SP+tJAy0PammIAyuakVGnAY1F1s5niAifH1o6mq9XuKFF5VS+4I+heRFtiUxalATO7hskX0uda1k4iUBOJudQu2Jw/n9k5aFOqzcVS5klrH8Jjk5qQZtT3QybddyceXEaG/UgdYzevemhhvrSBYTp14OooEu7yXTLa41G7+wrJpXXpXGpvS829ZP7U7zvntQ+5cp9lz+S/BduIM9ddRFfn5NCeWWlMotKZVEyTxSQ5INhbarMFIm5H6E1nqha3shdDrFOUznymfOzJ37LSmVNMO946JJM2RxS0rlWJaePY9onMJxVKXPAV75YZDiO92SUsmUs60fbAFWNXdZljcLISoXaH4aaJHz/wJgE8YwsaeBhbLRBrFTCOErhAiSZflqkedIz6DyqjTIIWsM0XhStucFVL+UsbsUmdy0NmefV34tPRLhP9tfrDkXpz72N1VKsXjeR+swrYCTpNXS14lYaJvZQi1URNW9YJHTPe2ZRiwaNYn8dL19T/QlaNpehzl5PJftJLZFQ4jJa4vtMItjbfSMGDcKz2WW5/79vqZEnStezU2AqLoXFCuRffGVqPKD5fOKHYc4mRlEC7c838R7gZvo59bN4jEPUTh+rFaRHzFmSauqRfLHemUpulevNST2CW9WH/5L8Rs94Z5Ny9jNpu2qK4cTOcJ5xVoA5LXlWB1tnkagYgug4nBWBq+H5zkuL73ZiGMvO5+TvjDUXP4KVcbmmLRk5yXQhb6/k94fNFe0nV1ckzMtvndoPEdt7oH5BPY1IJcspQJKfsdc/o0ihTuANu4aXc+2YXmEkUVxddVVdA7qDXYK98stPIjvovyRJVlGZBTjDahV2qx7xbfEb/1phSC+1MrdrAiGSo9TnI2XVlRn7iMzqOWiLGWXuSIQz2z7CL8KIuabRN5/tDrjyx0DjPkGdXRqePEG6nXeSHctVKbJdizh6SFKEXLMnon9mjDuf0sUu8L/epHoj+8i3T7LUx1fACDl03RT6cr8BWJWPzWZPa1DizzVssdqcm5WEG/UWA9AsuTGmtrlTP6g1OcaMejjPHK/du5bLTovX7r4KJdeqgiGPFt/6Lenaf9Hb9P2U4u3MtLP+mrbGloc7YLrOGWuTPS1cw7VKrAKWTZ7WVR9K4H2ZXpbOABiz8yxuK/YDlVZlmUhhN1PuRBiMEbTDa64o68cyMKw78hN1dcKNWde8CN8XHFnWEyo1KjVSp3/QrIfHreKFqrbMgxUWl98s4XQaHg6/IhZEsT0OxUpt/tOkasRSTagyi7aEyydOM2eXjV4Z4mKCQF5voFttX6hxWMvoVu7h8wnG3B7SIrdHCHWYGj2CMNCfrb7uCTJtuIkD1E0Ymc2ROubSePKx3jOU/kSF7ddTFmu8gHji181qyGR3fpzuoAmWd3FjeouRRec+XJOeSbXWmqyKWfLEtOWPI5sMN6fNSrE0d87/xiFC/aGB55HvcQf78PK1Z108ybczLM/L/yqA3PKOkZlnR++pyXkA8pVyb0s8qg/fxGKekfJlhlmHRXu13PNLUKIICD3V7kM5GfDspl/Q+w9Ts1NQzjbKi+GtXbT004qOOU4Lr7ViG0NvyQ3bj1blrhxMJAw8oS7yt0duZZypkcyKqL+p/hp8deGN+SdcpMpyE+z4WY1DIfyCvkKrQvJdZQ/9IZ0N6ImnLJavk86dor93aJ4/2eDSYMH6DJxA/OjOjDjlWlmWYLOwJ0qrmaFwm3Byk9aM+6r4iVO/X+G0GhI6NuA5e3zSlDmou2JTmTqNficMheMbit3UyGrAZ0rtGdV5Dq7zplLrZALrVBzqtlCq8edzU5hwMkX8ub+U1m8f7AekVJmXuninrkfcPSJXYWRWwOUHBurgL7CiMZAkjV7ey5kvR5DRskkINTZOgj5kk3TMMPgXmsViUJVN75ExAcHFH1OfVqLw02/V7RNn285AzR0+TXmJpW3eu74T5swfNhKRWglQPUdvUl9J1jRpvL2ZEOLqYq2MYsG2lwEQIo9y54e1agxdTg/JfshyQZG+53j0BszzAR7lR+HEj3DfqHsLLgUKO2mRqAPfuhQtQVCp0NUj2TnR9PNBLskG9B2TcGtXTzlZhYuHHVr9yANcGVzhrL4dUngqj6F8Vc64NYu3vTxXfRQaNsKq8JdCPEjsAOoKoS4JIQYCHwGtBVCnAba5GwDrAXigDPAt0Axgrkcg1zI+0E+7+EYb4NKjbpAnp1LvKuxNmMuhKBslQSFDXL8zWpUXGV5mSqdjuOz1c9YPb026m6hyUoZ571QbVG+YFCZf3Hf0/Y9fNKJ01T4bDsL6tegycHuhfaZm1SeKj+mKFYNDkGlJvUpx9ZlHrG3GX8zj+rYT+3OrXedyyb6X0V629r88cePxTKx6ePO8XF4Hc7pHXim7EDTjaO43sSCz+chrMKWaJmeFnaZlRXPiZIZUdxJOYrMpxowb8A3WEo2sBdXXm3EQJ/J5JpELuhT8DqnFPYZTzXgt5qTyG8jXB5Xh+BTxykK7les2wP1J73JbJytyAq9IaXifbYAnWhgALfm+SgYKI3Zo445kQ3JyZTtreLzLZFmhZJXXKuLvPeoQ+Pmh9BqmFV3saItxZCB7pT1uGvp1BmWx9VRlPhTFd+8+p+G15ayjK+4GncL1AHr0nR881gLpETbycZGNnwWkb9Eo7sbK7esUCg6tqLz6fZI3ZXPVnRq7D2Po/8voVRkqDoLSYOTzRyPjkIdGEBakEFhEvniemvKzFcuC/VuQpE6bCuqP29d83W5I8iWJYVw35IeRMBMpYPnTstwdj+i5EJ/aX9fKv7teKiidCeJJL39CS7FQc3Vr1D1s93/BbaYUgFNWChuC400Hp9XWlkolUDYysEEb1KhSTPgaid1gHS9wOpUpabF6OEO1eR1v56J6toB6x0fwmb8p4S7RTggLW7O9eX4I9+QP93+yIe1caXoByDFkIHqX1+r4+/ZFg0FytQVhO6OjJRv8nOSgpn429OEy3mRAvpW9XhvgpJIqUd8KyqPuGHVkXo/kdKxDhXVG8mvRfoecS7d8/9X3HmhCXeiILuMgbiI2Tmtedc5xZBBzT9GggwRy/Q4jQ/fIFnkMnqIe4//jHDPfqI+y+tMpuCSc2WqJ1HTLtgdvuSi0Ss0ZgChL/CWEILabxxSNN0x6Knw4xmrglWTal27uVNVxlXk/UTz4psS/mbeykE0qEl8XxTx9Z1Pt0fqAdL10l178nJb2WpimD2I8LvF3XLljCFx/x+hUmN4tBaooMJLZ9hVxbz4yeYM2JwSzY0sL6JecoxX5iEeHPx3hLuH2mzZmWLI4I2fhxF2yT4Pu/xoHV6PsK3Ee3PvQnhYbICw4W0T9flZrnfLpJIm30tGCNRly5JeN5RR035ShBJ2PdsGqY8G/dVLhYzmHLhrskjRujicFVtS+Cnsbx57fAgey/8fCnch0FQIYs3S+Ra7pBgyGLhsTI5y4HhCnyNQubpa75QPioCFh3AY/xnhXhhaHXqB8Pf22G2VSa7kWPy1PQjelmHV9ZzYJgKvfFENrYJiWf7Jo/zRayJeKkGAOi9zrltca9K6qpCul5xgB1gS/gfNe75SouXBSgOEEL7Adxhpm2TgReAUhRDm3Z8Z5iH96QZsmD6d/CbEgug4eCTh65xMx2sDNJUr8fNW2xSlXDzfqAv6y8WvufD/HaVWuEuygWuTI/DAvIxdfgidDqlBDFXGKaNTNqaruXneDz+9nckuKjWRryjHypYlqv07kMid1s0tzoTnxQwy8sUSfxJ4mE/6HSa/6WlfZhZDJowicP3FEhfsYOSebz1mGwd3RhZZ6MFevH29FsG/nbun2X9WMAVYJ8tyVyGEC8YstrcxEuZ9JoR4EyNh3j3Lob41pAmBPc4jtcwTfOfHN+WzXgvNTIhgjID5uo8xpNXt2EkMJZEyXwi0m4JolxPJ5K66ZpanYQ3P/rWfNIP9RH9rrtdAblV0PYT/Tyi1wt2AjNepJKuhUKrQEH75aY7iBpJkA6O+HU7UZ/bTwKpctHQtu1fRdkmfTuQrF21OCLIFBhfrccbXG7rjpSr8J8qWJWpsHUDgD674/7bD6UJRaDRoVemF7vsk8DBhrzYiaojzhPu5NP9ia2s3uqYT/lvxTUZCCB+gOdAfQJblLCBLCGGJMK9EoPb25tT0CNN288ijvBW0jicXvWxq61trk9kqs+qWvmgOeeJyFwJ2Gp+BexlS2LX8vgJUAvZhoI9j/qI2HifouMh8OWxI1xA1+P+fj6HUCndbcatpgFlc7Ze3q1Jx+hGHbujbzz9CTZe/sMRtURCTz7amRx37lp2q2jG0+XKLok2SDZw8XIlI8gSc9GiSouBIfkT9PpToMcdKpPK70Ok4Nb0mv/jPoCDtQS6qVLmGuqy/Q7UxSwrHms2n5rsvE/pesU1GYcBNYL4QojawDxiFZcI8BQryJhUFdfWqJNQrPLs2y1NwtnVBpkMPszJzZscluuJ7SUabdu9EurpcORKerAJARa1lMqufkv2Ye+kx0/bcyB+p5EAocWGIcXEv9Npc0qfwXN/Xizy2zOEkDAeLzk150PDAC/cyfS8ohPvZ7BT+uhGNKtmxOqxl/z7P6Ww/wrR5Tqf9mcEgFfKgyDLq+f7GBbwduNDBj7fLKh2xeiSiZ9yyavZJM2RRff1wosceLxHBrvLwQF8/il3tpuCuyrPpb86Acqo0YlyMwmpDzGraVekLDgh3odOh9XE8o1Tl4YGfu/mqYtLtaMJWFE2mZiM0QF1gpCzLu4QQU8irWQAUTZhXkDfJ0knU5cpx5oUyxPZ1bnGJ+M5zoLMxUuzbXS0AkK7dKBEnuDowAOHiwp3GFdj1WdHfIzY7lf+tHUqVMcZQXk3FEDasr2LS1CXZwL4sy09AbRe4LmWSIQtFvQZrCNF4Wp1b+C9DiP4owDx2/x5BXa4cwtW6KUp/6bLNbKylW7iriw4XFA1q0iP4T0XbE1tGUuWFgw6f8uTrlcyqoXwwpw/BiYWbeLxPJDHpdjhjyxSPejdblsx+NI/V3rT17cSGmNWmtvN6PSG/q4tdY9YSUttUZ+OMmWhF3sMz6XY4G3o34tQgb+Kem21ql1XCmKFoJ/WvXDuKE83n4yi1UVrL6myuaa4drvqwFZ6HnBJnfQm4JMty7mDLMQp3S4R5DkH6SUdsTMlVDerikUKXXb8D0HLAIFz+3GvlCPtRdmUGC0PX29R3wOuvUiWnRoDK1ZWfd/yiMKfuy5J4N8xyqck2R5OZt7wdficNbJ80q3gTL4C4Z2czoH4zrjR26rA2w3ulxE9hv1vt91S99uiv2ma2KjXCXe3nR4WQPO52rVBzY4JE2U6Wj7ld3dPctifjMM+4OiaSVV2+BvIyM9+/WZ2Kc09Y1KgNR0/y47n6JuHuKgRpdUPR/WHfc19zzUiqxikz9Py+38GVck0VxTTO6/3w3nG+5ByPAjMz15L4+pQ9dALkRor2cYsWM+6zwfh/a78ZpFj0wRbe+fYTTxcOWZavCSEuCiGqyrJ8CiPVxvGcTz+MXEr5CfPsRrcT1+jutZP8xVdKErPnTCHNYHzcz+n9mRkVeU/4+HdmSHzUzPgQe906YDFyrcvpdmT3EBitXYXjn9YRVE45hKzX89SWDkWet8Wfp3i9jH0+oekhG4mNz5vhoAmj8Z/r/Kiw2Pn10HlkETHuLh//vQyAKloZW+6FT3esZPiro/BYYV2JKTXCXR9Tia21Fpi2mxx6Ds85vkUek+WtfMol2YAhxXI4mDVkB3gSrc1bGrU70ZG0aRVwTyz6Qros8COlTgaeKlfKqj3IGJmI69+6Qiuwq319aNtVGZKWKWfjelVrU3ZmqkGH/nrJxXJneisFe5IhHbcFfgBUWZrJsvY+dPNMAqC1m0RaeYG/A9p7QVxN88YFxx3Wwy83xmfPFWe+9EYCS3IiZeKAARiXGstyyPPOA3aVf9K3rofuHaPJvqvXDjyLqF86/mY1/viiucX9+XG7prBq2slvxohxSWTO30GW5/l2OcSOQxb35+LuHxFMDFpCQaFUc/JwPC4bjWOaTBmPy8rnR1UrGu03d9DlJOhFbBxA5e9VaK4WTZeR32Rizfn++5utWeHT1uL+1GAVR8Yo/RnuKhfq5LOMdBn1D9v7hANgkAWiY6LdptCbQ5tQvruSkH1mxcW4qzKZsbgVdXT2RQXVcnHl0Xd2sTaiKcFfFB0wUmqEe0HcOFWOKquLKLem0/HxK/MUbSezM4l5x/FwRbcPryo0ykD3uyTsNlgVGL47LpEhSyYX7JbaS4n+dARVxprPX7i6Mq7cGhREYynlCf14733nVMluU4/P35+taMuUDfjsuIgeENsO8uXHvej2aZ4g+WngJN6c9mSxIokk2YDubW+bv/+l1uZa/57rlShzPtbhORSELMsHgfqF7DIjzLOGGyOaklkGsmLSOFN1bU5rnmA/lpVOp00vK45xi9URssS2aC/fR6oTXn6gafuDxr/R19tyCLFOaFlrmoc5wl4ahFubplbPu7nGRMrmy7XIlLOJ+WsIMT9eQH+x8LBcqUVdYrtriI/8iVyznOsJNzQbnVvg3PX33UXqwf4hFQivnnfNetXZrShWA/BO2ZNQNq/aU9W3hqHOso83p2KLCxavdfOwv+0aKxefBx7k6jPe7NU1JetbyzKy1Ap3W+AhlA4iCYGc5UD2nRDoW9aljnee5vDOjZpceasK2sSTRRyYc95r12m4aixxzxgFo1ao2fv8JB6//BpBXylv2uMfhBKQjx/+YGYmUz7phl/2/U8KutTGhRZuRbsjVQUoGMqo9OhjKiG22y7ck6LMnWEiW7JZuL/+xGrrnUoLPNx47ZWl9PYydzyvTPUkLjOA1VdrEtnfcZI3+cAxIvvnbb8/5xluNd1YaN8QlwTTyssS4tt/Z+OZ837HW1IqkxMaEzngkMUSdOrqVYntqSa+U56/5Pu7Aehu33u1Rn/pMpH982LiV7zfjDLP5/mxBvseN4tUOzWw5PwjlrAxXc2h9LwShiP8TqETWhaGboZhm2m40vIqvtQId8ktbyqSbECVee84XNVly/L53BmmJdIlfQrrJz+G36YdNkVeyHo9rteU5gw/tTsZZc1vWu/yyYrVwYtH+lJuwf0X7CovLyQX5XyzZYlm24cRnngmr5/eSH2cG74WovHkymt6KnRVm2pyWkPdUQedNu9cpGVqKY3lOqIiEujtpQyHTDEY0+s/+2AoPkt24lJkHTUHzjl4D3/iXei+zKda02H2NwAWw2ztRbYs8emNZhytZ4D862YhEC55DlPvOTeJD1tq2k4zZPFj33aU233/7/9K47fz5/i8axYSG0gHdyX9sbOuV6acbQygsAGj5oylwud5CmLTOBUNdAabfFalQ7i7u9Jpap6msS7dnciPj9ltXnkz/lnItq+gNsCJj8Ko6WK01WfLEi/2GEGZPfvsMpOUOSFxOCvDrIh1UciUs8nY42/XXCtrbyE93tUpJfzy49yYmux4fiL5tbHJiVGE94tVcH14/7KfwbFDWL56rulm39lwLk+3HIFmo2Pa56cJ1VAlJtv0IjU8Vodqrt8r2m5JqYS+lVGqWTBzcTAzkzejHwfAJ+veMyjq1u6la4Tx/COPHeIp9+LzuEStHkbVkQcB5Ur6dv/G/PPR16ZtN+FCrikmyZBOj5gnIFlpCiktmFe9KvNU0aZtoVYz7+R6h+i9C6L+lFFUmGLbs1IhW3mPfBDTFPcN3vxSCDFcQZQK4Z7tpWa03znT9oakGkgp9of6XV1emYAMG7McVWpEvWrcjfBgfts5ijeh5k46kp0xwR7Ld/H2y8/we9QfprbssnpEPpIttbc35b3yqg+lGbIJ+cu+71lVa+BqE1dCimYLthsZoVn4F3Dw/XOzKoYC11POzuL8M96KdHdPlSvSuAQ0hVsC8qBSc3VMI2YFfEF+n8O8fx8n8rx1QSe0Lpzpp6V5gfdn632DCL58werxpQWFOdrv3cll0/m/6fk8U1zyVpyygIU/TLNbgC1tN51/D0SbtVfVLShU252TFMzygW0RydadtvcLBXMCZKBf9xGFVoUZt3ARrd0sqxaTboezbkiec7zS2bPoHbwH5MxMMoaV4Qnf/tyOcUPF2xb7lgrhXhC7vqyPt8Gyo8AZ0AQFMnvFTEIK3MgqBGfedaPytDqIbQftGlMe4MLo5fWZHGSMJ47tMIuY7wdR4UctFzpC2ZA77Ikxhj5JsoGOx/oghbvhY8eq9M+0AEI+c67GZ3isDhMe+9VsqZc5IQgN5i/L8EnHeaLJs/xTPS8SsHPwYf5U+RVpmlH7eLP8lYkKqt/orS9Q8U/b1kgXxtUntoOy0taaNFeyDviBquR5dYqL6XcqMnNhJyrgXOeho5D3HFFElQqgzaw3MNhBBfNWj2X09b5FQ51tYYd9zzfnyJIaBGwvHdfAHliKIBozYwjZXsq27QO/pN7fL6OLd8Xtuky5bXkPeXEjuqRjpxBA4PkK/COVs9ivVAr3koamQjAZCzQEqc1Tw9VCRezjC6i/cRj+2+wbVx9/nvW/NCVl2FY8Va5ohZozLedz9rEUMzri2rteoFL/i0h37Ut+qqBJhIbVYedh+yZnAULrQpc5G80cfstSfHC5lVaoqUS6k8SVXdWgel7bUN+TzHt9tMI+aAt8f/PA9XfrL3JNWCidnttuFoP/6Zv9qLR8e6kvxzb9TkW+2vAUkQ7wHd1LhHxi3/z+aleNvt6bber79vVaHPi1BsHTSvc1sBcFgyYAnm7SmyqzJUQJvsT0ly7jIlte+Rcjk+T+4vIr9ajt4hgtb8K37mystqpIp0Snkf9y5Q3r4WAFUenz3dRc9YqiLb9gz5Ylau3uSaXB15DuWi/+W/G368xJCjZtN3ZVc6Zb0Xwl9uDKK/Xp5aWkQsiWJcb91d2uItjuKhckK1X54kbHEKK2Pw9BaDToFqTxeeBBRfsFfQpu10p3Yew02cDBzExmfd+JyFEluxq9H7iY4sfBzExis82FTJIhnYOZmabP1vGNrcZm/1fg1i4esf3+mp0eWM09JUKPXyGad1G4NbgJSZHwQ8xU8nNfpxmyqP7HCIRO4nTr71ALFe+XO87N572IX1oR/XnbeWpkvZ4qS7NZ1dadzh7mCQ+x2VmEDLhmc1y4FHuWU2nlwcf5/NaakAo0eP6w2XVsvL8n0a8dLVIbDtqhZ1/vLOrp8tbwIc0uovb2tvjSMkSl2k3/CoBazdDgTYqmW1Iqz0x4nbJb73+kRVG4dMSTcWGNCC4lphhnw6XtecbRCH2remxcPFexr9OxXri1izdtu1kpUfkQzkWpFO53qqosBHI5Dk2FYJq8tJ9pFXaRX7CPu16HtT80JeqrXahqRGJoLZssutMq7KLld0/jYjnRrVCo/j3A1ME9MMxZVnjRD+n+x3VowkLxWpzM3EpbFe3Vd/QmdNAVJCuZeLo1e3hlZA+21frF1LYsail9/LpDIcLd0OwR/mj6DbaybeZH/Lt1edxtK/l/t89vPkbAksOl3hzjTFwb1ZTZo75RtL04fyQVP7r/Lw7tliM81UTJFeKZlvJARDD9V2HVLCOEmCeEuCGEOJqv7QMhxGUhxMGcT4d8+94SQpwRQpwSQrRzZFKf9l4IKnWRfcKXSZzIsiEVWKXm2pimlF9xN0ew5yFRSmPfmLoET9wOBglx4SoNPxnJvsw8T/mXVX4mqU9juyu6q//Zz7TB3TiWVTgnenFgcDdYvT7WcK5HBX4qkCF3VZ9CpQ8NNq8qdJPKkCjZlo6tdzcvg/j29VqU2XXdwhF5yPYzmBWj2PB9kxIjTyuNiP+sCd0HbqSxq1rxGdFzNVkbQq1+RL3q1k9SDMjZWejPX1R8/t/Wsy0lsEVz/x6YBiws0P61LMtf5m8QQlQDemB0tQUDfwkhomTZxoh9O6DddpTrkicxRehuol51Tr/gxZGukxXmgE3pKl7c2h+fPa4E/Ju3rJfuJBEwcxcXR5ehns6ocdfTubDsky/pJr+GzxL7bKaa7ce4JnlSPZ/+UkYlkdS+Gp7LHLe/7n9qMr2j+yMdtz/dXhNakbMDQ1jTTxmOCND0jzFEHbadOVD71wGa7x3EkUY/2D2PbFni15WPUelM0VqnOjCAOjWVTueN6WrKHC/dtnZn4tqopvTt8LcZTTTACN+LjPC1bjYMe2kw7k9Y9yFVnLSP2z3r4nUxy+G8hYcoHbAq3GVZ3iyEqGzjeE8DP8mynAnECyHOAA0BuwyjZdQpqCPDkE6dsd45H14cvoYV555AlWnAoFPx3BfrGel3ntyCEwvvluVYegg732tI5GoL9j+DxLtz+tIlH6lQJY0nSV1S8Vli13RAkvgtsS6t3fKqwARpPLnWOZMqy2wfZtvXDeGLvKQlT5XO7pVELjKqBHBy0EwKCvZx1+sQ89VtJHsIwAwSkpS3+NMKFYmNK+CbmmZWxCMpTKl5/5pahvDvrJfVS3o8nLVV8uhdN6Wr+HDUQFz/+m/bb9Xe3hiiKgEwb/RkhW/DEcR3tlxAIz/a7HyRJ0Zv5ccj9YlMqqHYpzp/vdRq4+rAAAwVAxw6VpyI/0+uAotjc39ZCNEX2Au8mlMouAKQXyW9lNNmBkW1Ghcfwn8eTdzzxoe4uSv0G1WGqBH2sQ2O9DvPyG+/NWtPMWTw0c3GHOlTFenYKVytOHYqrrrB+D7VeL9cXmWWMTU28mut5hgOW+eayYWs17N7cmNufLpJUczaXnidc141+Lje5i+Ft6/X4kjvKKRY+1cCPr94ktgoDT+1O54qV7Z/PYumY4bitTRPuKc+14jv3/yaXPbAlamefDvwWVSXD1gY1YjsJ+rz4afK33PA+kFE/f7fFuwIQeJT1djxVe5LzSjYJdmA3ooVu7BaqvbgryVGMr4JrY+YUaTVnDScCpONJjtbGEzvJc6/WIVjIwtWrbINrfoPQrve+Vz39xuOhkLOBCKAOsBV4Ct7B5BleY4sy/VlWa6vzVIRPf0m0+9UNO3f1elrbg4pgjlfkvjgbGer54nPTqH5hDEcaeWHdMx8WVvo0KfOsHR5C965UdPUNtjnCvHP2c9e4vvzAfo9OVAxVoOw82iCyts9Vi5UCK41c4xJZWB9pQP1nRs1OditikMmHgDf1ccYeVHJrX0jX72FjE4NSX7hroKW4Yt3+qDaYl2wX+gnKTL/bkip+B4plTEATkXszAZsmDjZrD1mS3+6RDQr8rMxvXi+mKKwa/Rklp7bQv9jzqud+xAlB4eeFFmWTV4wIcS3QG4JkctAxXxdQ3LarEKKPcvkVR0Z9MJUdEJLgNqDJW9+xctde+DS8YaC3wSMmoPHgCzqPzmMbeOnWtRYOk9/g+BZ2+322lf8aDvLNc2Y8FIe98XQrmv5431fu8aRMzPhVBxL9jRmwlPGsX4K+5sG33ajTEfbKqqkBStTuNVCRaWecaQXsxhNkiGd335oRvBpx6MtDMnJHPqlKYzdZGr7vPMPfPtmFHd61Gf6R1NNJoVtGQaOZYbgfsM6tcOFfhJnWirrYc683YDAb+8/NXJJ4tbqKJbWmG6Wtl917jCqLL6JlFH0Ku7zF/rwqYvtAt71g6sKyoyikOu36uhxlVOHzPngE/XunGgg20wg5wz4bStDjOc16nvMs97ZAnpP+Z1LWUZl6UqmD+caOj8I4n7AIeGeW2osZ/MZIDeSZhXwgxBiEkaHaiTYHtxa5cND7HheZ6KdjXFx54uI5bzr2hYKuan1l68QsNnNaCcuYG1oeexpLu8NpspvNxwOxxIFfLXN3GP5tdPLuFqy11uAqnII+9tPgXzFkjtWPMp2C8WnC+JWbXNTSq+gnXz36DN2UyTkx9LkCIInFj9OXBiMJoPcpLCauqucmt6dte0mmWquSrKBoTNfJnjidtQUTXqmjgynf03zea35+vF7Ro0shBgDDMJIK3IEY7GOIOAnwB9j0ewXZFl2TmFSlZrYafVYWmM6DXVKRSX856FE/XLXJh+U2H4Ie3T3W981pmrMMLP26s3OWCSn8lS5KkyWuciUs4meNZxqH14x1vosQai9vTnxRTTbK04qlAsn7I9BuFwzV/gGPb3erEKTsYarUdFKMqRTZ/YrIAuq/JiN6t+iV5ilGVaFuxDiR6AFUFYIcQl4H2ghhKiD8cY/BwwBkGX5mBBiGcZyZHpghD2RMoa0NEbOHMqRsXm2s1ouak5+GE3kK4XzqYjUdOYkRSmIxwAuXCtD5Af7kYpB0hQ29QRze5Q3FfCtp3Phen0NoXbSiRviL1Bv1RgT37u9iPjhNsue91HwcHfzTOKLam52UySUBEK+O8qH/WoyvtwxwFj1J77Tt+R/mU24VYOKs4/a9KLNrOhnLJSQD9syDHhecYCr3wEIISoArwDVZFlOz7mnewAdMEaJ/SSEmAUMxGiiLBbUvj7caRfDsc5TFVFdaYYsXjzfnphP49Ffsx4y6gh8Fu/Ep5D2uFea0qN73q81tdJqq34jndAS3/Fbah8fToXfXZDOxBfZ3xGIR6qTEuFJpreK+M7mgQEAfc61IPqbNAwHzV9As13bsq9xHj/6osobFJQWPiq3nHsXwvVDiMys5TSqj3sNW6JlehbSPLeQttz+HwMfOzoh3R3lolsr1OBt2Xmjv3yFdX0fRb1QzomMMeJkq++o+sUIqo47aGbSsRVSYiL7UiqbhDtAtndOjLkdS09Zr0ed7DjTg3TsFG/+0ZNnuk4341YpDUh7rCpPes2CInTGA3cqIt11XEANO9yboHvr9NIAbkKIbIxvqatAK6BXzv4FwAc4QbhL0aFs/3oWoBTsf6SVJfFR+ymsnYHAqdtJnJq3/d3hunT1MV9xhai1ZlnHh96YQYzPcMK+TrCJYsMWqMsZCbJOverC2VaFK0nZssSxLD0JrTIwZJgLdoCIV3fmFXNUqdlxRk15dSrlVMIsUzvu2dnUC+1G4It55FyGxMRS50y2hAeWWyY/5H3HWPpee0WbVqg53XUGqe1rFWvsY+OVx+/o+hWaSoUGAJUoot89zoEs5+Zj1nM9R1a7wirJ2QfdH3vp/dsIi/vHXa9D1iDHo4XSDFmEvHXvHihZli8DXwIXMAr1JIxmmDuyLOdOxGIkmDPQJ+4p5kSFl9TwduPfWm6MDH3U7DP0YptC+58YMoO7y8o65dxCp2PZgdWsPbSBs63mW+w3/25FxoU1sl2ZM0h8HF6HkaGPUm/VmEK77Ku3jLWHNpg+GU884shXuC8odcI9YMVJXrr4qN3Hea0/TtjaQYoKJ2qhIubtIw7HhAMISbmS0NlQAaUkIJcAZUE9nQuXWzgh+kSWiVhe+AP16tW6HHk+DOm07eyXt6MLKRqsv3dOOiGEH8acjTCMviMPoH2RBymPHyyE2CuE2JtN0WbBa2Oa8tGPyoVwxNKhZHYvfSu0wpDQ1ZMOzZ+hQ/NnePKJHornb2X1RTx+2HHn5JXXmvLG2SO8eXxPkVWQan05nA7Nn+G39vUcPlf0e6fp0PwZ2nfqXWS/r6ZP5/yHTRw+jzMhGtSkfA3LGeKlTrhLiYlcz1CSIw+quxV1lbAijzMkJ1Pt3UskGpRC5s3ADST2LSKk8j7hGZ/9pDzfyOb+hvQMuq8f7vR5dG23DU35QKePm4tNsxvZbXvtMmRTyUzGdrQB4mVZvinLcjbwC/Ao4CuEyH0bWowEU4T5UnR1+2x3FA7UqnOHEbkwGf1V2yKp7jf0l68gnYk3/sbxykzZsmoPHvO0LfwYQF3Wn5R14aZPz74bae0mFVnXt9ZXw6m40jgHewj+CkJKuG38DodPU/+9YRy04Kurp3NhZNffSVkXzrWVMQ6fr7hI6tMY76+v4F2EBH8ggobnHm5KlbMHHTo2TOuJe++rqH/zQbpTdGHge4laLq4kRqltp9EySJTZp4FO1rvag3fK7aWrV4/cYAGnYl2aDr/TDyRNwAWgsRDCHUjHmM6zF/gH6IoxYqYf8JvFEWxAcvfG6BoabeqSbKDG9n5ELL5pd2Z2aYGs11N984sIlcyrtf5isM8VKqhTuPJaU0Km7S/SXKKOiSSuRzlO1rKeiLQuTceofd0BiPj+JPoE5/kl5Ows/L/bwTP1R+LiZ5yvj2c6ux/52dQnl/IhUUqj+euvkRub6xsn4f5LyZdOTO7RGKnHbZaFb6RhEf0eCOEu61U2ZarK2dmsSw2lr/ctRfvaasvoGtATSpFwdwa6jPyH7dtq2ZWAtHJqS9750PYs2+Lgu6vNnF7r9V5AluVdQojlwH6MUV8HgDnAGuAnIcSEnDaLgQXWoKoRTfiokyyuvAmATFlP5RdOW41jL82QMzMJ62nkMP/sm05EPzWb5q6eHBk7gzb7XkSbaPm7XWjny8mXLAv2Jcn+SLLRvPrJ4Sep3N0YwVJSxrqooXnhzuqoCBauyvMf1HG9RC0XV/zU7hzJR1PS7kRHVHHV8gY5fb7YtAaaiiEY/JUcudXHHOHbitbD5EqdcBcaDS4qx5xn0q0Evpjfjd6vTFMU4lALgexavLTs0oh3yp4k7NXHiBpo+zE+8eZh2bK7zu4IoP86ZFl+H2PYb37EQZHKkm1QqZmweqE5X4xabZnt8wH7bSJH7uLVA8PYM8EYTJRLa2ArJNmAIUclTjFksqhWI1Pt11DubVFtKfYsS6JDTNsfftWVEz2mAyii1/6M+R3W5h3XbMQQ3FfutotCpSBOfVbOLJnPVpQ6m3tirwZ8H/a79Y4WUHHaIWK29Fe06YSWW588+Mzf3hf0bC6mYudyM5XGB7uSkuObcFe58OVv80ju1sDKkfajkV886qpV7D5u0bHiy8/SCk2FYBad32wm2N1VLiw6uZ5F5zcX+knsXzqcePcKjd8dQefoFnSObkGv6Lb3t6h4AVR5cz+do1vQpVFnJNmyXPn9m8mc+cp2v5qzUeqEu+SCwjOeLUuoEmzXug2pqVQZcYmWx55WtPu7O7Y8yh6lZDc8kOlxTyM38kOVZSDNULSDzhoMh0/i0zGeWitGm9qqu7hReeQphMa5C7nXy5zl/HMBdkcrhX73YESK2Ipbq6OotV9Qa7+g/trzFpOByqo9CLDwefnNn4n/7MER8HGfNWHkGz9b75gPJ7LSaNV/EK36D6Lc6jMYkpONn1LG2ChnZ2FITkZ/5RptBwxmU3rhYtRH5cacp7/lws81C91vCUKno+peLbX2CxY3+c5ivyXJ/pw6ZznctNSZZeQC1+lwlkTkB8fsqrgjJdzm/JXKigLOvYJ3sbjJUxYrmJtBpSajQz2+rjqd/BWAhi4eSuil4le+2ZSuIminfdqI5u99jJ0/kLbD88xOXR/Zx/HKldCfu2D7QAYJn9PKC/1xyGp6dXsN7x8c45nXXrjFy5cbmRVEmTvoG8ZPbobBSmWn/HCNv8W463XMaqY+iMis5MGcGouLTdnb1/sWFzr8wzyvxxXtIRtk3FbeH5bMs181xuBauMnhxWb/mPm+rMFXZeDc08YXe9TtQCil9MImGCS06/cyYu5QsrxlfGsksKeuksu7tZvEzLpLeHH6SwDEfHIR/WXLJTPVVcI48Vo51gTNynnGzRWdatv7kH3GC9cEgbj7kcWxSpVwV7m68tLoVeY7DPabVGLev8XL1fKETV/vW6i//52pE7rhv/q41ciZs4tqsfrRySZuFIBHDz9L+NSTTnHi/JsS7VAxhLAll/i+T7Apa3Zi+QM8UaE2wh7hDgQtPMqrA+ryVZDR4Rmm9WTxp1/y8tFBdtEa50J/8RKbVjQl6eVN+KjyKmWrChL02DJW/HlWHH/EJNzVQnCnXgBedsTKlxbU9L1JPV1F6x1twDtlT/LOM8rfJtKvP0E0RJUto1uzx8KRzoXK1ZWUJ2uz5fkvC+V1KQzDLzcmXcpTkjTCwKyQLQrfWJDG00TREXV3GOVDcsxzMrit2lMs23VJIuQTo7KX2rURo4PqMzlImUndws1g+l6PHB+O1+UQszFykRilIb7zDPIbVQ5mZjLlel6yWPk5rmjXGzmWigr+LFXCHaCO6wWKSmO3FfpzF4jrHcHwJYIZFYzaaG+vBHp/PpN2p16A3ZadMlKLusxuvEAh2G9IqbhO9ENKuL8CRn/uAp+s68LA7nmUkHHDBRF2csxId++y51YYl8ptJiTnAY3QepIZ4IGjrueKUw9yYrALjfPlm1TV6slaVRbdK652RfVUWqBmY2M1rd0kdELLZ5/M4oNbA9H+9d+oDnRBn4JkRVaphbFQTFE43eJ7aAFns1MYWe9pkGUMd1OQs53DZ5YfKg8PhKsOQ3gFtkyfjS31cCXZwAV9Guc6eCkKfahcXTl5MhN3IVFOrTFLUortN9MYbIoxQ/n53Z2Rs7IwpKSWKvt7fngs30XsoXDi/9pEmLbwa3Pgf/Zzzo850w2XtnnUKlpso+EodTZ3Z0KKPcvZEZFmTo8LT3pZOMKI1CAXBY84QMvdg9H87VhYn9rPj4iG9mnWRSFwl/FhzsXT0TaamgrAveNlmq0dq2grP97xl5eclUXvlSNYkZIXuuWjcmNjtVWcGOOD4bE6No+lXb+Xj4f0Z02a8aFv7grnXpCLXTu2tGDYoz0YGvpYkZ9hjbraPF6E1pO1hzey9sjf3HzR8UzNonDu+3DWHvmbdb8tsvmYbZkqhoY+ZlbByZCRwdjKTRga+hi1N5mzUuaHu8qFNfvWsfbI35x/s2S+m7MgnY5jaOVmXNCnWO9cwhByKVjqeIsyciPRGpWrK+8f30Zj17wHeF9mFu/UaOmwU0Xl6sqZedFGDScHV/UpPP3e6/h9Xwh9rEpNzB6hWFpd0qcw6JkhyPuOOTQHdVQEy//+UUGwNP5mNbbXdtAOq1Ljt8XHVOD6hpRKh/Gv4f+d/XS4l95qyvYRX5lMKVf1KXx6oyWnW7piSE52aHrqmEhOvuVFXBtl+Fv4r0OIGrXPLuKlwB3eLAzdDBg1uLlJkazt3gTDUdtNR7vkjdyVbzvOQVEM1K/tKrtUnYD3EaVjXjodbz28UQjUUREWd7dcccCMvhaMy/jLkg9rEutwtkHxwqtUXl4M278PtTBQ1+VWoWaYiI0DqDrBwr0iSVYzlDXlA5F98hSuiX8uorqLW6F9T2SlEadXFqr5+H/98VrqeE3ikoA6MhxURt05oVE5dn1mO79ckiGdnu36mwI3RGq6RQrlou7tUmeWKYhrknexbG2GjAyk28oIkyCNJyEvniF9mbuZo0/t6UFbH6WDquXWl4k4esLhOXDtJq0O92JnneWOj5EfBolLkyJJm7IOd5ULAWoPMn0dk10Vv9zLroHePOFupNMN0ngyrOy/vObd1WHhLp04Tfi8uuxrlqVwJG7u9BVDJvayy/l7aGkNeMMo3N1VLoz0O8+kEe2IKlrZKzU4frUcza/FOVZ7VJaLzFb9dUJb1g2qzsZqSj9VHZ2OOmRQ2+UvOv02iPLdz9nFjHpmUmN8qhi5E100Ep09cp8RpWCv9dVw1OkQfiS9WFm1+mvXIR+lcb+PxiLpBNltkjjaWFm4OMbFnRgX5XdZOfYI+3pXBUAyqAh6/myJmKXsQX4upbJ3U3jE00gdMu21aTzqam4wCf9lCD7HjUqtSpIpe3xnsX0MpV64v/3Ni5RPK150SpkDKi51TjHZlgFmha3k2Q5j8ViujO44/U512rttIr/Fym2fe/HsfCqBWqU0DX2/vwlROG4/9vxtHx2GdGNTjZUAZDVIQeh0ds9TliSG/DWAKa0Xmx7iGBd3jk8IImqAZa++Naj/PcDza0fyWdufTDz0IRpP/H64S2Iv26N7Qn69yIQXoxX87u+3XMlPj7RFPuDYSupeQnMzFUmUTNSH1087ueXThGd7ty20sEaIxpO99X+gzsiXUWdCwL40q8VdbgxvyttP/qKguc6PlamevHu0M7IsqDjniMMKQFHwn2tcgSYnNKam6IVKyOxtsNgi3fW3FbeZ6r9lyxL1XhmJKof63+OaAc9l91er11+9RsAM4/XsU3sIngHmVojIxelO540v9WaZeuOHUXZ28arvCI2G6rsMpsiQXERu6k94r4OmbXWVMEas+4On3PM0g4m3I9j0ZAz6i5ccOndGp4a0mrBVUbnmscPP4t0jodhcN3f/iGBH7RWAsVbsiHpPI91KsHJU4TBsrMiGmLwqJDszJD7o3h95T/GyAbOfqM+qedMUDrNuca1J7elm8zVVV6/KB78vVhBsRS0YRtj/dtuUuXk/zTK593ZJIqt9A0Z98yNdPIq284ateYlqn95EH3cOAHXVKsjafPqdCub9/l2hppd1aTruGlx5c8dzRPa7t5QSQutCjyPncFcZFZcKmsRCtd/C8PLlRpwdUDTpIIC4nuDY6uo+44E2yxi0xX8mZb2erZObwOdF35Syu44n3FLJH61zMaOMw4IdIDVQbVaS7OotHzzuFD/qJvO3ANJqZuGucqGcWsPlPlUpP9mxVc7N3yqSHS2ZtKPGrmqyvV2KfYO4Hb/KHYMez3zP4rLwjVQdMozK79h2XaXjsXT/azixHWaZ5vdbz694beqzxiV9KVBQ7idc1u1hTtMmPHVwHap89SbVBeip45/6liG1m3CukbHPiDW/KxQZI5SCPTcYYcqzz2A4fJJIKyUSSwJydhY/RgebtpN79GTLV4VHnRT8ztMq7IL11sm8IhcNI/zNfHH5/4F7qtRHy7z1yhJU7u7WO1qB16VMs8rwj1S6qKC7vdHYT/FwAPx+1PFiH0LrwuBXi0UcWCSC/rhEZk7tCE+VK7W7H0VTIdjKUYUj+I9rCi5uZ0F/+QpPf/w6sdnKpai22l1UHjYW8JBlokce4bkzT5maorSuvL99NYbmdZw42wcX0s2bPB3Tkk4xLegU04La37xcaL9pFbby/fktfH9+C+3dik4se/3aI6bxDEdsp+4taXgv32uaV/5Pu96DHB7zQO+vTdfl+/NbjA7RUg65SW3Calo2i5V64R6hvVmsYhu5UP+znwlxHRVty8I3kl7LaKxT+/rQ6KUDijf/2ewUoqYUL9ogwkVZWi5blpATi5etaAkLQzeTWtux4kDy5Ws8sk35cNwaaXtWqeWBZcrO3sGvd+somnc2nIsqwPZKPYaMDG5/E0qmbDSmqoWKhjotkrbU38L3DNLdu6ZP6I8XaTHoJdPnm0Rj3VCtUBOk8SRI42mm5eZHzLYXODSytmm80qTJynq94rvmfrT7Tpu+79yk8naN6alyNV2XII2nKdKltEATVJ7IPTrFp8XsnUWW3Sx1ZpksJyQwWcKtDRXIriYVekGud6/G2grKcKU2f40m6oBz63Y23Nubqq8fsotO4V7AkJZG+UWuVNf05lgTY4TCN7V+5P12g3D5s5jXQAiHMlULwvuf03Q/05GVkX+a2s510hL590NGy4LQn7+ILl/xijnVn+Lgc4eZW2mr1WNjtr1A2R/cEdtKnpvcmTAkJ6Nba8zS/brWsxx4bp8ZHYatOPG6L5HzaiO2O5ZDUhwIjYYzn9dXULEY3CXWVFhn1ndlEeOUKuFuyMhg+JzhHH3F/iwuW1Bp+RUyX8m2qch0kiEd9zPF07DlR6rir9oC+avxbCiDIePe8KnbC9ffd+OvaQg5/FTNXeGt6QuY8lgro23bQVwd04RhvpOAPKeqChVJdcvjEX/e8oEFICXcJqtbAD9t9qOHlzFU78BzX/Nj2yqsblWjWHP8ryN44nZ2i6a8/Hy21b4hUzWotjxYgr0ggrals61JGBQi3HdmSCxOaGrafiNwo1kmcHyH74i4O5SotGoYDhZebLu4EDodGa3Nzb4GF8GJHtNtklNFwapwF0JUBBYCgRhrjsyRZXmKEKIMsBSoDJwDusmynCiEEMAUoAOQBvSXZdlmL4zrLeXyT42Mys/XKcxwcX2D0Qnz5Hqh0xHaWxmn+3tqCJW+3u+Qhq1yd0flX4YTw12oo8sT7IezMlBnlp7lbWFQZcnsy8yLT6+ru4Ps76uIQ7YXBi1mN6q7ygXNkOuwwr6xpOs3+HBRT9oN+RI/tTs+KjcG+lzgy9c6UeV/d+wKBRVCzAM6AjdkWa6R01Yi93VpQPAX2zn9hfV+Kg6U/GRKGI9N22UWyHBDSkWSZXpvG0mVF/K+4/id7ZkQnKcV50YLne0xi7pVulO+jzHjWrp7t1hzUnsri27I4SFs+u5bS70tjpMopZGR4+guyk9mi2FJD7wqy3I1oDEwQghRDXgT2CjLciSwMWcb4EkgMuczGLA9NasQ1NHpOP5BUHGGMCFs6U2TzTY/hEbD/yquUbR9NbmbXYkfprF0Os7OjWTx9mWcaJvH/zLueh3efKI3/nOdx+An301hxIWnrHe0A7o/9vJeq+e5mpM+XVbtQfVFsQ7xsuci5KvdTE+s6qwpUvHjHbTYl1ehRCvUnOg5ndgv69g71PeYF76+J/f1Q9x79G/Zh/6hzRSCHeBSk1T6hzajf2gzBtZVUoXvr7+U1Sc28duJf8yEsz1QeXnx64mNrD6xyfT5fc1ih8bq8MZY03zjj1imUrGqucuyfBW4mvN/shDiBFABY3X4FjndFgCbgHE57QtlYwD9TiGErxAiKGcchyA0ztF2T73tWWgVdTmqMu5CDxi11djsVDyuO2Yjlh+pyp9Np+OnzlvmJRnS+f3npoTEFp8qOD+kxEQObGgKg/8xtZV58xypa4s4yBpkGdIzFMyXE8sfYOACDy41EQ451mS9nmzZib4UWSboIxUDpjfjo+A/CNF4ohVqRLZ9jndZljcLISoXaL5n9/VDOBeqWtEMWm68+du4XweMFAbx2SmM6DAQw7mzhd+/+dqkhNt0aNudN1b9bCrMrRYq1MDQfXvJlh2zZGuFvlCrgS3oerYNaUP9Tdu+F45hsOE5tGumOQ/CI8AuIDDfjX0No9kGjII/PxPlpZy2+/4QVCpvXkhXVglOv+qiYIAcEtvLsUK3KjUXXpXNGOF2ZPhSceJe7oVBpqV/LL/jV6wxZFnmtkFDfmLSd4LW8fyLr+M/r/hp0c6AvO8YVxrDR7vbMjvEmOT213Nf0uPA6/guKlbSW6m8ry+81xTNI3fM2t1/8Snu930gofbz4+I85Yq+rGcqz3nmmk7cqLevG9KGsqiyZcods/EayTLSsVO89ukQHhu2R8ExlUfDcG8QtWAY7lcFXhcl3I/ZL49sFu5CCE+MFtLRsizfFfnCE2VZloUQdj3xQojBGJe3uFL8OHZHcWVAJvsfnU3uWz5bltC97e2QIJaa12Znk+mmsQDWpLny8qY+REn3xo5ZzfUSq5u0sL0oSSGQbtxkyNujmfnxFJPPIEzrydoPvuRJ9WuUnVN8YZItS1zZG0QY54o1zqn3a0BOunqY1hNt7+uoV/sUO/sXHLuvwXn3tsrVlWsv1gWg8zPbCy1eUk/TjdS0RniseLAdoLbgbq/GZPgaLcl6TzjSqOjAi5TD/lSe6thq2f+7Hawv35TRz2DGz+4s3JBSaX/gRYv7I7+9asomdgQ2CXchhBajYF8iy/IvOc3Xc5elQogg4EZO+2VMTA8AhOS0KSDL8hyMFeXxFmVMD1DgH+d5dVhdBVXAvGbz+bxOjxLxWp9qtpBcYZxiyOC7pGhUiSl2F+TQlA+Ed28oClUAjNrdg6hBJXNzCI2GjAAlw2JrN4nhT7lTuTjyV5bx/mEny15vQJ3APL6LALUHdQce5vKKMkgJ5qsge5ApZxP57RUcK4WeB22KcoSNNZfyTHBvcFy4F+u+Bsv3tq1QBwaArzd6fw8OvFO0ANtXbxmvBDXgzOFwBVnVAwmVGnWkZaqAfu+sZqhv4eyI+bEuTYeEQJtcvPyYihO2s/VaEza9td9konEUkmxgQ7obUr4kyW3JtSjX2XJyWHGfDVuiZQQwFzghy/KkfLtWYaTT/yzn72/52l8WQvwENAKS7LFL6i9f4VhSBci34mrhZmCCn2vxIuCFQFWEEpYtS/yQHM4fNf3BUDRFqdnQGg1nh0VwMkb5IN6SUqn6zp1i/0iWoK4QxP5Ok6GAdliv5Unu+DtBABvMb49vK26j3nPDKDdvj13UvfcKGtTE9fAn9D2HhyiR+9oenPi4EvEdLNfOLIipwXuI/WsTIys/VipMZo5CUzGYNf8Ujzk1zZDFlBqNMWRkUIHi+7j8v9vB5zu70WL9T8UaJ8GQztdRDe5pPoYtmvujwAvAESHEwZy2tzHe/MuEEAOB80C3nH1rMYaLncEYMjbA3klJ75Uje6ky2cj3wwskN7N3pDykdG3I71W/pqAgBKNgr7p8BDFfXgKDfTwymooh6L+HNVW+oCAvR8NfxxJ5rmS0dgA5LYPJtxswvpySHbGa11W2ayoVe/w9H9WHGebRfovf/opOVccS8arjbHtTb9eG9OJl/xYGtVAR9Xg8tgRECiF+xOg8LSuEuAS8Twne17Ygco+OFUHTyJ8TYAuitB58e34LAM998Dpl5pUuO/yYMyeo5lI0qZ3R4GJb2b6CqLqlLxFDjS4RQ0aiQ2NYguHYKTpUb6loS2oTxbYpsxVtTQ49h1+vIhQqg3PnZQ22RMtsBSytb8zo7nKiCUYUZ1La63eJzc5SEPaHeSRQHELM1CA1fmpzwZ5iyKDm768QPe4gegdCH+80rsC2mMJLjmlSVSX6ppYSbrP472aM754n3NMMWfy0pBUVrhdfa/Hcepaau3qxqM58Rbx+jIs7EzouZeF3bZBOnLY6TmL/JnTznkj+a7RgdSsqXyu+AMoo53iimSzLPS3sKpH72hLU1aIImmekV34zcB2eKtsEXM3Jw9E8epsDDYxaZW4iTv/XfmdKVEfC3ry3Al7l6kq5f1zQFpKN3Nw1GXcbv1dRCN/wIiG/mIut8PMpSIklJDxl2Wxsn3/O0HzYYGXblXSkRPPiKfcLpSpDNRfS6Tg6rxzD2W6zrHe2AULrwocjvzdrf/9mdX5e/jhRE3bYFFpUGK48Ubgt7mBmJuV3luwS7PrLjTj0vDLzc25SJBUmOse5Jt1KIPi5OwwYNsas9mMPr0Tm+xReLacgkkOFxZqSxUXD/ymLQkuygdMbw6lE4XzkpQ36VvWIfQHWmmgBzK9T2LpBiFRzo2T0L1e5cz6QWnJPDjf80dQ+wvciN57czA+q5iBDxDv7nFq8Qu1fhtPjzPMWDBqZ3yvNsMBZU3w+pah/+1FpqRrdWvNckXtN5yHdSsDtN+VKpLQZxEqlcFdHhrPs6ak444ZQeXlx6pso2rhtVYwXn53Cnp7VqXi8GBquEHzavHAbYRWtzO1oDRVKjhQStyevm8XtL71YDw+DEx1rBomgH04Q0+IFTjyqrJ15taknFW9ULpZHv7jQFCiCsiQ5gPCFl0rMz+FMiHrVieuuIr6dMktxVao7W5LzhGe1d6+gv2xeOEUCvM7E43azHu9MqsmEgDzu/fHljjG+zzEk2UCrrUNRZxpwjUuwWvKuKKhqRJMZ7MmdQC2n+1jK4bKPcGvi7QhuZNmWHBTxWRaGQ/ee6+VBRakU7mg11HJRaipXM3yAO3YPdXJiDPFPzCG/YE8zZNHqj7FUPX3Q4SnGf9IEzxq3aeq2BUl251h2FlW1alOigqfKlSzfe/8u10z1B5wbNSElJhL2QQBX/0hRFHI4/NoMasvDKf/1OcsHC4HeXXkd1qdpqfxb8QoIq729iZ8bykS/b8m/cvnw965EnCtd9TQLg8rDA/VXt4mPUpaRS5TSeHPhcCp+lF/pKLoilubvfexvE8CNA0aKDh+Vi+k+VAsV/86eA0DEsqFEvXsLDAbb6DxUalQeeabMSx+pONLIdkdvQSQZ0sksUKx+44CmdhSEKUapy/+HKJ3CvRBcfTcCjR1l6YTWhcuj6/N+C3PNusavI6k6xr5CzfmhqhXNyKfXMtLvPODJwcxM/tfkaZpviGOcv3UbtDOgrlqF9sElQ2hUGKTjsXQa/zq/vD9RQbKU2SQZMcNyeT9NhWA29lTa209nlYfdxavwhJsrGxrNVJROPJudQrkHhO3l/SObFRXHcvF835epuMl+W7l0K4EXQpsDkPR7WKH1emOfn4HheZkL+nSGV25mNbIm64m6rJ+bZxotLpHVk6+OwetnpRkNQzHvg4ewiFIp3M+862Z2I6ky7bNfn51Qj6N9pihSflMMGdT6bRRVxx3B4KBgV1evymOLD+QIdiO6LxlN5es7+XlKG8Z9mCfcs8tnOVTX1BZcfDqAteWUD3Dr451x3x5rd4y+TZBlyi7Yw5G3ylJJk+d4XtNoJiM9OyNZ+I4ZUeXxUTmXxlnt50fs18EEqpU2/3cvdcL7x9KdzKMpH0j3Tfuop6zZTqacTednXkR7+LjD/p9c573/iGyiBg8jtp/SdJKbRl9Z407vE8bIkumfPI/vwpyXiRC0OpxCkNboPAzQnLBLoCdKafTs8hJCKnz+PmePYbjP1MynF9blg4bKguKLBnVEtfXg/ZlQCaJUCvfoYMcZCIXWBWpFMqLjHwrBPul2OAu+bU/kN7scvsE04ZVp/tN+M+3c4xIgywT+eZFeA1ryQ5iR6yX2iTnUeOdlKr/r5KgFlZqh/VeTKWebvmOilEbavGA0d2wrPH2vkPpaklliV3Fxt3UUZ1rMJj9z3suXG3Gnvx/I9zbczG6o1fT1vkX+uW9KVzFq2ijK73X83swPffx5wpd5UuvWcFPb24N/NNEkq4UqZw7wz/Cj7OhYEzDWxBnpNxd3lW2+rmrTh5PvPY+QoPz+HZSGusxnJjVGG2JueppYZ3k+igIjFo1P4HyC8RpkZ2rMiMUeVJRK4V4Qc5KC0Sak2qSRXh5Vn5UjvyCiQHTG+hsxlJ9SvPDAC88GM85/pcX9+ouXSOoRQuzWVKK0HmiFmh6dN7NnYVXnZg8aJNY8Hs0vPz7CxmpGLeSOwYDf+tMlo7U7GdOOtaASji/HW7+rLDiRKKUR17cS0ul7YxJzFJqwUC4+q6yUtSTZn3e2PkPU184llZMPHCMon4x6O6wbUyJuEel7k4Whm03t8yttgUpb8h1ZuGDvGPskCel59ndZFoROPYIh2XKZt/sCIUh4sTFfdFxiJsQtIX9h+BtSKk8Ofo2AH4+Wvu9mJ0qdcE/v0pBJoVPIz8/y2a4niTxh3d6uCQslLcRgJtgz5WxSpofgUXi2uM1o2WOPWVuHUx0IWnPRFJ2hv3yVwcNH8+Rnmxjnf5rx5Y5Rq/PjBH3lROEuBDfn+zE9YgngGNPcvYC+dT2mxswi/xzPZqdQYUbx5hykvWP6P1FKo/7PY4mMK73alqZiCLJOy+UOQRx+NS+k9HBWBu9u6ErUyyVvSorMOcelJ+qzceY2AB5zzSiSqTDFkMGuTGOdW8MwT7wL5DSUpmpiam9vCPAHtZqN4yc5vFoMUHuw74OZtD47EN3525CcinT9hvUDnQxNeGVklXX6BHHB8iqr1An3tLJqRfISgCzZwBEhBFcmu3G2gXls/DOxT+O9qXhardC6mJWKSzKkI4/yRrqYr7KSQUJ7V0+Ma96L5Ithc5ky/RGH+OELQ0rXhiypOYkorY0Fpu8TMvy1NNQphUfnvUMI+ccxr6c6MICT74TR2G0qudWtZiY+QpWxO0tdjHF+tF13lNF+58zaX3p3DJH3mNFRu34vX0QYTRCpp2OLZDp85/pjnKiXq7aU7lXR+RE1ODYy98VZfDPgxkVzAai5qxfBz9xb4a5yd+eXLT/bRBHcsN0di/tKnXAviMNZGVT76IbVuOXM9vX5s+5kQCnwlqX4wPOZxeJZyWpXnwFTVvK050Vyb5z1aVomd3oe+fQZs/7aQ2fZklyVzh5GbbKlWwqvfPCI0zIGrzUVpV6wOxuaypXQLsgkLnI2irKFpRyVaqYwwvcsBSvrPPlkT/yO77uvL6VZjRszS6XmwouRHB1lTlD2efkdnD9vTH4a3eFFpGOWSa7uNTTlA/l61y+mbV/VNgo++7k4m53CyIbPgqHoq327XQQ7v1AqhzsazOPKeYlsWcXrtdoVuxqTJWRtCGV21A+mbZ0o/vNdqoS70LoQ3j9W0ZYtqzDcLJqTAkByVRGgVl6QG1Iq4+cPJ+SW4/bMrPYNGPPNkhwNxyjYX7nSgH2f18XzROHLaenuXfa824DHRoaxtdYv6ISWt7usYNl3jxYriQRAE1qR19utNmtv9ddoqiYVh6DBMbT5cwxV75ZsYonQaBDfZysKY+fix0WtCXYCQVRJwVWoFBEnZ7NT6Pfqq3iePOjUrFFHkKvwhC5z57GzQ5AF/Dppkuk50gktUVqj9hj+/TluZZUxHWuQBSkdsktM2FnCpbeaUrPjSTzUqVYVnMhN/Qn8RYdKL+N23XoFtDIbBI+9MgSASROn0VCnxVPlSlROXlbZP+H6mFqw07nPWdaGUKZF/uR0ha1UCffkZ+oyp9KX5MZEr0jxZtyqXlTJcmwZ3/7Ai4R8Vjxtuf3ETYqla6/4lhz/KYbAn4sWKLo1ezjfsSHk1L/t732D8a+XJWq+V7FuDtnTnYE+FyioCXqecLkvwsL7hNbm86YYMtBu8rH7HKmd6/F92FcUlppf7oDzw0xLCj8l+/G/tUOpsrx0mZH08edNhcobNx/LrCfn8YS7shzltEIKTVd5dxjqfD+90AtCP9hRYsyU18Y05bFnDpiKsxTE4awMnl73imm74jpwW2m7P0O6fgOP5UYTzM3PvAClGXVh6GbCh1dB26kJ7lcEAdOdo1TMjvqhRFbipUa4q729ye6XoEiQeWP3c1R5zYYHQQii3zyqaJp+pyIB72scDssSOh2nP3uEZX5fk6uxX9WncGphNIGzbftRq028wc52kilZJb7TtzxyZDgBpT+BslCc/19DWrpux1ZaiDsRylT02wY9wT/Z7/u43JpCneTRv40gesexUuXYs4R1aTre2v4sUWNK948fOXIXI5f2pEf0Piq4JDLYx3J27Jneyjj6JEM6z25+GVGI+UOz5bDVpEGhdUH/WA2L+18dsswUwlkQ+zKzGHWqF1FDnVOjeOalluz2u8Aj7ufp4pGXTR3XZh4AnydE8tfRx1D9a78jX10tiqzAvPvZVWy12HdNmiu7UyMs7r+iv2lxX6kR7voaYex+ZIFpu8XRLlQdec5mQfCoj9LhM2NxJ0IOOPZmFVoXzn5Yl5PdpqEVRsGeZEin1XdvUMlGwQ4gXbxCbFYgjV0LvyEdgeR1f+zN6upV6d91gyIGOmrhMCL+TixUuN5+sQl/DDOnQbYbKjWyzvwuiMvOJubtU0i2pNGXAoxdMJCoj0qv+Sg/Knc/zE60ZLXrwPPfTTW1e6p0RSY1+ajcTI7IgniqaWcMN4p+DkRIEH8tmWfXXJMM6Rhkmec3v0xkP+elJ2e3uMpOtKx6uS+Pj/sSQMEqO87/NL0XHeClmHYgyxgyMq0ywKrcjcef/0jLsSb5v2feMyLJBu4a8lYMb84ZTvAXlu+bFNkyl0+pEe6XWiqXJRePl6dK4rn7MpfkLo9wtM9UtPm81Y/tGUilCfaFrMnZWUyZ9Dx9P8jTcJKbpBM4uxgmlI9vFzsN3F6o6lTj8UV7zJK3wlamYjhUON/H7RqyYhUGMPZ8F2Q7I4ZUtapysP03FIyAGBP3PCLDui/mIRyHy5976RnW3LR9cWlVjjVZUsQRlvHLthU29rQvTLZX855IFy4RaSgZv0/A9B30nNUc1Grmn96o4FYK0XiyMnYTAI0+G0XgN5aFsMrLi59PbEAr1GjYiiWCtQm3arCjXt5LJFhy3KxcKoS75O/B+iF5Wt7mDKg685Zdy/f1CdXp7138kCVNeGXqjduvCEPKliVSL3o5xM3uftPAN4mhJrqC3x6dwetu7ZAcFO4eWnMb81V9Cq63S8bOqY4M50wPH/4oINh3Zkio0rIK1dpVrq4QYD7PU6ujCE62T3s9NcDbLGb5kj6FxPmV8M0oXt7CvUKjccOovP7MA5FgVhD5TSmVX0uhvX8f0kLc2Txjjl3j2BLWZyuGXGrC+eE5pooLJ0u2IpgsG8fX6xnwzBAQgtiRLsS1NWreud9r8uhZDKg5kKjBylyYS281ZVTflai5Zsbgmh+1Jw4naMtdVGlZyPpYi/3sQakQ7rpyGYRofAF4+3ot1ix8jPKxdryxZJmLX0bB9H9MTfU6HeVgRlOCJu+ySyiffKU8a4JXmrZXpHjz/rw+VJ283yHbrvuvu/jjWH0G/n0ad5ULwWqZqy/UcMwZo1Ljojb/Ll/ebF4ilXfUkeE8/ssR1vr/omhPMWTQc90rRB0u3L4pwiuxv8UM8mvbK1M9CdpivwlFlW3eNv5qO3ydEB8uhJgHdARuyLJcI6dtItAJyALOAgNkWb6Ts+8tYCBGtt1XZFk2D98pgGM3ytFiQxz6+5AI42zo489DPHie8aH6N8PN9ssaODJ0mgU+9+IjfPkQ3K6q8bpowGfvvfddyPuMRXHCFtYjLOMl4jvlUTW3cDMwvvmvfLikk+KYZ2J2WPRbxGan8sys1wEI/f0a0uk4pyoApUK4h7skA74kGdI50K8a5Q8X3za5MHQziWPW8cypUejWmGeWFoa0ZxpRPjrvIZyTFMxn6zsT+dl2pznt/NTu3G2STsB0+4+VmtfmhcAfrHd0ErKDfcxMMZvSVbwzbhQxf520fCNev0X32K6si15jahq7vRuRO+y3iTZ9tESZL78HpgEL87VtAN6SZVkvhPgceAsYJ4SoBvQAqgPBwF9CiChZlot8HrXXUtELx7mSSiOkO0mEfGr+jAqdjtbNnkUUUasYoKJnooICoTC8erUu+29XVLRFz0pEOu4crbY40GzcR9W7NWkf+ZTiHu/rfYu+LedbPX7CrWg2Xq/KtTveVMq5jiWxqisVwj0XBzI9UCXcdUiQatIkLuhTFHZeP7U73m9cRNoXiP5aEQ+YECQMasyC/00yZcduyzAwcdXTRI4rPbUo1Zv2M+G9/rSfaKnajXNxpo/57fHFhfZ4LN9V5M2YUS+cP6sqib0ivi19MS2yLG8WQlQu0LY+3+ZOoGvO/08DP8mynAnECyHOAA2B0nOD3GfImZnonjhntd+1JrXZtmSToq2MKoMorSs7c6x5uz5tgMdypY+rNJm15D1HUHfxZvNBUGOgqjadsmrr4Yz7MrNYMasVATO2U/wqx0WjVAn3oUuGEHrZsWfF5c+9PP35G2x66yuFjXZV5DoixwwjfJwF4a5Sk9i3ISvfnUiIxhNJNlBl7RCi5mYSvqtkln49a+xlf5UqDiU0+W2Mo/e5NvwU9reprZ3PEQ61fAG1g2n9lqBKMXfcGmTrVBDe/7uocPpKsgErypxF6A331nlcAC8CS3P+r4BR2OfiUk6bGYQQg4HBAK6FFGT//w6x4xAfhtdVtN3p2+T/2jv72KrOOo5/fi3tFShxLwxSaWXlZd3QzK5j4yWExRm3tnNvWZSSqeAWkyVjoRMlGNR1rpg43YzIZM7AwpaNl4UQZ8zIcDESh3SrWKCFUFoGztqXDRUola69/fnHOW1vS+9te+855zncPp/kyT197rnne37Py6/nPOc5v4dfPb2RH8+6DYDJhDt0MzgvK26YVQTAv/bM4+iCke+qf/DgSqbVBjNryv/Lv7EwCseRiGmbq7nj2TWsal4wKH/3sl/Q8dUFl+2fWTiHvAMT2fm049jboxe5t+ErzKtqdV408ulljNsnN6E5ycW/iLa1c+GhLPZ2DkyJvGtSN03LM51wxx6RWTiHH5YOHmt/6dxnYO3VI/52aAyez727goya5FbRaXlmDlEN/qpfRNYDPcCYp4eo6kuqOl9V52ddQaESTHL1zkNULrzH9GkkTf63mrnn1pL+9I8eZ258wR++PSi/90hwIRxC49w7ez9hwv9SPEhvlOkbD9BQcRPt0YGHdzdnf4oZqxuZkJ8HOKsDnd6wiAW7jrHls3+hICuH97q6+dlHS4je2ULPmQ9TPJHEPFm9jN7a5MeSe1rbqHj9Ed7rGnjaWF/2Ak3P3IpEUnMmEomQ8fkbuen1U4NmH3VpN8/vegCtqUvwa7hQvpC1eXv7/27q7mDGi9lJL1gSaYsf2MovRGQlzoPWh3XgLbhmIHYQOM/Ns3iAdnUZib7oFdH/nqOnpbU/LV+zhsXfeYzCX3cOyk9mxl2yhMK5t0Uj3LJtNXk/9+btMjlwmMX7Vw3K216wjw37d/PBTxbxxJ/foW7lJp66znGwH0cvUvnlr3F0UbZvV+teM/Opg1TeXc5/oo7zm5SRzbFvbOLSnTendNzOki+wY+/LPJc7eIhnT8c0rq+qSfhbmTCBjvJzg5aPO9sbIfvglbP2pYiUAGuB+1Q19j/Lm0C5iEREpACYC3jTYC1pR84b1UzZcRD9e72xcwiFcz/fOJHr1//Vu/mqqtxQ1UHVxzf2Z2VKBkWRCA0rN1Myqat/TLilp4Olv/kevac/9GU5PN9QpffUGYp/X9GflSWZPPHLnbQ+uTipQ2beMJvbflRz2bzyshNlbFtWOuKLVxqNcun4VUlpm0BEtuM8EC0UkX+KyKM4s2emAPtEpFZEXgRQ1XpgF3AM2As8PtJMGYvFJKFw7trlfcCr6PGTvPv1ImecOAFffHkt+VUH/H0Rwie0p4e5r3bxdufACyIP5ZznjoffJ3PqtWM+XuMj0y67Yu/ovcTZrTNHNYyUMXEiryzbNGZdU6jqclXNVdUsVc1T1S2qOkdV81W1yE2Pxey/QVVnq2qhqr5l8twtlpGQMKx3KCIfARcB74KwjJ6p40zXpLYp3Zmqep0BXUTkAmAqEPp4q2eT2qFr26Fw7gAiUqOq861u+mqbtNkUtp7Hh3YY23YohmUsFovF4i3WuVssFksaEibnPrYwc1b3StQ2abMpbD2PD+3Qte3QjLlbLBaLxTvCdOVusVgsFo8w7txFpERETohIo4is81nrtIgcdV9OqXHzrhGRfSJy0v0cOXjK6LS2iki7iNTF5A2rJQ4b3TI4IiLF8Y+clG6liDS7dteKSFnMd993dU+IyN3J6rrHyheRP4nIMRGpF5HVQdkdRoJq2wnKPW69e6wfWL+K0SyMsatWRM6LSIVfNpvqzymhqsYSTkzYJmAWzqrLh4F5PuqdBqYOyXsWWOdurwN+6pHWUqAYqBtJCygD3gIEWAhUe6xbCXx3mH3nuWUeAQrcushMQTsXKHa3pwANrobvdoctBdm2E5T7sPXug35g/SpBWbcCM/2y2VR/TiWZvnK/HWhU1VOq+gmwAydudpDcD/StzL0NeMCLg6rqfuDfo9S6H3hFHQ4CV4lIroe68eiPUa6qHwB9McqTQlVbVPWQu30BOI4TFtd3u0NIYG07QbmbxJd+FYcvAU2qesYvAVP9ORVMO/cZQGwIxrgxsj1CgbdF5G/ixNwGmK6qLe52KzDdR/14WkGUwyr3FnFrzC2yb7riLIJxC1CNWbtNYcS2IeUOw9e715juV+XA9pi/g7AZQt6uTTv3oFmiqsVAKfC4iCyN/VKde6pApg8FqQVsBmYDRUAL8JyfYiKSA+wGKlT1fOx3Ads9rhim3IOqd2P9SkSygfuAN9ysQNt6H2Fs16ade6AxslW12f1sB/bg3Dq39d0yuZ9+BpWOp+VrOahqm6pGVbUX+C0DQy+e64pIFo6DeU1V+1b7MGK3YQK1bbhyT1DvnmK4X5UCh1S1zT2HQGx2CXW7Nu3c3wfmikiB+x+4HCdutueIyGQRmdK3DdwF1Ll6K9zdVgC/80PfJZ7Wm8A33afsC4FzMbd7KTNkvO9BHLv7dD2LUS4iAmwBjqvq8zFfGbHbMEG27WHLPUG9e6ltul8tJ2ZIJgibYwh3uzbxFDc24TxZbsCZWbDeR51ZODMWDgP1fVrAtcA7wEngj8A1Hultx7kt7MYZc3s0nhbOU/UX3DI4Csz3WPdV97hHcBpebsz+613dE0BpijYvwbk1PQLUuqksCLvDmAJs2/HKPW69e6gdaL8aoj0ZOAt8OibPF5tN9edUkn1D1WKxWNIQ08MyFovFYvEB69wtFoslDbHO3WKxWNIQ69wtFoslDbHO3WKxWNIQ69wtFoslDbHO3WKxWNIQ69wtFoslDfk/BHA6QmmAOZEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# TI1: Strebelle\n",
"TI1, TI_filename1 = mps.trainingimages.strebelle(di=1)\n",
"TI1=np.swapaxes(TI1,0,1)\n",
"# TI1: Strebelle, rotated and coarsened\n",
"TI2, TI_filename2 = mps.trainingimages.strebelle(di=2)\n",
"plt.figure(1)\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(np.transpose(TI1[:,:,0]))\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(np.transpose(TI2[:,:,0]))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#%% MPS_SNESIM_TREE\n",
"grid_size = np.array([350, 200, 1])\n",
"O = mps.mpslib(method='mps_snesim_tree',\n",
" n_real = 1, verbose_level=-1)\n",
"#O = mps.mpslib(method='mps_genesim',\n",
"# n_real = 1, verbose_level=-1)\n",
"O.par['debug_level']=-1\n",
"O.par['n_cond']=49\n",
"O.par['simulation_grid_size']=grid_size\n",
"\n",
"# make sure no unwanted hard/soft data files are being used \n",
"O.delete_local_files()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'dim': {'nx': 350, 'ny': 200, 'nz': 1},\n",
" 'n_cols': 1,\n",
" 'title': '350 200 1',\n",
" 'header': ['Header'],\n",
" 'D': array([1., 1., 1., ..., 1., 1., 1.])}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d_mask1=np.zeros([grid_size[0],grid_size[1],grid_size[2]])\n",
"d_mask1[80:150,100:180]=1;\n",
"d_mask1[0:40,80:150,]=1;\n",
"d_mask2=1-d_mask1;\n",
"mask_fnam1='mask_01.dat'\n",
"mask_fnam2='mask_02.dat'\n",
"mps.eas.write_mat(d_mask1,mask_fnam1)\n",
"mps.eas.write_mat(d_mask2,mask_fnam2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAACGCAYAAAAmecr2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANaklEQVR4nO3de7BV9XnG8e/j4XIE5aaWMEICjBiktRJkUDOZ1NRJY0g7pk2awaYj05IxNTrTTjNpcGITkzYzaTtpZ5ymGjpQ1OAFG1NpS2upNcn0DxTTGIIa9KgYoAoRoxiJyuXtH+t37BbO5uDZe6/L7zyfmT177bXW3utdzHse1lr78lNEYGZmeTmp6gLMzKz7HO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuGdA0rclfaLqOsy6zb09cg73EkjaIel1SacfNf/7kkLS7BJruUbSQ5Jek7S2rO1anurS25LGS1ot6RlJL0t6WNIHy9h2XTncy/M0cPngA0nnAhMqqON/gT8H1lSwbctTHXp7DLAT+BVgMnAdsL7MA6e6cbiX51bgipbHy4FbWleQ9KF0xLNf0k5J17cs65f0DUn7JL0oaYuk6UdvRNIMSVslfWaoIiLi7oj4J2BfN3bKjBr0dkS8EhHXR8SOiDgSEf9C8Z/O+d3ayaZxuJdnMzBJ0jmS+oBlwDeOWucVij+SKcCHgKskfTgtW05xRDILOA34A+DnrU+WNAf4DvC3EfFXvdkNs2PUrrfTfw5nA4+MbJeaz+FersEjnPcDjwG7WxdGxLcj4ofpyGMrcDvFaSbAQYrGPysiDkfE9yJif8vTFwD3A1+IiFW93hGzo9SmtyWNBdYBN0fEjzrdsaYaU3UBo8ytwHeBORx12gog6QLgK8AvAeOA8cBdLc+dBdwhaQrFkdHnIuJgWv5xYAD4xx7Wb9ZOLXpb0knp9V4Hrhn57jSfj9xLFBHPUFwHXArcPcQqtwEbgFkRMRm4CVB67sGI+GJELADeDfw6b77OeT3wPHBbOjU2K00deluSgNXAdOAjLf85jEoO9/KtAH41Il4ZYtmpwAsR8aqkJcDvDC6Q9D5J56bm3k9xKnuk5bkHgd8GJgK3pCOYY0gaI6kf6AP60ptZPoOzbqi0t4EbgXOA34iIn7dZZ9RwuJcsIp6MiIfaLP4U8CVJLwOfB9a3LHsbxWnpfoprmt+hOP1sfe3Xgd+iOHJZ0+aP4DqKN6tWAr+bpq8b8Q6ZJVX2tqR3AJ8EFgLPSfpZun284x1rKHmwDjOz/PjI3cwsQz0Ld0mXStouaUDSyl5tx6xM7mtrip5clklvjDxO8ZnXXcAW4PKIeLTrGzMrifvamqRXR+5LgIGIeCq9EXIHcFmPtmVWFve1NUavwv1Mih/xGbQrzTNrMve1NUZln2+WdCVwJUAffedPYFJVpVjmXuUVXo/XVNb2Wnt74gSdP/+scWVt2kaZHTsP8vwLh4fs7V6F+26KrxMPmsmxvzWxClgFMEnT4gJd0qNSbLR7IO7r1ksN29fw5t5efF5/PHjvrKNXMeuKJR/Y2XZZry7LbAHmSZojaRzFr8Rt6NG2zMrivrbG6MmRe0QcknQNcC/F19zXRMSo/elNy4P72pqkZ9fcI2IjsLFXr29WBfe1NYW/oWpmliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mlqGOfhVS0g7gZeAwcCgiFkuaBtwJzAZ2AB+LiJ92VqZZudzb1nTdOHJ/X0QsjIjF6fFK4L6ImAfclx6bNZF72xqrF5dlLgNuTtM3Ax/uwTbMquDetsboNNwD+A9J30uDAgNMj4hn0/RzwPQOt2FWBfe2NVqnIzG9JyJ2S/oFYJOkH7UujIiQFEM9sXWE+H4mdFiGWdd1pbfffmbPBjszO66OjtwjYne63wt8C1gC7JE0AyDd723z3FURsTgiFo9lfCdlmHVdt3r7jNP6yirZ7E1GHO6SJko6dXAa+DVgG8Vo8MvTasuBezot0qxM7m3LQSfnjNOBb0kafJ3bIuLfJW0B1ktaATwDfKzzMs1K5d62xhtxuEfEU8B5Q8zfB1zSSVFmVXJvWw78DVUzsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLUC1++CImTeDgRYuHX/E4+p/ax+GBp7tUkVl37Do4gc88966OXuPSyVu55OTDXarIRotahPvZs3/Cpn9Y1dFrzF93NXP/xOFu9XLgMdh6fmev8c0bPsFTH/l6dwqyUaMW4S5En3yFyDIVQ/54pFlPOVHNzDLkcDczy5DD3cwsQ8OGu6Q1kvZK2tYyb5qkTZKeSPdT03xJukHSgKStkhb1snizTri3LWcncuS+Frj0qHntRoH/IDAv3a4EbuxOmWY9sRb3tmVq2HCPiO8CLxw1u90o8JcBt0RhMzBlcFgys7pxb1vORvpRyHajwJ8J7GxZb1ea9ywN0Dd1Kk98dj5H+sv/6NoZW2Dyus2lb9eOkWVv//jQz7j4nz/NSa+q9G2fvejHbHznxtK3O9p1/Dn3440Cfzx1HCFeUyaxYdlXOWfchNK3PXfKCiavK32zdhzd6O1+yu+loew4dArzv/Q0h/cMOaZ3b7f9p++Gd5a+2VFvpJ+WaTcK/G5gVst6M9O8Y3iEeKuprvb2WMb3tFizdkYa7u1Ggd8AXJE+WXAh8FLLKa5ZE7i3LQvDXg+RdDtwMXC6pF3AF4CvMPQo8BuBpcAAcAD4vR7UbNYV7m3L2bDhHhGXt1l0zCjwERHA1Z0WZVYG97blzN9QNTPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPL0LDh3maE+Osl7Zb0cLotbVl2bRohfrukD/SqcLNOubctZydy5L6WY0eIB/ibiFiYbhsBJC0AlgG/mJ7zd5I8zJLV1Vrc25apYcO9zQjx7VwG3BERr0XE0xQDGyzpoD6znnFvW846GZn6GklXAA8Bn46In1KMBr+5ZZ3BEeKP0TqI8PiTp/Deq67soBSY9/jzHO7oFcze0LXenvi2icy855SOirn6tFs7er6NTiMN9xuBPwMi3X8V+P238gIRsQpYBTBJ0+Lkex4cYSkFB7t1SVd7e/F5/bH67f/d7RrNhjWiT8tExJ6IOBwRR4C/5/9PT094hHizOnJvWy5GFO6SZrQ8/E1g8NMGG4BlksZLmgPMAzo7JDcrkXvbcjHsZZk2I8RfLGkhxanrDuCTABHxiKT1wKPAIeDqiPAVE6sl97blbNhwbzNC/OrjrP9l4MudFGVWBve25czfUDUzy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMdfLDYdk5sucnrPjsH3NkbPnbnjdwoPyN2qhx7tgD9N8VHDg05G+d9dRV0/+19G2aw/1Njhw4wKl3bh5+RbOGmdo3gbvP2lR1GVYiX5YxM8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDCkiqq4BSS8D26uu4y04HXi+6iJOUJNqhd7U+46IOKPLr3lC3Ns95VqP09t1+RLT9ohYXHURJ0rSQ02pt0m1QvPqPQHu7R5xrcfnyzJmZhlyuJuZZagu4b6q6gLeoibV26RaoXn1Dqdp+9Okel3rcdTiDVUzM+uuuhy5m5lZF1Ue7pIulbRd0oCklVXXAyBpjaS9kra1zJsmaZOkJ9L91DRfkm5I9W+VtKjkWmdJul/So5IekfSHda1XUr+kByX9INX6xTR/jqQHUk13ShqX5o9PjwfS8tll1doNdett93VP661fb0dEZTegD3gSmAuMA34ALKiyplTXe4FFwLaWeX8JrEzTK4G/SNNLgX8DBFwIPFByrTOARWn6VOBxYEEd603bPCVNjwUeSDWsB5al+TcBV6XpTwE3pellwJ1V98Zb2Nfa9bb7enT1dmWNlnbqIuDelsfXAtdWWVNLLbOP+iPYDsxoabztafrrwOVDrVdR3fcA7697vcAE4H+ACyi+3DHm6J4A7gUuStNj0nqqujdOcP9q2dvu61JqrUVvV31Z5kxgZ8vjXWleHU2PiGfT9HPA9DRdm31Ip3bvojhqqGW9kvokPQzsBTZRHN2+GBGHhqjnjVrT8peA08qqtUO16Yth1LJPWjWhr6F+vV11uDdSFP/d1upjRpJOAb4J/FFE7G9dVqd6I+JwRCwEZgJLgPnVVmSD6tQng5rS11C/3q463HcDs1oez0zz6miPpBkA6X5vml/5PkgaS/EHsC4i7k6za1svQES8CNxPcao6RdLgT2G01vNGrWn5ZGBfuZWOWC3+nU9AbfukiX0N9entqsN9CzAvvaM8juKNhQ0V19TOBmB5ml5OcQ1wcP4V6d36C4GXWk4be06SgNXAYxHx13WuV9IZkqak6ZMprqE+RvGH8NE2tQ7uw0eB/0pHa03QlN6uXZ9As/o61Vu/3q7qDZKWNx+WUrwT/iTwuarrSTXdDjwLHKS4TraC4nrYfcATwH8C09K6Ar6W6v8hsLjkWt9DcWq6FXg43ZbWsV7gl4Hvp1q3AZ9P8+cCDwIDwF3A+DS/Pz0eSMvnVt0bb3F/a9Xb7uvR1dv+hqqZWYaqvixjZmY94HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDP0fO8wSdcHmnPYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(2)\n",
"plt.subplot(121)\n",
"plt.imshow(np.transpose(np.squeeze(d_mask1)))\n",
"plt.title('Mask 1')\n",
"plt.subplot(122)\n",
"plt.imshow(np.transpose(np.squeeze(d_mask2)))\n",
"plt.title('Mask 2')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#%% Simulation in region/mask 1\n",
"O1=copy.deepcopy(O)\n",
"O1.delete_hard_data()\n",
"O1.par['mask_fnam']=mask_fnam1;\n",
"#O1.par['ti_fnam']=TI_filename1;\n",
"O1.ti=TI1\n",
"O1.run()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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"
}
],
"source": [
"plt.figure(3)\n",
"O1.plot_reals()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of non-nan data: 8400\n"
]
}
],
"source": [
"d_hard = O1.hard_data_from_sim()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of non-nan data: 8400\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d_hard = O1.hard_data_from_sim()\n",
"#%% SImulation in region/mask 2\n",
"O2=copy.deepcopy(O)\n",
"O2.parameter_filename='mps_mask2.par'\n",
"O2.par['mask_fnam']=mask_fnam2;\n",
"O2.ti=TI2\n",
"#O2.par['ti_fnam']=TI_filename2;\n",
"O2.delete_hard_data()\n",
"O2.d_hard = d_hard\n",
"O2.run()\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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"
}
],
"source": [
"plt.figure(4)\n",
"O2.plot_reals()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.9.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| lgpl-3.0 |
sgkang/DamGeophysics | notebook/ReadFiles_all dataOld.ipynb | 1 | 614488 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
"\n",
" python setup.py build_ext --inplace\n",
" \n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import sys\n",
"sys.path.append(\"../codes/\")\n",
"from Readfiles import getFnames\n",
"from DCdata import readReservoirDC_all\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"directory = \"../data/ChungCheonDC/\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fnames = getFnames(directory, dtype=\"apr\", minimumsize=7000.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'fname_temp' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-1d70b030f6e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfname_temp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'fname_temp' is not defined"
]
}
],
"source": [
"fname_temp"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fname_temp = fnames[0]\n",
"dat_temp,height_temp, ID = readReservoirDC_all(directory+fname_temp)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ntimes = len(fnames)\n",
"DATA = np.zeros((dat_temp.shape[0], ntimes))*np.nan\n",
"height = np.ones(ntimes)*np.nan"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(380L, 1269L)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DATA.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20150103180000.apr\n",
"20150106180000.apr\n",
"20150109120000.apr\n",
"20150112120000.apr\n",
"20150117120000.apr\n",
"20150120120000.apr\n",
"20150123120000.apr\n",
"20150126120000.apr\n",
"20150127000000.apr\n",
"20150129060000.apr\n",
"20150201000000.apr\n",
"20150204000000.apr\n",
"20150207000000.apr\n",
"20150209180000.apr\n",
"20150212180000.apr\n",
"20150215180000.apr\n",
"20150218120000.apr\n",
"20150221060000.apr\n",
"20150224120000.apr\n",
"20150227060000.apr\n",
"20150228000000.apr\n",
"20150302000000.apr\n",
"20150305000000.apr\n",
"20150308000000.apr\n",
"20150329000000.apr\n",
"20150401000000.apr\n",
"20150403180000.apr\n",
"20150404120000.apr\n",
"20150406180000.apr\n",
"20150409000000.apr\n",
"20150412120000.apr\n",
"20150415060000.apr\n",
"20150418000000.apr\n",
"20150421000000.apr\n",
"20150424000000.apr\n"
]
}
],
"source": [
"for i, fname in enumerate(fnames):\n",
" dat_temp,height_temp, ID = readReservoirDC_all(directory+fname)\n",
" if dat_temp.shape[0] == 380: \n",
" DATA[:,i] = dat_temp[:,-1]\n",
" height[i] = height_temp[0] \n",
" else:\n",
" print fname"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = ['1', '2', '3']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def strtofloat(input):\n",
" temp = \"\"\n",
" for i in input:\n",
" temp += i \n",
" return float(temp)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# dat_temp,height_temp, datalist = readReservoirDC_all(fnames[79])\n",
"# print fnames[79]\n",
"# # datalist = readReservoirDC_all(fnames[79])"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20150721000000.apr\n"
]
}
],
"source": [
"print fnames[705]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"locs = dat_temp[:,:4]"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mida = locs[:,:2].sum(axis=1)\n",
"midb = locs[:,2:].sum(axis=1)\n",
"mid = (mida + midb)*0.5\n",
"dz = mida-midb"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, IntSlider"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy import interpolate"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.linspace(mid.min(), mid.max(), 100)\n",
"z = np.linspace(dz.min(), dz.max(), 100)\n",
"# grid_x, grid_z = np.mgrid[np.min(mid):np.max(mid), np.min(dz):np.max(dz)]\n",
"grid_x, grid_z = np.meshgrid(x,z)\n",
"\n",
"def vizDCtimeSeries(idatum, itime):\n",
"# idatum = 0\n",
" figsize(8,6)\n",
" fig = plt.figure()\n",
" ax1 = plt.subplot(211)\n",
" ax2 = plt.subplot(212) \n",
"# ax1.plot(mid, dz, '.')\n",
"\n",
" grid_rho = griddata(mid, dz, DATA[:,itime], grid_x, grid_z, interp='linear')\n",
" grid_rho = grid_rho.reshape(grid_x.shape)\n",
" vmin, vmax = 50, 200.\n",
" ax1.contourf(grid_x, grid_z, grid_rho, 100, vmin =vmin, vmax = vmax, clim=(vmin, vmax), cmap=\"jet\") \n",
" ax1.contourf(grid_x, grid_z, grid_rho, 100, vmin =vmin, vmax = vmax, clim=(vmin, vmax), cmap=\"jet\") \n",
" ax1.scatter(mid, dz, s=20, c = DATA[:,itime], edgecolor=\"None\", vmin =vmin, vmax = vmax, clim=(vmin, vmax))\n",
"# ax1.plot(grid_x.flatten(), grid_z.flatten(), 'k.')\n",
" ax1.plot(mid[idatum], dz[idatum], 'ro') \n",
" ax2.plot(DATA[idatum,:], 'k-', lw=2)\n",
" ax2.set_yscale('log')\n",
" ax2.set_ylim(vmin, vmax)\n",
" ax2_1 = ax2.twinx()\n",
" ax2_1.plot(height)\n",
" ax2_1.set_ylim(15, 21.)\n",
" ax2_1.plot(np.r_[itime, itime], np.r_[15, 21.], 'k--', lw=1)\n",
" ax1.text(0,0, fnames[itime])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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yOZ7U4zjdy+bGJK8SJlDk3ik6/jaDMgksgZ0DZfv6y85jpbDLRnKQd3ZBlazz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x86a6c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interact(vizDCtimeSeries, idatum=IntSlider(min=0, max=300, step=10, value=0), \n",
" itime=IntSlider(min=0, max=DATA.shape[1]-1, step=100, value=705))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def vizDCtimeSeriesVariation(idatum, itime):\n",
"# idatum = 0\n",
" figsize(8,6)\n",
" fig = plt.figure()\n",
" ax1 = plt.subplot(211)\n",
" ax2 = plt.subplot(212) \n",
"# ax1.plot(mid, dz, '.')\n",
" itime_ref = 101\n",
" DATA_ref = DATA[:,itime_ref]\n",
" grid_rho = griddata(mid, dz, DATA[:,itime]/DATA_ref, grid_x, grid_z, interp='linear')\n",
" grid_rho = grid_rho.reshape(grid_x.shape)\n",
" vmin, vmax = 0.9, 1.1\n",
" ax1.contourf(grid_x, grid_z, grid_rho, 100, vmin =vmin, vmax = vmax, clim=(vmin, vmax), cmap=\"jet\") \n",
" ax1.contourf(grid_x, grid_z, grid_rho, 100, vmin =vmin, vmax = vmax, clim=(vmin, vmax), cmap=\"jet\") \n",
" ax1.scatter(mid, dz, s=20, c = DATA[:,itime]/DATA_ref, edgecolor=\"None\", vmin =vmin, vmax = vmax, clim=(vmin, vmax))\n",
"# ax1.plot(grid_x.flatten(), grid_z.flatten(), 'k.')\n",
" ax1.plot(mid[idatum], dz[idatum], 'ro') \n",
" ax2.plot(DATA[idatum,:], 'k-', lw=2)\n",
" ax2.set_yscale('log')\n",
" vmin, vmax = 50., 200.\n",
" ax2.set_ylim(vmin, vmax)\n",
" ax2_1 = ax2.twinx()\n",
" ax2_1.plot(height)\n",
" ax2_1.set_ylim(15, 21.)\n",
" ax2_1.plot(np.r_[itime, itime], np.r_[15, 21.], 'k--', lw=1)\n",
" ax1.text(0,0, fnames[itime])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function __main__.vizDCtimeSeriesVariation>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
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nuz7GVh7RTzjzKFG8/PH8nmx+Gtpo7PqG7Mw+KRrwh6mU4iMRGctzs/iRTDZb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb27bd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interact(vizDCtimeSeriesVariation, idatum=IntSlider(min=0, max=300, step=1, value=0), \n",
" itime=IntSlider(min=0, max=DATA.shape[1]-1, step=4, value=0))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(380L, 1269L)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DATA.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = np.linspace(mid.min(), mid.max(), 100)\n",
"z = np.linspace(dz.min(), dz.max(), 40)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-18.0 -4.0\n",
"5.0 105.0\n"
]
}
],
"source": [
"print z.min(), z.max()\n",
"print x.min(), x.max()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101, 1.01010101,\n",
" 1.01010101, 1.01010101, 1.01010101, 1.01010101])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diff(x)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from SimPEG import Mesh\n",
"from scipy import interpolate"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hx = np.ones(110)*1.\n",
"hz = np.ones(40)*0.5\n",
"mesh2D = Mesh.TensorMesh([hx,hz], x0 = '0N')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"203"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(range(90,900,4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference data 20150126000000.apr\n",
"0\n",
"16\n",
"32\n",
"48\n",
"64\n",
"80\n",
"96\n",
"112\n",
"128\n",
"144\n",
"160\n",
"176\n",
"192\n",
"208\n",
"224\n",
"240\n",
"256\n",
"272\n",
"288\n",
"304\n",
"320\n",
"336\n",
"352\n",
"368\n",
"384\n",
"400\n",
"416\n",
"432\n",
"448\n",
"464\n",
"480\n",
"496\n",
"512\n",
"528\n",
"544\n",
"560\n",
"576\n",
"592\n",
"608\n",
"624\n",
"640\n",
"656\n",
"672\n",
"688\n",
"704\n",
"720\n",
"736\n",
"752\n",
"768\n",
"784\n",
"800\n",
"816\n",
"832\n",
"848\n",
"864\n",
"880\n",
"896\n",
"912\n",
"928\n",
"944\n",
"960\n",
"976\n",
"992\n",
"1008\n",
"1024\n",
"1040\n",
"1056\n",
"1072\n",
"1088\n",
"1104\n",
"1120\n",
"1136\n",
"1152\n",
"1168\n",
"1184\n"
]
}
],
"source": [
"timeind = range(0,1200,16)\n",
"hy = np.ones(len(timeind))\n",
"mesh = Mesh.TensorMesh([hx,hy,hz], x0 = '0N0')\n",
"itime_ref = 101\n",
"DATA_ref = DATA[:,itime_ref]\n",
"print \"reference data\", fnames[itime_ref]\n",
"# model = np.zeros((mesh2D.nC,len(timeind)))\n",
"# model_ratio = model.copy()\n",
"MODEL = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz))\n",
"MODEL_ratio = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz))\n",
"for i, itime in enumerate(timeind) :\n",
" print itime\n",
" F = interpolate.LinearNDInterpolator(np.c_[mid, dz], DATA[:,itime])\n",
" F_ratio = interpolate.LinearNDInterpolator(np.c_[mid, dz], abs(DATA[:,itime]-DATA_ref)/abs(DATA_ref))\n",
" MODEL[:,i,:] = F(mesh2D.gridCC).reshape((mesh.nCx, mesh.nCz), order=\"F\")\n",
" MODEL_ratio[:,i,:] = F_ratio(mesh2D.gridCC).reshape((mesh.nCx, mesh.nCz), order=\"F\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from SimPEG import Utils"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Model = Utils.mkvc(MODEL[:,:,:])\n",
"Model_ratio = Utils.mkvc(MODEL_ratio[:,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Model[np.isnan(Model)] = 1e-8\n",
"Model_ratio[np.isnan(Model_ratio)] = 1e-8"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# mesh.writeModelUBC(\"/Users/sgkang/Dropbox/dammodel.txt\", Model)\n",
"# mesh.writeModelUBC(\"/Users/sgkang/Dropbox/dammodel_ratio.txt\", Model_ratio)\n",
"# mesh.writeUBC(\"/Users/sgkang/Dropbox/dammodel_mesh.txt\")\n",
"mesh.writeModelUBC(\"dammodel.txt\", Model)\n",
"mesh.writeModelUBC(\"dammodel_ratio.txt\", Model_ratio)\n",
"mesh.writeUBC(\"dammodel_mesh.txt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x9bd5e10>]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Cp7yA8N1yi7T33lnXAmgtrrnXwDV3AHmW849QVMA1dwAAMCiEOwAAgSHca6C3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\nQGAIdwAAAkO4AwAQGMIdAIDA/D+7iQu11dNe5QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb286b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(Model)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<matplotlib.collections.QuadMesh at 0x9cd8ef0>,)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Pj7W/eOfl4xvca6+3cz7PgvqP6+yNjz+LtWj5emv7GHnb70u/ztmiQz5mWXEg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\nx9o7re3XxJ/397az20YXSvrsrdKBq77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"text/plain": [
"<matplotlib.figure.Figure at 0x9ed41d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mesh.plotSlice(Model, clim=(50., 200.))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dzu = np.unique(dz)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def profile_time(i_n, color):\n",
" figsize(6,3)\n",
" ind = np.argwhere(dz == dzu[::-1][i_n])\n",
" nskip = 5\n",
" for i in range(0,ind.size,nskip):\n",
" plt.semilogy(DATA[ind.flatten()[i],:], color)\n",
" plt.tight_layout()\n",
" plt.ylim(50, 200) \n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sungkeun\\Anaconda2\\lib\\site-packages\\matplotlib\\scale.py:93: RuntimeWarning: invalid value encountered in less_equal\n",
" mask = a <= 0.0\n"
]
},
{
"data": {
"image/png": 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nTiGXy/Hx8SExMZFNmzbh7u5eZPWV+HCJjIyk00ed2Mc+NLM0yGVyhn41VLdy\n9smTJ6ldu7bueCMjI3hhFKm1ar2ysoRIo9XoCVTOnJEFIT+BOnLkCDHRMWAhJjHI3p5+KBSvbumL\nCbHFjhfaWZQuvv+Cg4MD48ePZ9SoUaxfv54pU6Zw4sQJYmNjUavVuLi40KNHD5Yuff3S54WFIAis\nWrWKSZMmAdClSxdmzJjBV199lcsV6efnR2hoKAcOHMDDw4MhQ4bQunVrOnfuLFlWEoVGVFQUno6e\nBA0N0omCo6MjISEhqFQqSpYsqXe8sbExvAjM02j1LagsUdp5e6eeQHn4egDksqReRU6B0mq1LFu2\njJ9++gnbr22Jy4z7IMUJijjVUXbym0/w/5pCHjMyMjJi6NChJCQk8PTpU549e0ZMTAzTp09n/fr8\nlzMvbARBYMiQIcyZM0e3bffu3QwYMCDPH+xHH33EwIED2bt3L2fPnsXd3Z3x48czcOBA0tPfPNmr\nxP8Whw4donv37iQlJb3T60RGRlLSuSQ1nWvqbbe3t88lTiAKVIY8A0sjy1xpnrI8Ql/u+VJPoLIs\nrZweo5SMFDI1meRF9kwSoaGhdO/enZ07d3L8+HGMTXJnqfiQKBYC9b4nwL4NRRLF946xtrbGwcGB\nrl27Eh8fz7Fjx975NVUqFT169ODChQtcu3aN69evc+rUqQKf36RJE6ZPn86GDRvYtGmTOA/lPaLR\naqTx0vdIQEAA7du358iRI8yYMeO1x2u1WlJSUlCr1a89Nou0NHGNsYiIiDyFKD+MjIxQo8bCyCLX\nGFR2wcouUFmMOzoOZaZS99lqkVWuNcuykMvkJCUlMXXqVGrXrk3lypU5ceIE5cuX/59oO/8LxUKg\noPiPQb13CtHF9zrs7OwAaNu2rW5hxXfBvXv3cHZ2Jjw8HH9/f0qUKEH16tVp1qzZG5fl4+PDqFGj\nuHXrFmFhYe+gtrkJSwpD8a2C+afnv5frScCsWbPo3Lkz27Zt49ChQ6/sHPTt2xd3d3fs7e2xtrbm\n9u3bry1/xowZmJub06JFC/bu3UvTpk0LXDdjY2M0ggYThUkuCyr7558v/wyQy0J6nPhY73NIXEie\n1znmd4yKFSsSHR3N9evX+fbbbzE3Ny9wPf+XKTYOfEEQiu8YVFHwHgXKwMCADRs24ObmRq9evWja\ntClLly7Fw8Mj17HBwcGcPn0af39/du7cyYYNG+jX79VLnWdkZLBw4UIWLlzIlClTmD59ujjA/B+p\nWLEiI8dlqcSjAAAgAElEQVSMZMHCBSSnJKMVtKSr00nOSGbX7V0kpieSmplKQEQAvm19eZT4iJOP\nT5KhzsDezJ6nKU9Z3XF1ntmmcyIIAjNOzKCBSwMO3z+cK2PC/xJqrZp9d/ZhrDCmZemWLA9YTkpG\nCtObTcfCyILHiY+5Hn2d9hXaF/kcnNDQUHbu3EmVKlUwNzfHz8+PNm3a5DouPT2dvXv3Mm3aNI4f\nP46pqSne3t706NGDTp06IZfLadGiBTKZDIVCXMdr4cKFfPfdd4wdO5Y6deqwZ88eXWetIBgbG6MW\n1BgrjF8pUFkZK5Iycrsos1tA2TvpMTExLF+xHAwh9Ekox44do0aNGgWu24dCsRGo4k5RTtR9H2SJ\nzNWrV1m/fj1eXl7UrVuXKlWqUKFCBYKCgjh//jxxcXE0a9aMli1bYmFhwfXr1/MtUxAEDh06xKxZ\ns7CxsSEwMLBQ0jU9TnzMr5d/ZY9mD8wEbYIWo/l5C14FuwrUc6mHb4AvW65vAaCcbTmauDdh7529\njG04Nte6UJmZmZw7d45Dhw5x6dIlVBoVT5s/RWWl4uygs9T8vSZaQcv+u/sxkBtQ36U+JcyLNk9k\nQXia8pSZx2eyLmid3vZytuV4kPCARecW6ba5WrkyUD2Q73y+Y0idoknQDC/GhUqWRC6X8+WXX/LX\nX3/lKVDbt2+nZs2aTJ06lalTxZW479+/z7p16/j4448BcHV1JTw8HDc3N0xNTQkPD2f79u306PF2\nqwrb2tqiQoWJwiTfIInsjDkyRu+zTCbTa1MEQWD6X9M5cOwA9y/dp0ftHuAKgwYN+n8pTlCMBOp9\nrCXz1uQVxZeWBmZmeR//OpRKSEoCZ+cc1xEgLAxsbN67QGVRqlQppk+fTteuXdm9ezfHjx8nODiY\nhg0bsmrVKurWrauzfhwdHRkzZgxRUVF07tyZrl276n5wqampjB49mn/++Ycff/yRTp06YWDw9r3x\n5Ixklp5fyrab24hLi6NfzX782O5H9t3Zx5rANWz7bBs9/xIX6VvSegnfNPqGYw+OUdO5JqFJoTRa\n2whXK1fCxoWh0Wh0dTkfdl4nUElJSezbt49FixYRHByMra0tCdoE6A1EAMthbdJarG2tuRd7T7ey\nr7e7N2e+OvPW9/auiUuLY5LfJD1hqluqLr5tffFe783I+iP5uLzYiN9+dhutoKVrla7sDd7L5zs/\nJ0GZwGTvye+93kqlEqVSqbNqevToQdWqVWnWrJlephlBEPj+++9ZsWKF3vnly5dnwYIFLFiwgJSU\nFHbs2EHr1q05evQoKpWKTp064eaWO8deQYnKiELjqcFIZvRKCyqL2LTYV5YXHx/PwpCFCE4CdISV\n01ayfsH64t02vmMkgXobtFqoXx/GjIHBg9/s3LQ0GD4cNm6ExEQ4fBiaNYNSpeDiRWjcGD7+GDIz\noTAi1L7+GmrWhGG516x5FZUqVWL69OlMnz4932M6duxIcnIyCQkJTJo0ieXLl9O1a1dKlSrF4sWL\ncXV1fTkZ+y1Qa9Ws/nc135//nkeJj3CzcmNW81n0r9UfhVx8dbPWB7JMsKRntZ5su7mNST0mcbz6\ncbZv386Fcxe4f/8+C7wXcCvgFl9++SV//PEHAANXDuRU6in61ezH4ROHGT9mPBYKC6ZOmUr37t25\nEXcD7/XeTGw8kUl1J7G3xV6WLl1KRLUIJsZOhBc5jLOvHVSc0Gg1zDwxk4VnF1LWtqxuHacKdhW4\nPPgyAAmTE7A2ttZ1LCo6VNSd37lyZ073F1eRPfH4BGMbjs2VHuhtEISCeSMiIyNxdnbWHVu6dGn8\n/f1p1KgRY8aMoVmzZpw8eVK0ZFQqfHx88i3L0tKSgQMHAhTakj0pKnFWr4FgkDuKrwCBNDkDHKJj\norE0tiQZMXt8VsTf/1TbWMgUD4HSWSiFsHDt5s3QsaNohYAoJufOwRsMfgKiWNSvDwsWYIDo4lOk\np8OzZxAYCOPHw9ix0LMnWFiI5yiV4jlt2sCyZfrlbd8OvXuDJpsrIHvD/dFHYh3d3ODqVfE6P/wA\nP//89paUWg2//w7u7m8sUAXBGBgwQEzlPGzYMHbv3s3hw4eJioriq6++YtiwYQW2mhKUCVibWCND\nxtXIq2y8tpE/b/1JtRLV2PjZRuq71M9zrMjT3hOAXp16ce3aNbbd3IaxypgjR45gY2ODTCZDEAS8\nvLy4evWq3rlrf12LvKGci5EXCU8Ix/BLQ8KFcE5an6TfYtHlOaXJFOb7iEER/fr1o1+/fozdN5Y9\ne/fAixVQ5DK5nlVW1JwPO8+IQyNQZiqxNrHm8JeH8Snjg5GBETObz9Rbpj1r+Yf8yMpefvTBUY4+\nOPrKdYsKwrlz5/D29n7lEjIajYadO3eiVqtzRdU1bNiQ48ePs3XrVipWrMiIESM4e/YsderUee+p\nvLKeo0wre2UUX0HRarVYGFqQnCkJVBbFQ6AAQSsgB/JOGPIaSpWCQYNg3jzo2xd69BAFAWDXLhg3\nDp48gZyTOjMzxe2BgaLLzd4eunQBT08IeRlRYzkZHNLTqf/zLzDyF3Fj48bQsCEcOABZ7gYTE5g1\nC7p3FwWsVKkXNydAQgJMmyZaS+bm4nVcXeHKFfEYPz/xn4MDxL5wBdy4AVWrQrlyouB5eub/DCIi\nwNERsgcfrHmRPTou7i0eagEwMYGzZ6FJE8zMzOjTpw99+vTJfVxyMhw5At7eL5/Jxo3QpQvxikxG\nHhrJtpvbcLd2x0RhQtTzKEbWG4l/X3+qOlZ9ZcPTpXIXLn58kYZzGlKmTBk6d+nM7ujd+Pr6UrVq\nVXx8fChbtizm5uZcvnwZzYsOwsyZM/F74ofWTQuPYUmZJYwdO5a9d/bSY5c4JjGz2UxmN5+d65oN\nyjYgrF0YYXfF6MGr/16l0fJGXLp0Se+4v+/+zcXwi/Sv1Z/bz27TtlxbcbxC0OgswMIiMT2RVf+u\nwv+RP8ceHKN/rf58UfULfMr4YGjwMgVOdnEqCHKZnFP9T6EVtFgttCIgPIAGrg3eup6XL4uWW9my\nZbl69apeZgaABQsW6FntP/74Y64yWrZsScuWL5dfb9GixVvX57+ge67q3CKiFbTIkOUZnexp70lI\nXAgCgt55Go0GMwMzeBHslxX1JwlUUaJWI+PFF5pX5+zRIzA0FAXj6lVo3lxs2B0cRAvh+HGIjIRv\nv4XyL1YM3blTFKXHj8HfX3TFxceLYmJqKjas167B3Ll51ylLnJo1E4WFGzhld7cNGiRaWKamMHmy\nKCCPHsH58+DkBC1awKhRokDcvy+KS1AQzJghWnibNonlREWJ/7u5iWNPAAYG4OsLM2eKjT9AcLBY\np2vXRIswOBhUKrh8GUqWhE8+EZ9J3bric9i2TRTJ+/fF81NTITRUtKQKwqxZ4vNaufL1x4aEQJMm\n+e9PTwdr65efFyxAuHKZqGN72bqqPxPbwNeX4cCoFXQKGMfZAWdp4NLgjXrDDeo34NmzZ/z2229M\nnjwZmUzGuHEv55Q8fvw4V/7BH374gfErx3OMY6RfS2f0stEYGhjSrUo3etCDCY0mMK/lvDyvV9a2\nLKEpobrPMTExxFyO4UH8AxaeXcijxEcEhAdQ0rIkpgpTFp5dCEBdk7qEacJww43uht2ZOHFige8x\nJ/HKePwe+HH72W1C4kM4+fgk3u7etCnbhi2dt+Bg5lCoFoVcJmdsw7F029mNq0Ov4mDm8MZlXL16\nlTlz5uDn50dQUBBeXl48ePCAkJAQIiIiWLx4Mffu3WPq1Kl06dKFypUrF4tw6ujn0TiYOeSKaMxy\n46lUqjwF6vRXp2m6PrfnJmvirUar0UuRpNFq9N7TZhua6cr6/0rRClR8PFSpQoUuKuR5jbfs2wef\n5Ui5b2wMGS/yi5QoATEx4t+GhpAV7qzVitZNFjNnig15ftkqjIxEwXv6VPw8fLjoEqteXfz8CWhl\nEF2jOtwIFEUkPl4UhXnzRLceiNs1GujWDfbsEf9lp08f6N9frOeAAWBpCVk9yIwMuHUL6tQRXYcy\nmSjI9vai1bFwoSiIr+LKFXH8CkQRNDZ+aQ1+8YUoOLVri+7D5GTRevT0FK+dJe4gHpeQINazXj39\nawiCOI6W9X29yjoTBHiRyohu3cjcvZN5p2bzfQMN1FDQKNOJ5YcjGB0AHByNGqDsfUi4LI6dvUHy\nSwcHh3wncuaVHLdatWq08WnDseBjfNb6MzFtDWJk1b9D/qWyQ+V8r2VlbEVKRoreNjs7O/bd3cfa\nwLXMbj6bha0W4mnhSf+J/bnhIq5FdCVdtJZjwmK4svaKTqC0grbAGaujnkex8tJKfrz4IyYKEwZ7\nDaaFRwsmN5lMLedary/gPzCpySQ2X9+M81Jn5rSYw6Qmk/QsMkEQUKvVeSYtvXnzJmPGjKF79+60\nbt2a1q1bk5CQQPXq1UlLS6Nhw4bUr1+ftWvXvtFcpLdFK2hRa9UkpieSlJ7E/pD9LDq7iJnNZvI0\n5SlPnz/l+KPjWBpZEhwbTAW7CggILG69mC6VuwAvE8SqVKo8o/g8rHNP04CX6Y00gkbvPI1Wo9ep\nyJoXFRwbnM9NaEXPiVXeuz8Eilag7F8uZqbINtmyVGqq2GiOGweNGonBBMEvvqSuXaFlS7HxDwsT\nBerrr8X/a9aElBSxAf77b7HxNTaGHTvExjYrnU/PnqKVAaIVNWMGyOUvszdkvSQZGRAYiGyvKHYy\nZKIIAdjaimI0ciR8841o0XTpIjb8ZmaidffHH6KI9u0r1sHRETw8RAsuJ8bG4OUlWmFhYaKb8uxZ\n0YpLSoKAAFFIzc1fWHVA5coQHi7ec05KlhQtywcPxM9mZqL45UeWFTdhwsvy69cXrdHLl+G338R7\na9QILmRbpvrSJfG5PXsmCuicOfDLL+K1GjaEn34iwwC2xfnz1SwwFwQW3HVjbMupyGOeQY1QcIoW\nvy8QRRxg4kSoUgVOnxbH+HJ+N4WAtZlo2XXy6aS3PWfYOevWifdTU0yBY25kTmpmqm63kZER1p9b\nM/7YeJa3W87oBqNJSEiglU8rqlSpQvSwaP5N+pdP/vgEgAb1GhB2JAytoGXJuSVM9Z9KWduyNHJt\nxLpO6zgYcpCIlAgczBzoVLETt5/dJigqiD139nDonpg548xXZ/B29y60Z1EQrIytCBwaSKO1jZh5\nYibWxtaMajBKt3/KlCksWbKE2bNnM23aNObNm0fHjh2ZMWMGd+7coXz58nz33Xe64+fPn0+TJk2o\nX78+Dg5vbpG9CfHKeGr/XhufMj5cjbzK9ei8p0eMPjKaxm6NaerelJH1RmJhZIGThROWRpZsvLaR\nrju6sqrDKgbUHoDW90cwAqVKqWflxMTEoFQq9QbU5TK57piszkhOC0qr1SKT536/f73yKwtaLcg9\nXviuXPfFCFlRpW2RyWSCUL061KpFNYvNzK8yle4xC8k0gE9C4OAf2Q5etUpsCOfMARcX/YK0WrHR\nytlwZQ/TzswU/x43DmbPFq2lrGPU6tf21O2myKgcI6N3WnWGbb/2coezszh+VbKkaEn9+CMsWiRa\nftu2iWLSurUoav7+b/qAcm9bulQUEIAFC2DqVNF95+8vjlX5+orCplSKQRcZGS/FcOVKUeR//jnv\n6zk7Q3T0SyHYvVvsDGRhaCgKRrVqYt22bHm5z8REP+JQJiNNIXDdCYIdYOQnUC/GgOln5PiEZGKQ\n3yvXvLn4fZ7JEbI9ZowokBUqiC5dR8d8H9ubsPHYRvpf6E/UsCicSjjlfdCCBZA1JuLrC35+xDao\nTkXD34nPEIW8ukl1bqTfwMrYitiJsRgaGDJ37lzOnz/PwYMHUSgUPEx4SLkV5bA3tSdOGYfDOgds\nJthwP/4+3u7enA0V3bl2pnbEK+P1qlDOthz1XOpR27k2VR2r0rZ820Ifw3oTlJlKtt/czoC/B/Bx\n+Y852OsgII4rjRgxgik/T0FTWgMxYOVghZWnFWXLlKV32954lfTC094TS2PLd17PtMw0gqKCWH11\nNRuCNujta1e+HZ9V/IyvD34NwNNvnmIWFsWaa+sY3/OnfMs8+fgkLTeK419r/oJBn4Gh1pAeNXuw\n/rP1jF43mg1HN6CsrORTzaf8bfR3rjJqOtXkWvQ11n26jsXnFnM37i4AJpEmuFd0JyQ5d0aJZxOf\nocxUci7sHN2rdudB/AO2HfdlWdCvJBsJxA64g71bxVznFQUvApMKpSdZtAL14trVh8v4tvIUuj9b\nRKYBfHwPDj1qLLqj5s/Xj3YrAuymyKj0TEaftBoM2xb0cke1aqJrzM5OtM6uXBEFL7t70dFRHJ96\nU1+6tbUY4DF/vmi1lCwpWmtpaaKlUtAs3kZGokBnZ/Fi0X2Zni6WmyWqDRuKY2vffitalSqVKIrf\nfy9asa8gUw737CHVEJY2hv2V5SgNxB7jL5/8zNd1h4lBtRkZoiWmUomimJ4O9+6JHYdx40SRGjkS\nTpyAO3dEYcxefwsLUWw9PUXrNC1NdGFmF9MCcv7yeZpMbILKT4WhoSEqjYrnqufYmdrBuXOoJk/A\n6NxF8eAuXXQuW6UCbKeAygAEGVQzqcrN9FuEjwvHxcqFIwFH+GLIF+zZuAfBUkujMAGzp89QKgRM\na9Sh9bG++Cdc1tUjZWoKKRkplFlehgxNBilTU7AwssD/oT8qjYqPK7xw20ZHi5aqrW3ujloRsCFo\nA1/t+4o2ZdvQOLYxK/etZN738xh+aLjumPJ25bkffx8Paw9crVw5F3YOACdzJ+qWqktwbDCNXBsx\nyGsQFe0rolQrcbVy1bkOQ5NCKWlRUi/Q41VEP49m5+2dLD2/lCdJT3Tb25Zry5KPlqDRanCzdtON\noQUdWMONywfpM3ev+A7t2aOfB/PkSdEVfv48PHwIjRuT+kUXLHbk7U5VxCvoVrMb28K2Ya42J1Xx\nwtLOliUnr04IgInKhApaY26Y5M44YWpgglKjPwxipBHfQYDYibHYm9nnOq8o+CAFal6lSfSIXUKm\nATR5bMjZTRrRanqVW+o9YT9ZhmesjL45BeroUZgyRQxAWL8ePv1UtAAuXxYDNEJCRCvnFWKSqkol\nLTMNaxNr/QgrBwexgX6F60MQBFJUKZgZmuXfo65ViwPKa/zTqwH1zDxp6t0LuzreWBiJofFpmWkk\np8ZjXNIN2+zv/08/iYEeWcjloNUiAH9/UZs97Ty4pXzCgo8W8e2+8ZxNEecjOaRCBZkdayacoYpj\nFTLUGQVKJZQn58+LY2rJyWJn4Plz8PERn2tWAEnWOOSGDaKY7tv3corA1Kmi8KnVooWZw1IOCAig\nYcOGhD8IYsy6buw2vAdAvXC4VQLSXnwdVoaWlLJ2wV6lYGCzsXQ1rInt7nrIAI0caseZcN0qk4gJ\nEXTc2pHL0ZcxMjBChQqnFDBTg2kmTDgPHz0URbzSKFjVYRUlLUvSwbMDAEFRQSjkCqqVqJb7WRw9\nKk47yEroe/iwOJ75998vx0DfI8nJyZw6dQrfMz9yQnUSwVa/HQkaGkQF+wq55ohFJEfg/8gfhVxB\nUnoSIw+PzDMIwKukF0s/WorPJh++8/mOaU2nAS/H67SClojkCOxM7Vhz+XcM/I9Tyb4igzJ24m5b\nGhsTG2o61WRA7QE4Wzhjaphj/DYzU/xd9usnBi599hn89Ze4T6sV3dh9+750kecgwwBMXmS7qhcO\nl11f7rsU35U2NgcISf2KEpa/AWCoVZApF915ljJLUoQUzq2Brp0hKpuumKlevnf5YZkOKTlGCZTT\nlZgo8hg6KAI+SIGaW2kSPZ8tQaWAtvdg160qWFy9WSTZFARBIDE9EXMjc4wMjLCfLKNMPFSVu1Fn\nwARWXlrJl9W/ZHaL3CHIBSk78nkkxx4c40niE+acmgPAoNqDqORQiaolqhKTGoPfosEsXRxIioUR\nZW3LkpyRTGBkIJuubyIpPYmAiACepjzVlVvBrgILWi0gU5NJfRexwboRc4N93w9kg3vu3pq3uzfn\nw87rNQ6tSzbBGw8ig84we3MYmpJOhI3oTah3dWIzEgm4eYRATTihSaG0r9CebTfFcbwWpVsww3sa\nPmVbI1OrxbG5vMbZ3paAAIQzZ1CuWAZRUZhmipZLRpMGmD4KFweKC8KyZeJcMzs7cHYmcfhwZm/d\nxMbJcpKMxOfgkQBPbF+eMrviUFw963L72W3dpGBzI3P+uvOX7pgyCfBIBh5xHoR6hCIoxPfaOFMM\nrpl71Yrfqyp5Yi5agjXjDLlmn8nJ6I95XtqFmI8a81Xtr2D0aDHwJiuwZOtW8f3//HNxjPJVVKr0\n0g2aFcqfE6VStFyzR1U+eABlyogdkPxYsUKsV5cuhIeH88/ChRht3EivVNE6CK5SiXq9QklVi1nB\nh3gN4feOv7+6vi8QBIEMTQZaQcuu27uIS4vjm2Pf6B3zRbUvWNZmWZ5pmgBcTEoQkS4GS32a5My+\nH3IsChgYKHZorl0TraCAANHyzsLISBxfrF9fdIHv3Ss+c40G1feLCGrgwe70QB7HPyQjPoZbccEk\na9KIIw0NAkM8urLqyW5dcdWj4WYJePIDuI8Xt1mlQ/KLn0TlWDOCHdI4sFXB159qCbcU3z1jNWTk\n088M+hWsMiDzt59JcrFH6VaSMtalqbO6Ls/SnqGeqS7yvIlZfHgCNUzG3EoT6Rn7PSrFyy/T0siS\nP7r+Qbvy7ZAhIyIlgpOPT/I05SmuVq5UcayChZEFtia2GMgN2BO8B2MDY/rUzGMuTg5iUmMYc2QM\ngiCw8/ZOnC2cGVBrAOfCzpGuTudCuBgIkNVby96zMVWYopArGFZ3GKUsS+Fq5YqrlSv34u5Rw7kG\nNZxe5s3K6vHFpMYw4dgEtt3clmv1zSqOVbj9LP/MywbI0SC+xFbGViRnJFPfpT6uVq7Udq5Nebvy\nzD01l+eq5zxNeYqpwlRvEP/K7+D5KBkjAyP8H/kTmhRKYnoiTuZO1HSuibOFMy4/iC6jTyp8wqF7\nh6gZq+Cag1hPBzMHjA2M8Snjw+dVPuejsh9hamjK/rv7xTqUrJ270q9AEAQS0hMIiQshKT0JE4UJ\n1Z2qi661HN/RioAVHH90XPd95MQlGWachi8Oh2GcoSZlxxZmJ/2FraktvTw6Yjt2CpsrKKkRLY6H\nrfWCYEcoGw+f3AM7JcxrAX8ftaODZweEixc5qg5B+fUA+mfuJEWVgrbfI2RXrkBoKGnlPXC5PQgn\nCyfd2IGhGjKTAWtwVRqxY7uKuk/BUAubv+tO38wdWGHCnAdutJy1jtpbm2KSCVPOwJwXyQ/2tPyN\nzs2/BlNTMhLjRKvzRUCORgYG9g6i69XFBaysRJfr+PF59/B37hTdwg4OqGUyNIIgrl00ZgzGK1aQ\nNG8ewqhRYoYPmQy++w46dxaFKmfH4t9/xekLQLqBAcc1Gj4BYuvVw75dO2QGBuLYsJcX6X17Ed/l\nY0q5/bd8i1kdxP2bptMv6le9fUPrDMVEYcI3jb7BMl3gYe3S1B4yi8BV8ygfD6ZqMOraXRSikBDR\n+s5iwADRI1G9uijSWaLfpMlLF/zp06h8mrOzCmwe1ZSjT8Xx0N41euPl7IWRgRFlbMtQyaES5VaU\ny7v+2yqi6HGX1vb1OPrClWuhgucv2o9P78DflcAFSyLQD3DylDsSon2Wq8y7P4Fn9piICxfAxgbX\nQ62JSIkgblJcrt9PUfHBCNQP539g5eWVPEx4yFarMfRNWI7mRSdg42cbORBygJ23d+qdZyAzQCNo\nMJQbkqnNe4Gvmc1mMrPZTC6Gi+MHTdyaoEWLgcyA5IxkFp9bjO9FX5RqpW4ynaWRJSXMS2Bnaoex\nwhi1Ro2RgRG1nGux4tIKbJTgkWmNZdXqnA07q7tW1qB3duxN7UlXp6MRNKg0KmS8nGle2qY0jxMf\nU862HN2qdOPgvYPciLmR532c7rIf87YdqTNUdHn81eMv3KzdXpsq5mnKUx4nPiYpPYmS/UZQ68Kj\nl8Ek+fD1ga9Z2Gohtqa2LDm3hMn/iLnX3qZnphW0pGSkcCniEpefXubfyH8JCA8gOjUaQ7khAgLp\n6nQczBzQClrilfEYGxgzruE4bj27RYoqhbi0ON1zaezWmLbl2uJh7UHU8yhkMhmPEx+TnBLL1rs7\nX1Oblyg08OldcaxMK4OHtmCogVoW5Th1v6k4uTs9HSpWhLt3EQCX8XBqPVSIRxSHiAimdTRjTW2B\nZ/KX6/kggE+SHft/jscsE3Fsr3Rp+PRTYv9Yw2qHUKY1z6RekgVL9jyndT+wVYpuP7/yMo57CBhq\nReG8UwLcE8A5FZxT4EBFMHjxnn5zHhb9A7IvvhB7/D4+YvSlhQX06UNCbDiRlpBmCKUT4bBSxmC5\nHFsHByZFR5M1O6x1qVJMWLyYdn36iGO9gYGwerU4xw/EyNApU2D9ejSffsoMjQaTXbvoNXEiFUaN\nEqM+dfcuiJ8jIsSgkg4dRGstIUF0wVavXvAx02yErZiPe8JMPjerx+juy6jlXEsMroiNhbt3xeCg\n2S+8GGPHikK7fLloLVlaisLj6SmOE3t4gEJ0K6ar08XfpUxGTGoMpW1Ko5Ar0Gg1XIu+xoid/VEI\nMjrW/RKvkl7ULVUXd+vccwhHruvGz2G7aOrelDOhLwN7RsWWY731Q0ZU6sfiBxtynWdhYMpzjZK5\nNx1ZXvYZ8S+8oHWcapOuVXHr2a1c59wcFEhVlbU4FWbBAnixDpr5HCPSUKGdnomsmKwu/cEIVLcd\n3bgbd5cbUdfxtfiKMc/XgwzsUyF2iYDvRV9mn5zNgFoD8A3wBUTXlKHckBOPT+iV19ClIRcjLuos\njOx4lfTiZsxNDOWGepZFTgzlhlgYWejS42dty9Rmir1khVhWI9dGIMDPV35mqvdUWpRuwc3om9iY\n2LD43GLCU8JpV64dh+8fxs3ajeYezbkXd4+6peri7e5NYFQgf976k/vx92ng0oD2FdpT2bEyrcq0\nooPZ0uwAACAASURBVPOfnYlXxhP2MIgRLSbSo+8SPCs2Rnb6DMjE9Cf34u5xMfwiAREBxKTGYGtq\ni4ulC23LtaWMbRm0gpZLEZe4EX2DkSM34Xj7sTgOU8BUPDtv7aT7ru5cHHixwFkD1gWuY+DfA7Ey\nttKNjdVyrkUZmzI0cm1Em3JtcDR3JCUjRZwjYuOhNz5xPuw87f9oT+dKncnUZiKXyRlWdxh1StZ5\n5QD5mSdnWLzgExwb+rAhNHfEVPbw3vyY9MyTxQdV4nw5Kytx4nNYGPz7LxMT/kRjbcUPXX4XQ/U1\nGu6M70cd8636YwUCaOe+GAf/6itxYnZsrFjOokUI/v9w8t4/TGkjI9bejIfGL9/DSWehuuDIyMaJ\nJBlkYpEBz3N49EwUJlgaWRKbFssnpZrTO86FjD07iFdkcryKKZEWEGqk5FkesTimmaDQQooxLIzz\nwjgskqjnkTimgYdzLU5m3uC+tYYSlepQ26cXThmGlJu+lIpVm3Ov4+dM/GEZDnYOrF69Ov/lKGJi\nYMgQcQwQRLeZ6sUCfYaGorh37ChaW9nLEATxOd27JwbQxMdDYCCZcTGkrV+NzVTo99SRDYvuivMC\nV67UjRVpvZsg/7ybmFqsfHkwNORRwiPWBa7jxOMTxKTGcC/+HuaG5jiaOxKvjM/VNuTF6o6rGfh/\n3J13WFTX9vc/ZxpD7x0UBEEBFQuKvXexRRO7SYyJJpaUG5OYponmmmI0aqLGVDWWWGM3VuyKvSsi\nIFV6Zwoz5/1jywCC0dzy3vzyfR4fmZlT9tnnnNX2Wt/VfEKdRmBlSFKj1DBr1ww+Pjuf5l7NuZB5\nocZ2mgp476jEB11ly2fDA/3RpcCZw075/K6ewETDRjJNhehV0KbMmeKSfK7XQYx/4aULNevczpyB\nNm0ImSoMLnlKTo2ynf8l/jYKqvGSxuSU5ZBdls0s62HMKtsIEijM0DukL7vv7GbdU+t4JqI2Hb4s\ny1y+f5m04jQOJR7C086TQKdA1l9bz4brGxgYMpBnI58lrzyPF3e8yGvRr3Eh8wLedt580u0TEvIT\nsLeytyziu9u4427rjrXK2vIAVtYrSLMk1CaILnHlyAJBQ1SoK+SpX59i9+jdT5xh9GcQMVXJNTch\nWENy4LabyHy6X3rfsk23wG7Ya+wpNZay/+7+Oo/TNd2Kg9/qxctfRw+mzJJMPGw9ahSKXsq8ROTy\nSO5Ou0ugc02+NJPZxDdx37A3YS+N3RrTP6Q/m29sZvXl1XzW8zPC3cO5V3iPASEDsNX8ucxFk9n0\nxN5ambGMmLUxHEw8aPluStQUlsQtoZe6MWP6v4NKoaJrYFe87LzQV+gpNhSz4fI69LKRgG/WcDrj\nLPM6wtIjDkz66WpNr+ABYpNiee/QezXYygt1hTh9WjOzVGUC48cIQf0HafCyLPPZthns2PwFryrb\nUtoygnERo6FzZ86ln+PInf1YZ+TQPqw36dbCA+8Z1JP88nwqzBV8d/47Pj3+Kd0Cu5FalEqotR9N\nHRrin1jB1p9+49PkdDyV1lgXl5FhDxnP9Mdh3xGKDcV83Al2VWPLal5gywWnUhrkOGFPBWE4cCfc\ni7j752uN++VWL2OSTVy+f5kAJ5GEkFWahd6kJ7csFyulFW42bnwfOJ3z1gXsyTiKr40XmaWZxKdd\noby8mDMpJ3EzqHFw98WQm42pvJSAIgU5aiP3HSTszRqyVHrS6ig8ffYC+ButORmoRhkUzN6iqjE2\n92pOoHMgx+4dI6tUrEUFOAUQ5ByEl50XTT2b4mbjhqu1K242bpQaS/Gw9cDX3he9Sc/2W9vpWL8j\n1ipryoxlNPFswq2cW5gPHeTuFzNx7xbDVt9ifknYyr1qtz2y0JqLjuXYm1QUK2t38LUySegf1FRU\nri8pzeBeCpnVsuztjBIlapmOmVZkqfTcqiMvKm5iHK18WtX80mym/z+bMFnfhAEfravjafvf4G+j\noCrPLc2SeNd6MHPLt1pSMYeHDeftDm/XLpp8AjycOXY3/y4NnBv862OtQ0H9t6Fztke6k8DJVp50\nfRZ61e/GuBbP42DlQKBzYC2OuuvZ1/Gx90EpKSkxlJBTloNCUhCxNIIm98GhRVsW9/8af0d/5p+Y\nT3xePJtuVC3svtTyJZYNEBlHZtlMq29bcWLCCUtmUFpRGm/8/gaHkg7hYu1CI7dGlkQBG7UNVydf\nJdA5kPzyfJILkzl27xjt/Nv9S/fvj3Cv8B63cm7R55c+BDoFsmv0Ljb+cywv13sKp2kzkLt1RXr3\nPeje/Y8PpNdz7JdP6JjyEbvXq+lzVVdnosD17OsMXT+Um1NuWr6TZRnFR2LbBbvhtb5CAOneLBQe\n2BPAycmJxMREnJ2dH7/xQ2ixvAXLBywnylewfJw/f56+ffvy6aefMnbMGJTXrgkl6eICWi155Xns\nubSJCxd2syphC41ywKNMrL+Vq2DSWSjUQrt7ChwMZq65g40RPn+5PzkRNmQbskkrSsNKZYWhwoBC\noSDCPYIyYxl38u5QZiwjtTi1xhitlFbYaeyw09hRpC8iX5ePldIKvUmwwNgbldipbchV6DCaKyyc\ndZUeeIRHBEkFSWSUiIQHO40d0X7RtPZpTbBLMD72PjR2b8yMfTNw1jqz7to6not8Dl97X15v+/q/\nRfM058gc3j/05A0ph953ZbNnzTC/SqGi9J1SrOYKOeTv4E9KUQqdnCMxlhRx0ni31nHa3YPMEB/u\n6tJr/Xbi+RO09W9b6/u+v/Rlauup9GvY74nH+9/Gf1JB/TWClkD1IIy6An4d/uu/fKyH05r/HeX0\nv4IkyyiVKjxKQZ4F/P42NO35yO3D3KsWpu2t7PG2FyzQ27fZEetawumWClp8W6UsJrWcxJe9vqRv\nw76kFKbQa3UvXmjxAq18WnE2/SzHnz+OVqUlpyyHN35/g9WXVxMTEsOmpzfR3r89kiSh+VhDa9/W\nLO2/lKVnl3Ix8yL77u6rMa62fm3ZOmLrf6ShnyzL1F8o6GMmt5rMN/0Fce9MtyGQIhaWpaxsse7x\nOFhZ4WgvPJ2GdvUfmcXmbuNOWnFaDaNHkiRuDNqHpltPgqr3oLN/8uJTtVqN8eH6tCdEfaf6XMm6\nQpRvFJcuXaJfv34sX76cwZW0YA8YLwD2Jexj5KaR5Jbn4u/gz5Aukwi28eUfR9+nvXUoLik5tIus\nFK41Q6FK807cE53JNObTMh3O+YgEoYbOwey/uw8Hs5op9YYT7B1Op9bDeWXvNLbf2k5ZRRlWKiv8\nHPwY0mgIjlpH+gb3JdQtlIyidCSjES/XmjRAsixjNBtrlFoUlBfg/Jkz45uN5+dLP7P/7n5mdZ5F\nhEcEjlqRibhumPAclg6omUzxr2LDtQ28f+h9Zh6BvtmOHPxkIu6eDejk1YYZR9/noy4f0cK7BTIy\nb+6YzpfnlxA65AU48WmN41SYK2pEViqjCc4e9bHzs+PklSoF5aJ1IU+XR6kGjKq65fqj1ttlWa6V\ndPV3wl9GQf2vPLk/h/9eynuhrhA7jR1KhRJZlukwspyzX7nCK+L3/nGv8VK9f+KkdaJAV0BMaIwl\nHv5H9Q8DbkPfUlBu3kOuQk98XnytdZ1gF8HDF7UiylJY+WHnDynWF7Pu2jpaebfi+suCbqeZZzOG\n/jqU+o71MZqNHE85TtSKKLoEiHbab7Z7k2Fhw4jyiSKtOI2YtTG0+74d11+5XieTdvXrfhx2xu/E\n38Gfi5Mu1sxYCggQRdLl5WLN5wmb0Gk1Yg3s1fZFNDv4Hm+0fQNn65oejYu1CyWGElp824JrL1ct\nXjdq1p3Ww6q20yvhn8fmUd+pPj72Ppb5CXYJrpNnT6VVcTL1JF0du+Jg9efI1Pwd/JmwbQKOiY68\nOPHFmsrpAc5nnGfd1XV8fuJz3mj7BvN6zKtRKzet81uWZ6DMWMbp1NPk5eZRXpzKEJd2mH/5BfWl\nK2h/P0ipGmyNlbWm5UD1pJ5KxTCFdUeOYBiyEr2uFPvENJERaG8vCJQfwNuh7hR4SZJqPR9O1iKe\nFuoaiukDE78n/E6HHwW9U/WQV5mx7D/Sk0uWZSbtnERsbAM6HboLWfF0qBaurWTMAEF71qxeFJwH\nrzquSa1Q11AcleOrMFfU4u3TqrWgg0teQEntkglXa1c239jMxusbKdIXMb3NdA4nHWbTjU0cTznO\n6dTT3Jp66/9EV+c/i79MiO9tq4HM028DSXhQho//OgpLmiWhNkPbIjdiF9ZOAf1P4OkNT9fIWGyY\nC4v7LsZp4lT2BsMv3UX6qUapoZlnM8oryrmadRUvOy82P725lvsvyzIyMi8PVrO8hZlnQoYyue00\nig3FrL68mgiPCDbd2ESpoZQglyBkWebA3QOYMVv2fRLYaey48coN/Bz86vw9pzSHgesGYpbNTGs9\njXMZ50gsSCSlKIUSQwm3c2+jUqiwUlqhUqjQqrQ0cG5ApFckkV6RaJQaZh6YSROPJuxJ2MOG4RsY\nFDqI02mn8bH3IbMkk8Yl1jh36we//sqFjyaTs2wBIa4h+Dv6cynzEp52nvjY1xYiR7Ys5LnDrzHZ\n3IJzXUJYd3Udbf3acjb9LG+1f4uugV2p51iPhosbAnBo/CGWxi3lRMoJFvdbzFv73uJ2XhUtTWvf\n1pQaSlEqlCQXJFuMDYPJQHPv5iTmJ+Jl58W0NtN45fNXMDYyojfp6RvclwEhA7iUeYl8XT6Hkw5j\npbJiSKMhdAnoQqGuECuVFd523miUGmzUNrT+tjUuv7nww8c/0L9//xrXdTr1NNHfCzaTASED2D5y\n+xPdy0ciI0MkPJw8KZROfLzICr1wQWTJXb5clRzxMJRKUc+1dm1VIeyfgDRbYlyzcfw8+GcA4nPj\n6bZSrL8NbTyUvXf2Umosxfi+EaWkREZ+YuLdh3H35kk6/9SZlE+NIj39MR7x9lvbeWXXK6QUpdT6\n7eFWGz52PqSXpKNRaixEtU8KlaTCQetAXnkeLtYuqBVqvOy86FCvAwcSD/B2+7cZHzn+yS/0v4y/\n5RrUW5oYPjVs/+sqKBO0Lf7vKaiQxSH0De6Lh60HV7Ku8M249bjMmS9qXQC+/BLjtCmolWo+PPQh\nHx35yOI56Sp0RPtFM7PDTO4V3mPRmUUYTUbMspnkwmTW7tAycsCju/O29WtLQn4CpyacItA5kJ8u\n/MRz254D4Ks+X2EwiXYClYrkxZYvYpbNaOdqLW3UK1FqKOVi5kVOpJwgsySTvQl7uZZ9DaWkxMHK\ngZjQGDr4d8DTzhN/B3/C3MOQkUktSkWj1JBXnkdqUSr77+7nfMb5Gum79hp7S0ZmbnkuKoWKCnMF\nbjZufLpdx89ttByxy8HX3hddhY7c8lwcrRwp1BcS7h5Oga6AMU3HkFeeRxvfNnz4+zv4JWRzSvMy\nfP01yQXJnEk7w7Jzyyy0R/cK75FXnmcpb6g8ZyWej3zeUjz6cDM/WZZJKUrh+L3jFBuKeWnHS/QO\n6s2JlBMUG4pZG7WW0Oah7Izfyc74nVzIuIBaoWbj0xtx1Dqy/+5+DicdxtnaGaWkJL04nXxdPuW6\nchLyE9CqtJgVZjxsPPC296aVTyt6B/dm8o7JzOw4kymtp/x7D+WfgcEAO3cKTsrMTMEAs3OnIECu\nbBTZs6eozwoMFATIlQklXl6i0PjsWbF2WK8eKJUYTUY0czR1zu21rGtELK1i3FjafylLziyhY72O\nfxzuMxhEevqJE9C+PbnpCbxpe4If3Cdw5NbvzDz7Kcc22Nesn3oEVl1axfPbnufznp/z2t7Xavzm\nau3Kj4N+ZOC6gQB0rt+Z2ORYOvh3YEXMCsZsGcO5jHMAlmf0cRjSaAjNvZrzfueq9bH2P7THz96P\npQOW/i3roP4yIT6zbPpvRtD+bcgSf5rV4nbubW7n3sbbzptmXs3+kOAzrzyP0U1G42nnyfpr6xk+\nfD0/yQX4h4dbmNMrQzKzu87mwy4fopAUZJdmU6Ar4Ldbv1lehko4WjmSOV/CU21D9wkXKbZVE58b\nj6+DL6WGUgr1hTT1bIqXnRcgmt4BDG48mOe2PUfmG5l42lWFZ2RZpsRQwuGkw5bar8ySTNp+3xY3\nGzfOpJ3BLJvJKcthbNOx2Kht+LDzh7Sv1x6z2Uy9hfUIdw9nYsuJta6/MsxYz7EekV6RdKjXgQ8P\nfYirjSvfD/xeELGW5XAp8xIBTgGEuoUiyzJL4pYwbfc0fo10JuZEDqt+OE290NaYzCZik2Np7dua\nnLIc0ovTqTBXsDt+N9eyr3Ey9SQtHEJ5Z082jBEhtvpO9anvVJ/h4cNrjK1YX4zDPLHNsv7LyNfl\n8+Y+0SqjLmaDSkiSRD3HetRrImpoXmwpWo1XmCuY8PoEEi8lMqLfCJp7N+e9Tu8hzZbQm/SW64v2\ni+a9TjVbiOh0OqKioggeHMwd+Q6YILU4ldTiVFr7tmbo+qEgw8wDM1l3dR1lxjKifKJo7t0cjVKD\nn4MfQc5BBLnUXWT6L0OjEcW+Q4bU/q2kRPBRnjolvK0vvqjJ5FAXwsIo/eSDqs+BgYI+7NVXoVMn\nwtesIeHlTWxU3sTNxp0JO8Xc2pYaMVydi2bGO6KWKyVFNAJ9/XXw80NOTaVCAfftIGEV9BstCvB/\nuboGgwp8nbDUGD0O2aXZ2Kht6gyxa5QaYkJjLJ+7BHQhNjkWSZJo5N6Ihq4NLQrKw9bjsQoq3D2c\n2ORYttzcwu2828zqPIuE/ASuZV3jRMoJ1jy15g/3/7+Kv4SC+ivppcySTGzUNpQZyywFt5VI15Tz\n7oF3Ka8oZ2f8TjrV64SMTFx6HK7WrthqbJGQaOndkpTCFOLz4zmSfMSy//hm46nvWJ8CXQEL+yxE\nkiQyijOITYoltzyXNt9X1RwFO0K9ko9hOAzN8yK1bBGOq34nPi9eFBv3+JSGLg2xUllZig2ntp6K\nq7UrE1pMIC4tjusZl/m8w8eElxjwyDhHk5AORHpFcq/wHoHOgSTkJXA69TQGk4F9d/ex4vwKJCSG\nNh7KlNZT8JrvhaetJ6XGUhysHCzUSgFOATTxEL2yHK0ceb/T+2SXZtPKuxXPRDxDiGtInWGWDcM3\nMGX3FGa0n/HI+ZdlmWP3jlmatd2ZesdiGXrZeeEV7GXZVpIkS1+mPRnd4cRGCBU0T0qFkm6BgqbB\nTmNHgFMAAJ3qd6o62fnzkNrysaGcSuZtT1tPRjYZiYRkUVD/ClQKFVPHTKVdu3ZkZ2czf/78Glln\n17KvEepWm5nabDYTExNDWFgYYd3DLDRZ73d6n4+PfMwvV37BLJu5M/UOU3dP5ZnwZ2jg3IDY5FjO\nZ5xHb9KTVpTG1ayrSJKEj70P90vuY29lj1al5cC4A5a5LjOWEZ8bT7GhGF9731rlBn8KdnaiWLey\nv9qiRVWErOXlQvHcuiXqpbKyBEHr6tWUzngNRj04hs2DNaaFC8U/oMHGjVQ+SYdH27CqYRlnym5j\nxXvcdn+PoHxIdoTAAsi2gR1uqezurGFDQ0OtIVbWKDUq0ojOz4+BvkLPuYxzWCmtLJ1vq+PhRK3K\naFFlsoOVsur3ugxXrUprkT1NPZvyevTrjI8cT3xuPO1/aM/qy1XdBIY2GvqXoTn6T+MvoaAEniyk\ndy79HKXGUkJdQ3G1ceV27m2slFZ423uTWpTKpcxLnM84z96EvfQK6oWDlQPWKmtik2Np7tWcb89/\nS9/gvjTzbMaPF3/Ey84LJ61oexDoHMiv16qyBzVKDQaTASRBN3PHpoSvTn/FqCajKNGXsPLyShq7\nNa7VW2b77Zox//qO9bFWWfPzpZ+RkPCy82LRmUUEOQeRkF9FVZM3Iw97K3uu3L9CQIMWbJw1nKxT\nB5jd9D5WsppFEbMxySYuZFxg4vaaXkjPBj3RVeiIz4tn0ZlFFrbk52zV/OSv58iu0bCrduGqjdrG\nEjrbP3Y/yYXJrLy0kk03NuFg5cD0NtPxdfDFy86LBs4N8Hfwr8pmmy2hUqieOMV1UKNBDNswjJ8u\n/sSzkc/W+E2WZTr/1Jmj945iq7YlxDWEPaP3PFYw6itE2rKpW1eMWzey+8YWhjSuw4qvC5XksU+Q\nfXf0OUF+W7nYvX7Yet7a/xZJBUlPdq6H0KpVK1JTU+nevTsrVqxAVY0FoFLgpRWJBXNfB1/Kysro\n0KEDNjY2rF69GrVaTbRfNMWGYoaFDUOr0vLuwXdZOXglQS5B7Bpd5QV0rF+zAWCFuYLMkkzSi9Nx\nt3Gn1FjK4tOLGbp+KBuGb+C7898x86AgZ3XWOqOr0NGjQQ9u5tzk1AunuFd4DzuNHSmFKcSlxzG1\n9dTaZKx/hOrtcWxtBZFveHjV7xMmwKpVlGTdgKUiO9V61F3K35VFiK60VHhHxcUcnD+F5iNeI/nU\nqyAnWg4RMg1szUpKFSbGqFuy2njOkqHasV5HJraYyLCwYQR+Fcj90vsihFhWVtUM9Q+QXpzO4HWD\niUuPw0ZtU2eG3cMJH5XrUZXddKt7XdWV2bcDvmV85HiCFwVTXlFOTlkO9hp7Vl5eyZimY2jo2pA7\n0+5wJPkIVkorJm6fyOimox/LLvN/Ff9TBWUwGTiTdgYZqKjW7bZCAasvryalMIVTaado5tmMlZdW\nUqgvpFhfbKENqkTl+gBU8doFOgXy6fFPcdY642ztTL/gfly6f4nBoYMJdQvlVNoplAol+bp8ApwC\nSMhPoHP9zpx+4TQAjdwaoVVpMZqM2H1iBxI4G9TkU8qK8yto5dOKBX0WEJsUS0xIjCVU1qFeB8qN\n5aiUKg4lHkIhKUgrTuO538SazostX2T5ueVYq6wtykklqXir/VuWDLLm3s1BBxOt2sGFM7xr00c0\nJ2z+nOWaF/ZZiEJSkFOWg6PWsVaYQZZl0pIu4xfVHTQa0mK34xoYjpXSCoPJgN6kx05jV6enM7jR\nYNp9346zL561sJ4/Cg/fiz+CSqFiVJNRLDq9qJaC2npzK/dL7/Nex/eYc3QOPRv0fCKrvTI0kjv2\nKb4KuMsnvw5lepvpzOoyy9Lg7ZEvr1pNmj3MMmxk7Sfv0NKnJdYqa8LcwyjQFaBVaWnm2YxRTUbV\nag74dPjTPB3+NJqPNTUElMls4nr2dcLcw6gwV/Dbrd8o1hcT4hpCO/92NSxdDw8PVq1aRf/+/QkP\nD8euiR0lDiWM2DQCGZkpu6agUqhImZ5Cz549iYiI4Oeff7ZcSzv/dthp7HCa58TB8QfRqrQMbjSY\n27m3cdY6425bd8GwSqGy8EdW4pv+3zB+63g8vvCgiUcT1g9bT7fAbrhYu/DlyS8p1hez/fZ2XD9z\nxd/BH0mSLGHTt/a/xSfdPuGdju889n7VhfzyfOyt7Gt5EqWmKiopXYWOnLIc0SZDo8Hk6MCsw7OY\nU/8w37qO4sgD5TQ4dDDjmo1jyZklxCbHggyrjefoFtCNg0miqPvovaM8E/4MNp9UZf6tOLdChJ5t\namYDZpdmczv3NsmFycw/Od/CSONi7UKvBr1QK9WikelDqKWgZJkrk6/gai3YHqq/r87aqszRkU1G\nolFq+HX4r0R6RWI915oKcwXHE4+z7NwyXLQuGM1GNl7faDGEx20ZR+L0xEfe7//L+J8mSShnVymW\n56UO/CBXcdxVFvUpJSURHhFoVVoGhQ7iqbCnaODcAAnBx+Zg5WCpDi/SF1FmLLNkXQU4BWCvsSfK\nJ4qbuTc5kXICK6UVgc6BvBL1ChnFGXjYejA9enqt8e2/u5+z6Wdp4NSAZzY+YLKQQKvUMrLJSCa1\nmmRhDX9SVBeUBpOB3+/8jpO1EwoUuNq4EuIagnT5MnJREdl9O+Hx4muiE2+vXoJLrJJ37EmRni6I\nPhUKwZP2J+iOngTSbAknrRP5b+U/fuMHuHnvPAO3PM03McswlBbR160tJeUFhP/Wm2X9l5Fbnkv7\neu1xtXa11LpUR+Xzmq/L51z6OUZvHk12WTZ9gwXzSCXe7/Q+EhKX7l/CRm3Dnjt7mNhiIs29mzMo\ndBClxlK2Hf2O1w+/g4O9G6NaT+Ba9jWCnINYfm45ugodPRv05Gz6WfJ1+ThpnYj0isTbzhtnrTPz\neszjbPpZuq3sZjnnbyN+Y9SmUcjI2KptMZqNeNh6WFp3S0h0b9CdaN9olsQtYcfIHbSv196yf5dl\nXTiSeATZRmZii4msOL8ChaSgx/EeqNVqtm/fzs2cm4R9E4buXR3auVoivSK5mHmRkxNOEu0XjTRb\nPF+d6nci9tnYOufvXMa5WqwEh5MO08i1EfsT9zOqyagahsvUXVMxmo3kl+fz6/VfKZ1ZavEkZVlm\nzZU1jNkyhp0jd9K9QXeUCiVm2YxGqaHcWM6ZtDNsuL6BBb0X1GJdyS7NxuMLDxb0XsCr0a/W+O1I\n8hFi1sZY6ImOPHvEkrZ/POU4T/0qeoAt7ruYqbun8u+gY72OfNrjU0s27Pqr65l5cCZ382sX1FZP\nlGnk1ojZXWZzO/d2jeJea5U1r7d9nblHRQfhkREjLetE90vuM3jdYE6lCa5QX3tf0orT8LL1Iso3\nqlYEphJapRZfB1+0Ki1DGw0lyjeK4RuGc2vKLeo71d1e/n+Bv00W363sW2hUGgIXBjJOastKajJW\nDwodJDJXvJvjpHXC1963hgVaYa7gVs4twj3C0VXo+PDQh6w4v4Io3yi+7ve1ZeG9EvOOzeOdA7Wt\nvO9ivqNHgx5cz77Ophub2HZzG9nl2ThoHCgyPMjmqdZwDGBa62m81vY1Sg2lhHuI8ES5sRxrtTVL\n45bSyK0RXQO71jiPWTajq9BxLesavyf8zuIzi2tQFwHs+AVW9/FhnWs6azbCyCwP0SzPw0O0p/8z\nSEqCLl1ESnBKigiPPKZ78J+BNFtCISkwffBoL+pc+jlKM++xcuN7JBTfQ1FYQmwAFlJggKBcQd6a\nWC0JyV3pwLCGg+jg147QwChe3fuqpevso9DQpSHxefF1/jYodBC/3aqdCj3zCMyasQt1776WFRM4\nhwAAIABJREFU7yoJftVKNbIscyHzAvHXjxFXkUwDlyDi0uNIKkjicNLhGsdysXZh+YDlDG40mLv5\ndynQFdDat7WFEHfOkTlYq6yZd3weIOp79o7Zy8pLK7mVe4vj945TYawgtawmK8Pzqc+zfOlyDiUf\notfqXnVe39ZntjKo0SCLgmru1ZzzL9WkLErISyB4cdU7Yf7AbDGYpNkSMzvMZG73uTX2OZp8lE4/\ndUJC4oPOHzA7djavR7/O/N7zLdvoK/R0+7kbJ1JPYKO2YUjoELbc2sLr0a8z5+gcy3anJpwi+vto\njj9/nCYeTXht72t8f+F7QKxlLuq7iLFNx1rGtPLSSl7b+5olXP0oXsUO/h1qEDhXxwvNX+C7C99Z\nPjf3ao6uQodKoeLjbh9TbiwnvTidN34X2bLedt7oTXryyvOI9IqkY72ONHJrxIiIEZxNO8uzvz3L\nthHbcLd1Z8mZJXxx8gs8bD0sFEuV6FK/C/6O/my4vgFdhQ47jR1qhdrC89nYrTE3cm4AIgHiWvY1\n2vm3I8wtjIktJ+Jt542MKEyf0W4Gp9JOWdazU19LZeSmkZYM1/TX0y2F+X8F/G0UFLOqPg8hki08\naAYow54xe+jzS58a+7zc6mWW9FuCJEmkFqWy4twK/nlMMHBnl2Zb4rzvdHiHdv7tkGWZNn5tahSw\nGUwG1Ao1BboCdsbvZOyWmq05NAoNBnPtRVRkUAJWGpFA8ShMajmJZeeWYau25YUWL3Ay9SRn0s48\ncvtGbo0oN5bjbO3MxcyLNUgleybADLkdPTzbImutqJg9C5VCRWpRKlqVFlcbV/QVekv8P7MkE5VC\nZekWyu3b0L+/aM6WnCwWpP+DfZpsP7KiTDYgfyhjNImwQ5mxjJyyHC5kXmDLjc0YqoW/1GYJkwRm\nZJxlDSiU5MvlIIGjbEV4uT2qrBySnUGnEP1v4uvgJetUvxMjI0aSUphCu9KR3HhpGuFH3rQkQCQW\nJIr7e3sn847PQ0LC+L6R3Xd2czHzIoeTDtPYrTHPNhtHS7/WcOSI6BPFAw9NlpEyM2H7drh+XSzq\nV2LxYvQvvYDTZ2JtppXBnYvKbCqUQkEdGHeAZp7NHrseYJbNjN48mnVX1/F0+NM0cm3EwtMLkZBq\nZXR1C+hGh3odWHNlDXfy79R5vLfbv023wG41FNhXfb5iWptpyLLM/rv7+Tru6xpK+rMen5FSlEJC\nXgK77og1qy4BXSy8enqTnqT8JM5mnLXsE+UTRVx6HPN7zifAOYDcslzUSjULTi7gcpZYi60ecn+r\n/VtUmCv44cIPNUiYHwW1Qs2sLrMIcApg7tG5lBpKSS5Mxsfex5KkU1ms/jAqOwVUojp3ZSPXRmhU\nGq5mXX0sefCy/svoGtiVENcq4sJlZ5fxTdw3jI8cz5IzS0gqSMLPwY83273JlNZTOJly0lJEDJAw\nLcHCYDP78GyGNBqCSTYR4RGB0Wzknf3vsOiMeK7e7fguc4/OrWEwVEKaLfFRl4/47MRnlBhK6hxv\n9pvZVe/8XwB/GwWVX57PktNLeP/Q+wyWmrGVS+JHGeRZoiVDiaGEcmM522+LorhJLSeRWpTKjvgd\nlmNplVqi/aOp71AfXYWO9dfX1zjX2qfWsvjMYrJLs4nPi8fByoH6joIuRqvUonuolXI7v3bYW9lj\nNBuxVlmzM36nxYNys3GjW2A3CnWF7E3Yy7qn1nE16ypXs65Sbixn7929gKjLmLxzMs+EP4OvvS/e\n9t50CejC9ezr/HL5F9RKNb+N+K2GR3hp90+0PvmcpY1zp0Q4Egj3VG/S3ricFKmoRpuR6oKgetih\nrV9bWvu2JiXlOs7HzrDimAvS3USxuGzz5yvui/XFfH7ic/bd3Ud+eT7ZZdkYdGWUmHUgCcPhZOpJ\nC6OzAon6emsSrcpYc9CFej2eInrChyh9fIVg3jQatVLNyiErWXtlLTP2z2B+r/kkFyRjq9SSVppJ\nSn4StzKukJKfTG5FESqVFU52rnjZeRHgFEBeeR5OWidKbqQQaz7He91nYzQZcUjLQZ9wC8nBAUNE\nGKnl91Er1cQmxxLsGIjLnljKDWUYG4eQ4+tM13WnsevRj1P6BG6Wp3BfqcNB1iDpdGTaCXJPo1qB\npFBS32xPSHIJ/Qrc6TFpCddun6HvjjPYRB+o4RF2s4kgzKYe2WU5NA5oxZiOr5D57Ze0z7eHn38W\ntUKdOhHnqmfzjc142Io6pnf2v0NGSYaFr64S1a36R8FeY0+xobjW9yEuIRToCiirKHukgKuOYOdg\nJrSYwKnUUxZlVtc7UglrlTWSJFmMturPJFCrLU6oayiZJZkU6gtRoGB4+HDOpp+lWF9MVllWreM7\naZ2IHR9LE88mfHnqS97Z/w5t/NrQO6g3vYN6M27LOG7m3qSRayMK9AW8Hv06fRv2JcIjArNsZu2V\ntdwvvU+Iawgxa2P4ut/XaJQazqafZVKrSRTrizmVeopXWr9CxDcRJBYkEj813tJeQ1+hZ+GphXxw\n+INaYwMsoVVZlrmVe4sZ+2bgYevBipgVf2ikvLDtBYv3uH3kdmLWxtSq9YIqBVV5/qGNh7L5xmYA\negX14veE3yl8u/BPs5H8N/G3UVBjN49l1eVVAPSmMXsRLi8yxITGcPTeUTxtPbFR29Sis/8jtPNv\nxxtt3yDSK5KJ2yeSWpjK+Gbj8XXwJcIjAl8HX+7k3WHH7R0sPr2Y0U1H827Hdxm3dRxf9vqSlj41\n28xLs8Rc25iUlH4slIDRZCTgqwAyijPqZF3oFtiNrNIs8srzMJgMeNt5k1SQhN6kx2AysPnpzbWz\nzbZuxfTpPzlddIM+A4tpdh9OBihoZ/LlonSf+TFL6OMWzbVbR7jtYOJK1hW23tpKTlkOgxsN5vL9\ny9zNv0tTj6YWaxaga4aW+0odHTqNpri8kLXJO+juFkWBoYiKwgJ0Jj06rYoJipZ0PJdNmrMK12Hj\ncGwUKdjhr/2Ku60HPg6+VOjKuHv5CFm20OMupDtKXPJVgdnEyHgttsU6dJKZSIeGDHp2Hn7dBtfi\nuTucdJgPDn3AkeeOMHbLWNr5tWNy1ORH3k/96RMUvTiOwtQEUnu3JTPIE42dI3mNAyg9fItjqetw\n8W+I0tWdpOsnCMkFbQXobDQ4tGyHe7cYDicfps+XIrZfoAUfzyDUifd4tbuRngnQ2CGQNg7h+B2/\nTGav9pgC6hHQuhe2DULRaAUV07n0c6Teu8LXez7GK9WApC9gcxi1wr+9M2zplWmLVlfBOy3yKHqo\ndcbtnUEUpybQ8qWa3wc6BeJt782JlBOW7+Z0ncN7h2rWQlVH/4b9cbF24UzaGUsDxcdhab+lTN5V\nNd/t/NvVOOfDuDLpCmnFaVzLvoartSth7mGcSDnB+czz+Nn70cavDS28WhC1IorM0kzWDF1DW/+2\nyLJMgFMAZtlMVmkWwzYM42bOTQaFDmLj9Y0UG4qZ1noamaWZ3Mi+QUOXhlzIvCAIY5G5cv8KMjKJ\n0xMJcAogNimWN/e9yZmJNSMS229tZ+C6gWx6ehMDQweiq9AhyzI9VvWoEb2oTN2OCYkhoySDbgHd\n+LRnFYde15+7WsK2LbxbcD6jNqt7Jb7u9zWv7HqF21Nu09C14RPNe3UMXDuQ7be306NBD/aN3UeB\nrsCS1FMdlQpqUKNBNFvWjJ8G/UR5RTmTd06md1Bv9ibs/Uu1e4e/kYJy+GdV76Y2ciCnpao0UT8H\nP9KL02ni0YQ7eXcoNZaysPdC3G3dKdIV8VKrl4jPi0dXoSPUNZSs0izUSjWZJZk0X/7HHV4drRxx\ntnYmqSDpibKP6lJQHD4MOTkYhgxk0elFmMwmRjYZicP+o8xdPobVvb3QKDU0dGlIv4b9+OLEF/g7\n+rO472KifKJqW1dFRbBmDXz7LYXXL+D/OpxbDuOGwKk6qOW6pKkJcgxk9MbbdJw+H5WzKwQHc6ex\nJ2fTz6JWqHFMTGfO7neIdS/FTg9D0uxJl4s5EAQ2BqhXCPZ60Gi0HPfQ4VWqoMBWgclUgfGBR6A1\nQngWRGRBgwJwM1kR6teMrpM/Q9G5S9WAnn1WFEOmpkLXrn8YSkzMT6TLz11InJ5I/YX12TVqF008\nm/zhPQBEd9dFi0ToLV+Eiyps7FGVFYsW3Rs3wptvis6zKpXoLDtypCBP9fQUIc9PPhHsBvXqwTPP\niGMB7N4Nffr8wcmroKvQ4fVJMIVyTd60ouwXsHp9BpoGVQKrMOUOSy5+y5Gcc/x+T2SRaVVanj2p\nY2VLFWWKCpzLId8aZtr1w6VRJLt2L2Z6g5G0DO+BHB1N5x86MaHVRHzsffB38KeFdwuctE41nqH8\n8nzul94npyyHuLQ4Lt+/zJTWU2i1omYyxI1XbtDIrRExa2P4PeF3nmr8FOHu4cw7Po9JLSdxK/cW\n++/up2P9jnzU5SNCXENq8RM+CrdybnEn7w79Q/rX+fuaK2sYvXk0O0ftpP+a/rza5lUW9FnwyONt\nubGFob8OtXSLnX9iPsmFySzqu6jGdnfz7xK0KIhIr0ictc5cy76Gi7ULN3Nu0rFeR4r0RTT1bMqc\nbnMoMZQQ5h5mGcuJ50+IsL9STZh7GKsurWLanmlYKa2I9IrkdJrI6q1cM5rUchI2ahu+6PUF5zPO\n08K7xb+U3r3y0kp239nN2qfW/uF20myJj7t+bCnkXjVkFWOajkGaLdEloAvn08/ToX6HGjyB/2v8\nbRTU9azruNu64/6ZO20I4LSUZPk90iuSvPI8gl2CeSXqFQaGDvxDJobqOJ16mtWXV9PMqxnlxnJi\nQmPwc/DjUOIh7pfe51z6OXbd2cXivovp2aDnYx+wGgrqI6MQZpX8Zw+HzWJiYMeOqkLEJ8WQIYKr\nTJIo0sj4vQ5Fy11gwgQk28/pkahg2xYril5+AZdeg1A/NdwipC0IDxeKU5Lg5k24cAH51ensCFfz\nWUs9WR42RHu1oqlVPaa7DUBlRqy9+PhweOtCUp0U2HvVpzgzmbGx09np/zads22xdXAFkwk6d4ag\noKrrlSSRFVjx59iUDSYDdp/YsWH4Bt4+8DZXJ1998kLD1FQ4elS09N6zB9PpOJRGvaidKSoSiqmo\nSCiligqx9rZ7t6DYGTwYrK3F+F1cRP1Tu3aC+mb4cNGw8MUXq1qB/wHmfFHIkZtX2KtejcpjOWZF\nbTqeh2GWzZQZy5i2dhw/Jm1ha8UwIk/cxfXND7E/PYh1G+CZ2s1UBdzdRamBm5soZtXpIDsbwsLE\nmG1sBEluSIglU1OWZRILEnHWOpNdlk1Dl4aPfNbNsvlf5rD7M+izug97E/bia+9L6uupf7htelE6\nvgt8MbwnFMioTaPoFdSrVokCVCm/6i09jj9/XGQ2ItW67ocTRgBGRIyopTBkWRadqb/0Jml6Ug1m\nlf8fkGZLzOk6h3c7vcup1FMWome7T+wYHj6c3fG7q+q4/iL421AdNXZvbPlbNptFFgKALDpI/kvQ\n62nj1bJmJ1ijERQqegaJdhVjmo5hAY+23AAhAO7fFwvoD6CoMFWFq2JihPW9ahWMHSuy49RqQeUC\nQkE9LAxMJrh0CeLiRDvqmzdFpt2VK1VEmqdPw9njyCmvieN9/jmbO7oRmKHD+lY81tbWYr/YWLGf\nySS6lXbvLhb0H2qWJ7m7E5OjJubHdMhKemQzvS6Dq6X4NoIxXab98fyAmAuTqep6L10StDaPqcTX\nKEXt0PANw3mr/VuPVk7p6WKuEhLg6lUxt3fvCg+oWTN47jl0qXnYJl0XfZi+FzF9XnhBzF1yskjP\nDwyEvXvhxg2xX1GREO7Xr4vtO3cW93nqVPjgA5g3T7RB9/AQFD1Go2BDKC8X/7p0QVXhiHNxByS7\nHx8/T5XTJSmw09ixIuQNDNu20GlEDM4fjxM/nobmX62Hg5dg2jRxbpMJ7twRWZj5+YKC55NPxPZN\nmgillJwsjJsGDcTcgIXoVJIky0L947yg/x/KCbCwI9x45cZjt/W2Fyn9qUWpBDoHcir1FO92fLfO\nbYc0GoK9xp5do3fR8ceO/DL0F9r5t3vksYNcgrg15RahS0JZM3QN3vbe9FrVC+NgY41UeEmSSCtO\no55jvdrKyWCA1avh+eef4Mr/fUT7RVv+znozC7VCjf+CJ2Pu/7+K/4qCkiRpENAfsAd+kGV532N2\nwfw4JonKGp4/8nbMZhFaev99sRANgom5Uhk83FFWlkVFup2dsEoXLRKCbe1awdjs6ipaUFdfH+3f\nX9QUbd8uLNoZM2DSJDGu8HC4d09sFxEhBGCHDoLGJfsByaybm2hx7eMjBPCoUZCTg0mpZsnbqZxe\n4IG3WQ0NwOznj2LoUIbUqydSzL29hXWv10PDhuJvnU4IMRAKY9kyobCuXBGC/YcfqjycMWNEmCsp\nSSiqDh2gTRtxHSEhYrvbt2HTJqEYBgyA3r3FOQwG4W14eorPWq0QkCUlYjzffCNCfCCUgVYrBD+I\n+QkLE3Ok14NSyU+3GvNs6A1a6ZwFSeipU4Jk9NAh8PMTLa3z8yEyUoytZUsYP17cy2qsC8VbTgoF\nNW+eIB196y347jvYtk20Fwcx35cuQdOm4vtK+PsLjwwEWem+fTB/vhivo6NQeq6uVcpJlsV3zz1H\nw65LCLh6E278UPV8VM7LY6AsLmX1ZiDoKjyg/bNYwN2frtpQpRLjqkT79jC3Zhp4DXz3HUycKLgb\njcY/zR35/wM/DPoBs2y20Ef9ESRJ4unwp+m3ph/bRmyzhOfqgrXamqJ3xHLBmRfO1FpHrgshriE1\nPI+2/m1Zf209Y5qOqbFdQn4CQc7VuAuLiwXZ7cqV8O67j1ZQBgNcvCjk1o4dQna0alX3tg+QVpSG\nr4NvDY9WRmb52eWMjxxvWWuqrEP7O7JHVMd/RUHJsvwb8JskSU7A58BjFVSd+ikuTgjTbduEpTJy\npBC4lUIgPR2WLBHCY/t2IZABvvxSrENERAih26cPhIYKAZOcLF5gWYaC2qmqPP88dOok9k1Nhdya\nnTJxdhaCNyREhIYKCsQDGxcHmzcLIahWi/Pq9XDsmDhedrYYw+efC8EbEoJ58xYUn38GCOdx+lxP\n7mjDcJLusvw1yLXyQR/SDT9NlghPBQaK4zk5gYsLckEBssYKRYcO8NNPYmwTJ4rzVzbDi4wUFvXd\nu8LS7t5dKHKFQow3I0OwTh89KgRaYKBQEPHxwktbs0bMhUYDL70kXkwrq5oeolYLrVvDt9+KFgzv\nviu2adEC+vUTc37+vDingwPk5jJG2Zziszn0WfghFDyUXebjI+7h0KGPrdsyOorKfAyGGo36sLWF\nbt0EG7zJJMaXmirWpYY/0AotW4rv+veHV14R17RsWdVzMGtW3R1yX3uNpxaOqP39tGmwfHlNxSDL\nYr6rF0gXPkgjT6nWpmH1arGO9u+UAbzwgjB4WrUSxxs79vH7PA6FhcLoavIEa4RPgD/bPHRmx5ks\nP7ec7+aPpkvj6CcSyJWdhp8IlbWB2dkMtWnJyeTjtRVUXgJBdv6ipcjcueJdr46tW8Vz5OIi3pOi\nImHk7t8v3tXCQujRAxYsEO/D9Onot2+hOC0RN3fRLHPD8DDmxn3JpfuXiPaL5lTqKXoH9Qbgo9iP\nMJqNuNm40dSjCQ1cgmo1RPy74okUlCRJ3wMDgPuyLDet9n0fYCGgAL6XZfnTh3Z9D/j6kQdu0aLK\non14zcbHRwjPHj3ECz96NKxbJwTviy/CihVC2AQHCyE8ZYpYfzGZhLWyeDFERQkF17+/8KqKioSA\nj4kR1i4IT6L4QXru8eNCeJlMwhrftEmc71CXBxeMCKVs3Sro+gHZ1RWp2lqQSanieuAA6vmY0Jlc\n2TfpR5wzb9K0XgX+t04gDxxoSfhSACYklNW0c7DuOiW+7oAO9xPb4MQ27gZ0o0HSQZKiR5B7z4nG\nmQdZbf8y92iFa2ECzZWX6QLCUwAwGkl0bUlg7jnuZtnRwFOcUe4/AKOnH7mZRjwyL2NSaNEoFMKT\nWLBAMEWDENJHjoikhEqUl4sw28WL4n+lUsxFp04i4cDJSSjkZs2EEjxzRmyTkSEEcaUXptfDtWso\nrayY4uICnbsLQermJn5zcxPHHDFCeImTJwvBW1YmXv6HWqQrDA/uo8FQ0+DQ64U31qKF8MyCg4Wy\nffrpqmetuFh4vbt21WawXrBAJF2cPy/GVB0//VTjo6NeIt9aFs+kp6cwEnx8hAfUvLnw3qKihNFi\nNovPrq7Ca6/E2LFCkcfECCPnX4WNjWAc+fLLf19BbdkijASAOXOE4VGJ/PyqeyHL4t1yrM388aeR\nlSXulyzD9u3U+/FHGgyAz1ziWL1QDT91F8ZDcLB43ps2fXyDyvJyEUE5d048l4WFQg5kZdVYP/UK\nh+MDg6F4jTCmoqNBpSLh5E6CNxyAU99WeeFt2ghjrqxMePYDBgiFlJUlDLywMGEkjB5dNYz8bI7P\nep5nf40mzQEIEN+PvQSrdsAzYU/T3r89Ye5heJQrSLt8Cmwh+L4RVytnhm0YVuOyGmg8yTQ8KPR/\nQu/9/xqeKElCkqQOQAmwslJBSZKkAG4D3YF0IA4YIcvyzQe/zwN+l2X54COOKa+d2Jaz904xP1qm\npcGTc1YPJluG4sKXsZ34CoUNfLDX2JOUcR3Dp59QvHkdXiUiA80CDw9WtVRRnJ/Jy5n1hGD7/fcn\nn4XNm+Gpp4QA/Ogj8RAmJAjlZmWFpBZxf3s9FM2DcqUt1qZScnDBiUJUmMjBFTeEt1WAI7aUoMb0\ncAYyF5qMJUkVTL9bC7Eqy8fYsg3KQQNQvDVDWHITJ1Jip8HLdiklD5Ybcm39SS915JpHV/oWrMXR\nkMOR8MmkdhuHT/xhUKrosvNN0n2j2PDqcTw2LMHuwjFamOPwNKWTr/XGXZfKKqvnUZv1jDD+wk1C\nuE0oCfbNuVXszVLpZfKHvoBTAxcUnz+wM+7dE57UG2+IMGVQkLCkPT2FQvjmGxEWjYoSayOHDwvB\n3KBBlTdz6JAIu3l5ifmtDM81bfroEFRFhRBSBw8Kj/PqVfH9iBHCMk1LE+fPzSXr7fl4HFgn1ooC\nH8Hdp9WKMVQycTwoxqV1a3ENWVlCmBw5An37ilDqnAcMCAsXwvRqVFgVFeDgwMoeKxm3XXhiPnOc\nyajIR745QhhRIJRz5fqcUinO5+0txr5qlfi7Rw/hGf/6a1X42dZWhE1BKFxZrqWUH4uCArFPeLhg\nElmypPY21T3ghAThLRcVifvr5yfGOXq0GOOwYSJLU6USz2hBgRD6IBJNKg28p54SBt5774kIg7u7\nMFxycsQ+Go24T6GhwuPIyRGCfOvWqvE8LI9sbYlbOAOnAU/RcE+cMJ4KC4XCqURkpHhGIyJEQszW\nrcIIcHER96BSmTo7i2uQJPH9jQfrYB4eIEnscy9mXosyDqT3EGHnBwZPv7EKJjv1JEZ6wDB//bpQ\nRpX31tdXGAMODsIIMpksz2zh/h3UPzWCz45Y8VK7qmjM69Gv42Pvwz/2/QOAZUedeOn9rWJeK5s7\ntmmD1H4fc8vbMTHNk3eCk3k+24/1d7ehV8LyatFC87Q8pD/7nPyX8D/J4pMkqT6wvZqCigY+lGW5\n74PPbwOyLMufSpI0FRiHUFoXZVn+to7j1WCSaFruxmXrnCceeDR+uAaEsTOpShF523rxuv/TTJix\nDud1W4UQtLKyCAuTQkIZf4eMk/soN5Zye8dKot0isV25FhMyWndvYfFXQ7YNeMwAZLDTQf+vd9B+\nmDeD/M7hrsoDSYFy3U+k+DqS4ayk+MIxAoOi8LRyxz4xBdWkSSI0ptOJh37lShGmXL1ahGOqCeln\ntz5Ldlk26wf/guOnzmRsa4hHxz7iYT90SGzfpw+6uJMUXDiJ4sJFPKzdSLOpwPfeAwHw8stCgSxd\nipyaiqxUoTDWwYwBFDv5c7vZMFYyHq/YtTzLz2gwUIoN9RBrMxWSikvjF2Ad4kd6vbaEdvLk7Flh\nqHf/vA+qA3vFiz9+vEgwcHD4j/L9AUJZxcYKBdWsmRA+SqXwbO4/MGrCwqqSHurChAlVSRSdOokw\nTaVlHVq7tUVlQ7sa65kgwpgvvcQ/Xirmi+ViHcVrjiP3KwqRZyE8js6dhWA+fVpkeebni/8PHBDH\nePVV+Mc/hCKoCx98IOZz+nQRDejRQygPa2txb+vVE6EiV9dHe1vNmwtv18ZGnLs6pkyBr78WCuX+\nfWGEhIWJZ+xJMXGiCBubTMIwATG+SsUVESGiD3q9UGo2NmIecnOFklAohGIzmcR1+PiI+ejSRSi3\nPn3EPejfX4ROz58XyjAtTRzv2LGq9dh/BRqNOF7fviISM2oU5zU5PG93kItflIixZWfDxYs0OPEM\nu3+qILR6tF+lEsaKQgHr18Pq1ZxSZtDaqyUKL2/WNFPw4qU5lJqrCpztjQq2JbWlU5fxKMzCSNp/\new/RqWD3a7Vuw507Czmk1SINvczcI2pmHnwQsq/0knQ68rXQeIrobZX17DXc69e9Pvf/G38VBfUU\n0FuW5RcffB4DtJZl+QnSv4SC+qB7d6QDB5jdGQK8bElqXPUiHR5/mI1Xf+XZFs9z8OwGRpwu5eTA\n5kR9uJySC2fYM70fR8tvEZJlYr5PEpIMXZLghhu8dlrCKMn82FzU70y6YsVxTz1zOj16PACx31vT\nKgVOEs06RnDMphcRVuvZOP1tsUYmg0YtWnBsGL6BoY2HMnnHZL49X0v/AoIb7srkKygVSlQKFQtP\nLeTruK95qcWLXMm6yoHEA4S6hVo62m67tY0yYxkDGw5gW/wOupd7sz90rngJDh4ULATAoHWD2HZr\nW41zTT8Fs6664dgwgrtRwQT5NRWJB3o9JCejv3CWO7Gb8W3SHqcXpoiECYMB9uyBLVvQ62UKPlpM\n6b1cvFZ8jE2ZMBauSE0Iku9gQzlF2JOCPwetB7BP15HzciSrPf9BWMFxNBVl3HBuR+sZp4TVAAAg\nAElEQVScXSS4tiEhbADlY1/ifoUrx48LvaXVCschOlrkZzzR+q4sC6/kgw+EcPL0FCG27dvh7FlM\nR2IxKMF62AixjpabWxW+rQ5fX7H/w4cfNQr5g1koQusotly+XCiy76q43Jg9G778ksmjClm6TFzA\nGwv7kpibwGbXl0Um4EOFyYAQeOnpQrGGh8PbbwvL/949cX2RkUIhLl4swlCVOHZMJLw4OQmv4/hx\noRAqw5n29kJYdu0qQnsREVXnz88XXsSePSLZxWwW624XL4o12l27hNJ/8UVxc2RZCN1584QyX7tW\nHCsxUSiPVq2EYK/eLqPyHuXmivBperrwloODxZw3biwUSkmJCMXpdGJfPz9xL93dH/0g3L0rvPae\nPUWotkULocjKyqq68r79NrrlX5M7tC/u1xI5O/cVIlv0ozA7Fe9eQ8XY0tLE/FbOiVJZ59pi3pG9\n+O7vy/qe3zJw7kbYu5eCiGD8nkqmMGkEyp8FqQAREdCiBUUxvWh0aSLXb3TjW80V3gq9R9wPKlrp\nXIgcWcglZz3dSzzYeLEhilkfUX54P54VVmL+XVyEAqysw4MqhVvplanVSGMT+KSkDe+ctRZK2mQS\n82htDTk5eD+TSqadjDwt78972v8hHD58mMOVRgowe/bsv4eCkqOiYNAgJON7hJU5cN22iphV/spJ\nvIR+flWZVgqFsJqLisTNNBqhrEyE0RwdobCQ77s48kKXQpyV9uSbROihaSbICiXt0zQ8c6mc7SHg\nWQovx8GOEDAoIc7TlR9alzDJdyP9W0fwc+JcTmUc4emwpwXh5QMF5WLrYqGc8Xfwp0hfxMyOMxke\nNhxrtTVxaXHMip1Fn6A++Dn48cmxT0gtSrX0ovkzaKF3ZV/A+7jYuMKePZT/uIJCfSHR30VbGL+b\neDSha2BXXp3akL3BEmHeTYjPjadkZgmKpcvgyhUupJyhRZSoih8UOoitI7bWPFFhoVBWlSnor74K\nX30lboVZxngiDtnJGVV9XxTXriD/tg350CGUp06Q79+En146RYjjfTy3f8cB6wHobv4/9s47uoqq\n+/ufm947IQkhCQRCCQGkg/SigICIoIigNFFEmjRRgSCiIFiQKihdUBBQeq/Sewm9l4QECCE9uWW/\nf+wkl0AoPurj87p+e627kjt35syZM+fsfr77MpUS1tAw5TfWeHdgUZH3SQkoidGoCYdXruhtGjfW\nW7/xhhp+BoPw4/Efsbexp2mJptjdTOBEn/YsdDrHuZqlWJ2s42cjBookCyXvwOacmPu3q8FcuxZ9\nx/+OISVFmeInnyizBdVIAx8A1MxhCHfwIeXtgYRNGZJfuORaUV9+ac1QrF0bhg6l+7IXKL5pBh9e\n7gGzZqkL7Glp/35tJzvHsh0wQJWJPXvUYrh+XZnYfSVosLOzuu0aNVKB5Oen57q4aAB/7Fh1rw0d\nqsdErM9zv3UzZ44Oei5lZ+ua+vVXdXfv3q0p/Y+L7aSlqfCcM0eZfmqqzpmKFbk6aTRZ2emUPJWg\nAu3IERXQ7u7qCouL04lQurTuw7O3V6a8c6e63cxmFZQiKiRffFETpESwVHqGvdkXqVyyHvYOzlw5\nt5+Xt/bkUNwh6jpEsD37bB68UjGvYmRmp7Om0zoqfqcCqmvFrjQv2ZykzCSmHpjKkIq9aOBfnUPm\n64TbFqLqtErcdQbz9EBsps9g+8guDCl/i90/PDwEl72gWE7YNljcCXQpzLOhdfi67miqjCnO1OUW\nqjZ43bovMjdGXKKEPn+pUuo6DwmxZv+eOKFCOEfYGEYa+MzcgKGj7rNunZz0fd69S+AAuOkO8uFf\nCwT9Z+h/xYKqAUSLSNOc73kuvqdsT6RLFzh7FkPjnZRNc+ekW44vW0DuB+52cLAu5lyaNg3i45HE\nu6RWqsu1Ki+xeaOFOzuiiS43ii9mV2LQ5UPM8m9K77ePMn73RGyPHeZ0WlHqj6iHn5eJQie3UjjA\ngHufrlicHLH95DGuKVFUhfRPLRyLP8b43eOZf2w+u7ruyoPof5DG7RzH4I3W6rEX+lzA39WfM7fP\n0GdtH3Zd28XYxmOpULgCbzevhUdQPJ17pDDgXKV87Rws+imX9qxlaFR8Hlp33IC4vFLtACmOBoa/\nFsDZ5yqx+pwG/F+0K8fPCXXpnf0bAWdu4NZ7AFOu/MKhtw+RkJZAuHd4wZlAo0apxQKP33AcHa3x\np9xNyw/S7duYvxyH7ZRpKvTeew8KFSI5Wfhp1052nDrN+pVuJFzxgg4twbbgDb+dojoR4RdBhjEd\n+08/x9EEvPACJ7yy2JJ4hjijNRvudK/TDNsyjMUnF9OvYk96UZWUqhVwd/LAK8XInV8XMnXtKPru\ngYxJ31Dj7d7M6byGuD0tKV6xLtVfG4R37cYqvEwmXfTlyinTzk17P3yYN4eFcP5oGjuPusHAgdz7\neQ0OjevhHOqvsRs3N7UkHkWVKmnGY9u26mICvU9MjFpIUVHw449qcfTsqQwpIUGv2bhRU+IvXVKX\nZ+6m42vXVGgYDGrNuLpqTOR+i3LNGi3fsnevCqNLl9Q1euqUWkitW0P//ioct2/X+1SvrsL91CkV\nRDt2qDswJUVdjPXrK2PdvBlsban7hpkdoTAitQoXvIQXzMVpf6swHDxImj1w9Qqul2PzC837yd5e\nf0tOznc4zR7c7svTcMuC1Jw91e1iYHEkVL0B+4s8etifRN+shn7NIbrI64x4eyETq1g4WQimFgDU\ncN4HKg9052PbhvQf+hvxrhA8QPtwJ9CLdZOSKJELnxgSotsXjEYVPi+/rGM6fbq6CLds0fcXHKyW\n1Zo1ULw4hk11+Cy2LEOr9re6JO+z/gIGGYh3A3k34ZF7HP/b9E8JqDBUQEXlfLcFzqBJEnHAPuA1\nEXnyDjxyBJSNjSJHDxfKJrtw0jMHJVxApucElHPM//Q04cbJe9x6vS8nbMqzMHAAsbG6bcfFRROI\n4uJgXJetDAptQNaXXtj++COGFk0ZtW0Uw+oNe+JmxNeWvMZPJ35iZquZdKrQiUNxhzBbzNT6QTf8\nORkhY7SO184re6g9uyadrnVi3g9adK5WrVpUrVqV+Ph4rl+/jmdhTwpXLkxEQASRhSIRMWAwWLd0\nZWer4rt8ufKRXLd2WMhdLnf14ZmY4hyOzF+PJso/iq2dt+Lj7IPJdN+WIINBg/5799JzUFmmuelr\nqG4OZK9tHPOXQLsfD+O4ND8MVI3gGvSp1of2pdtiEFFlYNgwa5JAAfPDZDFhtphxtHMkPjWepaeW\nUtitMK1KtSI5SwFtj8UfY96xeXx38Lu86xpdhFP+BmLdCp5zLc870ObKM3glhPF9+Xi2B94ic9ES\n7JNL0aiRyozFvxi4HVmXqzM3UbGKHb//DvWbx5F4PAnvuVYfvIOtAx6OHtxOf3xc0y3mTVIj5+R9\nL3MLlvwMZRq0Y09xB07ZJzEibRXtTtkQbutH0MVbOBmF2lc8cTNqpo4lKJiYWG82+LxK/1bnMeRm\n+S1cqC60gujsWZ0E4eEqQDIz1WPw/vsQFqZMf9cutXTKlCnYDXbvnsbj7t3Tj9GoMbXJkx+OOz1I\n7u4ai1u1Sq2xQoV0GwLohFy4UJON2rRRYXbihFo8kZHg60tckxocCjTwbGhtvJy8OB5/nEv711P8\n/B2am+ZwLS023+2iDAHYZmRyxCmJMrdg+yzwCygOfn6IAQwenurGjIhQy61iRU1Sat+ee47wa2no\n/IhCyT0OwLSVkOXtjpPZwFeRyQx4HpadrcSLt315w3UDNgLt7wRw595NIu5ASOEIfCyOdG+azTyn\nM8xZBned4Jwv2AhMqgbbAz/k682f0SyjCN3XJqhA6dhRIbJCQjhV0puX2sPp3BwUOzsuVCpGZPNL\nZGHi9pwAfHu+r0rHunUaSwWNqe3fr0oPaHbktm16LChIlYYcMkTD5xvhgxEb1OXwAAUMsSXexYIM\nt/zP7Hv7rwsog8GwAKgP+ALxaHLELIPB0Iz8aeZjnvrGBoOI2QzffoshqT9lkp055ZmjTQkkdkth\nyTo3Tp1SJe74cY2ltm6tSWIhIapIWCy6vjw91TNgm5WuWt2+fapZ/gWUC3XkZIQud4exefNRzlye\nBx1eYKDvYFJTs/H0DGLjxhQOHvyKyMg6ODrW5tAhAw4O1ylWrDjnz1fHbDbkS+4qiKZPh5rV7hL1\nqw+9UstTIbsWPXymUSzOi0vpVSl7cD3e3irc9u/XZ//2WygXZUCqVkX27MOmbRtk2TJWDe7GvLRD\n7HU+w9rZ6ZRYd5RDhTNZc24NnSp0oueqnqy/YE0y8cwy0LZGV7YdXMLM2Un4ZsDRTQuYcWgGWeYs\nkrOSKeNXRhGUs+7RPepN7mQlseysol472zmTYcqvEb8a+Sprz68lyD2I3pXeIevCWe5eOY3X1Vs0\nNBfFq0Q5EsoVI7JuW1w8fB8aD7NZ4/jr16s36+rpdM5ddSTmtC23bkGj0jeYeLox5uOnSPPay8qz\nqxhYczCezm4YzUb2XN+D7a1n+PHKGNLkFrEZl/is0Wdsv7IduzMw6uQAwiOqk/3jYj4f7sPP995j\n1tHZj50PEZ7huJ90oPTRulSoeZBBEw7wecgUvrR5kfe/W46jOZbM5DvU/uInojoP4WzrOoTnFNrz\ncfbRvTxnzqgldPCgbmd4wFoAlEkfOaKW0LPPqmUWEqILwM9PrYxHMaU7d3TBjB+vLsjTp60bq+/3\nSJQvrxq7n5+6A319VUt/+eW8rMizd84S4BaAh9kO7OzYvWYGdY/0xcPGiURLwYIw41MF5fX1KMyU\niib6Vbvz0DlHu+wjyxbqzq7L4FqDuZp8lYOxB3F3dGdSs0mUdgjEyS+Q+h8XYbvdDWwNtgyrO4zu\nlbpzbsYYLCeOU3fuNoXseoCSHbVcy5PI7OZCenY67tkwvTIcCIJpx4szv7YXb5Y4hLPFlrgP7nL9\nojuzZ6vxaGOjfOeZzE9Y12waF9uvUV6T4xI9d+ccpz7oRqvWQ/J7F+7eVc/PlCnaQO/euuCLFtWY\nk7u7fr+PDNEwZgMM+XKPWrIPUJ6A+pdCHf2jWHxxccJPP0H/JAOlk5047ZnjihBgpPZr8GAVSllZ\nGlz/b6T6i8Ddu0JychJTp37HF84KJutkhMzPtF82NoLFYsi31nPCYNjaqraf/lDZqBQcHReTlfU6\nkA24U7r0TqpWPcS8eTPZvv0QdesacLRLJOtjX4ZmVOFD/5cZ7LCVr1Y5M7LiMk6eVC9OYqKGMX7P\nqdMmGNhHVaqzj92ujaiRtploRhBREoomHafOraU09jmEX5NnuHZN3d9dusBNmwOcPWfh8OYGLAm3\ndthF7Ek3GPP1Phfev2VEy3xVP0++exJXB1cWxyzGzsYuD3E62CP4b6tTk4v+s7v7D7T4tTv+hYSU\nFKvB5+6uvDwmxproZTBoRvGYMcoPsrfu4oXPniUjXejbV71qb/ZI5pXFr7DuwjpWd1jNtstb+HzS\nae6tX8EdZ/D7/kfsW7bGZ2RxsuytxSbtTe4Y7R4ud5FLTibItIPXsiIoWbwKQ95dwNoScKNLW4rU\nfI5lZ3/jZPIFDt07zYLnZ9C+7CsYPDzUErp+XV1AW7eqdh0To8f9/DSbr1Yt3ZScna0m9aVLGkPa\ns0evqVdPky/uzxrcskXT2xMSVNPLtfrq1IFKlbjmamYGB5hid4Q7Npk4mA20PiUsKqenzV4GHbNL\n0avCDb4rncrzGUU45mMkLiMBU6sD2AaHqKvO0xMMBk7fPs20A9M4kXCCYI9gjiccfyxaeC69Ve5N\ntsTuJD41nt+7/k75wvdlLRqNKmz37s3PvD088gt8g0GFQG6c5xE0uyJsDYNDvx5lHc9zI+gm9TMP\nkZaY3+tQvboaOgduHCK+RjeyJvyHsGwF0Zw5uqhXrYK4OKuAmh6DpXTZvIz+o0fVAzvxni0Jbhbu\n9Ja/Sh//0/SvEVAgFCoEt941UDLJiXPeVgF1qr0QFvbXC6TchKPr19UyCw5W1/nmzULp0lkcP57C\ntWu+qFGYQyOsFlSLs8KIEXqtwaBzfvFidc2Fhqob/9VXdX1cuKAhgsREGD5csFhMVK1q4vJlGwYM\nsKdWLQO+vtq2m5sbcXFxbNrkjrdbIvV3+jLkTi16mF4ipFVFTr45hmZ2G6lcWWOu9eqpxyB3O4hg\n4LhzVVoH7uOXW/V4JmU7nzGUl99wIyLzKIZFixj14gGG/1Y5TwO8n+IIQNzi2ZP5HHbOabQasJMp\nq2zwHnGNepUCCQgwcOaMejnmz9drynxpwM3ei2pnnr7k+58lER3Xffv0r/fsr3nv4vuEhUpe8kUu\nmhSoR+rdd9Xr1bu37l+9dEk9WuG397LDWIOgAAtp6QYiItSr9lCsORcJIjcV29+f5s0h5nQ2a3Zf\nJnKapqn3NBwmMdaLMWMgxDOEX0//SovQJtjv2svx6wf55ehCbnnasTr1CFfdH2NGowk4LSJa4O3k\nTZB7EL2q9cp/gsmk8ag9e1St37xZrR+TSS2l2rVVo6tWTV2ET6JLl+C557gde57pleGjRnq4/wl3\n3tqaQtn3rKdubTiXejXa5w1ULixP1NQoTiSceCptPheBfHLzyVQNqsqUA1OY0nwKp2+fxtvZmzXn\n1pBmTGPQhkF8VOcjPm34acEN/fqrJlEYDLoXLypKNzsbjZp0sXSpuhhElPl36aLXzZql7roiRVRI\nb97M/I9bsSYglYEfC4VMcVzdcIbNlvpERKgh5Oqa/9Yz1uyl//repH6dv/yH0WjdbvVH6cABfYUp\nPyyiyfev5gmoQ5ar7IsryrVr6lUID9eEzK2htiS4WjB9LH/57o7/lP41AspiEc1YjX5YQEl0wf0S\nUUWxIMBps1l///hjq9YsohvPJ0/WkNaqnGCnk1PB2cgAISFx1KhhoUyZIM6eNbAwwiqgcmNQfzWF\nhITQuXNnoqOjyU65i/M3fjTdWJfyvzfnsG8TvkrpTrHEQw8tktxncHI2kOzsz6Q+52jzQ3NK397J\noqID2H+tMJ+2PYrjLz/yQYO9HHGoxu+/a4jJ11cXk58fNGrrhavRuvu5s9MEZmf2xQYzgk0+oebk\npIrxzXgDmTiyeG4mNWvqotm9WzW769c1pPHMM3rsxAlVbNPS9P1VrapaqLOzNf28oAVtMinv3LJF\nE58WLtS2y5VTIdMq5nP6JXxIyaKZ7DvqSHa28p1GjTRPICQkH3QfRqPmAFy4AKlrd7CDunw3PoXQ\nSDdatlRF+5VXtG8Wi3q/Ll6E+JjbXMvwIz1drbPbt1UJMRrBteQBni0XwtVT/pw9qyGG6OhHAyuY\nr16h0YgwugW9QOsPf0UE3F1tiU+Lx9Zgy/GE47yz8h3cHNyI9I9k86XNuNi7UK1INfrX6E+lwEr5\n4qlmi5kbKTdwsXd5osWanJX8UHG7pMwk2v/SHgdbh3yW8aU+FwnzVjff1P1TaV6yOaFeoY9sOzYl\nlot3L1I75PFgwbmUbkzPw5QriC7dvUTxb4uzvP1yWpZq+VRtPpH279fYmldO7aUzZ/SFBgWxsH5h\nlvonsOhneSrhsmj3Tl79fjDDiuzk7Fkr5Obt25ofM2+e5pUkJuqcDA5Wd/XFi3p7Dw/lT7mG7dix\nuvvA3x9eqXGVictDMUTD2A1QvEsS4ZU8KV3aul8a/v0uvn8UzTzfVoonPE5amiZBlCypC79/f2VU\nBw7oC712zboxPJciI1U5mjxZ/799WyhSxMjt25CZ6ZDThzTatdtMixaFqV27LEWKuOHgkD8deWH0\nX/CwT6ABAwbQr18/jhw5wsihmvlXqpSBnmXMHIv0ply/wzB8gKY830f3W5geGQl8WGMzbDPDbXjl\nJSNnFziw8Bc7OgONG1qoVV7j0Pv26WJycFBXZNMHKqZOGOeNpb89E8ca8Q1ypFgxVczd3VUwODsD\ndgacJIv58/NnLYeGapx/504NoeRSVJS+81zA9wepaVMVEPHxuqhNJjUQgoLUGAgL08e/X5vdUCUZ\nEmDL8hS8vVVr2fAo5Mf4eOx//51JtY1Q3473Twhch/a1r+MZ6sW4j5yYNN+L9HQVss7OykTKlIFO\nnfwIDtYErJQUaNVKLXEXFzBfq8L6c5rdPWmS9r9kSRWO5curME5O1mPFi0ORIqFsnSXKK8M1LFSv\nHgQFBRAaCmXLNuTMe2fz1keGMYPd13czdudYqs6w4szdXxk6lyILRdIiogUdy3fE19mXS0mXqBlc\nE4PBwPYr26k3ux4yQjifeJ6p+6fy1Z6v8q795vlv6Fu9L42KN8JsMedDmX9cQclcCnIPIsg96Inn\n5dLjhBOQl6X6NMCvT01VH8Dpu2+T9s1aHTkfv/qpLZ9CAUaKhdox5Vvdd92xozXP48cf1ev6wgvK\nu4YOVb4lYsVbvnZNFe369TU/Zu9eK6YxhAACIw3w/PO07fYIbed/JDHi76J/VEDduGHFNH0Umnls\nrAIvDBumzKBCBT0WHZ0fZSU4WLXmChWE8PBLREfbkZmZRHJyCKVLDyc5uQ63b5clKyuS+vUn0qqV\nO5UqVaVq1TK4uPxF2tmfoL59+9K6dWsaN25MkyaNYRBcvX4A26IladXZB/qh/qkHBNRD5OVlzcLI\nzuatXg7ErbKHA9C4gRmeVeb6EBnyR5R79XdgmsmB1b9mc/yyIzdvatNFimg8OCAAZts6Ym/KpGJF\nbdPdXRmxk5MKPnt71fYSE3Vhnj+vLsncUEB2ti7UyEj9u3atetJcXHTdZWTo/T7+WLfP7N2rSSEG\nA/rjvHlUv6Uaf9AX/WD7Vu2Yr6/GUpydlXMUK6Z+0cjIfM+Yy5o9a6gLrB/gXXMaa463pkSEA4W9\nsqhy8DsiVy7DZa4Rp1qVFaOvqC+FC6sVdn+c8bPPtO+jR5oob3eScxfL4+OjczwjQzM2k5P1+V1c\n9P/x49XSXLVKhfH69RoOeucdzVOoXRuyspy5fr0hWZsbwuFrUGo5TvZOeNZajaclmFZBtWgR2YiT\ncZc4dTuGsTvHMnandbfHM4Urs6LDcj7arDnaNiNt8qpAd4jqQKYpk9ejXqdNmTZ51zx1fa6/kZzt\nnRnVYBSBboFPPvkByoXYS0vTT1aWxoevX9e/9vaqvCYlaULlwYNwyb4hIS+ffnzD99/DYqR4mD0b\nC0gU7d9fd1bkyo/jx1WRLlTIujXNbNa+ffmlbtk7efIR3li7x7BpHx/IuPXUff7/jf5RF5+zs+g2\niBEGwu45ctkr1y8HXa4KGzaoNuriYgWADgtTzbRbN/3u4gJly94mJmYr773XF4slGw8PD7Kzs+nb\nty8hISEULVqUrKws3NzcqFixInaPe+EF9TX673fx3U+3r16i0KzidDhaiIhlt9hSty6/ennhtXy5\ncvr0dCuEU14nc1bC4cM6OIcOqW+zZk1dfbkAsHXrqql5fxmH+6/Poe6+y5hu7orNhfOKnC5qwcbH\nq+Vw9Ch0G+iFF/d4r5dgF3+Db35RX0XbVtlYbO3JzNTtHA0aWGvphYdDmOddgv0ycXE042OTBDt3\nkn30JPOde9BhZCmc0hMRo4lzOxO4OvE37l29x8rYSlQx7aYXUx4ar4sUo1gpRwxnHsNcQkOtO4T/\nBBnf6Ib9nO+pVk29RQ5k8fs+tTD9/ACjkYwefXGePZXkZ5sSXXQmE38JpFIljd2VLKnjN3euatBf\nfvmwEmyx6CtbtUpflb29at1RUfopUkTdRBs3WgH/Y2JyqoMUMmFXZjU3XTaQ6rsDy92iUGolADbp\nhbG4xBOU0RiPhGb4GSJoVLQFd++qNe3urq5We3udXoGBOk3KlHl4uv03yCIWbD+xzStY+CTat08F\nQ278N3eLlaurKkzOzjoPHRzUmsl9Vn//HJdbpfUsuDyODZ0eXXzBZNJ3N306ZASvwbn+BPa8t/ZP\nP6vZXPD4GkYaGGvfnMEfFlwxN2B8wP8VLPy7KC1NtZiAqQ//NmuWahqlS+u+wrt3dYtILvRYcnIy\nixcvZsSIr7h58yY1a9bklVfaMGTIEIIfhXH2Z+nBObBpk87uv6IUgYgGW0wmPLJ1ZVWoWJF3QyPx\nDQ9nYu/eDAPOnDlDqTZtNAPrAYsAeMiCyjNlwGquXr+eX0AVgGf2/VwH6OqgcDitW2MwmQjycyEo\nyAHi42lcx4vUYQ6QAZM6H1Dz6Be99henjqoqlijBrcFN8Cvth2HrFjWBZi63FtbLpebNcUhJoeuO\ncnlmjQGIACLKloXmdXl5mhWZO8XRF/csa9pyKc7QzDWB5QRTgnOcxwpbJLVqYbC3130mufTOO2rG\nrF7NObsytA/bQ5+BDlTa/x1RP9xXuBF0H9OLL5Kx6zBblt6lzpLl2M8WjEYDHzKa0XwMt1ZBlWaQ\nlQ1vvYXzyZPw4494TJ3KV8uKMWrS93y/1IeoqOZUqqRGXf/+D7+6XLKxUU/Ug96o+ykgQJP3cim3\naoTBYAe0QqQVly/rb7HZp3E0+yHpPiTE23D4MLgFWDFfc+H90tPV2r17V4WexaLeitwqNj4+Gvur\nVk2FWViYWr4rV6p765VX+NOUC7iQng4e3jpfx443cuW8PbGxer8bN/T+Hh46pS0WVWATExWUw9tb\neYanpwqlpxWsWy87YLyQY+726WNV8NAxGDxYheCNGzB1KrzztREc7OG9JzT8FPS4PhoeY0TIIzxP\n/xb6RwVUcLAuAEY8vB+0Vy/1aN1fY/D8+fO0avU+K1aswM3NjXLlytGiRQveffddQkMfHbz9W+jO\nHd0416GDOpz/E8rOVud0bg2fHDLYoEXwDDa4OTnx3nvvcffkSZg6lerVq7PVaES2bKFCeDg2RmP+\n9KKbNzVaCxoEat7c6iLItSCOHtWNg7t2aYDnVgEuAldXNZdySzbk1pjKBWW1scEtN2uialUVSMOG\nKddavVq1j+XLKZTLiQsV0myrbt103OzslHv4+lqrx44Zo/48UPy7V19VRAbQWGAErrUAACAASURB\nVMGwYVC3Lu6rVqn5cesWB+adwvSNPQ07FWFT/VVMbhLOqC0pzP7FjbkXn2XuxSH0951DaWDuhLuU\nLpJChRZFNcnGYCDTwYPREz0YNw4+OtOXH1jN86znCiGEcpW2697CybYh9eq1p2TjZNxfnIG0aUNq\nylIVTqClFkQwt++A7U8LNE342WfzwIBd3+lEX6AvcFVeoWPPT2h+aSPph05zsPNEPDyU6f+ZcMKD\ntThzS3sBFCO/tfwo4I+CSERfe2KixsoOHVJr7epVjfX9/rsKi3379NhABefm0CEVMiVK6Kvevt0q\nfG7ezFn36LS4dEmt8zNnrMf9/OB2cjZ8CO9Fu3Ky0WhCLm/D52YM2SYbUkzhHG00CtfihTHaOOJS\nOoRatcDGYtKMxqAmauq8/LKmby5dqoHSrl2tQMNB+eNl9jb2ZBszdT6mpsL33yNBQeztNoMuEyth\nQBjxkTNv9nTB3tUBc4SJXt/9F+CF/iEv1/8Eicg/8gFkyhSRGTNEGIGE9HUQotHPCCQzM1Pi4uJk\n27Zt8vPPP0twcLD4+PhIsWLFBJCjR4/KX0pnzoh89pmI0fjQT4zQPjkPRSQuTpJ/WiXmdq9o0YZG\njUTi40WWLRM5ffrp7+funlv0If+nRAnJttFxGNCnmcigQXr+Bx+IgPz888+y09NTOvn65l1jLFVK\nLE5OErt+vQhIZrNmYgoMlOyoKOny4ouytmJFERDzkCHW+9SvL/LRRyLR0SIffiiJb76px52dRebN\n03FYv15kxQqR2FiRzEyRS5dEDh8WOX9eZNw4kQ8/FJkwQaRqVZGFCwt+zpQUva6AcS2QLJanH0PR\n7jmTJuk7D4kkJOT7bd8+kRYtRH716yoC8txz1sdv2FBEQE6HNM53a0u2UYxjxkt8vMjmDSaZMkVk\n5EiRcuVEihcX+cG+hwhIEh4PvTszBqnF7zJ/vrUPWd/PlfTnWuU7b1748Lz/wSKhoSLFwiwyY8Yf\nfvz/Cp0/L7J3r8jq1SJmc87BpCS5NHe7THftJ7GRjWSr2wvyrM0u+Xq8Sd4ptVkasUHGOX4on9t9\nLIPtv5Ixdh/JpMBP5d1nj8jX7ffI3FaL5LP3bsgHH4hMnGCWNastsmenSa5fFzHeTRE5cEAStq0X\nopF41/vGuUWLgtdNuXIic+cW/NujPu+8I7J2rYiLi4iNjewt4yFVhviIpV5dmbx3kvSb+rIcLVzw\ntRaQha+VF9q1E5Pp7xt7opEvPnk+77vFInLnjsjKlbr83Ef6C9FY38v/AKlY+YvkxF/V0B++MciI\nESNky5YtwgikaL/8AsrOzk4AeeaZZ6RNmzbSrVs3uXbtmqxatUoAuXjxYsGjc/euPtb9K/3MmSeP\n6rx5eZPv2lVL3uUrui3LE1AuQ60TNIYysqZQxwIn7+7IrnKl11gZ+HqsVK8u8uKLIsHBIlXdTsrZ\net3E6FNIBOSOVzH5vdmnku7kJQKS+ulXIoGBYswRUB2iB4nkCuLBg3Nel4i8+mq++00HaeXqKoCU\nAPHz8xMg77P4hRdEQEwgyTVrijwwdhaLRZxAUkeP/t/kkI+htbNi5RKhYg4rJuLmpgrDp5+KfPON\nCs8VK0TeekvkhRdEDh8Wc5ZRli8XGTUwSS6WbipZM+c/+Saiw7JkichHRecU+M6/a71aTHEJsmmT\niK+vSIUKIg0aiNjZ6SleJMpSWouAHHOolHfdj5Gj5aM6W+VEeCv52fCKODiITPv4mhiHDhP5/vuH\n+pGWJrJ/v/blAXn815DZrEy7a1eR2rUl3S9YqrJX+jpNk8/4QNYsSRPp2TOv/zd8IkU+/lgE5KJd\nCRkSPN/KxIsXl7MNe0iyvfcfExwgmSEl5FIRPyEaue6ec/zePe1jSsofbu9pPocDkArvIPtWr7Py\nogc+v5RBfoyyfnf40D6vW/HxIj/PzRSTnYMkVqz/p16DxWKRdee1H4N6PSc9e4rUqycSFCTi5KT/\n9+kjwkAVULdu/anb/SW0ZcsWGTFixL9HQImIiMUijECCH7CgjEajGAvQujdt3CiAxMbGWg9mZ6uW\nLyKyZ48+Vp8++Sdg+/b6W64V0aSJSGSk/l+27EOT9RAV5TtvPfd+ATW31CiZ3nu97PctJ428e8k3\nAZ2kq4dq1ZNtS8s9nPK1czT0GVlRpIXMC+0taXYPW01TeEfsyBILyCkiRCBPQL00fpxVXkycKHma\nYps2+v+HH4rcuycXL1rks89EevVSy2DcOJFJk4wycmS6nD2bKCIiWSdOyLKuXSXM3V169uwpWVlZ\necOXkpIigFy4cOEppuH/EM2eLQLyPV2VSaSkiHzyichzz4lUrizSoYNImTIiYWEixYpZx90p5x0V\nL65W4h+gFStEinNefnB+N689Mwa50bZP3jnJySKzZukrO3FCxGRS49qcZVSFo0wZMTm7FsgkFxva\n5vv+2/ADcr7NIBlRa7108l8rA/lCZtJZdlJTTNjIaf/asuXjjWJct0mfu2VLkU6dRIYOFbl9W1Xt\nvXtFfv5ZZOdOqwl0+rTIpEkiw4eLLF6slknVqiIGw1Mxc/OceVI57Lbs3Zvz0Nu2SYZDzvyuVk2M\ngUXl2wrfS2ioGulzvkmUzZUHPFXbu21qScmSIgO93xOikYvv5SiC99PfIKBOFELK9EJsKyyQoH5t\nZMAgk/Rf0lveX9jlkQLLa5CdXLumay/K/VL+McrMfvxkionRBZvzTi6fyZSfRp6WpYuMMvDjrfnu\nM2xkuixfLnLypOSzlnw+VyF+L1dK/g/Qv0pApTZtKoxAivS1zyeg5PPPrS/7o49Um6xZUwTkCEjS\nuvVy790hIlOmSDaqpma/3rnAiffVS9vFXLXaEyfoXYNXvu/JUbXE9NvKfALK0dFRbGxsBJDhIP1y\nLL0G9etLh/bt5cOoKOnVq5f0bt5cRnnn1xzvgMSCdMRGujduIh/UqZfD4PL3Y6dNZSEa+aSWk5gx\nyKY+v8r2H87m/Z7mGSBJYeVlev0fpXngIbEjW9r5bZKNhdrLnpKvy9g3Y6RKFZHw4hZxdBQ5e1Yk\nKSlXPscJ/CxhYQulTp27EhkpUqFCpsA02bfvgLq5/n8xoipXloTI+lKKU3L37hPOtVhE0tNFFi0S\nGThQZPRoleQvvviHbmkyqSfJv5BF6rE5/xz6o/TccyLVdF4mr98t28v1fOIcfdQnC/unO7dSJZED\nB/Ifq/bA2ujcWeSbb+R61POPbGflbyapVOm+uXLlSt5vw8ovE3d39UonJj7wDrZsEcu48ZJyzywZ\n0+eKlC+fd52lx9tiycoWS6YqT6u7vi9EI7tGj5Lly0WGDRNp1Up1jiL28XnXRQdOe/Jzb936+N9b\nt5YL3khYX+TdEbVk7BvF87vh3d3liiey7/PesujYTzJnxyQx2iCp9oi7u0ijWun52ku08ZGzu2/L\np5+K1Cx1R/btMUt8vHUojEuXW88/flzk6FFJdA7KO7anSH5BePvMEZFt20Ru3hQRkTt37kjjxo3F\nMNggRCPp6el/fP79TfTvEVA5H0YgQX3tVBDkCiiw+kce8VmG+vajGS7jeV8EZCMNRUAm0VPeZqo8\nzxopV07k1NjfJLtlG9n+xgyZ+eKvcsvWX5Jxk2m158n7ba/IpAkm2bUlM6/tewEBMnPmTCldurRV\nQH2AtG7dWo4fP64TIufcKeXLy0vNmsl7rVtLNsgnjo4yOSREhlaqJFPeektWzJghCS1byt3ateXU\nvn3yySefSMvq1aWanZ285+oqlx94LpNBx6Hfs0VlES8/HeN58NO9u6RXqSP2ZEllw0FpzDopX/im\ndHwlS0qVMklk5HoxGF6WsmU7SM2aHwjEiotLtoBISIhIly6qULdurbx8yRKROXNEpkxRBr1okcjy\n5f+gMMthiKsmXxJ4gBE+LR05IhIR8ccf4u5dGew0QbKxk53UlNv4yN1iFf+DDhRAhw+LZGaKJSNT\nTjVUgXX2zU/lQKPB+d7vYIevZKL7UOlR56Rk2znJ+B5n5IviUx+eBx06iOTEIJ/q89ZbIhkZkpkp\nUrKkyPH3f7D+FhUlAmJHtri6PhB2tFhEQK436y7jxmmc5KmpUiURGxudYPfRrughQjTycthYadJE\nQ0ZLlohs3qxWhOXAQRGQO3vOPP6Zvv1WJ/ETnv2mK1JoEFLlfXfZ9lVffUAQcXQUCQjQyZ+vg7vE\nYmMrzSMvy+Ki/axt9e0rN+2LSJjtVXnF6TcRkLf4TkDE1zVDupTYLuttrMJ/QcdVcrp4MxGQhIav\niOzYIdtaROUTUHFu1n4ag4NlpJeXDGjWTAp9UUiI5j+ebn8H/UsFlG0+AXW05tsFTqJs7KQls0VA\nPvMdJ+FO12X7dvX/xsWJyNWrOmkHjJafZ9yT19pmy9ZNJvndo5k4kS5liJEUV38537KX7Fq/QZYt\n+03GjRsnderUkXbt2kkJT8+8exkMBnF0dMwnoPLoxg1JCw6WKwaD/HRf/1IdHERADjs6ioDMMDSW\nD3hbjhIg27GRTU75XYBJERGSVKOG7KKyrJy5SwTkZEQzIRr5sqaes5saMsPubcn0CRBLQKBkO3vI\nFrtGcju4gmQXVddVeni5vDbNBtu8/y0PjF9c0aqS+txLIr17S0aXLnKqY0eZWbGi1PIIkzJlmklC\nfKL8OitRypUTGfS+SZZ/dU6ie9yQ1i9kS5uXLPJWF6O0apYtjRtpzerq1UUOb00S09JfRX77Tc5+\nsUyWDtolX/S9Lt9+bZIFCzQRZuZMkd9/V0vuT1N6ujrhHR1l/Wf7xIfbcufcHXlktNpszi+Ecv83\nGtXNu3Tp0903Pl7k88/F7OomJmxEQCI4LU1ZLbEVnn/y9X+UHhScEyboezx4UMxmDSX+9pvy3i5d\n1PNbKjhVRhpGSGuWigGLvPSSyPLfLBJ7JVskNxEm95OzVvJ9Tp2Sq1dFXntN2xMRZdCgfkuQ7GzN\ne3mINm9+KL75Z+jgpI+FaGRin68efdKkSfreW+UkotSv/3SCODg4v+UYFibJ9sp/PD73kCxjprqC\nPT1FbtxQxcHdXWT3buu9k5LEbGMnq4u9+1D7ufMj97ODZ+WWTSG5hW++42l27pKGswjIMEZKZmKa\niIisP5c/DnbFs+Dn8P/U8/8E1N/xyRVQYxhcoID63tBdBCTLL1D2f71Drl3MFklJkaT7s3icnEQW\nLBApWlRjSjduqLn8wEvc/M47YrS3ukAGYU0gcHR0lOrVq4uTk5N4enpKYGCgzHletZuMBQt0wAsS\nUDltpTu7FDhx0hy95SdekV21B8oOr4aShZ2soplc/WqRmiDXr2vMJK+5dHHIiV+ZX2otRCPlX3hB\nbvXuLZsW35EzZ0RiY2Nl0qRJMnnyZBk+/LbcJ0slKEjEy8MsrqRIk3I3ZHyrbTIrLDrvhGiGyXmK\na/sY5LhnLUlo9KqkN39ZUhu1FAG57uUv6QaDWBwcVKO1tVVzqnBh/f/+Z3Rzk3uFS8iOoq+JgOy3\nrSbrHZrLeqeWct6vmtxzDZAsWyc55V1TdoS/KSvC+8hvnh1lvP0Hstz3TRGQdYM3Svr6HY+f7RkZ\nyoWHDNFP3bqPZzy5vviLFzUrc8oU7XvHjhp3yY1Bjhwppqo15PYPyzSroVUrFVSPyjacN0/n2/PP\ny/Y3Zkj7Vy1iT5aASE12SkKJGo9/jr+CjEYNQjyBLBadXh065H9tB57pnn+sMnM8BjVrijRvLtK4\nsVxr9a58yKfSkbmSuPesZmLkKlVmswbV/ku09/uRQjSye/HXjz4pKUlkxAgR14JjegIqbVu00OQZ\nEPHxUatq3jy13ipVEvnpJ+n0hrsQjUT1RNJs3ERAPm60S6ZMUVl+fsRcSfYKljm998ucOSJtG9wW\nM4Y8JdBUvqK82SFbKnHg8XMUJKt5a9nxwUrrd+xl8zprzGrFmRX5BNS5d14RqVVL9rRqJcv8/fU6\ne3vxH6i/W4OB/zz9qwTUvgYqoAL62eQTUOaI0hrUfYBO790rdUADjA+mnNasKZYC0reXRkZKp+e6\nSYOItvJs9ZoyfPhwef/99/OEVL169eTdd9+VLVu2iCVXa83VVl9/PU9AuX6ARkMDA/PafiVim1Su\nZJHkCT/ku+cZzyr6/759IiBnA4tISMhPEhCQLV26GOXsWZ2M586dkx079glkSe6YxJSK0HGo0VyG\nDRsmIiInTpyQ+zPzAOnTp4/s23ddMjMfM1uOHRM5diwvBGM6fU6SKz3M5A841pLXg7fKp13HSm0P\nfb6bv/wiFrNZg1jjx4t06ybi4CAyYIDIqFEiPXrkXZ/y4utidnUTywstRH79VTPBnn32iQtVQA42\nHChxuy4q892+XWTaNG07QpNGxNVV5O37LOqwMJGmTUW+/Va291kkApL6/jD9rXp1TZUHFaxPuPdo\nhqpff+5ckRo19N12765ZgBMn6rPmpvTXqCHSrZusKDNQfhp9XkDEk7tSiHjJdPESmT9f+9W9u2rc\nT0tZWfJ35glv3qwJjj/QJf/zjx+vf6tWFZk7VywLf5LR/t/I77UGiblBQ00icblPAfv8c6sC8F+g\nHfNGC9HI9l8esKAsFpENG3T951p3BoO64Vq0UCvnP/D5lhigiVoVO2vseHeX7x7yjvbjKzFiK7sc\n68sS+1clAwexuLrqHO3dW2TlSonZmZNJ7O4uEhsrlnXrxRQSpvsVRo3S33J52+bNYtm1S+IN/nJz\n22l9Wbt3y4w1o/MJqBMta8jVwEA5Z2srFoMhL5nFPycGJfclPf3T9FcKqH8U6kgABg3C4DKOgHs2\n3PS0KISAoCXfzWYrcFUOmUwmJk+eTN++ffXAhQskLFqE/4cf5p1jBjoAHrYRlDZfx8MRPmcLYjGQ\najxJ37f34urkgKOjI682a4bvnTtaN8fGRje3HjyoDeUUezKM0K+uWZB68RVYtIgMOzucTSbebXKC\niWsi83aCx9hXINJ0DElOwdDrXYU0BpInTKD0mDHExdUElgCzgS6APS4u4aSn/8Szz/ZizM6d3HJ0\npM3QLFjbipk9WnPz5k0+zHm+mTNn8s0333DsPsTVs2fPUrKkoieYzWZsn2br/PHjYG9P6pHz2Pyy\nCJcl8/L93IgNbKLJw5f5VcDLPpuicYrMG28TwKbwt3nl2njsMh8oXufkpLs5/f0V42f/fq1LtHKl\nljz39sa4cSv29x4uZpePunZVqIAlS7Tf5crl/TR/vu4ljouDgN9/0dITnTrpRuPQUFixgmu1XqVj\nyDbeKrmNjj1ctBxDRAQtHNez0thUUT3T0rTdS5dy8M0yFLbg2DHdlP1gfZL7KIgbrGswhij7M1rC\n/cYNhZZ6/XXte9myD+/CHTXKimp78KC2X6SI7q7N7UNQkKKUFCqkG5UbNXo8Lttj6NQpOF/hZVoa\nl+Ydi3UqRuFWNbDt/S7Urs3KlVrc9ciR+7qbmalIvampVkiJunUVM+jZZxWRpGRJ7ftfDFy6adEY\nGp8aysao8VRo+qYitaemKvDjvn3WqsHPPQdr1mBG/jCGYFp2Go52jtjZ2BE+yIGLbkaec+/Huve/\nzjtHRKdDXn3Is2dhxQpmDoyhlN0FnnU7Bu+9p/xjyxaFG0tNJdFgINXbG7fq1THWrYu/nR2GxEQF\nXYyJUdQXFxcQIfvYKUyeXly0z+K4Sxodupjy9XPZjnLsOn2XAdOmUbh4cR3zH3/E/2QXbrnyr4U6\n+mcF1Lhx0KQJhmUVKXzPhvj7BdQrMbqw76fMTBgyhIz0dPYYDKTs3Inx5EluGwyU8vSkflKSnmZj\ng5PFwl28cCQTRw8nbDzdkaxsbBLiSfUMws3ZolvaIyKUKZw/r3Da9etrJVMvL91t/vXXGO69D+QI\nqJyawRmAMxBqU4+rlm1MnDiRhg0bEta+PS7Hj+uszs5WwefvDxUrIiJkZmYyf/5BeveuTlaWdRe6\njU0cZco486ZpNqUzVtKq6yZY2xb2KH7QSy+9xM2bN9m1axerV6+mbt26fP311wwfPhyACRMm0LJl\nS6pVq8bp06e5efMmZcuW1eqtT0N79oCfHykDRpB17Cx+lw9wnuK42KRxXUJoJctJd7XHzSORhAQX\n6plPc4JyOPhcoUR5F65cCufSFWe8SaQIN2hit5V5bj3x8rPjnXcUvcDGRtdjEedEDCuWK9jcL79A\nYCDZzV9kq20jzh1Oxevcfu4k2RLhdYumST9xvUQ9TpZ8kQtuFdlCAwoVUgSaypWVx/ftq/zK01Nf\n5969CsyRK6c3bbJWy65cWRHwAfwNCVwnGIdSxfXili31x9+0QjABAUh8PKkDR+LUvyf2vh7g4EDZ\nsrC19sf4J51l3eJ7LOMlor7pTq+28YobBKT+fgSH93vhcO6ktv3884rNExiowHb9+sGKFcrxatfW\nzu7erVAK1aurULxxQxlZYqIVdK9jRxg9uuB6M0+guTWm0DR+Nv6e2XD0KKU5RZxHaYoWhTffVLDb\nOXMKABPORV61s1OYqtOnFclj927rdzc3eOstRRmJjdW1ZbEoAomLi4IwengomkiNGtZyF7ntPyh4\nLRaWLfmUNidHMLpYdz669D1SYRm8/bbiowGMHcuPdTx5rcZbmCwmHD91ZOoLU2lfrj2xKbGULfQA\n/wAiJkaw/639eDopOrjdJ3a8XfltJr8wmWIfunLZMZ2bfVIo7O2Wd01WFkyYoLiJJpPqMRcu6OuZ\n6T+YN2+No1O30uwqkUltqc3mr9ezKysBQ+nKHCxRmqMLF1LHYiHZ15f6bdvi3aqVol7fvq3jaDBw\no20fTvj58kq7UyR7pj7U75o/NWfkt8No0qRGvuP+gw3/J6D+DjIYDCKOjpCVhWEEBCQbuOkhVgG1\n/BldtJUr6wJdvBiTjQ0pdnZcMRoxeHtzy8kJ861b7DQYCKhQgRJeXjy3YQOprv58UGwRY5dFMO25\npThfiqFbxmQcnQwsXKgTbfdusLdkFbjQMzIysLGxYePGjSxdupSZRWcC4JYFKQ8UtW8YVYEtx4/m\nvhS2AvWA+vXq0blzZ4oVK4aPjw/29p5cuOBNTIw7q1dfZ/t2b0QceRhtygzYQrQB1nZlfNuhlC+f\nSvnyFSlcWNHbHR1V6R45Es6cmUdc3C2ysm4ARmASUVEVOX7cDqhPr17vMGlScQ4cOMCsWbPo2LEj\nNXPwxR5JO3aolnzjxkNwMLl06NBhpkxZx5Urx3B1TWft2rU0bdqUGjVq0qpVN7Kz/bh3TxdyLrCv\nh4dqora2qnA2aQL1SsfjuHShteCToyMEB2Py8uOYSw1+TW3MBUsx/Asb8mAFL1xQ/nf+vP41mRRu\nJztbu3vpkqLa2NsrH/jhBz2WW2TVYtH/ver8CHGVkFsKIZ2ZqfXvjPGJ3DydRMb2/Ww8GcgO6uDp\naaBzF6Fau+0MeK0K+3e6EhwMDQxb2EQjbB6BiXberjTebtnY+Hjj0Kg2Li4GDCdj2HvnKAGb9hHs\nHorIUxpG587BkCE6AGvXqrD7A/Ttl0ZK/Dya5vtHwsmT9PuuDBMmQM+eii0HaqQtWfLoWlYFkojC\nZk2frg/i56c4R76+ak3s2KFYTiaTCrDbt9U7kZVlFX5gLXkcr5WKZ9XzoGuDZN5ya8CM1C1ItPXU\n71aNJN4VRmwdwd7ue6n+vVbU7Vi+IzeSb7Dl8haOvnOUeUfnMe65cQCsObeG5gua82rkq3zX4js8\nHD2x+cRAuHNlzg8+QJFxIcSmX8tj9kajrrPGja3GWj5yTKa/3Q98Pej9fIf3t9pP1eVV+aDWYD5v\nMpakpCTc3d2ZNm0ao0eP5qOPPqJLly64uLhgMplYu3Ytvq3HUNO8E5fBXmS4JD10q9Z7ZrJldxdO\nnsy/JA0jVQ78n4D6i8lgMIikpYGLC4ZoA4WTDcTfJ6CilyziDYcx7DUa2XHtGolJSdypWZMNu3cD\n4O3tTbVq1ahSpQp9+vTB398/t2EuO5Tk9s6zVKkCd7+axb0V2wjbMhvQSVerljL35s31ktTUVGbP\nns3q1atJSUnh99w66rmU4+Jzy4K+0304VqQILY/f4C0SObx9O6MnfMuSJVdxcWnAsvQ1PMcxvDxH\nce9eVSAU8AB0Vjk4nCc7uwQeHkewtXWnU6c7HD26nW3b5pKQsIk33niDW7ecOdhyGaz9Gvb0w2Aw\nI2KLwaC84H4KC4OsLAtxcT8Abz00zoULJxMaamDfvj3AfMBCoUIfUaRIaWxsFFjTzU1LPrz5Zs5F\nIiqc/gDobmxsLCtWrGD79u0sWLCAdu3a0bx5c8LCwggPDwcKU7SoAyaTeukmTFCk83v39N5fffWH\n+S2gUIZjxqgAOnoUWrfW440bK9p32bJwq+IQ7iSnYlk5GdAFHhuLKgGnXiL+26UsXgzDh0NEhTuY\nfU7yjG8d6tbVuVK4sHDugoUPRl9nbZkwMDkyKCuT6tWhbeeb+EoyrcwH6fBZOW79tBHP0kHU7B6J\nR2RRDpzzZOXiDN6dWo7AdAXJnR/6EZ26jKaYXU3k+11cvapCs3lzxbE9f14NJ3d3PZaRob9nZYEx\nW2i48xMClk/HZtpUtfqexko2m7lVtw2XTmZS7ch0CA2lRw+YMcN6So8eOobOzmrcubk9ujnOn1dL\nMzxcXaTp6Wq+5ta/yVE+adNGcfAKmrzh4eq9uHZNNYasLNUSbGzAbOabipn0f16ocQ32FIXMyT44\nrloLvr4Y5oXnNTOw5kDG7x7/yK7OarqM6kUrUXZGaN6xsqbXSd38HlefU2WtzPI7nGlQAYv7db5w\nERIStBQKKCZznz5w/nImhX2diIyEwECh4q82BB5oSVyVFfnu93z486y7sI5+1fvzddOv8v22Z88e\nxowZw6FDh/joo4+Ij49nwYIFbDpzlSJk4DvImUTXjIeeYXP9WSxZ3Jm9e+Hnn7WuWGp2Ku6fuwM8\nNeL7f4P+PQIq594FCShGCjCE6tW34+npwfr166lWrRqpqamcPHkSs9mMjuEU4gAAIABJREFUzQPx\nqZyGOWMoRWj6aS3m9/PPsGQJa7p0ISkpiZSUFGbNisTX1xaTKRqTyUxMjB03b6YAB7Gx8cdiqY29\nfTkiI+2JiqrJvOJaIdQtC1LHCAbDFnrIYaYxADtmY+bNvNuPd+hBM5utNPPfzdWricAW4BRwCdhD\n3bql2b5928P9Br777jvefvttDQ6ONOQIqP44OrqQlVWYEiXacvNmPJmZRejZ05Xy5QsTHh5OaGho\njhAI4pNPPmL48E8A1UzbtWvBrVvPsXXry0A8WgjtFiEhJylcuBKFCrlz6pQyeE9PdW1HRKiwqlpV\nmaSdnTJIk0n/HjmiXppH0cWLF/nhhx84dOgQ165dIyYmhsDAQNq1a0ezZs1o2rRp3rmrV1+mV6/C\nxMU506CBuvdbtYLOnRVo9MEKwg/SlCkKLHzlijLXXPfU6NEaT3lp3Dh+S/kYi002RAsYLIRVukC1\nhjdZ5FoXYtrB4kVUqQK9+5r4ID6EuNQ4ZoVa6HLFhjZFe7Ls2jQE4ZnCVTgcfwBMjvRNzWDCNwYY\n7AfpvhT6+Uye5+l+ikmIIcI3AvuUNO7GZXJ+wiqqftcdQzT4XSvLGvO7BAzqRJ3K6QxsFsOAZc9S\nvqoT9eur8N6wQcM9bm55IVHOnIESN7bxvaULqaHlKPRRD3xfaZRTRfIBsli0DPH48Zg8fXG624Go\nt2awq9tu/P3VKOvQMoUtB9y5G5+Nq7cDr7+QxKlzdvz0xVXKGo9wzXQH7ztpHGoSxeojixm7zV7B\nfB+81YutsBn1KXzzjVZutLXVTp8+DWFhZNgKl5IuUdYjnHumNK6nxlLGvyzlppQjzCuMUQ1G4ePs\ng9FiJMI3gqFft2dM8s957Z97O4YSAWU5HHeYStMrPX5i/EF6xqshh5M265ecat4DB6p1GRYG6y6o\n9QXQuHhjNl7c+MQ2nQwe9KrRgxfsx1G5stWDkJAABw/uYvbsLzh7No1JkyZg0/UZou5kU+Y9uFNA\nHcd1jecw5cpvXJoxltplSzB5MpxPPE/JiRp7vjvkLl5OXg9f+A/Qv0ZAmc3C/PnXePNiCIWS4ZYH\neQKq8LS3iI+fTq1a0+jc2ZaoqCiqV69Os2ZtWbduPyJXH2rTZAI7ewMb7JtT8cZsbG1t+b5dOxod\nPUrP4sXx8vJiw4YNQDVgL4GB47h5szciTg+1lY9G6FjnCihYR1cO8wNDMfApDg43yM7+Ccjg889H\nMHTo0AKbcXV1xd3dHRcXFy5evEjbtm15+eWXGTBgALG5MM7wkIDKJUdHR0wmE2azmaCgIAoXLsyJ\nEycw5pbReCpyALLzvs2aNYvOnTvzww/K39atUyGRnKzeGGdn9fatX/+wAgywYIG60Zyc9PzciqAV\nKqgBpkXZkli5ciUbNmxg7ty5FCpUiA4dOvDyyy/TrVs3zp07x/HjwpEj6t2ZM0etrEKFNOTQooUy\nCUfPJEwWEz7OPpy8dZKNFzfyxYZZxI1fye87hS4rXufciHVQcTa4xYHXZagw39rZtV9D0wfqXBx/\nDZb8iJ3PdeSFnpjDc+ruJESCf0yBI2iX5c+B7odwMBWi7EJHMNtR5hcjMTFWQ0EEuq/oyqwjsxjy\nzBcYkkOp7NGC2MsuNP/pDUo+P486l2zZvsJTXdg5lFa+Jk793sHWwVa1gPDw/DfPiddkZcGhzUmc\niv6ZyINzqcBRDno2IDmkHIFRfpQo64hbcqxmkaSmwuuvM7T4RcYkrwagonM1Gn7/MV+93opz30KJ\nRNhYHBpd1CWY6gCu2dC3GUysDl0PgbMJJldDXW3R0Ro7W7AA/h975x0eVdW1/d/MpEwqISEkJAFC\nEhIg1CCh9y4C0mtEBRSxgQUERAOICCgqKgKigoB0pIPUUKQIkR5qIATSK+nJlPX9sVNJQHwenlff\n5/3u65prZk7ZZ5+9z9lr77XutVZ8PPfGDKb6hec5+NxBOtbqyLXka1RzqIajtSN5xjwORx0m5JcQ\nknIeL7nes3WeZe/1X8kxl11N2FrakmPIechZpZDiBy43wawDrUo/Y6dzJNuUUfY40YDmgQc7tAoL\nvlxAn5GtuJdxj903d5dJAFkE60Rr8quWT1VTDllV0UZ35LUa37HwuzQwW0CmB+jTwWxBQC17rg3T\nQCrgXHER27uvoPevo+hdazC/TVzH+gPXGXe0F3cTb/LDVuh7Qv50Mvc/hf8aAUWR3v5DTTkBZTXH\nioKCBTz33EusWGFZrHZ/rTD3ygsvKO3AgQNqILx9W6nBW/knczdZx91U1dODAgP5OiaGuS++SHJy\nChs27MDD4yUiI2cBOtTK5k0gHguLGtjYRGMwWGE0pmE0XsbX9xqRI1W6ghIBBf3YzGYG8GAvjBkz\nhmUVzC4BtFot5gfZYLaAO2Cn7purcDvpNrW+rAV7Pqdn5b3s3r0bgGXLljF+/HgKCgqws7Mju1Ax\nbm1tjaenJwsXLqRTp06MGTOGU6dOUatWLfbvL5npbdq0iQEDBrB792569uxZvN3b25vIyEguXbpE\nw8KEWyaT4k3MmqVyD3XpolRjLi7q//HjSntTGq6u6hMRUXb7oKEF1H36AE61bnMz/TLh18O5EXWD\n+1H3MWWrwePliS9zKfESAS4BRN2Pwsu+Jt55/Th2ooCDOytD0HfQYG2F7crNbvTp4sq2qIenPalk\n5cT9gvK6fc6+wOBmnVlvHFl+363OdK0fxNhu7cgnk0UnlnHinZ9xGTOK9LBRmHx3QMOf1bFzU6jt\n5YyTE5w+LVTqM5v7QdOxiO6MscaB4iJt83zxuFmPm/WVWujzZqFMqNITxo9X7MPNm5Vh7fp1dcJT\nT6nZQtF/UOlX/fzUcjchgbjIHOSbb/C4XrIyz8CBXBtn3HJVipUC3zr0b36Vnf4lxZhDQRsKS+61\nZIHDda5VSuGLX5+j9uloer0fhl10Q/KdYzDap2Bz3xVr6zzS9Zn09nqBjc8v5kzsGZJzknHSO9F+\neXsAPB08GRI4hAUnF/B07afp6N2Rd/e9W2GfNHZvjLXOmkr6SkTfjyY9L50g96Z429clMbIaNtfy\nGXh4Kn2Hwdc9v+bTE5/ybMCzONs4U9OpJtU1LejVL4tc/S243RFyXQDBysZAQW6p/CNaA4gWRAcI\n6AxKcImW4gHHMhc8TkPvl+luMZZ9+kmY9epd7Vu7Lw3zGnJ883EO7j6ImIVhw4YxZMhQhu/7DK8a\nBq7nnqjwHssgrjFUOweAxY3+GGsrRqUu2xOTXUyFp3y+R00colvU5WKqYs72Nq5gu4XS2oR/Xpmg\n+2lkZ/1/AfVEodFoxMVlLrm5e8l59wBVMiHZgeLnZVfzXcycuZnLl7+mcWNrjh4te761NTRposhO\no0YpnezJkzksWhSF0egJ9AX6YoMPdxmFh+WnFBhGAnqsra8gYkdBQQ2aNZvLc89VxWjswNSpx8nN\nfQYobSH+FD5UL5h9HmTN9QPe4NMxUby9bAEawNLSsngVM2vWLKZPn15yugVghlMnTjFs+DBu3b0F\nVQEHQF9YzdLYCgNfHMjGlI1UCp/N9VVjcKvkRlBQENNXTycpO4m0vDQauTZi2BvDSEtOAz04xznz\n6fRPOWE4wYjOI7icfJkvvv6CG1tuQCZgC206tOHYrmP07NmTiIgI7hTlh7KAo4eO0rZtW1atWsWI\nESMeqw8z8rLITLHHxUWtHCwtwSgFbDt3lO23NtDMMxg7Kxte3DkcS4MLcqcNnra+VLPyJy3dhKNr\nGqfDF4ImherNPfCrXZ+k3DgupZ4rvkaASwCpuWkk5Sj9mcZkhS6qBx7yFC1b6NiSOJ98bTqtKw/k\nt7TCjIlnXqK57UhOnQSsMyGhIUHz+3In/Q4pm6ergSyqA11f2cM+G2W3c07qTerv3eHiCHivMkQM\ngPUb+fxzJTtmz1ZCOS4OFm4/SOefOqtrXR4ITlE4X5nMV9OacOtuNguujSdPk8pI0wHeHO2O2SUC\nEwZ2XN9BvjGfI5d3cST1j+J7nGJ3nXderI1z6dmzCOzcicHKgp/i9xCTn4ykpRJz5iBR+nxSbSDV\nBkadg543Idi2NqYhg4hPiSZm2yq8MiC1/hjqn1jGZN/JfBqYgDloeZn+8zoSwr12Zd0LKkKDe19y\n0avQrcNkATpjhceN8fmIZbfeLynf0Ys2Ndpw9/5dfO0aM7L+KCySg9h3LIVNqypTx9+S7dvVrbq7\nq8nP9evKTuzrC52c/mBpeFMOf3GWgCGNcXdX5aalKfLnSy+p/zpdSY7Ov4ratZWaEwDbJHjbE63J\nlmDvutQ4WIP129ejN+lp0qRJoVAagqurKxqNhqgoaNFSiI0BrVaNx3l5SoNw43YeS349TFAdF87F\nn0NrsmX0rhG42roClF1Jllrl6TZtwBTRB3x/xd4jjNu/L+C4nQ8vTMzBzymAWq7VOBV1gbhDz5K/\nJ5QkczWqkEJiglBkhv+78V8joPr0GUj9+uP42LJLOQF1caDQvn0GqamOZc5zdIwgI6MRdesWcOWK\nprAsE/Xr9+bixVTgFSi0CVWvnsDdu7uAb3nrrY2cPRuJlVUMVarsZfXqlcBUYHaZ8rt0ieW5512I\nvieMGqFn2bItzKAfAPoCyNsGBMDU8VNxPGjkve/mMfLNkRzJPcLd/Lu84v4Ki/YsUlrELB66ZC+H\nk0ALYC1QHWgNHpnejOg+iPk95vP0wqfZlbyrwlMt0i3Q2mqp8WsNbva+CXko4QewGZrVacbpeqcB\nCDoXxN3UuxgyDKQ3TVfXAtgF/F5SptFoLONPZRYz8VnxAHx16is++U3RGbOnZpOSk4KtpS0uti70\nX9efX67+wmvNXuP4veP8EfcHH7T7gPfavIch14Z9+5RdJT4eijLYA1hZpVBQYA1osLa+S0CAmRYt\nalCjhj2jRpXla1y8qFiYEyZAbvs3ocVC9EtuYdVoMxluO3EL+4UmdSsRE6OOBTCaTBjMBk6f0NOu\nnVqonMzYwLyowUqdt+giFK2HQzWwdx4cLz/zt7NTGrMrSVdYdHoRr/h+xsBNfbliKJv2O+rNKGo6\n1Sx3PigVbtflnTgQHUZtq3bEpCajiW1O3acSqF/LjXdbv8OlxEscuHWA1RdXk23Ixjq7Ntrk+lgm\nBFNDdNhZanHyvUuSWzh/5B3jxSaj+eHs92WuM7zBcESENZfWVFgPAL3OBqMRNGZrWlqMI7BRHkkF\n90jKTsLbyRtPB08+7PAh32w9weTXXalduS4Rpi3gcQbyK2GV54Vku2KIbgI5VWjV1sDV+Nt42tQm\n476mOEdmRXj/fcWcz8tTZJaGDZXd08NDCZ0ds8/Te3pjGnKeizQkN1d5AbzwguJoPA4mTlSuWufO\nKXcpe3uljtbrlUdA9eqqDqmpcPduCgMH3sXbuzErVyoNq4hw/fp1/P39y7lsnDmjhOQff5S9ZlgY\ndOyo+CIVEU1iM2N55vOpkO9IgTmXNs4DWX/xF9LCO7Ps7QF07qRh8eIwNqxpQWS0LQtdQnkz5cNy\n5djbG7iX50glYx4x9+7h6en5eI3yH0JYWBhhYWHMmDHjv0NAWViIYpl+qCknoJgh9OyZy5EjL/Lm\nm6uoU0dHo0ZmGjVSg2bNmi24c+cNlE1lgCrUNglyqgAXoVJ/cI6EPDi+6TivvPIK58+ff6ASgKUd\n1PYGl6twJo7A5wZyWX8EnKCqviqJeYWWb4HS+rwP23/IsS3HOFD5QNkylwAvA5FAKfNBnSp18Hfx\nZ/vx7VjYWWA4ZKB1u9Z8NeIrgrsFY0w0UvOdmtzZc4cer/dgT9weuAHcA+JQnseF6FyrMwdul1y3\nr/RlO9sxa8yMyxvHYv3ikoO3AFWANo/RKcuBWKAqXN1/lZFbRhJtiqama01Ox56u8JTwl8JpurQp\nAJ92/ZRFZxbxarNXmdhiIgWmAhaeWsj4ZuOxsyqvfxBRRID4+CJCwFpu3TqGl1c31q69ya1beuzs\n+mAwVEOn0zJgAHz0kaZYWOXkQMO3JxPpPg8+jSPA0x0rK6hbV/m57t9fMpC9+qoyeK9cCXPnKnec\nq4a9XAvujveeP/CyaEJUFAwaBH1G3aJhjZoMHaIr9qUtwu7dyl2uNOp+XZerKVeJfC2aY/cOUaNS\nDTp4d3hkUy84sYDph6aTNSWLQRsGsenKJgAqaby4L/eKj9NmVif4xm6efzqQ4GDFeTCZ1Epjxw61\nkjA5XUPbZBWW1f/AyecW9g5mYrPuUs2hGrfSblHPtR4aNDRyb8Trwa/j5+zH4ajD1Kpci+qO1XG1\nc31kXR+E2VxxinI7O0XHdnFRg39BgeojjUaZzgoK1Ornz1CliirDJvISZ40NqM9FLlO/3HHDhinb\npIOD6vO4OOVaN2+ecr3y9v5Lt8W3315m0qR8pk8P4rPPlG/dg4m6zWZFMjSbVdsvXaqes7+KQ4eU\nx0BAgHK3MJszWLFiBdu2Pc/58+dp27YtLYNXcfz3keRPnUHM6A+IilLk2ipV0lm0aBEXLizlamoK\nNllZpKel4eT0/0kSTwwajUZ+/VXw8YHaqzS4ZEJKKQF19lnB1xe6dGmOtb01WRZZ3Cy4SXbVbMzV\nzehERzOnZtw238auwJtbVqeKy7ZPboQ43CTbWtloplSfwtwjczE7m6ES2FnYQSRk16zIuaEUjqG0\nfQ1UnawNkF+o2u6c25mYzBhq16nN273fpsOKDmpHBopVPgsGfjWQjW9vJDUmlcqVKxfdNxSyQQ/t\nPUSHDh1wcnLC2tqaaq9U43bYbcxtzGRZZuGR70HyiWQKkgtgoDon9Y1UOq3vxMwOM7mZepNuvt3Y\n8t0W3jcq1Ur9w/W5VPcSVAW723ZkX8pWAqq061MsivV+Ez7p+glu/m688OsLZW59ZZOVhJwNeWjT\nNPdsTo4hh+aezTmXcA4vRy+2XN0CPDlG0b1795g8eTKbN28hP98Wa+uPMRpDsLPTodXqaNhQy2FN\nKHSYAXPSeWlUJZYsKV/O9u0wZowaPI1GJbwcHOB69ikY24KUSSlU1juXY2sPGKBWWn36wNq18Msv\nKov7g/Bb6EdkWiSXBgqBgX/9Ps1ixmg2Enndik6dIPeZIeSFD+TjF3rRvZNthYEoSiMtTZFLVq2C\n9evV77ZtlUmrS1czLs4VsF2fAE6cUOqxosASGo0iyTRp8ud1dXZWAiU+Xq00bG0Vw02vV4LMxwdq\nZEVQuU0gz/hE4NW1Lrt2qfP27VO2zieJtLQ0evf+iqioAVy7Fsjw4WqFHxKiSIiJiUpoXrqkrh1T\naDLS61W9/yry85V58c4dWL0a7tz5mtdff50TJ05w6NAhLly4wM7td8nI/g1mzsQ0dSr79u1j2bJl\n7Nq1i7FjxzJ9+gfYevtjm52KySgVThr+DjxJAfWfeXIfE127CvcswircN+fmEHbeWcvvT//O0XZH\nOdvqLJkdMjHXM8N1MDmaOGk8SQIJZYQTQFaV8+RICdMnJj2GBs4N6F61Oy83fZmdI3bSqn4rvIxe\nvBH8BmdfPktX+wfC+qRBL+teKipRoQzXAMxQv12sXMgwZODt5E177/bsGLaDV556BV2lwqfEBGN9\nx+Js44yDg0PZsg3qY1NICzabzVhZWXH+zHnadmpLFsqT3NrGmhnvz0Bvp4fr4LjDEWdnZ86NO8eX\nr33J7TW3iQ6PZtOmTcVFX4q+VMQwJy81T9nAinq5iJ/xW+F3JLw36j2WzS9P6ohIjChN9lMMo28g\nf0o+23ts54e+P5BvymfZ2WV4O3nzcaePiw+tZP1XvDwfDi8vL1avXk12dib5+bHs21eXQYNew9m5\nNzk5zbh6dQsYC3WZRj01atzGVIExondvNShmZip1Umysomrv3aveoV2bdpGfn1fuvHHjFNV461bF\nZqxIOAEYzMr+uHVrxUzHP4NWo8VKZ0XduoqYsrLPOtJ+G8Rbr9sSGPjnbk6VKys11kcfqZVVeLiy\nrYwcCVVctIweXez7+kTRsqVyiPb2VisqrfbRwql0XatWVUzP7t1VdKinn1YTgl69lF9co0ZQuYp6\nl2Z+pKVqVeUu9dln/55wMpvNJCQkcPDgQdatW8eMGTNo27Yt7u7uWFjYUbeuL3Z2ymF706aSiESz\nZyv2/PjxajU+b55aYT2olHlcWFur58XSUqk0i7Q7RXUbPHgwfn5qDNuy9SDVqtVlwoTtpKdPo3Pn\nZMLDv8DT05LsbPXAnTx57V9vlH8w/l4WX2jJf+cMSC1aQT2IVGA/kIYafFOB6cDPUCm/Evc979Pm\nuTa83vp1hmwaAoBFvAVGdzVS+573xS3BjTlz5tCuXbsK6xN5JxK/5X743PfB4bQD0TbRpO5NxWAw\nYDVbLZv0Bsj7GLCBKlOqkBufy6Tuk/igzwfF5eg/0pNvyodQOHRIrZCgkDqu0ZTRY587d45GjRph\nZ2eHq6srFr0sCKwRyLa8bQD42fjxavCrODg68Pnmz7n71V1CQkKwsLBgz549xMTEkJVVGBalqC0/\nR622qoP2pBZzhlnZwZ4CDgCdQbNGRb0gFmUnqwK8VrY9LNZZYHzGqNiFF4DdQC707duXrVu3MmzY\nMI4EHSEmO4Zq26vx2duf0XdgX2wsbB4/vNK/gZiYGLZu3cr6uxs4rA+j3aHnuHf3GMnJycUO3BqN\nBjc3Nzp16kSdOnWwtCzryJiRlYF3T2+qJFTB0tKSNWvWFLMY/wo8PvMgLisOQoXPPlOOnVZWyvE4\nJUV916r1xEPV/SnMZrVKCQ1VbksjRiihGxysVil2dv/zdfpLuHED/P05+sMNQmb4sW6digL1VxEe\nHs7KlSv57bffuHr1KllZWbRq1QoPDw9q1apF06ZN6d69O/v2ObF+PWzY8ORv5WHIyVGrx6FDh7Jt\n2zYsLS3R6XR07dqV7dtOkJN3l2l8xMdMQ80YDwONUEZjRxIYR1WSSEk24+Lyz+jMJ7mC+tciTz5J\n3AFqgLH0Wk7AfqM9WQ2zIAC4B8MbD+eTTz6hRo0a6pj5QDa079Oel19+maeffhqzmNWKB5g5dCZX\nUq+w8sJKzNZmbty4URxQtSL41lQGowndJvD0q0/z2WefAZQb1ADIBXsLe1L0KdSsVlZJbam1VALq\ngXO3b9/Oxo0byxxbREIwmUxoNBqste5s2/UUdFICKis3i8P7DtOjXw+8qnlxOeMy33zzTbnqdO3a\nlX3so4a2BqmmVLLMSmhpRYvZwlyygipcJDjZOZH2RyljQAVxULWWWsXC/wPla1yoxthaGKduzZo1\nih5fCeLC4zh69CjDhg1j6NChvPbaa7Rp8zhGr38dnp6ejB8/ntzjuRzeF8bhsBWICJGRkezbt48L\nFy7g5OTE0aNHmTBhAtbW1gQFBdG1a1cCAgKoXbs2Bw8exOu+F+GXw1mwYAGNGjWiR48etG7dmoYN\nG9KyZUtcH2O6/mWPL7l6L5GC97OYNs2OwEANublw715JeCWAqVMVEaByZYiOVnFkK/I1f1LQalUM\n1+++g0mTVLSOtm1L9nfqpFYCTZv+5+rwb6Hw/Qg7osXO7vGEk4gwY4ZScxw/fpwbN24QHx/PuHHj\nmDVrFsHBwTg5OVXo5J+Xp1R2/y7u3FF2Qh+fPz/WttAp12g00rv3Oxw+HExamhPr19dDzQ71qJk5\nKHt7kaanFnAOrSYPBBwd/xnC6UnjbxVQu4fvJtgrGJe5LuVWTgH6AMLXhIM9kA8/G37m559/Ljmg\n0Hx048YNAgMDOXfuHEeOHCnenWfOY8WzK0jITqBdx3a4dXbDvYin+gj4+frh6+vLokWLKtw/efJk\n5s6di6PWEXEU5s2eR6v5rTCZTGqWrrNUKjwo40B78OBBAh8wUBS9JGazGa1Wixh8lPNeIdIy0qha\nrSrRsdH8uutXAN5++22WL19OSkpJBPBTp05Ba3B2diY6KxoKtZvGPCPogkHnDawvFlDZ6Q/Y3ioQ\nUAWmAiXY9oBrJVeSMitwsCyl8/72228JCgpi3bp1rFu3jhs3buDn51fcDjt37uT06dOkpKTQuHFj\nmjVrhlarJSkpiaZNW3HwoD29epW8sI8Le6sSmpRGo8HPz6/4usW3ZzaTnJzM3r17OXHiBEeOHOHi\nxYtYWVnx6quvYmlpyeTJkwkJCWHx4sXcvn2bVatWERUVxbBhw3jjjTdo8gjd1aDAQYRuCGX2bEda\ntGjJd98tJTAwEJNJCSiDAb75RqmJSmsgx46F9PS/GPfuX0Tt2ire3ldfKXvKvXuKbdarl1LR/fEH\ntG+v1IIhIf9ZwfnYKBRQq37WMqxi3/cyuHXrFhMmTOC3335j5MiR9O//PG5uPrRrVxsXF5c/PT8z\n868LqN9+S8TFxYC/vwfbtyczZ05lzpyxwGRSBIrAQFVunTrlCRelYTAYOHasO2lp7hQUfAKcRqku\nAD4DvgAc6dKlP6tWzWbr1mv4+zfCZZA1JGdS0Tz6vwF/q4DqUbtHcdqIBxWN4YUpL5oHNmfevHns\n2rWLuXPLe3NfuXIF79J0nVD1deLeCTQaDW1rtCXHkMOYMWMeq07eTt4P3ykUP+g3zt2AOhBxNgJ/\n/xLvxy7zurA/XdF6+vfvz8KFC3n22WeZOnUqJpPw3nvvFR/bq1cvbt++jdFoTV5eS+7HBIDtoWJf\nk/yrTYmLH8z5mCXFQuTNNz9lz572jB3bhnr1KjF79tO4uZk4wn7S07OApnDrHtRNAJMlWHYAXSHX\nt1AbWJD7gESqKJOEtvBjhqSkh3j/PzCIlXZQ9vf3p1q1auTk5HD//n2CgoJoURgfacWKFcyYMQOt\nVlsYQeMsOl1tLC111Kp1h6ZN4+nVy0SvXs3K2+8eQOsarR+5H9REoGrVqowcOZKRIytwyC2Eh4cH\nM2fOLP5/4cIFZs6cSbNmzXjqqacIDg7G39+fc+fOYWdnx/3792ndujV169blxx9/5Pbt23z99df0\n6tWLvXv34u/vTyE3hhkzYPp0pXLz8ioRAO3aKWN8QoJyNh8w4F9qFUzaAAAgAElEQVTOqPFYsLBQ\nNg8PD6Xqe+opJbCuX1ffzz+vPhkZikjyt6KwkXIKdHTsWHZXamoqN27cIDY2lh07dnD27FmuXbvG\n+PGv0rPnasaPL6m8v79ibe7dW3L/q1ap+/v1V7Xi+eorZW8qTA5QHIT4yhVlJ0pNhWPH1GRDRJE9\nbt2CkyerAklotTcwm/3Rag9y6FBdIiJs+fDDTOLivNDpCrCxMfHWW3qSk6/StKmG7t39WbVKy9df\nq2fg0qURJCfXQ6PpDJzF2dmZ1FQ1KjYPDsZ88iSRkZH4+Pig1Wp5qcgJ7B+to/338bcKqO+++47X\nXnsNKpgd7d69G3t7e9q2bcsHH3zA4cOHqV+/PpcuXSpz3Msvv8ySJUsICgrCz8+P9awHMyztvRQA\nO0s7knOSH6s+fyUi8IsjXuSb8G/Y8csOOtbpyPnz5zl48CD7dv4Gh2sByTg5PcPLL58lJEQDFDm/\nrgZcgCtERdXFy+ssIvHExtqBx7cQoAODDegyIWUw23d1xrrTb6CriYPDR9SoAVptb9q2hYUL4dq1\nPVy7BnTSEHXbFTgOp6/CH7Wg2SKwiFKe9ADZG2FzHCSNBhajJNY5MO2heNlViP6Dp/KL8RPatP6W\no4cnAffLN0LhIFvkqNylSxe1mkOpWsaOHcvWrVs5d+4cPj4+fP311+WKOHMmk+7drZk7dyWHDl0m\nKak1u3Y1YPVqHzSaY/j6XmboUCteeeUZ3N3dy9m36letj/mDh+dqehyYzWY2btzItm3bqFWrFh06\ndKBp06Y0bNiQjRs3EhcXx6ZNm4iOjubChQvUr1+/2Ka4ZMkSwsPDGT9+PDVr1mTevHlUrlyZVq1a\nMXz4cDp16oSDgwNubm4EBgZSvXpJ/f39ha5dISBAg1ZbwgYbMEBRrZOTlZro2WfVoFqz5pMfjzp1\nKvt/4kRFfnB0VIP4sGElwjQsTEU3qkiAGgxqe1H9IiOV0C0oUAKiKJ6jvb2yfSUmKrKKt7diyRmN\nimWZl6cG/hMnQBOnYz1gRsu0aQaeeiqc33/P58qVaqSn18DSMoa6dW8waJAPQ4Y8x7lzzVmwQF9M\nCGnYUNnc9u1TbXvpkmIBhodXvGr99FPlijB5slJ9FqF2bRW4w9ZWse+K2Ny+vnDyZC9q1+7ACy+M\nJCQkhU8+2cyhQ67Y29vj5bWOgQNb4O/vz2efXWLu3Pro9f4sW+ZJQYGWoCBFvFiz5j63bg3G2noU\nlpY3OHDgFMHBwcpOrNXSp08fKNQO/J/Dk8p8+Fc/lM4O+yFiP4kyGXUBMZvNAoiPj4+UOb7UR0TE\nZDJJTk6OmEwmIRSx+MCiOLvjkjNLZPTW0Y9OAfknKMqoq5+CzJo1S127g6rrsUu35dNPVTLNxo3N\nUhSJzdJSpQPv0kXk889FXn/9howff1tgrECYwEcCp6VBgy0C4wVuynML5kjr71tL5Y8rC6FI0Ctf\nqPLafiS+Lw2RqKiyiTNzc1XZPj4Jqt1eDCzVNlqxaDlE6NVBGNZd7a/ylICXODv3EJglsFzgimBz\nt0z2TkIRmi1U6a8bFohOly1wVGC2PPXUOwIW4u7uLkxVx3p4eEizZs3K9c1TTz0lzz77bPH/gIAA\nOXq0bIr3b78VGTWqfJunpYn8+GO+1K4dI1ptvlha7hUHhw/l+eeflytXrvxb/Vka06ZNk6CgIGnc\nuLF8+eWXMmHCBGnUqJE4OjpK7969ZcOGDSVZliuA2WyWb7/9Vu7cuVNm+5kzZ+T111+XPn36SIcO\nHcTV1VWsra2lWbNmMm7cOIE5otEMFr1eL6GhsyQlJUf27ROZMkUlup03TyVgHTxYpG5dEScnley3\nXz+RCRNEPvlEJS7OyHjyyXizs1U9ip7lPn1Kfrdvr5LWjh6tnr0PPiibcbZ7d5GaNctue/DzsP3W\n1iI6nVmsrU0SGGiQvi3uiIC4ESda7SEBEQ+PyDLnVK9eNulvq1Yi+/erTPBZWRXfX0qK+kREqIz2\nRqPanpAgYmEh0qmTSth9547KQv0wpKenCyD9+vUr3hYeHi41a9aUt956S+bMmVO83WQyye3bt8Vs\nNkt8fLw4ODhJXmEqbBX2DbG0tJSePXuWvQioLLwPwGQyyaxZsyTFwkIEJCoq6jF69n8GPMGMuv9o\nAZWq1rji4OBQfKxOpxNAtFptsYAq0zihiP4jffH/VedXydCNQ/+N5i4RUDZTVB2qVnWTpq+1EkIR\nB5dMAZHp00XWrRM5dUpky5aDkpFhLn7wy5RVZhDvIDt3GkSnqy6QKAQ2kVqf1BG3+W6qHZp/IQwY\nJnQbI/VeHVemnGPHVO95eoo0anSnUEC1FFgjMES9sE2WCX2dhRHt1X4X1Waenp4yefL74uraR6Cp\nYB1cTkBpW9sI04vq6SMwWOATsbG5IZAj8Ivwvk4IRTSaZHF3jxIYJ/CMQDcBR3F3d5c2bdpUOLHI\nzMwUs1kNKCtWPLr9Y2NFli5Vwr9du91StWpVCQoKktDQUImNjf2X+zU2NlZsbGxk7dq1UlBQUGbf\n/fv35aeffpKGDRuKn5/fIwVVbGysAHLw4MGHXstkMkliYqIcO3ZMpk+fLu3btxez2SzXrl2Tnj17\nSkBAgHz77bdy+vTpCq+Tn6/6fOZMkbfeEnn22eKs3+LmpjLNd+0qMn68yM6dInFx/3KzFCM5WSQ0\nVJU5fLh6xvv0EXnnnbKCxcpKZMkSkYUL1f+qVUXCw0Vu3lTC02hUk6nERJHr10UOHrwvx45dlA0b\ndstXX30rL7wwRdq27SS2trYCiJWVjdja2kqAs7MIyNKP5kpeXp4UNYvBoLK+m81KmO7Zo8rOzPzX\n7vP+fZVpPTRUCadHzEfK4OLFi8UTaJPJJGazWTZt2lR8H88//7zk5ORIeHi4ZD0gLYODg2Xy5Mky\nYMCA4ndCo9FISEhI2YuAmGeWCKiUlBSZOHGi6PV60el0klD4EGRkZPxrN/8fwJMUUH8/i+8ROHBA\nRUvILMoxA1SqVInU1FT0ej05ORVHNTaZSyzRDtYO3M+rQD2FMlgXObcZjUYsCnUXMTExfPXVV/z0\n0xoaNFhdxsm1Y8d0Dh2qRNblr6H9cfbvssPbmwfiYD2gMC9EXh4oVs67wDCgLr16ARRGZjeGcvve\nJLDWggPY2UN2AxWmJuLgLLp2VREX8vIUjTkkRGUW1+u90M2CBrWsuIgXEIqHx3FSLbeQp0sFXaHu\nyFwVyCI9/S3mzi2VZM2cg2IMldqkm6yiiQLNmrlw+vR6fHzOsHZtJ4KDOwIjQKtUoiIdqFMnhPj4\njkBdoBpgIj7+B+Lj51fYFtHR0Zw8WY8rVwwMGqSlDOPiAVSrBmPHaujSBTp16sGgQbE0aXKQsLCf\n8PPzw8HBAScnJwIDA2ncuDF16tTB39+fwMDA4j6tCHfv3qVevXoMGTKk3D5HR0dCQkIYMWIEO3bs\n4L333mPVqlV8//335QzuFwvjKe3fv582bdpw8OBBUlNT8fHxoXkh9Uyr1eLq6oqrqyutW5fYzfz9\n/dm1axe//PILa9euZdasWYwaNYqPP/64zDWsrFSG9dYPmNzu3VMBk41GpYpbskSlIAHF2GveHF5/\nHYrIr38FLi7w4YcV7/vkE/XuFNGkixASYiQ8/HdsbCqTm2siMtKaCxcuEBUVRUREBGfPnuXq1atY\nW1vj4+NDQEAAAQEB9OjxMvXrf4Wvry9WVsqtQ5OWBi4ujB03ukxi0dJdamurfKkeBRFl+wsPV2rT\ny5cVKcTRUakbd+9WNiV3d2Ur/DM1akqKCo90/HguoMHZ2ZnOnTsTFRWFvb09EydOZPbs2Rw5cgR7\ne3ucnJyoVasWH3/8MRcuXODYsWNcvHiRxMREnnvuuWI/RktLSwICAspd74+zGmwiIli+fDkrVqwg\nOTmZAQMGsGTJEirXqQOJiX9qq/3fin+MgJIKtg0aNOihx1eYC6oQJjERGwsnT17nbo4/x65dISgo\nmfx8OyIibLCxMWA0pmIwuNGsWQJxcVru3XPFyyua3NxMUlL8gU+AOcTFacoIqEOHlPI65/RAyDHR\nPFTD3LmKkRUbq+K0VUSHjYxUDqPFfG0AfgVK3q5e3RPZqbuFBlsV7Wl6Hu+EqX11alvSs54K3XPh\nghqoPv5YDUp37mjRfP0On6yZQi88ATvs7F4nMU9PJRcn7hsKGX/m1YAP2dnejBr1O9evL+bEiR1Y\n6upi4AFY5YLZGuhPeLgD0IVbt1zo1+8kMBkoAG2R7ecSYWGTHyjAH5iIivl0FZiECs2hsGxZHEuW\n+JCT049Dh17n6aLskRUgNVUNLHq9YkYtX67jzTe7MnVqV37/fTnOzonExcVx9uxZbty4wcKFC7l2\n7RqpqalUrVqVRo0a0b9/f3r06MEff/xBdnY2lStX5tq1a3/K7tIW2gC6du3K9OnT8fb2Jjg4mE6d\nOtGxY0eaN29OQkICVlZWfPPNN3z11Vd4enrSqFEjjh8/jp+fH507d6ZBgwb06tWrTHzD0ujXrx/9\n+vUjOTmZxo0bYzAYmD59Oo6OjhUeXwQvr1KJJlFx6hISlK3kyBH48Udl55k4UX3+LAel2WwmKiqK\n69evk5SUhKenJ5mZmezfv58zZ85gb29fnIstKyuLrKws9Ho9+fn5xelfiiZ7ZrMZa2trGjZsiIuL\nCw0aNGDo0KE0b978T++rsPHLfleAohBKVlbKhvXzz0qAJCYqm1d8vHK4LUJQkIqT16aN2u/ioiKF\n9OihBFZFwmnnTvjhB1Vm6VymlpaNgDf59dfphIWFUadOHerUqYNWq2XGjBnodDoKCgqwtLRkwYIF\nzJgxg6CgIIYOHcrAgQNZtmwZo0ePZv78+eTm5qLT6TCZfMtd/+q1eEYGBlKlShUKCgqYP38+b72l\nJpgGoxFLIDk5mSpVqvx5m/4vw9+cbqMQH4JdHmTrKRWLr+LzOnd+mgMH9mFvb01WVhYP1r8oBbJK\nOhaPtf4ajd9eQ9ZWay5figSgcuVk0tK8gA6Alho1HNFq65CY6ENOTtnorrNmpTLdqAYxmwJoeUrY\nsEF5gX/+uWLgrF2rXooihIQoIVKtmjLS3r+vBhFHR8jIMAE/Aa7AM6hAezZYW9/msw2RTLs4Bmud\nnsS8BDoctCGskxJo05rNplbcG4webc+XXyojbtHMUOE40KpsYwX8gl3bT8iWcKhugs/ehExnHBz2\ns3z5BE6ePMn8+fPR29iQN7mU4Az7gErusdyvsRnN/LtoNDrM5v1UqpRGpUoeREdHAC4QOhxMOpg1\nArUCqgH0R3n+5qBIGAWAtzoebeF/C8qumIwEBurIy8vEbDYSF+dMrVqKQeXjo4zmj8KLL6ooCiJq\nFlxisDcTERHB0aNHWbhwIYmJwVhatsVs9sTG5ibR0ct49tk6/PLLukdfoBRu3cpg9eoIzp07x4UL\ne9BobtKoUV1q1arF0KFDcXZ2pkaNGmi1WnJycti/fz9Hjx7l4MGDpKenM2LECEaOHFmG+fkgUlJS\neOWVV9i7dy8tWrTAzc0Ng8GATqejdevWDBo06LFo00W4cQPmz1fOuj17Qq1a+cTFxSCSSXKyC7Vr\np3HjRjyZmae5fl1HTo4vtrbu6PX25OVVws7uEtWr2wI+BAfHc/68B5aWBpKSqmBlZaagwISLi4ma\nNdNwdnYiLMwdg0FDairUr68iQKSlqfehZUsVuFWvVwzC3FwVkaFePVXPhAQlbPLyoG3jTFZudWT5\nF+kMf6USBQXKf+yPPxSBo1o1NfE7elSVl1foRtGypSrj2jW1ugoKUgIp6E9yHEZGRtK6dWtOnjyJ\nt7c3UVHqXT52DPz8hJycXGJj1XJRp3sPkTjM5h6IDHvsvihCRkYGHh4eHD16lL59+3L37l20Wm/M\n5tts3mzGw+M0ly9f5sXRo/lQ141PrY8xduxYXn/9dRwdvZg58zBr16ZyKXk0buRgMprQ6f4JvgH/\nRbH4iv8UCaiihKDFAqo5UA/lDKUDTOj1H+Hp6Uh09GIMhlwCAyfSu7d6OJcuhZxJGjBYw2xv1q+f\nVexBnpycQlhYPgMHCiJWpKWV78wRI9SDnpMDnTurRHlubirjL4BtAWR/rALcjhqlXqpp01TQTrU6\nUjOtiROVUCrK6loET0+IiWkFlM8d4+3tzRfrv2DIr0OoaleVuxl3abO7Pcd6qhw/lueHYfjlZypV\nukturhcFBSX9v3YtDB2qQQkAE/AGUB/8zkCLOzh5nCbdNg3mPw3ZW7C03Mjbb9ehTp1GPP/8c+h0\nPTBNDwHRMLrROPZtMhCdlQe1t8CnU4BBQG0UH12LcvSyVFG/DXqYHYMKV3EJSC/8Xe9xH4XHQnGa\n9geg06mZcNFjrAIQK1acnZ0axOLi1G97e6X2srFRA+GmTfDMM8LatRp++kmpx8xmFTDWaFSrtn37\nlCrY21uxt3bvVufXrw+3bgn37mmAz1m+3I1Rox4SCwll6/3jjz9YvXo1q1evZty4ccUOpQ9DfHw8\nZ86cKab55+XlsXnzZvbv38/48ePp06cPbdq0wdbWlrS0NGxsbIrDZ+Xm5pKXl8fRo0fJz88nLS2N\ndeuyOHasAwZDFhpNE3x9T5KVZYXJlI6dXSC2trYEBuqpWdOJwEAtIkrQb9qkFjGxsYqOXreuYhn6\n+6tvJyf1zmzfrmLy1aunjqtfX7V5pUqqLS9dUqrw2FjVX9nZSsCEh6t279NHXadGDdXfkp3DpBl2\n2JNJjsa+uI99fVV/OjkphmOvXqqfDQa17XF8uDIzMzl06BA///wzsbGxGI1Grl69SlphNNufftrD\n9OndcXDIpm3bLWzbNpns7AIcHNxJTLxJy5bNadRoGidONObUqX9t5dKlSxcOHDhQuHIyYWk5GIOh\naLJkQ9WqjiQkJjLfcQC+P37IhQu5rFhhSVRUkU+eEI87biRy84bwTyH5/R8RULuAnij10EXUILmW\ngABX3nmnM+PHf43BYOK5597E21u9SJcvx7CurhfaLB03XsmjShULFi0SvLwimTfPrzj1wrffqgjE\nBw+WRFdu317F36ooIHBpATXeICxZopzv2rdX1FtQL2f37sqXZfp05V9ROooAqPQQX3xRcb/VrFmT\npb8spfu27tSsVJM79+/QbXdPtCYLMnKr4xflgXvl6iTEXMFSInB2u0OAZw3ahfTDr3c7NH5+aFGW\npCKLnZU3aDsEo7NPJbvKTarMjSA914/KpJHEA8ljQjVY3erO0k57OGb6hDV3PiJbm02b/WkcO1a6\nUYqElDqHfHuYk0nFSAKOolZVj4NpwNNAWUPLypWK6luUZn7mTGUrMJkgICCBa9fc/rRkOzshPV2D\nwaAmMhMmqO0ajVJ7mc0laebDwlQ/FtGjg4PV4Dd7tpp4ODpCgwZqNn/3rtr28ccGpkx5PG/J5ORk\nGjRowKFDh6hTp85jtk0JYmNj+eabb9i4cSMxMTFotVoMBgN5eXm4u7uTnJyM0ajCfHl5eWFvb0/N\nmjVp06YNPXr0oHr16ri5/Xmb/e3IywMbG1Kiszl/wxZXVxUyqqIUFo+CiHD79m2OHz/OkSNHOHXq\nFJGRkTRt2pT+/fvj7e2Nk5MT/v7+aLVaZs6cydKlfmi17sALuLpWIT8/H51Ox6RJkxgwYAA1a9bk\n6FGYMqWs2u9xYTabiYmJ4a233qJVq1a89dZbWFi8jtG4EIBq1TYTFwfCAN5nFrN5v8z5AweamTJF\nS5OebmgSEzEUyD/GWfdJCqi/mcV3QTGBPkRs3yvFIvsQgXcEXEqx9ywEkLhCetKECROkdetF0r17\ngrRvf08gSZU1YKjQZk4ZllGdOiLDhon4+Sl2kYhihrVoITJnjsiaNeWpugaDYuRt2lTC4rOdgowZ\nIzJxosiqVSJ2diK3bysqcLduiv5raSnFlNlnnhF56SWRyZMVbVVEZM2aNeLiUnJfRZ9Wnp7y+9J5\nQihS603VDl82VzewjWfK0qYq+Jwq9Tu7sBK/VUdajEYajlPlpenLnpNoWU1mEixdLSYIoUiNV+3l\nTT6XwKavFfdFU06LA/fl+++zBEYV9kmGQLY6ZrKDwILCIhcLHC78nSGwRKCEFuzklFHq8gsF3n6A\nDdZKwE58fOYKfCd2dh9IaaJmq1atpVmzd8RsFhkwYIAMHDhf+vYNEfhVGjcuKNcswcEXyvyvUSOt\n3DEWFiaxtDRJWFgFlEsRiYpS/Vd0vI1NUVnKtWDHDhFvb5EHyVdms8iFC4plVhFeeukleffddyve\n+ReQkpIiSUlJIiJy+PBhWbdunZw6dUoyMjIeSY//X4GCAtXYhXTsx4HZbJabN2/K4sWLJSQkRBo3\nbizVqlWTatWqSY8ePWT27Nny+++/F1O8H8RHH30kzs5NxdIyS9q1e046d+4skyZNknPnzpU79vx5\nkfr1/7xOsbGxYio1wPz888/i6OhYAbv1J/VeYRR//1vq2QeZxizp08cgU6YoRmR0dKnC3dykzEvy\nDwBPkMX3NwuobmJhceIhAqr0x0X0+nECkYUUW+MDA803Au9Io0bZ8s03O6VlS6MMHaoosTt2qEYz\nm6UM7btFC3Wup2dJOd26iTz/vEibNiKVK5cqv0hAvVfyIMTElOzX60UqVVK/27jfkLxDxx/ZgY0a\nNSq+N1eQ1oUFxdup+/f5xEMIRT56o7NcuqTKreVtlo+nZYkN2dKlzl35bkqkZB36XaIXL5Z1774r\n74GMBrEHmd+4sewF2VoN8ZlWTezeCRBCkcFW/rLd0VGOjR5TTsARili/r36vaqD+az4se8w6kGYg\nT4OAjeqrdx1kOMggdKID0aGV69fjBSoV3qNNoWCbJFrtKrG0vFpYXKzAToE3CvdrxcLC4oF+txHo\nJFWqVJH69etX8EI/+HEQmCmWltECRoGexdXXau+WE06urpmi050ReF5sbSMkIiKyuI/u3RP58svy\nc4ENG9Rz9PzzJdtat1Z0eRHlN/PxxyKBgUVCVwmvmTOVn00R4uPjpVq1ahIWFvbIZ+X/NIxG1YgP\nuACURmZmpqxdu1aee04JExsbGwFk8ODBsnTpUjl8+LDcvHnzsYT1vXv3pHLlyvLpp/fLTTgqQlSU\n8k17FJYuXSqAjBo1Srp27SqA6PX6Ms+tr6+vwAEBk1haHpMRI0zFz5aAbGte3g+qCOb/cgH1N7P4\nWmI0lk9zoPAcKsueSsldZABdsACKDOxLlpxCo2lI27ad8PPzQ6ez4Pr1pxkyRLFzSiM/X9mXMjIU\nK+zkSRXipEYNpQv/4QelOkhPV/aG7t3hrbeUvUFbganAw91M1Cfr0DWsh0fHumjPhpO+5RBO86Yp\nlnnbtorPamen9IE+PupC2dmcu3cPExAGdC5VZtqWHfDbM2hsbCAP8rv1ITCwiMqrYepsRQXff9WW\n/XOgwMuH8eObUR0YMl/RuV988UXWX7zIu6DMUWmVQWMGe1hvvs6ZKj7c+n4Zz/YZRO79Hzl52EBv\ntmNheJF8SyNcvYrdptlgWMn5Su/BTFs4exb272dwZiaDi2ubiwZwN2fyBeCKiatAHczkt21ES+4X\nWtpygRUAeHv7cOvWLcAJFeyyLtAJGAyMw9r6MkZjPspGtxtIAQ6SmWlNcvLjRAPJBD7AYPgAS0t7\ndDoTeXn1sLOLZvjw59mwYSzp6Y0A0OlakZSkBeayc+dgJk60pkWLE5w7Z83Fi5707VvxFerXV3av\nH39UqsCoKBWxYOlSRdQ4d07ZbF57TbG/LCxUHqlDh9Rz1bq1SlEREuLG999/z8iRIzl9+vRjxYl8\nXCQkKAafp6eyB/1D8tg9FkSEs2fPcuXKFSJv3uQD4LfjxzFpNDg6OpKWlsaVK1c4fvw4p0+f5vbt\n27Rt25Y+ffowePBgli1bVkxSAcjPzychIYFjx46RkpJCcnIyqampZGdnY2tri5WVFadPn+bixYvc\nunWLV199lYgIR1q2fHQ9QdnbjEal5lWZeaPYtm07u3fvYu3atej1+uKQRCtWqHfA1dUVk8lEXl4+\nUBNwwWTqhXoPwMsrks8/b42NDfTvDzwNdo+IT3n/vnqb/gkoyqj7RPGkJN1f/QACT0tAQPti9VnZ\nFdSDs9djAgGFsw5rMRjUktloFAkLExk3ruTYuXPV9gkTRJo1E5k6Va2I+vYtW+ZjzwhKraAmT54s\nL/XrJ1etrMQIEl3kLQlyz8pKFtWpI3PHjJHTL70k2S++KObJk5Vre0CASNOmIuvWyRoXFzkEkgHy\nHch8kHHOznLz9k0hFKm9sLYQiszY82VxHcxmFTUgPV3kzBmR4GCR114rO2sB5NatW7Js2TL13wXh\nDV9xmOqs2lWrnEkzMzNl8ODBUqtW75L2eKuKEKoaZe/NvUIokpSdVKYduoI8BfIpSG7hqqvqREQD\n0rJ8h8kskECQYBBrSiKDlP9UFugtFhZTBWYIbBPIF4gWWCkwQqCxQNkVlpubW/HvHj16CCBTpkwR\nQLZu3SoJCQmSmZkp4eHhEhoaKiEhIdKkSRPx8PAQa2trcXdvKiCi070lixZdEVvbLAHTg7chIDJw\noGr70hg+XO17803VH0WPQt26ysn1QWRkiKxdK1Kvnsgbb6htM2bMkKCgIImMjCx/wl+E2azUkQ4O\nIu3aiXh4qPq88caTjzbxJHH//n3ZunWrvPbaa+Lv7y8+Pj4yZMgQmTZtmrzXpo00aNBAWrZsKf7+\n/tK2bVsJCQmR77//Xk6fPi0Gg6FMWRkZGbJ69WoZOnSo+Pj4iEajEb1eL7Vq1ZIWLVpI7969ZciQ\nITJ69Gh54YXXpEOHlTJp0hLZu3evxMXFSWqqiKuryKVLD6+v0ahWTxERavW8c6eIg4NB4H7h85Iq\nsEO6dx8qGs12geoCRaum6mJllfTA83W9+HezZvPKXgzkQMeHr6AStP/dK6i/WUAhLVq0eIiA+ljA\nWsC9VEe+J+AmYC/PP68eDlDhTpo1E+nZU2TSpLIDS6BbkuaU3qMAACAASURBVMyx+kCOhJnkpZdE\ndu1SKpgzZ6TEHf36dbXhyhWR33+XrB9+kMR33pGwxYtlVJMmxQLK7j0kvNC7PaJaNfn6ww8lISFB\nzv/+u1y7fFl2btki7du3lxkzZkjPnj0FED8/P+nfv7+8//778sUXX8jUqVMfMkgj0dHRQiji+6Wv\nUvHtKxFQD2LlSnV/RQ7kRWXEx8fLd999J4BYuFiI5q3q4r3AR7747QsB5MiRI8VlbNiwQcBK9Hon\nwQtxbecqR48elS1XthQLqwcfPEC6dOmifocivIm88cYbcvHiRbEEkdRUuTFzprxcwShvXL5cnB9y\n74B4eXk9sK2RwOsC2wVuCtwRFZ7pPYE28ssvZwuPc5CXXnpZ0tLSxGg0yt69eyW/dEyoB2A2m+XO\nnTvy5ptvCmx5oJomgTxp3z5F1q8X+fFHtb0iW1JqqlIHg8iaNQUyZsxHAh7SsuVNCQ4WuXGj4uun\npqrwPEajqsurr64QW9tJ0rPnfImIiJHs7EeH2KkIJpPIiy+K+PuXqBJNJmVz9fRU0R3+KdFwEhMT\nZfPmzTJ16lRp06aN2NnZSadOnWTOnDly5syZMvaax8XNmzdl1KjRAs0lIGC6dOx4WNq0SZQ6dQwC\nSg1vb6++9XplIy7qc61WhSRzcREZO1Zk6FAVlWLpUtX/s2crW3JRiKbS5+r1Is88c1OU7WihwP5S\nz9I7pX4nik5nKPxdIHZ2mdKvn4ivb37xMRrNYgkJebH4ngoKRARlg3qYljNB8/8F1H9UQEVERJSE\nESojoBCoJxAjsFUcHFaIigcnombXKuTLli2lXmaDQa7OXCfrGCQ3nYKkwNtPPUHjxyujU/fuJU+Y\nq2u5AfRhn9ICyvzUUyKnTz9WPJS0tDQ5efKkrFy5snjQnTZtmri6uj5SQHl+5imEIp8ceriA+v13\nVb3Jk9VMrqiMjIwMWbx4sQBi42ojvFtVaiyoKVcTrgogx4+XtY+BCiX1/vvvy7Bhw8Tb21vatG8j\nx24eK3fNomv069dP/W6KUBu5evWqFBQUyLffflvm2OUgC0BiHmjPX0DagzQpLK969eoPFVplP60F\npgr8KBAhihiTI2AUrTZNunVT4Xh27RI5d07k7t1HD/TLl+8USBMHB5HevQtkwoRloiZF3gLfCYjY\n2Zlk8eKHl5H6zc8yr/VGcXaeI71795bPP/9cmjQJkhkzRJyd1cK5WTORRYvKrqr0ekW48PNTq/vh\nw3PFwiJXNJoU0ekyxdo6S8aNu//YgurLL0WaNBFJSiq/7949FRoJVDifu3cfr8wnhaSkJNm2bZtM\nnTpVunTpIpUqVZJu3brJ1KlT5fvvj8jNm9ly7pzIzz+r1d7LL6vQSaNGiRSFbkxLEzlwQGTSpALp\n1StN7OxypGbN6+LgcEu02iTRaJIf+gpbWT32qy6gniE/P5G2bZXgCgpSMQi7dVNDyZIlIjt2JMmX\nX94Se3uTKHvqVdHrMwW+L5xQGQWMYmubKNWqiVSvbhQ7O7PMmyeyceM+SU1NFRH1TLzxhsi2bSJa\nrYW8+uoEyclRYaIoHKKnMUsuXizfrqmpInEadxF4lJnufxz/RQLKVby8vB8ioEQsyRcNwwSQZr6+\n0gHkYLOhsrFKWzF17aamOJ9+KvL/2rvy8Jqu7v2ezPNAREIiSAwxBYkYqqRRQxFqHtLSaFXNn4+v\nppaUGluUmmdq/iE1tGalIqYYQwgRJYSIIRKZh/f3x77n5t6bm4GGJNz3ec5zz9ln2mufc/d71tpr\nr9WtG+nlJcSxsiKnTmVGixaMDQzkwyVLGHnrFhP27CEnTmTSsGHcNmMGl3l780eAk8qX538//pgd\nGzfmD0OHMvjvv/n86VNmpKeLXu7qVTWCKgrUrq0a1FUskiQxPDycc0PmKk18P5/Im6BIEbATIP/6\nK4c8srOzuWjRIgKgeVkLYpw1neY6MeppFAHw7NmzateQNRcZmZmZ9PHx4datW3ny5EnGxMQoB5jl\ne3Tr1o2VK1dWbt+8eTNX3bQRTE2ARzR6g+UAd8yYQQDULxRJaS7mBPRoYuLK7duz+e235EcfCU1C\nT090Tl275jjLqGLhwhAC53njhqjOmDFkRkaGcmAbMGD58g7cunUrnz9/TlI4N0ycOJFPnz6VBWWq\noRmtrU/ywAHy0qVMOjs7c+bMO0rNxdFRvQOsXFn8Vq0qzIGyKQ4g//vfh1y79hB9fVfQwOAPGhjE\n0tX1IAcOnM958+bx3Llz3LdvHw8fPsy0tDTGx4uve0tL8vTp/D+azp0j27cX9/HxEYFeQ0MFqd29\nW7RmwJcvk7hp03Y2bjycFhbt2LixP31997FVqxyvzpo1CyaLChVIb28Rh9HQMIVGRv9HYAKNjR9p\nPb5KlfwJKSBABOE9fVrUc9GinH3du4tfMzNy+/bcMgUHB7Nbt27s3Lmz4v1wV55raZnIqCgRc1EE\nkRXlu3alcOdOsa7tmjLWrl1LAKxf/6JafQlwmulUWlmRHh5ktWrio8fKitTXJw/Dl7F65Sm/jiUB\n7xBBkUC2VoLahhwN57nKEwutWJGbALJVK9LVVZT/5z/kmjWMHzGC5zdsUHbQ5cuXp7OzM6tXr84K\nFSqwRg0xhtWjRw8uXryYYWFhzNQW0VWzwYuYoNzd3ZUdrKOjI6tXr049PT0eOXKE5x6cY+1FtYlA\n8JdT+RPU8+eigxGOPM7yi8EFCxYQAK3tHIiJJnT82ZHR8dEEwNDQUHXZAFaqVEmtbMyYMWokEBAQ\nwMzMTOV2z549lecCYGRkZK66hYaG5kkqFQH6ARwEMFrxXM8pfoMANlYcl9MRFG7x8/Pj8ePHmaRi\nj7tyRWgNZmZiPTw8p45BQUmsVOkGSeGV6eoqvPOmTSPXrydXrvyTpqamdHNzo6WlJW1sbNTuV7du\nXRJgsqRHA4NkZadiZxerXI+LE17SQM7rKq/LSEsjT54kFy7M2W9rS9raZtHRMYUVKsTTwCCNlpaP\naGGxic7On9Hd/SMCvamvH0szsx00M6tKS0tLent7c8SIETx48CAzMjJyjdGQQqNauFCQVKVKQpuT\nyRwQpnM/P3HM6tWibf7+WxBcfLwIrnrnjvh+O31afCAFBQnC++yz5zQxyemg/81StapMTPdpZHSJ\nQ4eO4fbtl7Qee/Cg0Lw0y7t1Ex8oZcuKCO1y8Nrk5BwjSEqKIGh/f9LePoPlymWyT5+7DAvLZlZW\nNqOiojh//nzFc9cnUJvAlwQ2EYgisJ8AOXp0NufOVb+/g8MGHjlynseP39f6H05JEb+enp4Exuaq\nPwH+X5mBHDiQ3LePDAsTU1bCwxVjoikpDN6/P1ew4+LEO0NQS2FOAspUFqrRzCMAjoQYfK8D8KcK\nFeik0jmEhYVxxbJl/Oijj9iuXTvWqlWLAFirVi327NmTp+VPJAUyMjJ4+vRp5ZfwKzV4ERNUzZo1\nlXLUqlWLFy5cUEZA3nJiC+ssqivmQZ3On6BkBASQYqxG1G/u3HkEwDJ2rsRkifY/2fNh4kMC4Hl5\nIpgCZ86c4Y0bN9TK7t69y2nTpvHmzZt8+PAh27VrJ8YKFXXu3VtEh5e3T5w4wXgND4KLFy8yLyLp\n06eP2vZvo0fzMMCt1tZcpfhnpgCcbmSU75iV5mJvb09JklihQgXOmTOHiYmJyrGon38WY5WA+Pps\n2FDMYenTR4yJZGVl8cEDYTLt0SNnbKl27busXXsUb92K4vTp0+nm5kY7Ozu6ubnRVDEpKhkSgWS2\nbh3MRo2yWb58Js3NfRgb+1jZHi9eiN+oKDIiQmgGsbGiY1SdkpOWJsw+L1+KzvTKFXLbNtH5t2gh\n0m7Izhh6etl0ckqhv/8zBgYms1+/x+zVK5xNmx6lpeWflKTZNDRcRze3MLZvn8YvvhBkPWmS+Kb7\n8ENxnWrVcjpEExP19BV5LYosD1oWdSeTFi3U93t5JdPLSxCYtXUCx46dxp49x7Fnz6/o4/Mxq1Tp\nSXPzoQRIU9OvKUnJXLp0M5cuzc51rzlzxJygrVtF22VliRQg335LOjmR9euL41xcSDs7sW5kJCz8\npqZikaeZmJqKDwIRtd+JwCIaGcUo7hVK4CyBx4rth7SxOUhDw10KogIdHIbRxEQ4SZibL+LMmYe1\ntA1Zrdp1uroe46efnuf582JeoLFxOIGDBMgyZbI4eTL58GaCIFCAj707sGvNa+Tx42Jw7Plz3rx5\nk5916cLT1tZcZGysNBmWBLwzBDUDYGaPHsRk0FiDoADQ2lqeSwPFF0ZOZ2RsbMyWLVuyT58+3L59\nO8PCwt5YyHltBJWUJL5ANc0icXE5ZaGhwk68datoaXd3MblP1uQAkSPp8uXLLF++PLt27U9j1460\nHV/jlQhKmBD+5sSJkzhlivxnOEZr6/8Qk/RoPcOaj18+JgBevHjxleXPyspSMXuBBgYGufI/ubm5\n8fLly8pz5FQE2pZx48apbX/22WcEwA0bNhAAf1++nNMBnlFMRjsB8CbADQBbAdTL47rVqlVjUlIS\nJ0+erEzRYmVlxR07dijrlZwsnAUOHSIXLnxGM7NWBMAxY8bkkvv2bXLMmCwaGMRQX/8JbWyWcMqU\nUAYFZfLxY/FsCTBD34hNmx4nAPbq9TXd3bNYrVoo27cfonXAPyWFNDBQ7XAzuWV6SKHzPGRni/QS\nly8LTW/0aKE99O0rSKdxYzFm0qRJJj09X9LR8ZqCOLJpY/OUrq73WatWHAMCHrJt2zT+8EMyN2x4\nwl27EnjnDvnTT2LMysUlhwwLswwbdoMzZ14nQDZufJoA2bnzJBVSa8eKFZ1Yv359durUmQEBX3H8\n+PFctmwZ16xZw4MHD/L06dOMiIhgXFwcIyM1vd3UF3d3QUKGhqSDg9ACTUxE2pFx44Qj0f794lmH\nhwuyf/hQPNe7d8mQEPHB8PJlNv/8cx/btm2r5b1yJ+BHoCUbNfqc9va5c9OdPXuW06ZNY/Xq1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H+T4FpUAPDAyEs7OzWllijRpIjojIdex/tZyfAaGp3a5VCy4REciKjcVelf3fq6z36dMHPXr0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\n6FAioSMoHXTQQQcdSiT+Hz4bx1z5N55RAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb684400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors = ['k', 'b', 'r', 'g']\n",
"inds = [0, 2, 4, 6]\n",
"for i, ind in enumerate(inds):\n",
" profile_time(ind,colors[i])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RgeTkZFy/fh2bNm3CmTNn0KxZM8yePRu2trbF0ibBa2JjY1WCFoYNG4a2bdvC29sbwcHB\n0NLSwsmTJ2FlZYUbN25g6NChucpo1qwZmjVrBuD1WI6mpiaSk5OhUChgbGxcKPssLCyKNHCjd+/e\nSE1NxYsXL9R2rmKJU1RK974Jb/Sgrl27prZjUHp6emzZsuV/pgf1vrx48YLz5s2jg4MDLSws6Orq\nSjs7O547d65E6m/SpAk3btxIhUJBa2tr6Xs0dOhQVqlShZqampwzZ06uHpGPjw+XLl3KlJQU+vj4\n0N3dnVu2bMl33OLRo0f8/fffWalSJbq4uHDPnj15Ps0L8uf+/fsMCwt7Zz6FQsGtW7eyc+fOKscf\nPHggfcb6+vps1qwZATAoKKi4TM6XtLQ06ujoFMl4KknWq1ePZ86cKZKyShMUYQ9KLQTK1dWVV69e\nVVsXn76+Plu0aMHdu3eXtilqTWZmJq9evcqTJ0++dXC6qJk9ezYBsEuXLnR2dua///5LAHR3d+f8\n+fMJgObm5mzYsCFdXFzo7u4ufddq1arFsWPHskOHDu/lgly/fj0BcNGiRdy8eTOPHDlSfA0sQwwd\nOpQAOHXq1Dzfj4uLY58+fQiANjY2nDx5cp75oqOjVc4pye9bToyMjArlJsxJ9erVefny5SIpqzQp\nswIlk8nUsgclBEq9efnyJbt160YAvHTpUq73N2zYkMufP3PmTAJgmzZtaG5uzri4uPeud/v27WzX\nrh0B0M3N7a15Y2JipN5WQkICnz9//t71few8ePCAHh4e7NWrFwGwf//+0nuJiYlcuHCh9OAwYMAA\nrl69ukARpaWJk5MTb9++XSRlVapUSUV4P1bKpEBduXJFrQWqefPmQqDKGGvWrCEATpw48YPOf/bs\nmRSm3KxZs1zvZ2dn888//2Tr1q0JgJqamiouxv8Sz549Y5cuXdi2bVtmZ2czLi6OFhYWbN++vcp1\nWb58ebEE0xQXnp6ePH36dJGUZWVlxcePHxdJWaVJUQqU2kzUfd2u/3+tTijtUze7BIVDGSChXE3/\nfTE3N8fZs2cBAE5OTirvpaenY/DgwQgKCkK/fv2QmJiItLQ0xMTEYPPmzVIwSVli+fLl6NatG27e\nvAmSOHLkCBQKBRYuXAh/f38cPXoUy5Ytg4aGBqytrXH48GEYGRlh4cKFSExMBEmMGDHiowrnt7Cw\nwLNnz1SOkcTLly/fu6zU1NRS2zFcXVGrKD6lEOQUK4GguFBGStWsWfODy6hevTpmzJiRa8mb5cuX\nIyIiAidOnFBZid3Ozg6VK1dGdnb2B9epjmRnZ+Pbb7+Fi4sLXF1dUadOHUREREg38K5du+LYsWMq\n0Wk1a9bE5s2bS9HqwmNpaakSyRcREYFevXohKioKly9fhoeHR4HKycrKQmpqqlhb8g3URqDUXZTU\n3T7B+6NcpqawIfDa2tpITk6WXkdFRWHevHk4cuRIntuEaGtrIysrq1B1qhMksWnTJlSoUAFnzpzB\nunXrkJSUhK1btyIzMxNVqlQps8vyKEPNY2JisHLlSgQHB2PRokV4+vQp/Pz8YG5ujpSUFEyaNAlf\nfPFFrvPPnDkDGxsb9O3bFz4+PmX2On0oaiFQwsUnKA3S0tKKpBxtbW3JRXXlyhW0bdsW06ZNQ40a\nNfLN/7ELlFwuR3R0NMLCwrB06VIkJydjzZo1AIBBgwaVsnUlR82aNTFs2DDMmzcPnTp1wsmTJ1G1\nalUAwPDhw3Hx4kVcv34ds2bNwrZt29C4cWO0atUK1apVw88//4yvv/4ahoaGSElJKZZVKT521EKg\nlCgFQPRWBCWBlZVVkZSjqamJ4OBgWFhYIDQ0FB06dMCoUaPyzf8xC1RcXBxCQkIwd+5cPHz4EBUq\nVEDbtm0RFBQECwuL0javxBkwYAC8vb1hZ2ensk0H8HqM08vLC15eXujWrRv27t2Lc+fOYejQoYiK\nikKbNm1w69YtmJmZ4csvvxTjT3mgNgKl7qKk7vYJ3p/27duruOY+FKWrcM6cOZg+fTomT5781t62\njo5OiayCreTN3v+TJ09gbm7+3q7NVq1a4dChQ2jdujW+//57fPrpp8KrgNfbxbyLcuXKoUePHujR\nowcWLFggrWqhZOvWrcVo4ceLWgiUcPEJSgOZTAZDQ8NCl6PsDaWlpUFPT++d+Yu7B5WcnIzQ0FDs\n3bsXW7duhVwuh4ODA27dugU9PT2kp6ejUaNGWLJkibQc1NtIT09Hjx49cOjQIYSHh+e7b5eg4HxM\nkYqliVqEmSsRLj7Bx4hcLgeAAokTUHwCdeXKFXTs2BHlypXD3LlzUbFiRZw9exZRUVHYuXMnHjx4\ngMePH+PWrVtwcnJCnz59cOnSpXzLy87Oxt69e1G7dm0YGBggKSlJiJOgRFGLHhSg/qKk7vYJSo/3\nFZviEKitW7di1KhRcHd3x927d9/qdjI1NcXvv/+On376CbVq1UKPHj1gYmKCBQsWwNjYGK9evUJg\nYCAOHToEDQ0NTJs2DX379i1SewWCgqAWAiVcfIKPmfcVm6Ieg1q/fj0mTZqEP//8U1q5+13IZDKM\nGDECMTExMDAwwK5duzBkyBAkJibiyJEjyMrKws6dO9G6desC9wwFgqJGLQRKiXDxCT5GlC6+glKU\nPahffvkF06dPx+HDh+Hm5vZe58pkMsydOxcA0K1bN7i5ucHBwQEnTpzAJ598Ag0NtRoBEPwHURuB\nUndRUnf7BKWHubn5e+XX1taGXC4vdK/8q6++wu7du3HkyBFp7s2HUq1aNWRmZoqJogK1Qi0ekYSL\nT/AxExgYiNjY2ALnl8lk0NfXL1SI+6xZs7Bu3Tr8888/hRYnJUKcBOqGWgiUEnV28amjTQL1QFtb\n+713161atSqioqI+qL4lS5Zg2bJliIyMhI2NzQeVIRB8DKiNQKm7AIgelKAo8fLyQnBw8Hv3okJD\nQzF9+nSEhoaiQoUKxWSdQKAeqIVAfQwuPoGgKPn6669x/fp1+Pn5IT09vUDnhISEoHfv3tixYwdq\n165dzBYKBKWPWggUoNpDUcfelDraJPh4cXZ2xrFjx2BlZYVevXqhSZMmcHFxwc2bN/PMv2HDBgwe\nPBhr165Fy5YtS9hagaB0UAuB+hh6KMLFJyhqNDQ08Msvv6BOnToIDAyEv78/pk2blmuO1NatWxEY\nGIgDBw4gICCglKwVCEoeEWZeAIQwCYoLMzMzTJs2DcDrcanevXtjwIABmDNnDiIjI3Hs2DFs375d\nuPUE/0nUogcFqH8PRZ0FVFA2sLKyQkhICGQyGSpXroyffvoJurq6OH78OLy8vErbPIGgxFGLHpQ6\nC5MSdRdQQdlAX18fmzZtwoYNG8RKDoL/PGrzC1DnHooQJkFJI8RJIFAzgVJnIVBnARUIBIKyiFoI\n1JvzoNQRdRdQgUAgKGuohUApUVcBUFe7BAKBoCxTLEESMpksAIA/gHIA1pI8+K5zPoYelEAgEAhK\njmIRKJK7AOySyWSmABYAeKtAqftSR4Bw8QkEAkFJUyAXn0wm+0Umkz2RyWSX3jjeRiaT3ZDJZDdl\nMtnEPE6dCuCnghqjrksdCWESCASCkqegY1DrAPjlPCCTyTQALP/fcXcAvWQyWbUc788DsJfkxYJU\noG6i9Cbqbp9AIBCUNQokUCRPAHjxxuH6AG6RvE8yC8AfAAIAQCaTjQLQEkBXmUw25F3lCxefQCAQ\nCN6kMGNQ9gAe5nj9CK9FCySXAVj2rgK+++47AEBsbCwuXrwoXHwCgUDwkXH06FEcPXq0WMou1aWO\nlAIVEhKCWrVqlaYp70TdRFMgEAjUAR8fH/j4+Eivg4KCiqzswsyDigHgmOO1w/+OvTfCxScQCASC\nN3mfHpTsf0nJWQBVZDJZRQCPAfQE0KswxnwsLr5Xr14hNjYWixcvRnh4OCIjI2Fvb4+GDRvCxsYG\nMpkMcXFxUCgUmD59OmrWrFlKlgsEAsHHS4EESiaTbQLgA8BcJpM9ADCd5Lr/BUMcwOue2C8kr3+o\nIeomSm+SmpqKsWPHQltbG9evX0dqaiq8vLywbt066OnpQUdHB2FhYZg7dy769OmDRo0aYf78+Wjb\nti2srKzQqFEjNG/eHM7OzoiNjcX9+/dx4MABxMfHIyoqChkZGUhOTi7tZgoEAoHaUCCBItk7n+P7\nAOwrrBHq5uJ78eIF7t+/jwsXLmDOnDmIj49Heno67t27h/Xr16Nr167Q19fPdV7lypXxxRdfSK97\n9eoFhUKB0NBQ9O3bFytWrECtWrUQGRmJLl26IDY2FoGBgdDX10fHjh1LsokCgUCg9qjFflBKSsvF\nJ5fLsXPnTjx58gQ7duxAWFgYAODTTz/Fl19+ia+//hoAMG3aNPTr1++9ytbQ0EDbtm3x/PnzfPOQ\nhJ6eHlJSUmBoaPjhDREIBIIyhNoIVEmLUnR0NDZs2IBTp07h0KFDUCgU8Pf3x5gxY7B+/XrY2dlJ\ngqkUqOJCJpPB1NQUJiYmePjwIWxtbYu1PoFAIPgYUAuBKg4XH0lkZmYiNjYW8fHx0NXVRUxMDAID\nA5GWloa0tDTExcVhwYIF2LhxI8zNzd9ab3FH8R08eBCDBw+GnZ0dRo0ahWHDhsHMzAzW1tbFVqdA\nIBCoM2ohUEoK4+IjiQsXLuDkyZMIDQ3F3r17oaOjA2tra1SsWBHp6ekwMDBAtWrVMHXqVNSqVeu9\ndy0tToHy8PBAeHg4Dhw4gJCQEDRs2BAvX75E3bp1sWzZMjRq1KjY6hYIBAJ1RG32g/oQUcrMzMSR\nI0cwevRouLm5oV69erhy5Qr69++PCxcuICUlBQ8ePMA///yDs2fP4tixY9i+fTvq1Kmjlltqy2Qy\n+Pn5Yfny5Xjx4gUSExNRsWJFdO/eHfXq1cP58+dL20SBQCAoMdSiB/U+O+peu3YNISEhuH37Ntau\nXQuFQoEJEybgjz/+gIeHB7S0iqdJJT1GpqGhARMTE+zYsQPJyckICAhAQEAAGjdujG+++QbVq1cv\ntrYKBAKBOqBW3Yj8XGgKhQLbtm1D69at4e7ujkmTJsHW1haXLl2CQqHAggULULt27WK/YZdW+LuR\nkREOHz6Mmzdv4unTp6hVqxY++eQTrF+/HnK5XCWvQqFQeb1v3z6cP38ed+/eLUmTBQKBoNCUqkB9\n99130iKDJKWb69mzZ/Hpp59CJpPB3t4eBgYG6NatG9q3b4+tW7cCAAYOHAh3d/dSnzNVkhgYGODQ\noUPIzMzErFmzsHDhQpQvXx6Ghobw8/ODqakpNDU10bJlS1hbW8PU1BTt2rVDu3bt4OzsjODgYGRm\nZuYqVy6Xv1cvViAQCN7k6NGj0vqqRYWstG5KMpmMyrobNmyIadOmoWPHjsjOzoa2tjY2bNiA58+f\no0WLFnB0dJQmxoaGhqJNmzaIiYmBnZ1dSdkKAwMDTJo0Cd9++y0A4PLly9i3bx/c3Nzw6NEjaGho\n4NSpUzhz5gxGjhwJX19fJCYmwsDAABkZGahcuTLKly9fpIJKEkeOHMGdO3fg4OAAbW1taGlpQS6X\nIyYmBklJSXBwcEDnzp0RERGBYcOGQVtbG4sXL0a9evWkcuLj42FlZYWkpCQYGxsXmX0CgaB4USgU\nOHPmDLy8vErbFIn/PewWyY2u1AUqNTUVtWvXxqJFi6TVFKysrPDkyZM8zzt+/Di8vb2RkJCA8uXL\nl5St0NPTQ7169ZCZmYlLly4hPT0dAFCuXDm8evUKTZs2hYWFBZKSkqCrq4uIiAjExcXBxMQEZmZm\nkovN19cXaWlp6NKlC6ytrREREYHDhw/D29sbS5YsKdZ2ZGVlYdGiRZg8eTJmzpyJ0aNHw9jYGFFR\nUahWrRru3r2LSpUqFasNAoGg8MTHx2PevHnYsmULqlSpgsOHD0NTU7O0zQJQtAJVqqPs586dw9Ch\nQ3Hr1i3ExcUV6Bw9PT2Vv8VBYmIinj59iszMTCxevBgAkJ6ejhMnTsDBwQHffPMN7O3t0bJlS5w6\ndQrTpk2DTCbD48ePER4eDmNjYwwfPhxVq1ZFzZo1YWdnh3v37uHKlStwcHDA999/j+DgYERFRcHM\nzAzZ2dmIjIwsdoHS1tbGpEmT0LVrV/j7+2PZsmXo2rUrjIyMAAAJCQlCoAQCNefRo0fw8/ODi4sL\n9u3bBw/srCqOAAAgAElEQVQPj7I71EGyVBIAWllZMTg4mDo6Oly9ejUBUHk8Py5evEgAVCgU+eYp\nKPfv3+fly5f5119/cdy4cQwKCmLTpk0lO95MDRs2VDk/MjKSVapUIQA2a9aM5cqV4/Lly1m+fHnq\n6+tL52lpaUn/GxgYEACdnJyoo6PDuXPnctGiRQTAly9fFrpN78OBAwf47bff0sHBgQDYo0cPyuXy\nErVBIBAUnFevXrFevXocMWIEU1NTS9ucPHktK0WkE0VV0HtXDDAuLo4kqaurW2CBioyMVF6Ad5Kd\nnc1bt25x//793LhxIzdv3kwnJyc6ODjQ0NBQqq9q1arU1tamlZUV9fT02LhxYw4dOpRnz57ly5cv\npXxeXl4q5ffr14+zZs2Sburh4eEEQE9PTxUbgoKCqKenx5CQEL569Yrx8fEq5SgUCgKgpqYmHz58\nSJKcPHkyZ82aJV0jkkxNTeXmzZu5b98+vnjxokDXoKCkpaWxVatWbNq0KRMTE4u0bIFAUHgUCgWn\nTp3Kjh07MjMzs7TNyZcyI1BKdHV1uWrVqiIRKIVCwYsXL7Jp06bU09OjtrY227VrxzZt2rBatWqs\nUaMGp0yZwsDAQOro6FBTU5Pu7u709fXlxo0b83wqyU+gPD09efz4cZVjN27cYGxsrMqx7OxstmjR\ngj169OD9+/eZlZXF7OxskuSjR48YERFBY2NjamhoEICKWM+ePZskefny5Vw9utmzZ+cSu8KgUCg4\nZMgQOjo68uDBg3z+/HmRlS0QCD6c9PR0DhgwgFZWVrx69Wppm/NWypxA6enpceXKlQUSqLS0NE6a\nNEnl2K1bt7h+/Xo6OTmxatWqLF++PAHQxMSEOjo6LFeuHN3d3SV33NChQzlu3DiOHj2a0dHR73QX\nKu1q1KgRSfLJkyfs2bMnXV1d+fTp07eeq+T+/fuSACndfjo6OgRAmUxGAHR1dSUAWltbEwD19PS4\nefNmzpw5UzpvxYoVtLe3VxGqmzdvSvVkZWUVyJ63sXHjRtatW1cqv27duqxRowbHjx/PpUuXcteu\nXUxPTy90PQJBWUShUNDT05N169blhAkTGB0dzXv37r3zvD179rBjx450cXHh559/Lj18pqamcvTo\n0WzevDkTEhKK2/xCU5QCVepRfACgr6+PH374AcOHDwfw9ig+AIiNjcWff/6J06dP488//4SOjg7c\n3d0REREBfX19fPrppwgICIC3tzeMjIygp6dXqEFE5bm2trZwdHTE2bNnYWxsDC0tLSQmJkIul2PO\nnDnw8fEBACQlJaFNmza5ylEoFEhKSsLRo0eRlJSEs2fPwt/fH56ennB2dsbWrVshl8sRGhqK5cuX\nw9DQECkpKQAAf39/ZGVlITQ0FNu2bUP79u0xZcoUKYhj1apVaNCgAXx9fXHjxg3cvXsX9erVK1S7\ne/TogfLlyyMgIAAxMTGIjIxERkYG9u/fjxcvXsDX1xeZmZlo164dGjdujNq1a39wXQLBx8ijR4/w\n448/QiaTwdPTE//88w9WrFgBuVyOo0ePYvv27Vi2bJk0P3Hjxo2wsLDIs6zq1aujZcuWGDJkCFat\nWoXffvsNtra2ePr0KVxdXbF9+3ZUqFChhFv4/hRlFJ/a9KB++umnfHtQKSkp3Lt3L9u1a8f69esT\nAAMCAtikSRNqa2uzfPnyHDRoEL///vsiCTSIjY1lbGwsV69ezcaNG6v0Vry9vdmtWzdpzKhJkyYE\noNI7AsDatWtz+vTp3LJlCw8dOsTIyEg+ePCAJLl69WoaGxvnGYihr6/PyZMnS4EVe/fu5aVLl/K1\ntUWLFjQ3N1cpo1KlStL/586dI0nu3r2b/v7+PHbsWKGvD0meP3+eEydOZO/evdmtWzcaGBjQ19eX\nkyZNYnR0dJHUIRCoO6NGjWJAQABnzZrFZs2aMTAwkA8fPpRc+CSZlJTErKwsTpgwgZUqVeLy5cuZ\nnJxM8rU3ZtWqVQwNDaWbmxuvXLkinZeYmMgrV67kGjJQd1AWXXxvCtSTJ084cuRINmzYUApoaNSo\nkSQE1apV44ABA7hs2TImJSUV6oLGxMRwwIABdHJyktxryOF6y5nc3Nzo4eEh2bB//35Wr15dJc/4\n8ePp5uZGACxXrpxKQIalpSUBsG3bthw+fDiDg4PZtGlTamtrMzY2lp07d2bnzp2laD+lKF68eJEk\n84yyi4uLk/LOmzdPxZYdO3Zw//79Ksc6depUqOuVF0+ePOHmzZs5dOhQKapx1qxZXLduHY8fPy7G\nswRlEk9PT544caLA+Y8fP86AgABWqFCBCxcuZJs2bdiiRQsCoJ2dnRQk9TFTlAKlNi6+hQsXYuTI\nkdJruVwOIyMjVK5cGenp6bhy5Qr69OmDq1ev4uLFi3gfu7OysrBkyRKkp6fjzp07uH79OlxdXbF5\n82ZYW1sjISFBWgJIS0sLlpaWsLGxQePGjdG6dWu0a9dOKsvGxibPOVuNGzdG9erVkZKSAoVCATs7\nO/zwww+58vXt2xcbNmzI085p06ZhxowZIAkbGxvY29vjwoULMDIyQnJyskrdHTt2RJUqVeDg4ABT\nU1N89tlnMDU1Rd++fbF8+XKpzEaNGsHe3l5aIkqJo6MjVq5cqdK2ouLZs2fYunUrIiMj8fDhQ+zd\nuxd6enpo0aIF/P390a1bN1haWgIAduzYAS0tLbHlvUAt+OOPP+Dg4IAmTZq8M294eDi8vLzw7Nkz\nmJubv1c9p0+flpYeCw4OhpOTE+Lj48vEai5lxsUXGxvLX3/9Nc8nf+U8onnz5vGff/7h48ePSZJ+\nfn4FCjM/efIkd+7cSVtbW1asWJGDBg3i2LFjWb58edrZ2bFPnz7s1asXnZ2dWblyZTZp0oQrV67k\nzz//zA4dOrBGjRqsVatWnm64N5OJiYn0/7Rp096aV19fX+odde7cmTNmzFA5X9k2Y2PjXD2fN+us\nXbs2HR0dpWtlYmKSy9WYV8rZMxw4cCBTUlIK9mj0gSgUCp4+fZqzZ8+ms7MzdXR06O/vzx9++EGl\nzQJBaZKdnU0A1NHR4ffff6/ipiPJ+Ph4btmyhXPnzqW/vz9lMhmbNGlSJHWPHj2aAHLV+TGCsuLi\nMzIykm7WY8eOVbmJ2traEoAkTEreJVCpqakcMGAAAdDCwoINGzZky5YtWa1aNcl9p6OjQ21t7Vw3\nbk1NTXp6etLKyko6tnTp0nxdfl5eXrnK6NOnj/R/TrHQ09OjpqZmgQSPJLW1tZmens7Zs2dLx+/c\nucMePXqwXr16udyK/fv35507d0iSCxYs4MSJE9mpUyeVPEeOHCEAHjt2LFedL1684Jo1awr8JSwM\nkZGRDAwMpKenp1R/UUy8FggKQ2xsLC0tLXnp0iXWr1+fbdq04aZNm7hr1y726tWLZmZmrFKlCtu0\nacO1a9cW+RQPdQ8fLyhlRqCMjY3ZvXt3AuDw4cNVbpj37t1j7969uWLFCpXG5ydQCoWCs2bNUhEG\nCwsLGhsb09HRUSVwYPz48ezbty/r1q3Lxo0b85tvvuHWrVul97/88ktevHiRX331lYpNenp60v+n\nT5/m4sWLCbweI1IezxkunzNFR0ezY8eO+YpSzrIbN25MmUzGzMxMvnr1igA4d+7cXG1u3bq1dI6O\njg59fHzo6+vLHTt28O+//5bC7ZVl6+vrS/8r/d7KFBQUROD1ON+TJ08K+l0sNCNGjJA+o+3bt3PT\npk28ffu2ECxBiXPhwgXWqlWLJJmZmcmgoCB6e3vTxcWFCxcu5N27d0vXwDyIi4vj1q1bS9sMFcqM\nQI0bN056qh82bFiuXsSOHTvYokULlcY7OjoSAG/fvs1BgwZJQQhKUapRowY1NTXZtm1blYCHHTt2\n0NXVVWUJojdT9+7d+fLlSx49elS6QT5//lx6P2fgAkmOGzeOAPjLL7/wzz//5JgxY9ilSxcpT+3a\ntQmAfn5+TE9PZ1ZWFmUymRQo8WYyMjLKVY9yYvKpU6fy/UIEBATw22+/5fTp09mzZ89c5ea8tq6u\nrvziiy/42WefqSzB9GbKyspienp6ibgcsrKyVHqqwOtlpdasWVPkK2YIBPmxZ88e+vn5lUrdCoWC\naWlpJMmoqCg2adKEN27cyDf/rVu32KlTJxobG7NOnTpq8UB35MgRTp8+vewIlBJlr+VNgUpNTaWJ\niQmfPn3KrKwsnjhxIs+bac+ePblmzRpOmTKFlpaW1NDQoJmZmfT+jRs3pN6Esuem/F9LS4sGBgas\nXbs227VrJx1Xjlv9/fff0jENDQ36+/sTAM+ePctevXpx7ty50rIjyiWLlGnDhg2S+yxnW8uVK0cA\nDAkJYUZGhuRynDNnDgHw0KFDudp4+vTpfL8YgYGBnDdvHklK42Z16tTJ0405f/58Aq/dmfPmzeOL\nFy9YoUKFXPmuX7/OSpUqsX79+jx27BhfvXpVgK9o4QgPD2dERASTkpK4bNkyenl50dzcnIGBgQwO\nDua+ffvEMkyCYmP+/PkcPXp0qdS9Zs0aAq/HoKZOnUoAHDlyZK589+7d48SJE2liYsLZs2czLCyM\nderUKQWL8+c/I1Ak2b9/f5XjynGcadOmMTg4mI6OjtLCqwBoZmbG4OBgSUgAcNu2baxduza7dOnC\niIgIkpRWSsjIyCBJrl27Nt/eRF5p8uTJbNWqFffv36/y4eTsoSnFLWdoeM4ylHOUtLS0pAVbAwIC\nWKdOHQLg2rVr+fnnn0v5P/vsM37zzTdctWoV7969y5SUFMrlco4dO5bdu3fnjz/+KPXC6tatqyLS\nypRzGSUALF++PGNiYnLl++eff3IdUy67VFIoFAru2bOHI0eO5MCBA1mpUiXq6emxa9eunDFjBs+f\nP0+FQlEkq2cI/pukpKQwLCyMY8aMoZaWVq7fc0mhdP8/e/aMTZs25bJly2hnZ0eFQsFnz55x1apV\n9Pb2Zvny5fnFF1/w4cOHjI2NZbNmzdigQYNSsTk/ypRAKSNnhgwZkuvmvXHjRi5dupQNGjQggFxP\n+lWqVKG3tzdlMhn9/f25aNEiqTwAkjtwxIgR9Pf357Zt26SL+PjxY966dUt6rezt5Eznz59n3759\nVXpbyv8XLVrEOnXqSCKj5Pz581KeEydOMDw8PNeHp0zKQVGZTCa1Laer648//iBJabXzunXrctSo\nUSplVK1alcDruWPKuVdvS507dy6QAA8aNCjP44cOHWL37t0ZExMjtenQoUMlthL7mTNnuHLlSvbs\n2VPqCWtqatLLy4vdunXjsmXLGB4eztTUVCYnJ0sPIHmhUCj41Vdf0cPDg7t371YLN4mgeLh79y7/\n/PNPzps3j/369aOPjw/d3d1pZGREFxcXBgYGluoEc3d3dwLglClTaGhoyNOnT9PMzIwNGzaknp4e\nnZycOGHCBH722WdcunQpGzVqJP0mdXV11Wrx2DIjUDl7OTmj33ImDQ0NKdrvzUAKZfL19c11gX75\n5ReS5M2bN2lra8vKlSu/1aerPO+vv/7iH3/8IQ2WKo8DkHppAPjbb7/Rysoq1yzv69evS3n+/fdf\n6fiNGzf4119/qdh969YtyS1YsWJF9urVSxIjZRt+//13kuTVq1fzHT+zs7NTGW8rTKpZsyYBFKg8\nDw8PafuTRYsWkXw90FxSPZqUlBQmJSUxPj6ex48f56pVq9itWzdaWFhIDxTW1tZs3bo1p06dyr17\n96pMGD516hRdXFyk9Q179+7NEydOqO02BoKCERMTw+fPn3Pnzp1cunQpGzZsSDMzM7Zo0YKjR4/m\nzJkzefjwYUZGRpaI67oguLi4sHr16qxbty4rVqzI8uXL09XVNVe0bl5p3LhxpW2+CmVGoBYsWMD1\n69cTgLR8kDJVqlRJJdwbAL/++utcH87s2bOlJ9+XL1/y3r17NDAwkI4pFAoeOHCANWrUeOvTNElu\n2bKFGRkZTE5O5rVr11QuuPJJRbmKxI4dO2hgYJBr6aAHDx5I+ZWrP5DkjBkzOH78eBXbo6OjKZfL\nKZPJ6OjoyPHjx6u0cdiwYVyyZAlTUlLo4+OT7xdUT0+PvXr1UgnoyLl6xZvpzXlXOdPQoUMlN2pe\nK2nkl2xsbJiVlSV9Tm/2RrKysnjz5k1evXq12BeaVSgUTEhIYEZGBiMjI7l48WKOGTNGemK2s7Nj\nw4YNCYCff/45SfLOnTscNGgQdXV1CbzunY8ePZqXL18uUJ3nzp1jjx49GBISInpipUR2djaDg4Ol\n72T9+vXZpUsXLlmypNCrzRQ3Tk5O/PTTT2lubv7W352ZmZkUfLVt2zYp6lWdKDMCRf5/YEHXrl3z\n/VCmTp0qRYfklZydnVXcb1WqVCnyC64Ugm+++Ua6Ib85xtO7d2+VsZvZs2dLoak7d+5Ucf8Br0PH\nMzIyqKWlxQoVKvDnn39m8+bNVfJs2bKFkydPll5nZGSwYsWK3LJlC/fs2UPgtZvP1NRUpff2Icnd\n3Z0k8wyuyC8p3a8AVB4o6tWrx6lTp3LIkCHs0qULy5cvL20poqGhwTZt2rB9+/ZS4MnmzZsZHx/P\nO3fuFKuAPX/+nBcuXOCmTZsYFhamst8W+fr7mJSUJM19AV5HFK5du5bnzp3j06dPGRYWxnPnzvHg\nwYNMT0/njRs3pGAOMzMzjh8/XohUCZGRkcGQkBDOmzePtWvXppOTE0+ePFli19/GxoY+Pj4MDAyk\nt7c3AwMDJbuuXLnCe/fuMTQ09J1LfTk4OEjj0G+mZs2aceXKlezevbs0nq1cy08dKTMC9ejRI8ml\n1KpVK5UPZfLkyfTw8KCNjQ0nTZr0zid/ANIHXNSDhjkFShlhc/ToUdrZ2XHr1q2cN28ePT09aWho\nyIEDB6rY5OTkRD8/P1apUiXP7roykEJXV5c3btzI9f7AgQM5Y8YM6YmQfB3Jo3SjBQcHqwhYYZK2\ntjYTEhI4b948uru708LC4r16UTt27FB5PXr0aGmcaPPmzVQoFMzIyOD169cZEhLCv/76i/PmzePg\nwYPZtGlT6uvr097ennp6eqxSpQoDAgL4/fff89GjR0X6eebFgwcPuHXrVl65ckUlqOXJkyecO3cu\na9WqRVtbW5qamrJ+/fqsU6eO9CRra2vLUaNGkXw9tunm5sYePXrwzJkzQqiKkKdPn/Lo0aP8/fff\n2bFjRzo4OFBHR4f16tXjsGHDuHXr1hK93snJydJvdOzYsdy8eTPt7e154cIFTpgwgSYmJrSwsKCn\npycdHR05aNAgOjo6slGjRlKwFvl6l1wNDQ1qaWlRT0+Pq1ev5q1btxgREcHffvvto9vluswIVM70\n5qrh/2skAbx18D8jI4MnTpzgpUuXeOHCBQK5x6SK4oIrBerbb78lAA4YMIA1a9ZU+UFkZGRI7dDQ\n0OC9e/d46NAhbty4kZ6engwICMhTFJT/37t3j7GxsZw3bx6zs7OlHwCQ90RdJcuWLcvz2ijD2d9M\nyki/vGypXr065XI5W7VqxXr16jEmJkZaQR6AFG2Ucy6XnZ0ddXV1c4lZz549VUT5559/LtBg7suX\nL3n16lWuW7eOn3/+OU1MTNisWTNOmjSJu3btKtKb0K5duzh27FiamZmxZcuWdHBwoKmpKdu0acOf\nf/6Zz549e+v5UVFRrFu3Li9cuCAde/LkCceOHUsrKytqamrSxMSElSpVYo8ePRgUFMQ9e/awQ4cO\nbNWqFXv27KniThao8vDhQw4fPpw1a9akgYEB3d3d2atXL44ZM4bnz58v1Z6E0luyZMkS6diMGTM4\ndOhQBgQEcPv27dLx8PBwLl26lJcuXeKsWbPo7+9P8vU9I+dvZvr06QWqW6FQ8NChQ1y0aBF9fHzU\nSsTKpEDVq1cv1w0zJSWFAHJtJ/GmiOXkwoULRf7EraxLQ0ODzs7O0o34zd10lfj7++d7I37T/sTE\nRElILCwscq3ioBTEX3/9NV/7Ro4cSQAqAujv75+vENnb26vM+cr54zAwMJDWywOgMn8sv1SnTh32\n7t071/EWLVrk2WscMWLEe4mMcruVoKAg1qlTh05OTgwMDOTRo0cLNZH4zp071NHR4bBhw6SoRIVC\nwbt37/KPP/5gt27daGZmxjFjxrz1O/XkyRP6+fnluXllZmYmnz9/zhMnTnDt2rWcPHkynZycWK5c\nOe7bt48zZsygqakpR40axaioqP9MyHx6erq0ncT27ds5YcIE+vn50dXVlU5OTqxWrRqdnZ1pbGzM\nL774guHh4Wo3aVu5aoxyHJMkDx8+TB0dHRoYGHDnzp0kc+9AkJiYSCMjIx49epSBgYEqv41du3a9\ntc5Xr15x7dq1dHR0VFmcQJ166mVSoPLqJZ09ezbXMeW4j7LnURzI5XL+9ttvHDFiBA8cOKBS/5w5\nc3KNW7wPb7YnISGBJiYmNDMzo7u7O7/66iv26dNHikIkyStXrrw1wCM2NpYAVAaIv/zyy3wFysbG\nhhs3bmS1atVUelr169dnZGQky5UrpxKxWLFiRelB4dKlS7nK09PTk9Y/zJne5h5U9kzedxuO7Oxs\nnj17lqNHj6aTkxNdXFzo6+vL7t27c+HChdy9ezdv3rxZoAVw9+7dy9atW781T1RUFEeMGEFzc3Mu\nXrw4T0FUzqFbt24dFQoF7927x8jIyHx3P83OzlaJHnv27BmHDBlCKysrlitXrtTm4hQFCoWCL168\noFwuZ1ZWFjMyMnjt2jWGhIRwwYIF7Nu3rzSfDXi9ZY2Pjw8nTJjA4OBgHjt2jOHh4bxw4QKjoqLU\neufmadOm0cjIiKamppw4cSJbtGhBGxsbyeNgampKV1dXamhoMCgoiI8ePeKOHTv49ddf09DQUFor\nNOfv4vz583nWdefOHa5Zs0b6LXp6ejI8PFwtJ64XpUCV6nYbOV9Xq1YNN27ceOd5ZmZmSEhIgK6u\nLjIyMvA2+58/fy4tg3/9+nWYm5vD0tISZ8+exR9//AFbW1t06tQJt2/fxuTJk/HVV18hMjISixYt\nyrM8bW1taVuOD+XNHW5///139OvXDzY2Nvjxxx/RvXt3aGhowNDQEElJSQXaETczMxNGRkbIyMiA\nhoYGAGDo0KH47bffoKOjg6SkJJX8lpaWCA4OxuTJk3Hjxg1UrFgR9+/fl94PCAiAlZUV1qxZAx0d\nHWRnZyM7OxsymQyffvoptm3blssG5edRUM6cOYOHDx+ia9euuHz5Mjw8PAp8rhK5XI4rV67g6dOn\neP78OY4cOYJ79+4hPDwccrkc7u7ucHBwgLOzM3r06IHq1avj9u3bSEtLg52dHQ4ePIjjx49j/fr1\n76zr2rVr+Pzzz5GUlISWLVvC29sbzZo1g6WlJRYtWoQJEybAzc0NMpkMMTExcHBwwNOnT9GrVy80\na9YMHh4ecHV1fWsdJPHPP/+gU6dOWLNmDbp06VKoHZE/BJLIyMjAnTt3kJiYCAcHBzx//hz79+/H\ntWvXYGJigqysLABAamoq0tLSpO+Iubk5Tp06hcjISACvv+t6enqwtraGlZUVatasCQ8PDzRs2BB2\ndnawsbGBtrZ2ibavqEhPT8fAgQNx/PhxbNmyBWFhYfDw8EDr1q1hZGSE6Oho6Onp4fHjxzAxMcHw\n4cNx8eJF1K9fH15eXoiOjoaVlRW8vb3RqVMnyOVyAEBMTAzs7OxU6tq0aRP69OkDU1NTyGQyTJ06\nFePHjwfwenfxFStWICgoCJqamiV+HfKiKLfbUBuBcnV1RVRU1DvPa9KkCU6cOPFWgcrKysLKlSsx\nZswYtGzZEj/++CPatm2L2NhYyOVyaGpqIjs7W8pfpUoVmJiYoFy5crCzs4OWlhbatm2Lnj174uDB\ng2jdujWA4hEoJdbW1rhw4QK2b98OT09PeHl5oUOHDvjhhx9QpUoVZGdnQ6FQ5PmDzs7OhpaWFr74\n4gusWbNG5T0vLy+cPn1a5ZiZmRn++OMPLFu2DCEhIahatSpu3boFNzc3XL9+HQBw6NAhtGvXDrVq\n1UJWVhYuXrwIS0tLVK1aFadOnSrUNXgTX19fHDx4EEePHoWJiQnq1KlT6DKfPHmCe/fuISoqCuHh\n4fj5558BACYmJtJ359mzZxgyZAhWr15doDLlcjnOnTuHY8eO4dixYzh58iS8vb2hr6+P6tWro3Ll\nyrC1tUWjRo2gr6+PqKgobN++HeHh4Th79iycnZ3Rr18/9O7d+617/pw+fRqDBg1CdnY2mjdvDjMz\nM2RkZMDQ0BBNmjSBn59foYQrLS0NERERSElJgampKV6+fImLFy8iMjISZ8+exY0bN1CuXDk4Ojoi\nPj4eOjo68PLygouLi7RXm0wmg6WlJTIzM5Geng4DAwNkZGTAxcUFzZo1g4aGBuRyOfT19aGvr//B\ntpYk4eHh+P7777Fp0ybo6enlev/kyZM4ePAgwsLCcObMGele8CH30CNHjmDKlCkYPXo0JkyYgNjY\nWABARkYGtLW1cfPmTZw/fx7Lly9HREQEZDIZevXqheHDh0vbv69evRqnT59GYGAg5s+fX+IPM/lR\nZvaDyplcXV3fOdYBvF5xgqQ0XyUvlKHPc+fOZf369VmxYkUqFAreunWLs2fP5qNHj/jo0SOGh4fz\nzp077/TfKuvW1tZ+a76CkF+7rKysVLYWUR5v0KABDxw4QD8/P+ro6HDUqFG53FdvrgGYM12+fJkt\nW7ZUOWZsbCxNSEUO9+qSJUuYlZXF/v37s2nTprnKcnJyyrVvV1GlSZMmSW7FD0WhUOS7ooVCoVBx\nzynzFmZSblpaGseMGUMADA0NfWve1NRUbtu2jV27dqWxsTHXr1//1vzKPbR++uknzp49m3PmzOGk\nSZNYtWpV6unpcfbs2QwPD5fGN16+fKky1pGVlcUXL17w4MGD3Lt3L3/99VcOHjxYCjZwdnamr68v\na9asSXd3dw4fPpwLFy7k0aNH+ezZM7Ua0ygOXr58yR07dnDgwIHs0qUL/fz8pAneAHjz5k2Sr1eG\nmR/BLE8AACAASURBVDNnDm1tbWlkZER7e3vq6+vT29ubc+fO5d69ez+o/tTUVGlahnJSfLly5dik\nSRNqaWlRQ0ODtra2NDAwoLe3N8+fP8+///6bX3zxBW1tbenm5sbg4GC1nFSOInTxqY1AKZfseVca\nPHgwybwF6vjx45wxYwbt7OykDy4rKyvfYIb3ueDA65UkCkt+7bK0tFQZ2/r3338ZFRXFDh06SHly\njv/Ur1+f48aNk7aIzq9chULBIUOGqGwdYmhoKEWdAZACGRYuXEjy9ZjWZ599xvnz5/PgwYPSZOqC\npjf96sDrVR0KOr9q+PDh9PT05OPHj1WCTTIyMnIFESxZsoS9evUiSf7222/5ClxxjWUoFAqGhoa+\n11Iz165do4mJSb5jVO+q78KFCxw8eDCtra1pbm5OExMT6fpWqlRJZezRwsJCEqMZM2YwNDSU9+7d\ne+96P2bkcjkvXbrE1atXs1+/fvTw8KChoSGbN2/OH374gVu3buXff//N6OhoRkREsHXr1jQwMKCJ\niQkNDAxYoUIFlitXjvb29vzxxx9zrR7zIaSlpfHEiRNs3LgxR4wYQeD1djg6Ojrs3r27tPQR8HrM\nWFtbm/Xr1+fcuXMZFham1g8QZUqglIOllStXfutNS7m6gbKXMXbsWA4ePJh79uzhunXrVPIW9aKm\nRSlQv/32m0oAQk6BymsfJoVCwRMnTkg3s4cPH/Lhw4fcuXNnrk0elSnnElIkOXz4cGlrEOB1L1RZ\nFwBpdYz58+fna3d8fHyheki9e/fmokWL3hqRqYxKUg4yW1tbc+zYsZINNjY2HDRoEBUKBT08PDh8\n+HDJ9lu3bknlREVFMSAggN988w1fvnzJTZs2EXjdy3kzyCEyMpKXL18u8bXM2rdvLz0QfCgKhYKP\nHj1iXFwcFQoF//77b27evJmnTp1iUlKSWoUelxRpaWk8efIkZ86cyfbt27NatWo0Njami4sLO3fu\nzCVLlvDcuXN5Bh3J5XJ2796dTk5O7NWrF318fNiuXTvOnz9f6lF9CDnX/FQoFPnOW1TuwtC3b18u\nWrSI+/fvZ9euXbl+/fpi3/W6KClTAqXsVr9LoJRPheTryC/l5F1l0tXV5e3btxkSEiLtq1KUF7yo\nBIpknttb5BViXhCuXbvGX3/9VaWsnHPK9u7dy4sXL3LVqlUEXi84O3/+fCmKDPj/9ffeNtdKmbcg\n6dq1a/m+l9cK6+9KxsbGuZa9eluys7NTeW1tbS1FLLZu3ZrHjx9nTEwMZ8yYIU2QbNCggcqNpLiJ\njo6mpaWlynqNgvcjLS2Nx48f53fffcdWrVqxTp061NfXl6YGbNu2jWfOnCnQzrcKhYLbt29n7dq1\ni/T+oZy4P3/+fJqamub5fVUel8lkbN++fZHVXVoUpUBpoZRRRp68blf+KKPTpkyZgo0bN6Ju3bq4\ndOkSqlSpghcvXsDW1hYymQyVK1cudpuVvHr1CikpKdDV1YW+vn6eA6t5kVdbSX7QIKebmxvc3Nww\ncOBAAEBQUBBCQ0MBvB6snDlzJu7evYu4uDgAgJ6eHiZOnIjDhw9j48aNAP7/M8gZOFIYbt++nefx\ngIAAyba8qF69Oq5du5bne0+fPi1Q3TKZDLGxsTA1NUVSUhI0NTXx4sULeHl5wd7eHomJidIgvkKh\nAP6vvSsPr+nq3u/JPEqCiFSESBDxERISJKZQMbQUqTGm4ocq1ZqKGKofnxqLaqgoaihKEVMQEoSa\nKW00piBCCIlEM0ly398fN+f03JubiZAb7vs8+3nuPcM+e+19zl57rb0GAMeOHcPvv/8Of39/HDly\nBDVr1izWs14FtWrVQnBwMAICAnD69GnY29u/9meWBzx58gSHDx/GpUuXEBsbi3r16sHJyQl6enow\nMzPD06dPcfPmTURFReGPP/5Aw4YN4evri5EjR8LBwQGurq6SAUpmZiZu3bqFM2fOID4+HqmpqUhM\nTMSzZ8+QkZEBMzMzJCYm4tatW7hx4waMjY0REhJS7O9YDoVCge3bt2P37t04e/YswsPDkZiYiHXr\n1gEAJk+eDEBpkJWUlISkpCTp3mfPngFQzgH+/v6v2INlh8jISERGRpZqnWVuxdegQQNcvXoVTk5O\niI2NLfb9b7LdIuMQBAGdO3dGXFwcrly5gho1aiAlJQXPnj2TTG3Nzc3h4+MjWeL5+PjA0tJSpT4H\nBwfEx8erHLOxscH169dRuXLlV2rjkydP8OOPP2Lq1KmwsrLC7NmzoaenB19fXzRu3Bjnz5+Hm5sb\npkyZgi1btuDRo0fw8PDAxYsXERQUhG+++abIZ2iCg4MD7t+/j969e2Pr1q0AoMIEAODevXtwdHTM\nV6c4lurWlUVhxIgRkgXe+PHjsWjRImzbtg29evXCzZs3UatWLcnsOywsDLdv30ZMTAwePXqEs2fP\nQl9fHxkZGbC2tsayZcuQkJCAkJAQHDt2DFWrVi12O+S4ceMGXFxcir3YmDNnDjZv3oyVK1fC19dX\nayyx3hT++ecfHDt2DIcPH8b58+dx5coVtG7dGk2bNoWzszOOHj2K58+fQ19fH2lpaahSpQqcnJzg\n7e0NLy8vFWvIFy9e4OjRo9i3bx8OHTqE69evo0qVKnB0dISdnR1sbW1RsWJFGBsbw8LCAqampnBw\ncICVlRXq1q0LBweHlzLVzs3NxeDBg7Fx40bpmLm5OYyMjJCcnJzvem9vbzx58gTTpk1DSEgITp06\nheXLl2PMmDHYsmULevfu/XKdqSV4q6z4xIgGTk5Ohapt1NVirwsPHz7kzp07uWzZMikwrLxMnjyZ\nUVFR3LhxIw8ePCilas7KyuKtW7c4c+ZMbt++XfIQNzAwoKOjI11cXOjl5UU/P78CaSwqrE5hEOtI\nT0/nrFmzJLWhiEePHhFQjbB+5swZApDSeBgaGnLJkiUFRn4WnyEGURXL7t27efDgQWlcHj9+rJE+\nTUkQ5UVUwxW3yJMvyjOhFkdVl5GRIalflixZovJsExMTbtu2rURRHc6dOydtbMudrIuCQqHg+vXr\n6eTkRF9fX166dOmVomNoO1JTU7llyxaOGTOGLVq0oIWFBdu0acO5c+fy8OHDJd5ryc7OZlxcHJcv\nX05HR0fWr1+fkydP5qFDh96YE+tHH31U4DtqYGDAO3fuEFBmL1iyZAkBZa44UpkO6MCBAySV39er\nGnRpA1CKKr4yZ1CxsbEElOk1CpuMRAYmMrRXRXZ2Nnfs2MFp06axZ8+eBJTWbNbW1rS2tmatWrUY\nFBTE0NBQlZetpMjMzGR0dDR79uzJtm3bcu/evSrmrPJS0qgKcoh1KBQKKTxS1apVpfNPnjwhAP75\n55/57qtTpw5PnjzJkJAQBgQEsGrVqhr3w8RnfPLJJyrtfvToEXNzc1WYX3GZjLGxcYHJEUtSfH19\nS9xnYhBeEQcPHmT79u1V6o2JiSmynujoaFasWJGrVq3inj172KRJkxK3JTMzk8uWLaOlpSX19PTY\nokULbt++nYmJiVptsVUUUlNTuX37dg4bNoweHh60tLRk586dJWu0kuRjevHiBcPDwzlz5kx26dKF\njRo1ktLKBAQE8Pfff3+NlCiRnJws7Vf98ssvUnqcjh078ocffmCVKlU4fvx42tvbEwD37NlDkipj\nWJAVJYBXMsbQFpQmgypzFZ+IatWq5VN7ySE6k65YsQLr16/HmTNnCryWJDZs2ICEhATo6+vDwMAA\nFStWhJubG3bs2IErV64gKioKKSkpGDNmDNzc3CAIAurVq4fmzZvnc4YV1S4GBgaSF/2rwN7eXtoT\nkuP+/fuoVq3aS9UptpEkpk2bhrlz58LBwQFxcXEAlHpuGxsb/P333yoRDQRBQPPmzVWcbzt16oQ2\nbdrg9u3bqFu3Lj755BPJix0Ahg0bBmNjY6xYsQIAkJiYmE816ePjU2yH3h49euC3337Ld1xUGxYH\nFSpUQHx8PCwsLIp1PQCsWrUKI0eOzKcu3rdvH3788UeEhoZCEAQMHDgQAQEBqFSpEiIiIrBx40as\nXLkSzZs3h4GBAdauXYvIyEj8/PPPyM3NhYuLC3799Vc0adKk2G2RIzU1Fb/++itWrVqFv//+G/r6\n+nBycoKtrS18fX1x+/ZtVK5cGc2bN0ft2rXx7NkzPH36FNbW1qhfvz4qV65cpqrClJQU7N69G3v3\n7sXBgwfRrFkzdOnSBZ6envjPf/4DKyurEtV35swZHDx4EMuWLYOhoSF8fHzg7e0NT09PeHp6wsLC\n4rVHUcjJyUH//v2xbdu2fOeMjIwQFRWFpk2bAlBGsKlcuTJ8fX2xY8cOVKlSpcj6W7VqhRMnTiA1\nNTXflkB5w1ul4hNTTKhbXqkX0VenefPmKqvejIwMPnjwgGfOnOHIkSM5cuRIyYpt1KhRHDt2LCdO\nnMjBgwezdu3aNDc35+bNm/ngwYNir0zFNryMBKUJ8my1zs7OXLt2LQVB4JQpU166TrE+kpKFY40a\nNaTzqampBJAvuRnwr2+ZCDGlSMWKFenu7k5XV1eVVCCis7T4X5PkFx4eXizJRx6vTz0fjnoSy6KK\nra0tR40axaioqCKTU5JKKbqwhIR37tyhvb09P/74YzZq1IgWFhYqWY3FthsZGbF///7S+7RgwQLJ\nN+tVoVAomJiYyHPnzvHXX3/lmDFjOGvWLE6aNIlNmzZljRo16O3tzXbt2rFGjRoUBIEODg4cPXo0\n161bx/37978RCSw3N5dRUVH88ssvaWNjw48++oirVq0qtto6LS2NsbGxPHnyJPfs2cNt27Zx27Zt\n9PPzY7Vq1Th+/HiePHnytdKQmZnJ0aNHc9KkSRw3bpwkKY0ePbrAzABi6dy5M7Ozs5mTk8M+ffpw\n4MCBnDx5MitXrszPP/+ce/fupUKh4IsXL1TM/6Ojo1USjZZkrO7evctbt25xxIgRWqUWRilKUGXO\noMSUEqJIXFARTaHFMnr0aMk03cbGhh4eHqxevTr/97//cffu3aVqKlraDEpuMu3i4sKYmBhp4tu4\nceNLt7Fu3bokKe1/OTs7S+fFyPDq6oXnz5/n8wHKzs6WEi2SyqjNcv+l9u3bS6pZQJm7S92jPiIi\nosCx/Pbbb1X+7969mwA4cOBAlePFiaQuL97e3lJE9yZNmvDbb79leHh4gR99Tk4Or1+/zmXLlqlE\n8ZDjwIEDdHR0ZGxsLLt27UoXFxdOnjyZy5cv54gRI6TJCVD6b2VmZjIlJYWVKlXitWvXihw3hULB\nU6dOFXldcZGdnc1z585x+vTpDAgIoJubG2vUqMGQkJBS35PJysrioUOH+Nlnn0n7P1OnTpXenRcv\nXvDZs2c8e/Yst27dyoULF3L8+PEcMGAAW7ZsSXd3d1apUoV6eno0MjKig4MDPTw82KJFCymhZUhI\nSIHRQUobK1asIACamZkV+p6pu8T07NmTnTt3poODA+vXr882bdpIfR0aGsqmTZvS1taWTk5OtLGx\noZ6eHj/++GPOmDFDpZ7i+G8qFApeuXKFAwYMkPwG1ReYZY23ikGJ+VDkUoVYxGR3gDKrJPBvlAI/\nPz/u27dPSpv+OlHaDEqeS8nJyYk3b96khYWFFAmhdevW0oqrJG1csWIFSUqp5UWGRSpXhwBeOhXJ\nw4cPpTZXrVpVZZzc3Nz43nvvcfLkyczKymJWVhaPHz9e4Aeuvocl7i+GhIQQgOQUrJ78sajSvn17\n5uTk8OHDh5w2bZrkO/fxxx9z586d0sa0iK1bt0r3zpgxo0DaxRxYX3zxRYGS2bVr16Tnbdq0iatX\nr6aLi4vGFBwKhYIhISEcNmyYtOd15MiRlxqXopCbm8tDhw6xS5cuNDc3Z4UKFejq6sq2bdty0KBB\nnD59OidPnsxx48Zx9uzZDA0N5YkTJwrMs6RQKBgdHc0JEybQ1taWXl5enD59Ovfv3881a9awe/fu\nrF+/vuTbY25uzkaNGrFnz54cN24c//e//zEkJIRhYWE8c+YM7927x5ycnDLbZ1MoFIyMjJQyclep\nUoWGhoY0MDCgkZGRFC1/+vTpNDc3pyAI9Pf3Z/fu3VUWXPKM3oAyE0Nubi4nT57MWrVq8ejRo4yI\niOC+ffs4b948VqtWLd/7q8koJzs7mw8fPuT27ds5b9481qhRgwYGBgwKCmJYWBgBaF24o7eKQeXm\n5kovhvqAieGMxLJ06VI+f/6cALh8+fJS79iCUNoMatmyZSp0xcbG0tzcXErUN3/+fDo4OLBSpUrF\nDksDgD/88ANJSvHhxBTupPJFB1AqqULGjBkjGV0AYGpqKh8/fsyuXbuqLCoKKurSsvixtmjRggAk\nK0R5okSxBAQEsG3bthrrFY1PvvzyS5KUYgz6+/tLal9PT086OTnxwIEDnDt3rnRvp06dOHz4cBUV\n6IULF3j06FGSLFJlmJSUxDNnzkg0XLhwgUFBQfTy8so32Z8+fZqAMuLHsmXLuGTJEtavX/+154JK\nT0/nvXv3eOrUKR4+fJhr1qzhjBkzGBQUxFmzZnHixIns0KED69WrR0tLS/r7+3PhwoXctGkTP/jg\nA9rY2NDIyIj6+vo0Njamvr4+9fT0aGJiQjs7O3700UfctGkTL1++zCdPnjA9PV0rDDzS0tJ48eJF\nRkVFSSrEqKgoZmRkqGSB7tChA83NzXnlyhXp3suXL7Nv376sVKkSg4KCePv2bemcXO3XoUMHLlq0\niN27d6ebmxttbGwKtdgVo6SsWbOGs2bN4ldffSXVm5mZyfPnz3Pw4MG0sbFhxYoV2aJFC3bu3JlH\njhxReU+0oX/V8dYwqIiICMlUWC5VaCqbNm0i+a8ksHLlytLt1UJQ2gzq2LFjKrTdu3ePZmZmKhZN\naWlpknXbZ599pqJyK6iNwcHBJCnF9nJ3d5fOiwFli+NVXxBElcLnn38uPROAimlwYmIiT5w4kS/8\nlLzI81YByth78v+9e/cmoNmys0KFCvkkOLHIJ4wmTZqoLHpI8uTJk/ms9NTL8OHDGRwczLCwMGlf\ndPPmzWzZsiW7dOnCX375hRs3buSFCxdUpFF5HY6OjrS1teXVq1fZqVMn1q9fn4cPH+bly5dVAvvO\nnTtXGhs/Pz9+//33Lz02pY24uDiuXr2aLVq0oKWlJatWrcpBgwYxODiY0dHRfPToEZ8/f16mGW3V\nkZqayi+//FLl/VBf5GoqH374oaSudXFxYW5uLm/fvs0LFy7Q3NycEyZM0GhtePPmTamOu3fvcteu\nXaxSpQrbtGkjqezliVgtLS05dOjQAt0gDh48yKlTp0rvbVBQEO/du/e6u63U8dYwqNq1a0vqHU0h\ncOSrcTE7pShxlcTX5FXxuhlUfHw8TU1N833sCoWCly9fluLotWzZkrt3784X+DQnJ4f79u2TGMXI\nkSMlaUGdjlcxZb969SoBpaqL/NcQQtN+3/nz54vNoMaMGUNAGU0d+DeunjymoJzRkMpkg46Ojirn\n5AYMpqamKgYYixcv5s2bN7lp0yapz6Ojo/PFBhTfx6pVq6qonb/55ht+++237NatGzt37iz5Tf33\nv/9lmzZtipwExSI3BBk3bhydnZ25c+dOTpw4kba2tq80Pq+KrKwsXrlyhbNnz6afnx+tra0ZGBjI\nsLAwrY/rl5ycnM+oxtPTk/7+/vzyyy+5Z88ebt26lT/99BPDw8N58eJF9u7dmzNmzKCVlZXK+Ivv\niLGxcYFR51NSUrht2zYCYLt27ejq6kpHR0fu3r2bCoWC165d49SpU3n48GEmJSXx6tWrRUrIYjLC\nU6dOaXWixqLw1jAocZ+lOAxq586dKh2wdu3aUuvQovC6GVRCQgJNTEwKdVJ88OABf/rpJ0lVNXz4\ncPbt21fa9/jkk0+4d+9ebtq0iUOHDiWgNBpQp+NlImiLuHbtGgFwwoQJJMmjR48SgEb116VLlwqc\npMW4gGIZNmwYAXD+/PkE/o3lp0nakWPy5MnScXd393zXihKf+FsMOCyOJaBZjaipLFu2jK1bt+bW\nrVt569YtKhQKXrp0SWWFLJYPPvhA2nCvU6eOZBVZUBEXFP369SOg9Ol6FUm3uEhNTWV4eDinT5/O\nVq1a0djYmM7OzpLVWUl8lN4EkpOT+fjxY968eVPy00tMTOTevXvZoUMHAkotwt27d/Mx+qysLD5+\n/Jj37t2TVOw9evTQmFYGUEr14eHhKnUoFAqGh4drfC8dHBxYs2ZNGhoa0sfHR/J/Kgnq1KnD6Ojo\nEt/3pgMdF4W3hkHJCdJksfW2MqjIyEgVOhMTE2liYlLszc6bN29y6tSpXLJkCX///XdGRUVx5syZ\nkkQhn4gB8NChQySVH8CrvMyiZDNp0iQVOjStDDWlhhdLcHCw9Hv48OGS5PPf//6XgDLaBaA0FpHf\nJwiCyjP++ecfSYUjMrnCypYtW1TqKg5jUi/t27enra0tg4KCOGTIEDZt2lTaQxMNeSpXrkxDQ0P2\n6tWLFSpUkKIHbN68WWMke3HvrFevXpIFWcWKFXn48OFSs0YVjRvWrFnD4cOHs0GDBjQzM2Pz5s05\nefJkHjhwQKNBR1kiJiaGaWlp7Nu3r8YoI2fOnJHS0QwcOJBpaWmMi4vj9u3bmZKSwg0bNjApKalI\nFxb1sm7dunxt2bdvH318fGhra8v69etLWRh69+7NtWvXStqPtLQ0rlixgs2bNy8xveo54QpDbm4u\nDx8+zMDAQNapU0erJNy3kkFpivQr31NQZ1CaXqLXhdfNoJKSkmhsbFxq1jj9+/cnoFShtm3bVurH\n48ePv9JGvGhaLm7oiqGLNPlgFBbRfPXq1dJveRoQ0ez23LlzBFSjsgOqkTFEiAYXU6dOLXTSOX78\neD4pvbBN7ILKqVOnJPXl0KFDJf88ebl69SoTExPZp0+fEtUtVzWJpWnTppwwYQKXLl3Kdu3a8dNP\nP+WGDRsKHcecnBzev3+fW7Zs4ahRo9i6dWva2dnR0dGR/fr147Jly3j27Nli+Yq9SSgUCsbFxfHr\nr7/WaOV24MABRkZGMiwsTCXVzLfffsucnByeOnXqpRYdALho0SK2bNmSQUFBUnsyMzOZmZnJwMBA\nWlhY0MTEhG3atOEPP/zAhw8fqhhTyJGenk4nJydGRkYyMzOzWGbyL168oJGRUaELktzcXMbGxnLO\nnDl0dXWli4sLJ06cmM+3sazxTjKo3377TeX6t4lBpaSkFPlylgQDBgwg8G/urNTUVE6bNo0VKlSg\nm5sb+/fvz9DQUK5YsaLYKzZSqWYUmQFJRkVFEdDsXHj9+vUCJ/isrCzJd0jOWMQ8OSdPniQAKTOy\nWFq1apXvOeLqWFQPFlTkJuWvUsaMGUOFQsEzZ85IWW9F9wBzc3Pa29szODiY8fHxfPbsmcY6GjZs\nWOzkjXI1pbwYGRlx2bJl3L9/PwcOHEh3d3c2a9aMnp6erFChAm1tbfnBBx9w8eLFPHz4MO/du6dV\nFl9iWx49esTw8HB+//339PDwIAA2aNCAkZGRPHfuHAMCApiQkMDs7GyuXbuWtWrVIqCMlzh27FhJ\nWzB8+HCNaWzk326bNm04evRohoSE8OHDh3z8+DEDAwNVXAxWrFiRzzjHyMiI06ZNK1E6FtFlQix3\n7txhWlqaRHdycjK3bt3KLVu2SE708jlRHdHR0ZKFaLNmzbh//36tU+2JeCsZlKbVo5xB7dixQ+X6\nt4lB/fPPPzQ0NCy1jdGwsDCNkQxycnK4a9cuOjs787333qOnpyctLCw4YcKEYr3s4ockrjJFRqIJ\n9+/fzzeecp2+GC9Q7qw4YcIEApCcfMUJSyyaohKIK231CUG9iL5hr1qcnZ3p7OxMT09Penp6snXr\n1oyOjqYgCLS2tuaOHTtUIkwUxnRKkuOqoGJjY8OFCxfy+++/58KFC3ny5MkyNbTQhFu3bvHWrVtM\nTU3lgwcPuHjxYlapUiXfN29kZMRGjRqxRo0aktOvaCn39ddfF7tPPDw8+OGHH/KXX37RuAB78uQJ\n9+/fzx9++IFjxoxhv379JP8icS7q3r07ly9fzujo6JeK0pCVlcWFCxcyIyODffv2ZZ06daT2de/e\nnRYWFuzYsaPKAmTixIn56klNTeWcOXNYqVIlfv3111oVMaIgvJUMSpP/jDx1tTqDWr9+fSl0ZfFQ\n2gxKPcpCRkYGDQwMykTlEhMTw2bNmjEwMLDIa3NycgiA06dPJ0lJpaIJycnJKpOo+nWi4cDMmTOl\n68aOHUtAmWQRyB89RBNEBiX3Z9FUCgubdOzYMe7bt69Yk1+DBg14+vRpnjt3jmfPniUAyRKwa9eu\n9Pb2ZsOGDRkbG1tgEFxN6qvCypEjRxgeHi4t2CpVqqSyeJPvpzVu3JjfffcdU1NTqVAoVFbtbwL3\n7t3jmDFj2K1bN9arV69AlwDxeyqM7kqVKuXbK9TT08snVcr92UQL07S0NF66dImHDh1icHAwhw0b\nxqZNm9LGxoYmJiZ0cXFRkZTEAAAA+NNPP5Vqn+zcuZPAv1ap5ubmPH36NEmyV69eKu93Tk4OT5w4\nwQULFrBfv360s7Njp06d3mgyzVfFO8mgtm/frnJ9QeafrwOvm0G9ePGC+vr6ZSayixOto6Mjhw4d\nWmjMM+BfI4nCGNSLFy8k+kT1rRwig5JLUKI1m1jU93c0QTTbVpdK1fd/Ctpvkhte3Lp1q0hmYW5u\nnq8/jIyMaGZmxuDgYFarVo1ubm48duwYSXLDhg356lAPpSNfXQPgoEGDpN/yLM5//PGHFF1j0qRJ\nPHbsGDt16lRgW+X1BAcHq0Syl+9HJiYm8tatWwWOuTqePn3KH3/8kX379mWzZs3YpEkTBgYGMjAw\nkFZWVnR3d5cWF2K6mSFDhvCbb77h8uXLpSgjEyZM4NmzZxkcHEx/f3+prfXr19co8QqCwK1bt/LF\nixdMSEjg//3f/3Hx4sVSJP3Lly+rGDDIi4mJCc3Nzenk5MSWLVty6NChXLVqFXfv3s24uDiSm5ir\nwwAAGWNJREFUSpX1tGnTit0PJUFBe4bZ2dlMT0/nixcveOLECbq4uPA///kPR40axQULFvCvv/56\nLe15nShNBlXmGXVFKOkq/vm3KbGbvr4+yJfLqFsaECORL1q0CDdu3ED37t3x+eefIzc3F+7u7ujY\nsSOMjIyk69PT04usUx4RXtPYajr24sULlf85OTlFPkfsM/WI0SNHjsSWLVuk//IMpnKImZoBZZbb\nIUOGYO3atdKxKVOmwMPDAx9//DEAIC0tDf/8849K1HR9fX1kZ2cjKCgIPXv2RE5ODqZPn47169cj\nMDAQAwYMUHnmpEmTMGvWLOn/rVu3VM6PGzcO69evB6Dskw8//FDK/vrw4UOcPn0aDRs2RHZ2Nvbv\n34/k5GRYW1vj+fPnqFevHh48eAAAUh0AMGrUKADAmjVr0LBhQ7Rq1Qr+/v4YNWoUBg4ciMzMTMye\nPRtZWVmIjo5GXFwcLCwskJubi0mTJiEiIgKnT59Gq1atMH/+fDg7O6Nq1aqoUaMGsrKy8Pfff+PO\nnTuwtbXFH3/8AQCws7ODt7c3wsLCsGvXLvj7+6N27dr49NNPAQD79+/Hhg0b8OjRIxX6k5OTsWPH\njnxjdf36dZibm+PAgQO4cOECjIyM8PDhQ/j5+eHJkye4du0aLCwsMHbsWPTq1QvOzs4wMzODvr5+\nsaKd+/j4wMfHp8jrXgYGBpqnWgMDA+ncV199hS+++ELqHx1QthLUzJkzJWlCPVqwoaGhigT166+/\nqnDoDRs2lAq3Lw7kbSoNiP5DYiFJQRDKzFRU3FsSExVev36dPXr0UEki2a9fP8bFxRH4Nzjl77//\nXqBkQ/7bb6J0LIeYDFIuQQUGBqr0i7ghLu8ndYgb42JCRrHExsZyx44dfP78OY2MjArcQFcf04UL\nF6qcX7RoEUml2bxoLi62RaTfysqKhoaG3Ldvn9Sfn376Ka2srDTGExRDOYnRC0xNTfOt+sWo/eql\nUaNGKuNSs2ZNhoSEcOzYsfT29qarqysXLFjAKVOmcOLEiaxdu7YUkkhTfcUpgiDQ0NCQRkZGtLOz\n06jtMDMzo6uraz4XB/WibvgiL25ubmzQoIHKdy+WXr160cPDgzY2NvT29mbTpk0l1apcVSi39i1v\naNiwIS9dulTWzXhpRERESCp7vm0qPnUG9cknn6i8qNu2bVO5/m1jUIBmc+03AYVCkS8auYj09HSe\nO3eObdu2lVRTfn5+JIvPoOrXr08rKyuVc6L1nnwPSq6PB4qXRVn0/SLJHj16SNc+ePBAusbS0lJl\nUpW/V3IVGpnfgCUsLEw69+eff+YbM0Dpx2RhYZHPiCMmJobLly/Px2wMDAwoCAL79esn7YMYGRmp\nRMYwMDBQUQUaGxur/G/QoAH9/f0lukaPHs2ff/5Z5TmWlpa0s7OTJv7iMiR1i1o58ywo2WaFChW4\nY8cOKSoIoMxUvH//fq5bt47Dhg1TYayi/5qBgYH07RsYGNDLy4szZ87k+fPnJedrAwMDNm/eXHqP\n3N3d+cUXX3DTpk10d3dnfHw8Z8+ezfj4+ALfxfIAZ2fncrXXVBDeSgalvmoaNmyYdKxXr14q1jgA\nePbs2VLoyuLhTTEobTID1oS//vpL6odu3bpJ/kyBgYFcunRpPitE0YkyKSkpX2QE0aRcbghRWOrs\n4jCogIAAAkp/GXlf2tnZSatsPT093rlzR0pRb2xsrFKf6CQsMkt1piO+k+S/74WVlRXNzMwKTWch\np010A/jiiy+4bds2ent7S1mdRcYcERHBsLAwjh49mqdOneLmzZslJlOvXj0V3xe5cU12djb79evH\nPXv2MD4+XqUf1KUbua+ag4MDGzZsyMGDB0v9WFhZsWIFT5w4QVLpL+Th4SEFKfby8qKlpSVNTExo\nY2NDHx8fBgQEsE+fPuzbty/ff/99Vq9enRUrVmT37t2l/TH1BZooZffr149Tp07lb7/99sZSb5QF\n7OzsVBZW5RVvJYMSUzeLZfjw4dJkUNb2/qXNoI4cOaJCqxjuqTwAANu2bSsxGHGyat26NV1dXRkU\nFCSZOYuGCpogv18smmLviaUgCU+MX0ZSul8d6vl75LSYmpqqXHv79m0C4MWLF1WMCkSIUowYHV6s\nA0CRoYFEZtq9e/d8zyWVBh+NGjWimZkZra2t86XgyMrKknyuAGU6lbCwMD5+/JgxMTHcvHkzQ0ND\nGRMTo8Isnz9/zoSEhHwJIeXF3t6erq6ubNasGT/++GP++uuvfPTokZT6RL2MGjWKgwcPZteuXenj\n4yN9v05OTvzhhx945coVbt26lUOHDmW1atVoYmJCLy8vDhgwgEuWLOEff/xRpMZgzZo1BCBZvL3t\nEDMalHeUJoPSGiMJdejp6YmM7K0yiNAEkc7ygpycHMydOxe+vr7o0qULPv30U4wcORKnTp3CnDlz\n0Lx5cwQHB0OhUBRYhyaa1Y0k5OjUqVOR7crMzNR4vEKFCgCAmjVr4ssvv1Q5p/5u2draAlBuXtev\nXz9fXVZWVkhPT1d5lvi7oI1wEaampgCAhw8fwsTEJN/5uLg4AMDz58/RqlUrtGvXDu+//z4aNWoE\nHx8fODo6onHjxkhPT8fGjRuxefNmjB49WsXIom7durhx4wYUCgVatGiBU6dOFdievXv34oMPPsDg\nwYNRvXp13LhxA0lJSbh79y4WLlyIpUuXIjs7G4IgwNDQELa2toiPj8eQIUNQp04dGBgYIDU1FQkJ\nCahRowZq1qwJHx8fLFy4EBMmTECjRo3QrVs3TJgwAbVq1VIxtCkORMMGsd/KE7Zs2YLatWvDw8Oj\n0PmLJBQKBY4fP4709HSYmZm9wVZqP7SGQalPWHLrKm3B62KUZNlZ8L0MMjIyAACVKlWSjunp6cHX\n1xf79u3D+vXrERAQgOzsbABKC7KRI0fC3d290HoLY1DFwZgxY3DhwoV8xx0cHHDp0iX07dsXY8aM\nUTmn3u+idV5BzG7u3LkYMmQI0tLSpGPiu1sUgxInn7t372pkUCIsLS2xYsUK+Pj44PDhw4iJicFv\nv/2GnJwcpKenw9raGkuWLEF4eDj09fWhUCggCAJycnIQGRkpWfbFx8ejRYsWsLKyQt26dbFy5UoV\nuj799FNUrVoVN2/ehLW1NTp16gQrKyuYm5vD0NAQ2dnZ0NPTw5AhQ9CtWzeMGDECT58+xe7du7F4\n8WKkpqbC1NQU7dq1Q4MGDTBlyhRYWFhgxowZ2L9/f6E0FgflgUFdu3YNSUlJaNasGV68eIHQ0FAE\nBwfj2LFjqFmzJgwMDGBjYwN7e3tMmTIFXl5e0twWExODgIAAPHnyBAkJCZg3b16xrA3fJWgNg1KH\nfOIoT5P3y6C8MShzc3MAmsdFnNAGDhyI9u3bIzIyEgYGBmjUqBF8fX0xbdo0dOzYUaMEJTK0kkDO\n1Lp164bk5OR81wwZMgR79uzRONEV1O9y5ivH4MGDMWTIEDx58iTfuaIml++++w6HDh3CnDlz4OTk\nVOi1LVq0QHh4OEgiNjYWERERCAsLg4+PDxwcHDB9+nSMGjUKAwYMwI0bNxAdHY27d++iTp066Nq1\nKxwcHODh4QF7e3ukpKQgOTkZbdu2xV9//YWPPvoIM2fORMWKFQEozb3t7Ozg6uqKmzdv4sSJE7hz\n5w6uX7+Oo0ePIisrCz/++CM2b94MOzs79OzZE9u2bYODgwMcHBykNrdp0wZubm6SxPqqECfykkpe\nbwqZmZnw9fVF1apV8ejRIzx//hxNmzbFiBEjcPjwYejp6eHKlSu4f/8+YmJiMGDAANy+fRtmZmao\nWLEiEhISMHv2bDRo0ACDBg3CpEmTypokrYPWMii5BFWeJu+XQXliUHfv3lXxASoI+vr60iS/fPly\nzJ8/H+vXr0f//v3h4eGRz28JUGU2TZs2xblz54p8jre3N3bt2lXoNa6urgCAixcvFlkfUDyV66VL\nl/IdK2oMW7VqBW9vb8yZM6dYk267du2k38OHD0dKSgr27t2L7du34++//0ZWVhYWLVqE6tWr4733\n3oOpqSnOnz+PnJwcVKlSBVZWVjAxMUGVKlWQkpICCwsLLFiwAA8fPsSgQYNw6tQp/PPPP7C0tMTT\np0+hp6cHMzMz+Pn5wdXVFYGBgVi5ciXatWuHgIAAfPPNN4W2t1mzZkXSVBKIDL8oybSs8Mcff8DR\n0RGXLl3C/fv3YW5uDhsbG5VrGjdujMaNG+PDDz/E+PHjkZOTg4yMDDx48ACVK1eWfBATExPLggSt\nh9aMvCYVn7btzbxOJlJeGJSjo6P0u6g2y8fP1NQUI0eORL9+/TBv3jwsXrw43/VyBiWXRoyNjQt8\nRkGSjhzvvfceAORzCH0VqDvfDhw4sFj3ibTIVYTFhZWVFfr374/+/fsXeE1iYiJOnz6NlJQUpKen\nIyEhAU+fPkXjxo2xevVq9O7dGwAwbdo0jB8/Hj169MCqVavw8OFD9O3bV2N/Hj9+HJaWliVu76tC\n2xnUjRs3UK9ePQBQkSQLgriXZ2hoWGpS5tsOrRl5dWb0Nqv41GnVNkZcXHh6emLfvn0FntdEV4UK\nFTB37lxpX0UOTQzK1ta2WJJUYbCysoK/vz+aNm2a71xxolUUB61bty7R9ffv3y+V56rD1tYWH374\nYYHnjx49ipUrV0oSbEERNtTrLAuI74C27MtkZGQgLi4OpqamuH//PiIjI1UWbDqUPrSGQalDG40k\nXgcWLFhQrlR8cujp6aFz584Fnm/WrBmOHj2q8VxWVhYAJcNKTU0F8K/xBfDvpGRvb48aNWq8clvD\nwsI0Hn8Zw4yKFSvmm9jloZ2KQlBQECIjI0v83FfFZ599hs8+++yNP/dlIc4BZS1BRUdHY/r06YiI\niEBycjIsLS1Ru3Zt3Lt3TyWclg6lD63hAoWp+Mrj5F0Y5OoAQRDKLYMqCpMmTcKzZ880nlu9ejUu\nXbqkshdz7969fNcVZqoOvNq74enpCTc3txLfp8nYoiQb+d988w1OnDhR4ue+ayhLCer27dvYuXMn\nfH190apVK7i7u+P48eN4+vQpUlNTceHCBSQmJqrsE+pQ+tBaCUobJ+zSalPdunVVmNLbyqAKg729\nPezt7fHJJ59g/vz5AJTSVEpKCoB/jRByc3MLredV+s3Pz09FaisuNElLJZGgdCgeymoPKjQ0FP37\n94enpyeGDx+Orl275jN+0OHNQGsZlDYaSbwuvIsMSoRcGpGrdZ2cnHDlyhXcuXMHUVFR8PX1LfVn\nGxsbFymhFRc6BlX6EN+HNyVBPXv2DDY2NqhcuTKOHDkCLy+vN/JcHQqG1qj41KGnp1dqk4c2421W\n8ZUUcmlJ9BMyNzdH165dYWhoCC8vL2zbtk3lnlfpN2dnZxw9ehQjR45EUlJSkdKaCE0GEToGVfp4\n0xLU77//DgA4e/asjjlpCbSWQb0rE7YYc+pdobcwkETjxo1x8+ZNaXJyd3fH06dPkZaWhi+++AKj\nRo2Cp6cnZsyYgYSEhFd6Xv369ZGQkABBEFCpUiUYGBigbt262LZtG+7cuVNgNAl5vigROgZV+hAl\nqDdlMJWcnIw+ffoU6UStw5uD1jCo8uAH9brwLjMoOd0KhQLVq1eHs7OzJD2HhoZCEAQYGRmhb9++\nuHXrFubNm4crV67AyckJly9fBgCcOHGixO9LUlISGjRogODgYFy/fh0TJ05Ex44dMX36dDg5OcHG\nxgZdu3bF2rVrERsbq7HNIkor2kFxpbh3AW/6+8/IyNDqsErvIrSGQanjXZmw33UVn3wSEmPKyY+r\nB8+0trbG+++/j127diE0NFQKbTRgwAA0btwYn3/+ObZv344HDx4UaUKekpIiOUzWrl0b8+fPx9Kl\nSxETEwOFQoGLFy/Cy8sLP//8Mxo3boyhQ4fizJkz+Pnnn/PVtWHDBvz5558v3xEAxo4dCxsbG4wa\nNQqhoaGvVNfbAB2D0kFrGdTMmTMxbdq0sm7GG8G7zKDkkE9IxZmc3n//ffj5+QFQBt5csWIFKleu\njO+++w7VqlWDsbExOnToIEX0Vq8zNTW1QI9+QRBQr149BAUFISIiAn/++Sesra0xYsQIjenIIyIi\n4Ofnhy1btiA7OxtfffWVRlVgQUhKSsK6detw5swZWFlZoU+fPhrN7t8l6BiUDlprxWdgYFDmDnov\ng8jISLRp06ZE92g7g3oZmooLOYOQG8UUd3IS+83Y2Bg+Pj7w8fHB9OnToVAokJGRgdWrV6NDhw6o\nVKkSMjMz0axZM9jb26NSpUpYtWoV+vXrV6znODg4YNGiRfmeK2Ls2LFo2bIlOnTogB9//BEREREA\nADc3N1haWkIQBFSuXBkGBgawtrZGSEgImjVrhgYNGgBQ9rGPjw/q1auHefPmIT4+Htu3b8+XHqQo\nvM6xetOQvwNvgq43zaDeprF6XdAaCUrThKTNk3ZBKGmEgPKg4nudUQ/k6S/kKr7iWnAW1G96enow\nNzfHuHHj8OjRI4SGhuLYsWMYNGgQ3nvvPSQlJcHFxQUBAQGvTgSAYcOGoVGjRjhx4gSqV6+Oa9eu\nYd26dejTpw8++OADdO3aFa6urqhZsyb09PQwYsQItGvXDuPHj0d2djaOHj2Ktm3bSvV9/vnn+O67\n70oc4b0sIlS8LqgzqNeNsmBQOhQOrRZRtG3S1uWDKn0YGhrC2NgYWVlZJVbxFRfm5uZSLipXV1f0\n6NEDADBr1iy0atXqler29fVFVFSUNLHVrVsX69evl541aNCgfPc8f/4cubm5yMnJQZ8+ffD+++/j\n2rVrkpkzADRp0gTVq1fHoUOH0KVLl1dqow7FQ0ZGhsYo+zqUHbRGgtKEd2HSflvDOb0MXoYplWW/\nmZqaFiuKtTosLS1hbW2NypUrY9euXejbty+ioqJQq1YtlesCAwOxZs2a0mquDgUgNzcXJ06cQHh4\nOOzt7cu6OTrIIJSVKbcgCO+GDbkOOuigwzsGkqWyciwzBqWDDjrooIMOhUGrVXw66KCDDjq8u9Ax\nKB100EEHHbQSOgalgw466KCDVqJMGJQgCB0FQfhbEITrgiBMLos2vAwEQXAQBOGoIAh/CYJwVRCE\nsXnHbQRBOCQIQowgCAcFQbCS3TNFEIQbgiBcEwShQ9m1vnAIgqAnCMJFQRBC8/6/DTRZCYLwa147\n/xIEwbu80yUIwheCIPwpCMIVQRA2CYJgVB5pEgRhjSAIjwRBuCI7VmI6BEHwyOuL64IgfPem6VBH\nAXTNz2v3ZUEQdgiCUEF2Tuvp0kST7Nx4QRAUgiBUlB0rPZrEaNpvqkDJFG8CqAHAEMBlAK5vuh0v\n2faqABrl/bYAEAPAFcC3ACblHZ8MYF7ebzcAl6D0N6uZR7dQ1nQUQNsXADYCCM37/zbQtA7AkLzf\nBgCsyjNdAN4DcBuAUd7/rQAGlUeaAPgCaATgiuxYiekAcAZA07zf+wH4ayFd7QHo5f2eB+B/5Yku\nTTTlHXcAEAYgFkDFvGP1SpOmspCgvADcIHmXZDaALQC6lUE7SgySCSQv5/3+B8A1KAepG4D1eZet\nB/BR3u+uALaQzCF5B8ANKOnXKgiC4ACgM4AQ2eHyTlMFAC1JrgWAvPamoJzTBUAfgLkgCAYATAHE\noxzSRDIKQLLa4RLRIQhCVQCWJM/lXfez7J4ygSa6SIaTFEOjnIZyzgDKCV0FjBUALAEwUe1YN5Qi\nTWXBoKoBiJP9v593rFxBEISaUK4qTgOwI/kIUDIxAKI7ujqt8dBOWsUXTe5zUN5pcgLwRBCEtXmq\nyx8FQTBDOaaL5AMAiwDcg7J9KSTDUY5pUkOVEtJRDcr5Q0R5mEs+gVJ6AMoxXYIgdAUQR/Kq2qlS\npUlnJPESEATBAsB2AJ/nSVLqzmTlxrlMEIQuAB7lSYaFOdeVG5ryYADAA8AKkh4A0gB8hfI9VtZQ\nrlBrQKnuMxcEoT/KMU1F4G2hAwAgCMI0ANkkfynrtrwKBEEwBTAVwMzX/ayyYFDxABxl/x3yjpUL\n5KlWtgPYQHJ33uFHgiDY5Z2vCuBx3vF4ANVlt2sjrT4AugqCcBvALwD8BEHYACChHNMEKFdocSTP\n5/3fASXDKs9j1R7AbZJJJHMB7ATQAuWbJjlKSke5oU8QhMFQqtHl4fPLK13OUO4v/SEIQiyU7bso\nCEIVFDy/vxRNZcGgzgFwEQShhiAIRgD6AChP2dl+AhBNcqnsWCiAwXm/BwHYLTveJ8/SygmAC4Cz\nb6qhxQHJqSQdSdaCciyOkhwAYA/KKU0AkKcqihMEoU7eoXYA/kI5HisoVXvNBEEwEQRBgJKmaJRf\nmgSoSu0loiNPDZgiCIJXXn8MlN1TllChSxCEjlCq0LuSzJJdV57okmgi+SfJqiRrkXSCcjHYmORj\nKGnqXWo0lZFVSEcoLeBuAPiqLNrwku32AZALpeXhJQAX82ipCCA8j6ZDAKxl90yB0pLlGoAOZU1D\nEfS1xr9WfOWeJgDuUC6ILgP4DUorvnJNF5RqlWsArkBpSGBYHmkCsBnAAwBZUDLeIQBsSkoHAE8A\nV/PmkqVaStcNAHfz5ouLAH4oT3Rpoknt/G3kWfGVNk26WHw66KCDDjpoJXRGEjrooIMOOmgldAxK\nBx100EEHrYSOQemggw466KCV0DEoHXTQQQcdtBI6BqWDDjrooINWQsegdNBBBx100EroGJQOOuig\ngw5aif8H8j+40voC/ZIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xaf1eb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(0, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MiIqKQnZ2NjZt2oQ+ffogKioK1dXVUFNTe2HHt7q6OszNzXHmzBl07NgRcXFxMDMzg0Ag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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc308860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(2, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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69Sr286rQn9VvO3bswKZNm7hlp06dwpQpU7BkyRIYGBhAo9Fg+/bt+M9//oP7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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc308cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(3, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SExPp6+vLI0eOFPDgVJhqBgwYwB49enDatGlcv359AXf9opJEImGzZs2op6dHAFy/fj1T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\nRGKSSIGSSCQSiUny/wOX0OME6GLpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x84d6780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(4, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wPz+fdd9TXDpA0ngaGBggMTERp06dkntu4Q+6MlBVVUVSUhLH4qgi8PPzk7uf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VsNsxFQ+jpJMJjDGpQ2GKpBOTg4YP78+SrHFitWDE5OTgZpUNK6gtSgdF1T2xqUvkLVIrjyFjVr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\nrS+DV5jlm58AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb2e7908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(5, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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A8MMPPwAAPDw84OnpKZxrbW2N7OxsaGtrw8nJCd27d8ewYcPQsmXLdytkKdG9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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xe0ad160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(6, 'k')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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yyZMnhWl/+/btBvNbsWLFV7rW4kqJFCjJvbIgWlClSpV65X1zEqgvv/ySsbGx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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb5f5cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_time(7, 'k')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 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": {
"042e8c72144746658f98812f3559d520": {
"views": [
{
"cell_index": 20
}
]
},
"ea16ebecae9d46fcb37ea761ea258da6": {
"views": [
{
"cell_index": 17
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
climberwb/pycon-pandas-tutorial | Solutions-4.ipynb | 1 | 128449 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>body {\n",
" margin: 0;\n",
" font-family: Helvetica;\n",
"}\n",
"table.dataframe {\n",
" border-collapse: collapse;\n",
" border: none;\n",
"}\n",
"table.dataframe tr {\n",
" border: none;\n",
"}\n",
"table.dataframe td, table.dataframe th {\n",
" margin: 0;\n",
" border: 1px solid white;\n",
" padding-left: 0.25em;\n",
" padding-right: 0.25em;\n",
"}\n",
"table.dataframe th:not(:empty) {\n",
" background-color: #fec;\n",
" text-align: left;\n",
" font-weight: normal;\n",
"}\n",
"table.dataframe tr:nth-child(2) th:empty {\n",
" border-left: none;\n",
" border-right: 1px dashed #888;\n",
"}\n",
"table.dataframe td {\n",
" border: 2px solid #ccf;\n",
" background-color: #f4f4ff;\n",
"}\n",
"h3 {\n",
" color: white;\n",
" background-color: black;\n",
" padding: 0.5em;\n",
"}\n",
"</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"css = open('style-table.css').read() + open('style-notebook.css').read()\n",
"HTML('<style>{}</style>'.format(css))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ligaw na daigdig</td>\n",
" <td>1962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sluby ulanskie</td>\n",
" <td>1934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The House of the Seven Gables</td>\n",
" <td>1940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mandala - Il simbolo</td>\n",
" <td>2008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Shi bian</td>\n",
" <td>1958</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year\n",
"0 Ligaw na daigdig 1962\n",
"1 Sluby ulanskie 1934\n",
"2 The House of the Seven Gables 1940\n",
"3 Mandala - Il simbolo 2008\n",
"4 Shi bian 1958"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None)\n",
"titles.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>name</th>\n",
" <th>type</th>\n",
" <th>character</th>\n",
" <th>n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Suuri illusioni</td>\n",
" <td>1985</td>\n",
" <td>Homo $</td>\n",
" <td>actor</td>\n",
" <td>Guests</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gangsta Rap: The Glockumentary</td>\n",
" <td>2007</td>\n",
" <td>Too $hort</td>\n",
" <td>actor</td>\n",
" <td>Himself</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Menace II Society</td>\n",
" <td>1993</td>\n",
" <td>Too $hort</td>\n",
" <td>actor</td>\n",
" <td>Lew-Loc</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Porndogs: The Adventures of Sadie</td>\n",
" <td>2009</td>\n",
" <td>Too $hort</td>\n",
" <td>actor</td>\n",
" <td>Bosco</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Stop Pepper Palmer</td>\n",
" <td>2014</td>\n",
" <td>Too $hort</td>\n",
" <td>actor</td>\n",
" <td>Himself</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year name type character n\n",
"0 Suuri illusioni 1985 Homo $ actor Guests 22\n",
"1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n",
"2 Menace II Society 1993 Too $hort actor Lew-Loc 27\n",
"3 Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n",
"4 Stop Pepper Palmer 2014 Too $hort actor Himself NaN"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n",
"cast.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>character</th>\n",
" <th>Batman</th>\n",
" <th>Superman</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1938</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1940</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1943</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1948</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"character Batman Superman\n",
"year \n",
"1938 1 0\n",
"1940 1 0\n",
"1943 1 0\n",
"1948 0 1\n",
"1949 2 0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define a year as a \"Superman year\"\n",
"# whose films feature more Superman characters than Batman.\n",
"# How many years in film history have been Superman years?\n",
"\n",
"c = cast\n",
"c = c[(c.character == 'Superman') | (c.character == 'Batman')]\n",
"c = c.groupby(['year', 'character']).size()\n",
"c = c.unstack()\n",
"c = c.fillna(0)\n",
"c.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Superman years:\n",
"13\n"
]
}
],
"source": [
"d = c.Superman - c.Batman\n",
"print('Superman years:')\n",
"print(len(d[d > 0.0]))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batman years:\n",
"24\n"
]
}
],
"source": [
"# How many years have been \"Batman years\",\n",
"# with more Batman characters than Superman characters?\n",
"\n",
"print('Batman years:')\n",
"print(len(d[d < 0.0]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f90845d4748>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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HUzX+mTpO/UhsCNxnxgYSbcAWZnEDjurbOBLY14wjBqzc6/X1/V9u2QWAjuM4TWIDws6p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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f90845d08d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the number of actor roles each year\n",
"# and the number of actress roles each year\n",
"# over the history of film.\n",
"\n",
"c = cast\n",
"c = c.groupby(['year', 'type']).size()\n",
"c = c.unstack('type')\n",
"c.plot()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9099ef7dd8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hsQ9UKMZRtOGQNFjSXZKelrRQ0nRJQyU9LGmxpIckDc7Jf6WkJZIWSTolJ32a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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9099efbef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the number of actor roles each year\n",
"# and the number of actress roles each year,\n",
"# but this time as a kind='area' plot.\n",
"\n",
"c = cast\n",
"c = c.groupby(['year', 'type']).size()\n",
"c = c.unstack('type')\n",
"c.plot(kind='area')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9099e62fd0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DRwHz8/F3Sb+a39tEuXcA37a5xGZxPzqITIxu6jY16Xdsmorr2RW4q9VCadVU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\nIAiGJJxEEARBMCT/H29+geb744nyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9099e34ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the difference between the number of actor roles each year\n",
"# and the number of actress roles each year over the history of film.\n",
"\n",
"c = cast\n",
"c = c.groupby(['year', 'type']).size()\n",
"c = c.unstack('type')\n",
"(c.actor - c.actress).plot()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9099df4128>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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D3XC1OK9fv2n9gDdi9cpyMiFpfeDnwP4RcX9eXelMA+TZPW/aDni/pG+Q3v4/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\nZmYFc5E3MyvY/wfqMbEDevIYogAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9099dabdd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the fraction of roles that have been 'actor' roles\n",
"# each year in the hitsory of film.\n",
"\n",
"c = cast\n",
"c = c.groupby(['year', 'type']).size()\n",
"c = c.unstack('type')\n",
"(c.actor / (c.actor + c.actress)).plot(ylim=[0,1])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9099d54860>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MMLOxZbs9CWzm7msAPwR+0+6CiojIPDVz3mb2KeBYdx8fl48CcPcTquy/FDDV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9099d59da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the fraction of supporting (n=2) roles\n",
"# that have been 'actor' roles\n",
"# each year in the history of film.\n",
"\n",
"c = cast\n",
"c = c[c.n == 2]\n",
"c = c.groupby(['year', 'type']).size()\n",
"c = c.unstack('type')\n",
"(c.actor / (c.actor + c.actress)).plot(ylim=[0,1])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9099cc54e0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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L8Vcs3Zl5IzQC47AY5ybMmhyc1KYX8B5hUApIWW29ksqeZoSYKDseOYk993i4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\n/gIX37FdoBhu3gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9099ccbac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Build a plot with a line for each rank n=1 through n=3,\n",
"# where the line shows what fraction of that rank's roles\n",
"# were 'actor' roles for each year in the history of film.\n",
"\n",
"c = cast\n",
"c = c[c.n <= 3]\n",
"c = c.groupby(['year', 'type', 'n']).size()\n",
"c = c.unstack('type')\n",
"r = c.actor / (c.actor + c.actress)\n",
"r = r.unstack('n')\n",
"r.plot(ylim=[0,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
andrewzwicky/puzzles | advent_of_code/2021/15.ipynb | 1 | 10316 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from session import SESSION\n",
"from common import print_problem, get_problem_input, submit_answer, neighbors\n",
"from bs4 import BeautifulSoup\n",
"from IPython.core.display import HTML\n",
"from collections import Counter\n",
"import re\n",
"from collections import defaultdict\n",
"from copy import copy, deepcopy\n",
"import itertools\n",
"from pprint import pprint\n",
"from functools import lru_cache\n",
"import numpy as np\n",
"import colorama\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"DAY = 15"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw Data:\n",
"'11956992691699626181157392791991439681856921722851821835662217958771161218637214'\n",
"Split Data:\n",
"['1195699269169962618115739279199143968185692172285182183566221795877116121863721498957174621167918242',\n",
" '9115625874265799911728162199785394821312124845672192542919338584962177612992393151111933224334135452',\n",
" '8131729216517615863391649615129757111193739768481218915512112595941796747611211317938536136799846916',\n",
" '4962593132118152776959137321183383866772192116141953231951591444941238923298442361423618911179121722',\n",
" '1416121522926541915424971183698194981419338198971913131598831476211215251194523121195812188825994191',\n",
" '1928733692818149228324516927257499752678492513169166313451293451242416536442231355632161758212412115',\n",
" '4991813117359923391742392452551991886811936769148871994981937912114913399457193319831421852811811411',\n",
" '9996916186114911181317116839536799955741529114391992149574162387517295536711111425429134625723713721',\n",
" '6413152742931664293329711273293938913869998819228825914291212594321245569621831111918122371115465372',\n",
" '6722188749914139715213996125538481197121421593279313213185899225935111987292443132111143755985119529']\n"
]
}
],
"source": [
"raw_data, data = get_problem_input(DAY)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [],
"source": [
"data = [\n",
"\"1163751742\",\n",
"\"1381373672\",\n",
"\"2136511328\",\n",
"\"3694931569\",\n",
"\"7463417111\",\n",
"\"1319128137\",\n",
"\"1359912421\",\n",
"\"3125421639\",\n",
"\"1293138521\",\n",
"\"2311944581\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"cavenp = np.array([np.array(list(map(int,row))) for row in data])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"cave = dict()\n",
"\n",
"for r,row in enumerate(data):\n",
" for c,val in enumerate(row):\n",
" cave[complex(r,c)] = int(val)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def adj(cave, c):\n",
" return [c+n for n in [-1+0j,1+0j,0-1j,0+1j] if (c+n) in cave.keys()]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def dijkstra(cave):\n",
" visited = defaultdict(bool)\n",
" to_visit = set()\n",
" distance = defaultdict(lambda: math.inf)\n",
"\n",
" current = 0\n",
"\n",
" destination = sorted(cave.keys(), key=lambda x: x.real + x.imag)[-1]\n",
"\n",
" distance[current] = 0\n",
"\n",
" while True:\n",
" for n in adj(cave, current):\n",
" if not visited[n]:\n",
" distance[n] = min(distance[n], distance[current] + cave[n])\n",
" to_visit.add(n)\n",
"\n",
" visited[current] = True\n",
"\n",
" if visited[destination]:\n",
" break\n",
" else:\n",
" current = sorted(list(to_visit), key=lambda v: distance[v])[0]\n",
" to_visit.discard(current)\n",
"\n",
" return int(distance[destination])\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2778\n"
]
}
],
"source": [
"print(first := dijkstra(cave))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"423\n",
"(\"That's the right answer! You are one gold star closer to finding the sleigh \"\n",
" 'keys. [Continue to Part Two]')\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submit_answer(DAY, 1, first)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def build_cave(cave, n):\n",
" tiles = [cave]\n",
" \"\"\"\n",
" 12345\n",
" 23456\n",
" 34567\n",
" 45678\n",
" 56789\n",
" \"\"\"\n",
"\n",
" for _ in range(n-1):\n",
" a = tiles[-1] + 1\n",
" a[np.where(a == 10)] = 1\n",
" tiles.append(a)\n",
" top = np.hstack(tiles)\n",
" \n",
" tiles = [top]\n",
" for _ in range(n-1):\n",
" a = tiles[-1] + 1\n",
" a[np.where(a == 10)] = 1\n",
" tiles.append(a)\n",
" top = np.vstack(tiles)\n",
" \n",
" return top\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"new_cave_np = build_cave(np.copy(cavenp),5)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"new_cave = dict()\n",
"\n",
"for r,row in enumerate(new_cave_np):\n",
" for c,val in enumerate(row):\n",
" new_cave[complex(r,c)] = int(val)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13672/2991909774.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msecond\u001b[0m \u001b[1;33m:=\u001b[0m \u001b[0mdijkstra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_cave\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~\\AppData\\Local\\Temp/ipykernel_13672/412344222.py\u001b[0m in \u001b[0;36mdijkstra\u001b[1;34m(cave)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpotential\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mvisited\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpotential\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpotential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"print(second := dijkstra(new_cave))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2789\n",
"(\"That's not the right answer; your answer is too high. If you're stuck, make \"\n",
" \"sure you're using the full input data; there are also some general tips on \"\n",
" 'the about page, or you can ask for hints on the subreddit. Please wait one '\n",
" 'minute before trying again. (You guessed 2789.) [Return to Day 15]')\n"
]
},
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submit_answer(DAY, 2, second)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
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},
"nbformat": 4,
"nbformat_minor": 4
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| mit |
hackpert/CBSECSPracticals | Question19.ipynb | 2 | 1155 | {
"metadata": {
"name": "",
"signature": "sha256:fb1a4f9027148bb2fcb16846378de195799c87dfec58431fbe4fb3c8e5e7b156"
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a class to implement stack in python (dynamic). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class stack(object):\n",
" '''Class to implement stack data structure with the following methods:\n",
" -push(element)\n",
" -pop()'''\n",
" def __init__(self):\n",
" self.s = []\n",
" def push(self,elem):\n",
" self.s.append(elem)\n",
" def pop(self):\n",
" p = self.s[-1]\n",
" del self.s[-1]\n",
" return p"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
hail-is/hail | hail/python/hail/docs/tutorials/06-joins.ipynb | 3 | 13091 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table Joins Tutorial\n",
"\n",
"This tutorial walks through some ways to join Hail tables. We'll use a simple movie dataset to illustrate. The movie dataset comes in multiple parts. Here are a few questions we might naturally ask about the dataset:\n",
"\n",
"- What is the mean rating per genre?\n",
"- What is the favorite movie for each occupation?\n",
"- What genres are most preferred by women vs men?\n",
"\n",
"We'll use joins to combine datasets in order to answer these questions. \n",
"\n",
"Let's initialize Hail, fetch the tutorial data, and load three tables: users, movies, and ratings."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import hail as hl\n",
"\n",
"hl.utils.get_movie_lens('data/')\n",
"\n",
"users = hl.read_table('data/users.ht')\n",
"movies = hl.read_table('data/movies.ht')\n",
"ratings = hl.read_table('data/ratings.ht')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Key to Understanding Joins\n",
"\n",
"To understand joins in Hail, we need to revisit one of the crucial properties of tables: the key.\n",
"\n",
"A table has an ordered list of fields known as the key. Our `users` table has one key, the `id` field. We can see all the fields, as well as the keys, of a table by calling `describe()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"users.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`key` is a struct expression of all of the key fields, so we can refer to the key of a table without explicitly specifying the names of the key fields."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"users.key.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keys need not be unique or non-missing, although in many applications they will be both.\n",
"\n",
"When tables are joined in Hail, they are joined based on their keys. In order to join two tables, they must share the same number of keys, same key types (i.e. string vs integer), and the same order of keys."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at a simple example of a join. We'll use the `Table.parallelize()` method to create two small tables, `t1` and `t2`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t1 = hl.Table.parallelize([\n",
" {'a': 'foo', 'b': 1},\n",
" {'a': 'bar', 'b': 2},\n",
" {'a': 'bar', 'b': 2}],\n",
" hl.tstruct(a=hl.tstr, b=hl.tint32),\n",
" key='a')\n",
"t2 = hl.Table.parallelize([\n",
" {'t': 'foo', 'x': 3.14},\n",
" {'t': 'bar', 'x': 2.78},\n",
" {'t': 'bar', 'x': -1},\n",
" {'t': 'quam', 'x': 0}],\n",
" hl.tstruct(t=hl.tstr, x=hl.tfloat64),\n",
" key='t')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t1.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t2.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can join the tables. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"j = t1.annotate(t2_x = t2[t1.a].x)\n",
"j.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's break this syntax down. \n",
"\n",
"`t2[t1.a]` is an expression referring to the row of table `t2` with value `t1.a`. So this expression will create a map between the keys of `t1` and the rows of `t2`. You can view this mapping directly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t2[t1.a].show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we only want the field `x` from `t2`, we can select it with `t2[t1.a].x`. Then we add this field to `t1` with the `anntotate_rows()` method. The new joined table `j` has a field `t2_x` that comes from the rows of `t2`. The tables could be joined, because they shared the same number of keys (1) and the same key type (string). The keys do not need to share the same name. Notice that the rows with keys present in `t2` but not in `t1` do not show up in the final result. This join syntax performs a left join. Tables also have a SQL-style inner/left/right/outer [join()](https://hail.is/docs/0.2/hail.Table.html#hail.Table.join) method.\n",
"\n",
"The magic of keys is that they can be used to create a mapping, like a Python dictionary, between the keys of one table and the row values of another table: `table[expr]` will refer to the row of `table` that has a key value of `expr`. If the row is not unique, one such row is chosen arbitrarily.\n",
"\n",
"Here's a subtle bit: if `expr` is an expression indexed by a row of `table2`, then `table[expr]` is also an expression indexed by a row of `table2`.\n",
"\n",
"Also note that while they look similar, `table['field']` and `table1[table2.key]` are doing very different things!\n",
"\n",
"`table['field']` selects a field from the table, while `table1[table2.key]` creates a mapping between the keys of `table2` and the rows of `table1`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t1['a'].describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t2[t1.a].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Joining Tables\n",
"\n",
"Now that we understand the basics of how joins work, let's use a join to compute the average movie rating per genre.\n",
"\n",
"We have a table `ratings`, which contains `user_id`, `movie_id`, and `rating` fields. Group by `movie_id` and aggregate to get the mean rating of each movie. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = (ratings.group_by(ratings.movie_id) \n",
" .aggregate(rating = hl.agg.mean(ratings.rating)))\n",
"t.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get the mean rating by genre, we need to join in the genre field from the `movies` table. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = t.annotate(genres = movies[t.movie_id].genres)\n",
"t.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to group the ratings by genre, but they're packed up in an array. To unpack the genres, we can use [explode](https://hail.is/docs/0.2/hail.Table.html#hail.Table.explode). \n",
"\n",
"`explode` creates a new row for each element in the value of the field, which must be a collection (array or set)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = t.explode(t.genres)\n",
"t.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can get group by genre and aggregate to get the mean rating per genre."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = (t.group_by(t.genres)\n",
" .aggregate(rating = hl.agg.mean(t.rating)))\n",
"t.show(n=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do another example. This time, we'll see if we can determine what the highest rated movies are, on average, for each occupation. We start by joining the two tables `movies` and `users.`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"movie_data = ratings.annotate(\n",
" movie = movies[ratings.movie_id].title,\n",
" occupation = users[ratings.user_id].occupation)\n",
"\n",
"movie_data.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll use `group_by` along with the aggregator `hl.agg.mean` to determine the average rating of each movie by occupation. Remember that the `group_by` operation is always associated with an aggregation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ratings_by_job = movie_data.group_by(\n",
" movie_data.occupation, movie_data.movie).aggregate(\n",
" mean = hl.agg.mean(movie_data.rating))\n",
"\n",
"ratings_by_job.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use another `group_by` to determine the highest rated movie, on average, for each occupation.\n",
"\n",
"The syntax here needs some explaining. The second step in the cell below is just to clean up the table created by the preceding step. If you examine the intermediate result (for example, by giving a new name to the output of the first step), you will see that there are two columns corresponding to occupation, `occupation` and `val.occupation`. This is an artifact of the aggregator syntax and the fact that we are retaining the entire row from `ratings_by_job`. So in the second line, we use `select` to keep those columns that we want, and also rename them to drop the `val.` syntax. Since `occupation` is a key of this table, we don't need to select for it. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"highest_rated = ratings_by_job.group_by(\n",
" ratings_by_job.occupation).aggregate(\n",
" val = hl.agg.take(ratings_by_job.row,1, ordering = -ratings_by_job.mean)[0]\n",
")\n",
"\n",
"highest_rated = highest_rated.select(movie = highest_rated.val.movie,\n",
" mean = highest_rated.val.mean)\n",
"\n",
"highest_rated.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try to get a deeper understanding of this result. Notice that every movie displayed has an *average* rating of 5, which means that every person gave these movies the highest rating. Is that unlikely? We can determine how many people rated each of these movies by working backwards and filtering our original `movie_data` table by fields in `highest_rated`.\n",
"\n",
"Note that in the second line below, we are taking advantage of the fact that Hail tables are keyed. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"highest_rated = highest_rated.key_by(\n",
" highest_rated.occupation, highest_rated.movie)\n",
"\n",
"counts_temp = movie_data.filter(\n",
" hl.is_defined(highest_rated[movie_data.occupation, movie_data.movie]))\n",
"\n",
"counts = counts_temp.group_by(counts_temp.occupation, counts_temp.movie).aggregate(\n",
" counts = hl.agg.count())\n",
"\n",
"counts.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So it looks like the highest rated movies, when computed naively, mostly have a single viewer rating them. To get a better understanding of the data, we can recompute this list but only include movies which have more than 1 viewer (left as an exercise). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercises\n",
"\n",
"- What is the favorite movie for each occupation, conditional on there being more than one viewer?\n",
"- What genres are rated most differently by men and women?\n",
" "
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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},
"nbformat": 4,
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| mit |
streety/biof509 | Wk09-Advanced-ML-tasks-Deep-Learning.ipynb | 2 | 77388 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 8 - Advanced Machine Learning\n",
"\n",
"During the course we have covered a variety of different tasks and algorithms. These were chosen for their broad applicability and ease of use with many important techniques and areas of study skipped. The goal of this class is to provide a brief overview of some of the latest advances and areas that could not be covered due to our limited time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deep learning\n",
"\n",
"\n",
"[Glosser.ca](https://commons.wikimedia.org/wiki/File%3AColored_neural_network.svg) via wikimedia. \n",
"\n",
"Although a [neural network](http://scikit-learn.org/dev/modules/neural_networks_supervised.html) has been added to scikit learn relatively recently it only runs on the CPU making the large neural networks now popular prohibitively slow. Fortunately, there are a number of different packages available for python that can run on a GPU. \n",
"\n",
"[Theano](https://github.com/Theano/Theano) is the GPGPU equivalent of numpy. It implements all the core functionality needed to build a deep neural network, and run it on the GPGPU, but does not come with an existing implementation.\n",
"\n",
"A variety of packages have been built on top of Theano that enable neural networks to be implemented in a relatively straightforward manner. Parrallels can be draw with the relationship between numpy and scikit learn. [Pylearn2](http://deeplearning.net/software/pylearn2/) was perhaps the first major package built on Theano but has now been superseded by a number of new packages, including [blocks](https://blocks.readthedocs.org/en/latest), [keras](http://keras.io/), and [lasagne](https://lasagne.readthedocs.org/en/latest).\n",
"\n",
"You may have also heard of [TensorFlow](http://tensorflow.org/) that was released by Google a year or two ago. TensorFlow lies somewhere between the low-level Theano and the high-level packages such as blocks, keras, and lasagne. Currently only keras supports TensorFlow as an alternative backend. Keras will also be included with TensorFlow soon.\n",
"\n",
"Installing these packages with support for executing code on the GPU is more challenging than simply `conda install ...` or `pip install ...`. In addition to installing these packages it is also necessary to install the CUDA packages. \n",
"\n",
"Beyond the advances due to the greater computational capacity available on the GPU there have been a number of other important approaches utilized:\n",
"\n",
"* [Convolutional neural nets](http://colah.github.io/posts/2014-07-Conv-Nets-Modular/)\n",
"* [Recurrent neural nets](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n",
"* Dropout\n",
"* Early stopping\n",
"* Data augmentation\n",
"\n",
"\n",
"[Aphex34](https://commons.wikimedia.org/w/index.php?title=User:Aphex34&action=edit&redlink=1) via wikimedia."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n",
"WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n",
"WARNING:theano.configdefaults:g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x1eff7c26cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.gray()\n",
"from keras.datasets import mnist\n",
"(X_train, y_train), (X_test, y_test) = mnist.load_data()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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mMeEFAABApjHhBQAAQKa1+9bCAPKNHj3a5aFDh7o8derUvOcceuihLs+ePbv6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\n2U66AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1effc5bccf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3,5, figsize=(12,8))\n",
"for i, ax in enumerate(axes.flatten()):\n",
" ax.imshow(X_train[i], interpolation='nearest')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
"from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
"from keras.utils import np_utils\n",
"\n",
"batch_size = 512\n",
"nb_classes = 10\n",
"nb_epoch = 3\n",
"\n",
"X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)\n",
"X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)\n",
"X_train = X_train.astype(\"float32\")\n",
"X_test = X_test.astype(\"float32\")\n",
"X_train /= 255\n",
"X_test /= 255\n",
"\n",
"# convert class vectors to binary class matrices\n",
"Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
"Y_test = np_utils.to_categorical(y_test, nb_classes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 1024 samples, validate on 10000 samples\n",
"Epoch 1/3\n",
"1024/1024 [==============================] - 1097s - loss: 2.3856 - val_loss: 2.3192\n",
"Epoch 2/3\n",
"1024/1024 [==============================] - 915s - loss: 2.3827 - val_loss: 2.3109\n",
"Epoch 3/3\n",
"1024/1024 [==============================] - 893s - loss: 2.3587 - val_loss: 2.3034\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\stree\\Anaconda3\\lib\\site-packages\\keras\\models.py:580: UserWarning: The \"show_accuracy\" argument is deprecated, instead you should pass the \"accuracy\" metric to the model at compile time:\n",
"`model.compile(optimizer, loss, metrics=[\"accuracy\"])`\n",
" warnings.warn('The \"show_accuracy\" argument is deprecated, '\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1fb3ed860b8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CAUTION: Without utilizing a GPU even this very short example is incredibly slow to run.\n",
"\n",
"model = Sequential()\n",
"\n",
"#model.add(Convolution2D(8, 1, 3, 3, input_shape=(1,28,28), activation='relu'))\n",
"model.add(Convolution2D(4, 3, 3, input_shape=(1,28,28), activation='relu'))\n",
"#model.add(Convolution2D(4, 3, 3, activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(4, input_dim=4*28*28*0.25, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(nb_classes, input_dim=4, activation='softmax'))\n",
"model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
"\n",
"model.fit(X_train[:1024], Y_train[:1024], batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, \n",
" validation_data=(X_test, Y_test))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 2.30337825203\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\stree\\Anaconda3\\lib\\site-packages\\keras\\models.py:621: UserWarning: The \"show_accuracy\" argument is deprecated, instead you should pass the \"accuracy\" metric to the model at compile time:\n",
"`model.compile(optimizer, loss, metrics=[\"accuracy\"])`\n",
" warnings.warn('The \"show_accuracy\" argument is deprecated, '\n"
]
}
],
"source": [
"score = model.evaluate(X_test, Y_test, verbose=0)\n",
"print('Test score:', score)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 713s \n"
]
}
],
"source": [
"predictions = model.predict_classes(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1a4sttnC5Z8+eLu+7774un3DCCUXnuOeee1z+7//+b5cXLlxYzRABAADQQXiH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\nJEnK2jcCBD/V8bzPcwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb3fb6fb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3,5, figsize=(12,8))\n",
"for i, ax in enumerate(axes.flatten()):\n",
" ax.imshow(X_test[predictions == 7][i].reshape((28,28)), interpolation='nearest')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performance here is very poor. We really need to train with more samples and for more epochs."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1fb3c26f940>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb3c283630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1fb3fb40f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"cm = confusion_matrix(y_test, predictions)\n",
"\n",
"np.fill_diagonal(cm, 0)\n",
"\n",
"plt.bone()\n",
"plt.matshow(cm)\n",
"plt.colorbar()\n",
"plt.ylabel('True label')\n",
"plt.xlabel('Predicted label')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building a model on the NIH HPC\n",
"\n",
"The [high performance computing group at NIH](https://hpc.nih.gov/) provides GPU equipped nodes on their GPU partition. This is an easy way to begin leveraging GPUs without the startup costs and maintainance requirements. \n",
"\n",
"Utilizing these nodes is relatively straightforward as long as you can connect to the Biowulf cluster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building a model on the Amazon EC2 service\n",
"\n",
"If you don't have access to the NIH HPC resources and don't want to buy a GPU there are a number of [cloud services](http://www.nvidia.com/object/gpu-cloud-computing-services.html) with GPU enabled machines available for rent. Probably the most well known service is Amazon Web services, and specifically their [EC2 service](https://aws.amazon.com/ec2).\n",
"\n",
"Anyone (willing to pay) can use these services.\n",
"\n",
"Using the [Bitfusion AMI](https://aws.amazon.com/marketplace/fulfillment?productId=dd1e96f9-9ede-4ff5-be40-3419bfca03a3&launch=manualLaunch) increases the cost but simplifies the startup process. Installation of the required drivers and software requires multiple steps, and this image has everything we need pre-configured."
]
},
{
"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 |
OxES/OxKeplerSC | examples/Example_cbv_1.ipynb | 3 | 92926 | {
"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": [
"# CBVshrink example1\n",
"\n",
"This notebook shows the basic usage of the CBVshrink routines. \n",
"\n",
"This is how you'd compute the CBVshrink correction for a single quarter of Kepler long-cadence data. The example we are using here is the Q6 light curve for Kepler-405, which has two transiting planet candidates, one of which has been confirmed by TTVs."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading in quarter 5 light curve data for KIC 9579641.\n",
"Object is located on module 12, output channel 2.\n",
"Reading SAP data\n",
"Read in 4634 observations of which 4487 valid.\n",
"Input CDPP: 46.373218\n",
"PDC CDPP: 48.474882\n",
"CDPP with 1 CBVs: 46.039991\n",
"Weights: [[ 29754.13757061]]\n",
"CDPP with 2 CBVs: 46.609279\n",
"Weights: [[ 29767.80738452 -1837.19643523]]\n",
"CDPP with 3 CBVs: 47.034639\n",
"Weights: [[ 29778.25369909 -1838.50144087 -1168.05468948]]\n",
"CDPP with 4 CBVs: 47.016348\n",
"Weights: [[ 29776.93269622 -1836.21629514 -1167.34633589 103.92456022]]\n",
"CDPP with 5 CBVs: 47.066080\n",
"Weights: [[ 29777.0849425 -1838.52687524 -1166.13893163 101.21811731\n",
" -202.05342631]]\n",
"CDPP with 6 CBVs: 47.239359\n",
"Weights: [[ 29776.22836325 -1834.99758388 -1167.62248547 106.11522416\n",
" -198.48067829 202.3941096 ]]\n",
"CDPP with 7 CBVs: 47.243854\n",
"Weights: [[ 29778.23266308 -1837.26334444 -1169.51759899 102.39325936\n",
" -199.62174826 200.79962909 -41.31929774]]\n",
"CDPP with 8 CBVs: 47.662966\n",
"Weights: [[ 2.97715640e+04 -1.82846262e+03 -1.16395558e+03 1.15767591e+02\n",
" -1.94748593e+02 2.07703585e+02 -4.12522984e+00 1.30118080e+02]]\n",
"Saving to file kplr009579641-2010174085026_llc_cbvshrink.fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/matplotlib/lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
" return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
]
},
{
"data": {
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4tC6K9V+E8uCOPAAzL2+PL023saVwdreWCaHkRDU7N6Qw9aN8CnMB9Ttewbv8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\nK6N59KXKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c564b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from cbvshrink import cbv\n",
"cbv.correct_file('kplr009579641-2010174085026_llc.fits',\\\n",
" 'kplr2010174085026-q05-d25_lcbv.fits',\\\n",
" 'kplr009579641-2010174085026_llc_cbvshrink.fits',\\\n",
" doplot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The figure above shows, in the top panel, the SAP light curve in black, and below it (with an arbitrary vertical offset for clarity), different versions of the correction. First, the green curve shows the PDC-msMAP correction, for reference. Then in different shades of red to blue we see the correction obtained with CBVshrink using 1, then 2, then 3, ..., then 8 CBVs (the PDC pipeline computes 16 CBVs but never uses more than 8, and we don't recommend doing it either). In the bottom panel are shown the light curves after correction, again using the same colour-coding.\n",
"\n",
"The above function call used the LC file and the CBV file for the relevant quarter as inputs, and produced an output file which is a copy of the original with additional columns added storing the corrected LC using 1, then 2, ..., then 8 CBVs. The output file also has new header keywords storing the weights of the CBVs and the CDPP estimates for the corrected LC, in each case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many CBVs should I use?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Despite the care devoted to avoiding over-fitting, the results depend very slightly on the number of CBVs used. In general, since the CBVs are noisy, you should use the smallest number of CBVs that appears to do a reasonable job, to minimise the noise injected into the corrected light curve. The optimal number to use really depends on the individual light curve, so right now we do not make the decision ourselves, we leave it to the user. \n",
"\n",
"In the example above, the correction essentially doesn't change after 3 CBVs, so 3 is probably the right number to use. As is the case for _most_ light curves, the corrected LC one ends up with is extremely similar to the PDC-msMAP version. In a small but signficant minority of cases, however, the CBVshrink correction is \"better\" than the PDC-msMAP, specifically it introduces less high-frequency noise and preserves stellar variability better.\n",
"\n",
"We are working on a \"recipe\" for automatically selecting the number of CBVs, we have one that worked pretty well on earlier versions of Kepler data, but we haven't fully tested it on the (final) data release 25. As soon as we have done that, we will update the code to include an option to select the number of CBVs used automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
KIPAC/StatisticalMethods | notes/bayes_law.ipynb | 1 | 43096 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Notes: Bayes' Law\n",
"\n",
"In which we will\n",
"* see how the concept of a generative model and our background in the calculus of probability allow us to infer properties of a model from data\n",
"* work through a simple example of Bayesian inference\n",
"* think about prior distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bayesian inference\n",
"\n",
"You've seen in the [generative models notes](generative_models.ipynb) how writing down a model with enough specificity that we could produce mock data is equivalent to writing down a joint probability distribution for all the models parameters and the resulting data. Let's abbreviate this as\n",
"\n",
"$P(\\mathrm{params},\\mathrm{data})$.\n",
"\n",
"Here I've chosen to write \"params\" in the expectation that we've chosen a model (loosely defined as the set of equations that _contains_ some unknown parameters) and are just going to be interested in learning the values of its parameters. This will be our most common use case. However, everything that follows is still mathematically valid if we broaden our thinking to include inference of the model itself from data. This will come a little later.\n",
"\n",
"Using the definition of conditional probability, we can immediately expand this as\n",
"\n",
"$P(\\mathrm{params},\\mathrm{data}) = P(\\mathrm{params})\\,P(\\mathrm{data}|\\mathrm{params}) = P(\\mathrm{data})\\,P(\\mathrm{params}|\\mathrm{data})$,\n",
"\n",
"and, through the power of division, conclude that\n",
"\n",
"$P(\\mathrm{params}|\\mathrm{data}) = \\frac{P(\\mathrm{params})\\,P(\\mathrm{data}|\\mathrm{params})}{P(\\mathrm{data})}$.\n",
"\n",
"In this form, the expression is known as **Bayes' Law**. Note that the numerator of the RHS is the factorization of the joint probability that our generative models encode.\n",
"\n",
"### Aside\n",
"Bayes' Law is also frequently refered to as Bayes' Theorem or Bayes' Rule. I dislike the word \"theorem\" in this context, since a theorem is a statement that has been mathematically proven, whereas all we did was apply one of the axioms of probability. Conversely, \"rule\" suggests something that is true most of the time, but not necessarily always. \"Law\" seems to correctly imply something more fundamental than a theorem, and embody the fact that there is nothing to argue about mathematically here - it's the law! The importance of Bayes' Law is not in the impressiveness of its derivation, but in the insight that the formal language of probability is the natural (and correct) way to describe the knowledge we acquire from data.\n",
"\n",
"On that subject, it's worth noting that the importance of this relationship was independently (though later) realized by Laplace, who was instrumental in developing the branch of probability that now has Bayes' name on it. So it goes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Components of Bayes' Law\n",
"\n",
"The above equation hopefully makes sense, at least as a mathematical abstraction. It will take time to gain intuition as to what it actually _means_, but we can start with the interpretation of each factor.\n",
"\n",
"### The prior distribution\n",
"\n",
"$P(\\mathrm{params})$ appears to be the marginal distribution of our model parameters, irrespective of the data. We interpret this to be a probability distribution encoding what we know about the parameters _before_ accounting for the information gained from the data, hence the name \"prior\" distribution. We saw this quantity already in the generative model context where, correctly, it had no dependence on the data (since choosing parameter values is the first step of the generative process that ends with producing mock data).\n",
"\n",
"Exactly what this distribution should be will vary on a case by case basis, but it's usually best to think of the choice of prior as part of the specification of the model (or, alternatively, of the problem you're trying to solve). We'll return to this later.\n",
"\n",
"### The sampling distribution\n",
"\n",
"$P(\\mathrm{data}|\\mathrm{params})$ we have also seen in the generative context - this is the probability of a particular set of parameter values producing a given data set.\n",
"\n",
"Recall that, in inference, our data are constants and the parameter values are unknown. It's natural, therefore, to think about the sampling distribution as a function of model parameters, with the data fixed. In classical statistics, this is exactly what's known as the **likelihood function**, and we will inevitably be sloppy and use use that terminology interchangeably with \"sampling distribution\".\n",
"\n",
"However, don't fall into the trap of thinking that the sampling distribution is normalized over the space of parameter values - it isn't. For a given set of fixed parameter values, it's normalized over the space of possible data sets. If this is confusing, consider that the sampling distribution is exactly what it sounds like: a distribution of samples from a population. Here, this might be a distribution of model outputs (given fixed model parameters) calculated on samples from data. \n",
"\n",
"### The evidence\n",
"\n",
"The normalizing constant on the RHS of Bayes' Law, $P(\\mathrm{data})$, is called the evidence. Mathematically, it is the marginal probability of the observed data, or equivalently\n",
"\n",
"$P(\\mathrm{data}) = \\int d(\\mathrm{params}) \\, P(\\mathrm{params}) \\, P(\\mathrm{data}|\\mathrm{params})$.\n",
"\n",
"This may not seem terribly sensible or useful, and indeed in the circumstances sketched out above (learning about the model parameters having already chosen a model), we won't normally need to evaluate the evidence. It's utility arises in the context of chosing among competing models. We could equally well have written Bayes' Law in the following form (and a stickler would have), making our selection of the model explicit by conditioning everything on that choice:\n",
"\n",
"$P(\\mathrm{params}|\\mathrm{data},\\mathrm{model}) = \\frac{P(\\mathrm{params}|\\mathrm{model})\\,P(\\mathrm{data}|\\mathrm{params},\\mathrm{model})}{P(\\mathrm{data}|\\mathrm{model})}$.\n",
"\n",
"Now we can see that the evidence is actually dependent on which model we try to explain the data with:\n",
"\n",
"$P(\\mathrm{data}|\\mathrm{model}) = \\int d(\\mathrm{params}) \\, P(\\mathrm{params}|\\mathrm{model}) \\, P(\\mathrm{data}|\\mathrm{params},\\mathrm{model})$,\n",
"\n",
"and so it will be different for different models. This provides us a way to make principled decisions about whether one model is better at explaining the data than another, a subject that we will return to much later.\n",
"\n",
"### The posterior distribution\n",
"\n",
"$P(\\mathrm{params}|\\mathrm{data})$ is a distribution over parameter values encoding what we know about the parameters after accounting for the information in the data. The is the end result of our inference - a function telling us the relative probability of different parameter values in light of the gathered data."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Example: measuring the flux of a source\n",
"\n",
"Before going any farther, let's try to make this stuff more concrete by working through a real, if simple, problem.\n",
"\n",
"Specifically, let's take our model for the brightness measured for some source in the sky from the [generative models notes](generative_models.ipynb), and simplify it even more. We'll do away with the multiple pixels in this model, and just say that we've measured a total number of counts. This might be the case if our source is so small and distant that it's effectively point-like, and we just integrate the measured signal over the point-spread function of our telescope.\n",
"\n",
"We know from first principles that the number of counts we measure will be Poisson distributed, and that the average of that Poisson distribution is related to the flux of the source by various constants, such as the integration time. So let's boil this down to the simplest possible version of the experiment: we measured a number of counts and want to infer the mean of the Poisson distribution that generated the data. (We could always convert this to a flux later on.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In other words, our model is now graphically described this way\n",
"\n",
"<table>\n",
" <tr>\n",
" <td><img src=\"graphics/bayes_poissoneg_pgm0.png\" width=100%></td>\n",
" </tr>\n",
"</table>\n",
"\n",
"where $N$ is our data (a number of counts) and $\\mu$ is the parameter we want to infer. We can write the corresponding expressions for the model as\n",
"* $\\mu \\sim \\mathrm{prior}$,\n",
"* $N \\sim \\mathrm{Poisson}(\\mu)$,\n",
"\n",
"where the second line says that our sampling distribution is $P(N|\\mu) = \\mathrm{Poisson}(N|\\mu)$. Let's go ahead and explicitly define the latter in code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get a bunch of imports out of the way\n",
"import matplotlib.pyplot as plt\n",
"plt.rc('text', usetex=True)\n",
"plt.rcParams['xtick.labelsize'] = 'x-large'\n",
"plt.rcParams['ytick.labelsize'] = 'x-large'\n",
"import numpy as np\n",
"import scipy.stats as st\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sampling_distribution(N, mu):\n",
" return st.poisson.pmf(N, mu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may have seen the Poisson distribution before, but let's go ahead and visualize it for a few values of $\\mu$ anyway."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N = np.arange(16)\n",
"plt.plot(N, sampling_distribution(N, mu=0.5), 'o', label=r'$\\mu=0.5$');\n",
"plt.plot(N, sampling_distribution(N, mu=2.0), 'x', label=r'$\\mu=2.0$');\n",
"plt.plot(N, sampling_distribution(N, mu=7.0), '+', label=r'$\\mu=7.0$');\n",
"plt.xlabel(r'$N$', fontsize='x-large');\n",
"plt.ylabel(r'$P(N|\\mu)$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, the sampling distribution is normalized over $N$ - each of these series would sum to 1.0 if we cared to check.\n",
"\n",
"As a function of $\\mu$ - the likelihood function - it looks like this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mu = np.linspace(0.0, 15.0, 100)\n",
"plt.plot(mu, sampling_distribution(0.0, mu), '-', label=r'$N=0$');\n",
"plt.plot(mu, sampling_distribution(2.0, mu), '-', label=r'$N=2$');\n",
"plt.plot(mu, sampling_distribution(7.0, mu), '-', label=r'$N=7$');\n",
"plt.xlabel(r'$\\mu$', fontsize='x-large');\n",
"plt.ylabel(r'$P(N|\\mu)$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this particular case, it so happens that $P(N|\\mu)$ is also normalized as a function of $\\mu$, for fixed $N$, but this will not be true in general.\n",
"\n",
"To reach our goal of evaluating the posterior distribution, we'll need to actually specify the prior distribution for $\\mu$. Again, this is something we'll discuss more later, and there isn't a universal rule for doing so. In this case, it wouldn't be unusual to choose $p(\\mu)$ to be uniform over non-negative real numbers, so that's what we'll do for the purposes of the example:\n",
"\n",
"$\\mu \\sim \\mathrm{Uniform}(0, \\infty)$, or, equivalently, $p(\\mu) = \\mathrm{Uniform}(\\mu|0, \\infty)$.\n",
"\n",
"One thing to note is that this prior is **improper**, meaning that it cannot be normalized. This is not _necessarily_ a problem - it's possible (though not certain) that the posterior will be proper even if the prior is not.\n",
"\n",
"Another thing to note is that this, still, is a staggeringly stupid choice. If we claimed to know, a priori, that the source's flux is not infinite, it's hard to imagine anyone complaining about that assumption. If we tried to directly implement a uniform prior over this range in code, we would find that both the prior and, therefore, the evidence evaluate to zero, so that the posterior is NaN. Clearly what we mean is for the upper bound to just be a _really big_ but finite number, and even then, in practice, there must be some values so implausibly large that no one would mind ruling them out to begin with.\n",
"\n",
"Nevertheless."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def prior_distribution(mu):\n",
" big = 1.0e10\n",
" # The construction below allows mu to be scalar or an array. It's equivalent to (for scalar mu)\n",
" # if mu>=0.0 and mu<big:\n",
" # return 1.0/big\n",
" # else:\n",
" # return 0.0\n",
" return np.where(np.logical_and(mu>=0.0, mu<big), 1.0/big, 0.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's 2 of the 3 terms on the RHS of Bayes' Law. The last is the evidence, which in general is challenging to compute. Thankfully we normally don't have to compute it explicitly if we just want to infer parameter values; for a given choice of model, we saw above that the evidence doesn't depend on parameter values so it serves only as an overall normalization. Working with an unnormalized version of the posterior is usually good enough, and we can always normalize it by brute force if we need to. In fact, we will lazily use \"posterior\" to refer to what is properly just the numerator of Bayes' Law quite often."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"So, we now have everything we need to calculate $p(\\mu|N)\\propto p(\\mu)\\,P(N|\\mu)$.\n",
"\n",
"Say we measure $N=5$. Now what?\n",
"\n",
"Broadly speaking, we have 3 options for computing the posterior.\n",
"* We could calculation it over a grid in parameter space. This is the brute-force approach: straightforward, but expensive in many dimensions.\n",
"* We might be able to calculate it analytically, ending up with an expression we can write in closed form. This is super-efficient as far as our computers are concerned, but only possible for certain forms of the likelihood and prior.\n",
"* We could produce random samples from the posterior distribution using Monte Carlo methods. In a sense, this is another brute-force method, but a more intelligent (less brutish?) one.\n",
"\n",
"Fully analytic solutions are available only for special cases, although we'll see later that some complex problems can be broken down into a series of special cases. Evaluation over a grid is a respectable solution for one- or two-dimensional parameter spaces. Usually once the number of parameters exceeds 2, it's best to go with Monte Carlo.\n",
"\n",
"But, for now, we'll stick with the first two options."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Numerical (on a grid) solution\n",
"\n",
"In this 1-parameter, computationally light problem, evaluating the posterior (including its normalization) over a grid hardly presents a problem."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def posterior_distribution(mu, N):\n",
" # NB - This assumes mu is an equally spaced array covering the range where the posterior will be significantly\n",
" # different from zero.\n",
" post = prior_distribution(mu) * sampling_distribution(data_N, mu)\n",
" return post / (np.sum(post) * (mu[1]-mu[0])) # remember to normalize!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we compare the prior, likelihood and posterior for $N=5$. (Recall that the likelihood is not normalized over $\\mu$, though the prior and posterior are.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"data_N = 5\n",
"mu = np.linspace(0.0, 15.0, 100)\n",
"plt.plot(mu, prior_distribution(mu), '.', label=r'$p(\\mu)$');\n",
"plt.plot(mu, sampling_distribution(data_N, mu), '-', label=r'$p(N|\\mu)$');\n",
"plt.plot(mu, posterior_distribution(mu, data_N), '.', label=r'$p(\\mu|N)$');\n",
"plt.xlabel(r'$\\mu$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This may seem like a lot of trouble to go to to multiply the likelihood by a constant prior, when we allegedly don't care about the normalization of their product anyway. This is true. But, of course, the procedure would have worked just as well if we had chosen some other prior distribution for $\\mu$.\n",
"\n",
"Aside: the fact that $p(\\mu|N)=P(N|\\mu)$ exactly (as a function of $\\mu$ for fixed $N$) follows from the fact that the Poisson $P(N|\\mu)$ happens to be normalized over $\\mu$ and our choice of a wide uniform prior. If both functions are normalized and differ by a constant factor, then that factor must be 1, of course. Again, this is not a general feature."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Analytical solution\n",
"\n",
"If both the prior and likelihood are built from standard statistical distributions, we can sometimes take advantage of [conjugacy](https://en.wikipedia.org/wiki/Conjugate_prior).\n",
"\n",
"**Conjugate distributions** are like eigenfunctions of Bayes Theorem. These are special cases for which the form of the posterior is the same as the form of the prior, for a specific sampling distribution. Naturally, the parameters of the posterior distribution are different from the parameters of the prior, and will depend somehow on the data.\n",
"\n",
"In math, let $f$ be the form of the prior distribution, $g$ be the form of the sampling distribution, $x$ and $\\theta$ stand for the data and model parameters of interest, and $\\phi$ stand for the parameters specifying the prior distribution (these are often called _hyperparameters_). In this example, $\\phi$ would be the endpoints of the Uniform prior, $(0,\\infty)$. Then, if $f$ and $g$ are conjugate,\n",
"\n",
"$f(\\theta|\\phi) \\, g(x|\\theta) = f\\left[\\theta\\left|\\phi'(\\phi,x)\\right.\\right]$,\n",
"\n",
"where $\\phi'(x,\\phi)$ is a function of the hyperparameters and data, specific to the conjugate pair, that we could work out or look up. (Up to now, we haven't been explicitly writing the prior as conditioned on its hyperparameters, because the hyperparameters have been fixed, but there's no reason we can't do so.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's see how this works for the flux measurement example. Our Poisson sampling distribution is conjugate to the Gamma distribution, which has parameters $\\alpha$ and $\\beta$ and the PDF\n",
"\n",
"$\\mathrm{Gamma}(x|\\alpha,\\beta) = \\frac{1}{\\Gamma(\\alpha)}\\beta^\\alpha x^{\\alpha-1} e^{-\\beta x}$ for $x\\geq0$.\n",
"\n",
"We could take this conjugacy as established fact (the Wikipedia says so!), but let's work it through this one time. For our problem, if we take a generic Gamma prior, we have\n",
"\n",
"$p(\\mu|\\alpha,\\beta) \\, P(N|\\mu) = \\frac{1}{\\Gamma(\\alpha)}\\beta^\\alpha \\mu^{\\alpha-1} e^{-\\beta \\mu} \\, \\frac{\\mu^N e^{-\\mu}}{N!}$.\n",
"\n",
"Collecting the terms that contain $\\mu$ together, we have\n",
"\n",
"$\\quad = \\frac{\\beta^\\alpha}{\\Gamma(\\alpha)N!} \\, \\underbrace{ \\mu^{\\alpha+N-1} e^{-(\\beta+1) \\mu} }$.\n",
"\n",
"Comparing to the equation above, we can see that this has the form of a Gamma distribution in its dependence on $\\mu$. Now recall that we neglected to divide by the evidence so far, so this is not yet the posterior. Since we know that the posterior will work out to be a normalized PDF for $\\mu$, we could legitimately conclude at this point that it will be a Gamma distribution, with parameters $\\alpha' = \\alpha+N$ and $\\beta' = \\beta+1$. Alternatively, we could integrate the latest expression above to evaluate the evidence explicitly. Here it's convenient to multiply and divide in the constant that would make the Gamma PDF complete,\n",
"\n",
"$\\quad = \\frac{\\beta^\\alpha}{\\Gamma(\\alpha)N!} \\frac{\\Gamma(\\alpha+N)}{(\\beta+1)^{\\alpha+N}} \\, \\underbrace{ \\frac{(\\beta+1)^{\\alpha+N}}{\\Gamma(\\alpha+N)} \\mu^{(\\alpha+N)-1} e^{-(\\beta+1) \\mu} }$.\n",
"\n",
"The collected terms above are now a normalized PDF, so that when we integrate over $\\mu$ we will be left with only the coefficients out front. So, we've just proved that the evidence is\n",
"\n",
"$P(N) = \\frac{\\beta^\\alpha}{\\Gamma(\\alpha)N!} \\frac{\\Gamma(\\alpha+N)}{(\\beta+1)^{\\alpha+N}}$,\n",
"\n",
"and, indeed, if we had evaluated the full posterior, we would be left with\n",
"\n",
"$p(\\mu|N) = \\frac{p(\\mu|\\alpha,\\beta)\\,P(N|\\mu)}{P(N)} = \\frac{(\\beta+1)^{\\alpha+N}}{\\Gamma(\\alpha+N)} \\mu^{(\\alpha+N)-1} e^{-(\\beta+1) \\mu} = \\mathrm{Gamma}(\\mu|\\alpha+N, \\beta+1)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One wrinkle though: we were planning to use a uniform prior for $\\mu$. Generally, we shouldn't limit ourselves to chosing conjugate priors just because it's mathematically convenient. In this case, however, it so happens that the uniform prior we wanted is a limiting case of Gamma (with $\\alpha\\rightarrow1$ and $\\beta\\rightarrow0$), so we can take advantage of conjugacy after all.\n",
"\n",
"Let's go ahead and update our PGM to explicitly show the hyperparameters in this case."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><img src=\"graphics/bayes_poissoneg_pgm.png\" width=100%></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can quickly verify that the analytic solution agrees with our grid calculation. (`scipy.stats` only implements the $\\alpha$ parameter in the intuitive way, making the inverse of $\\beta$ a generic \"scale\" parameter, hence the acrobatics below.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"alpha = 1.0\n",
"beta = 0.0001 # putting in exactly zero here will get us into trouble\n",
"\n",
"plt.plot(mu, prior_distribution(mu), '.', label=r'grid $p(\\mu)$');\n",
"plt.plot(mu, st.gamma.pdf(mu, alpha, scale=1.0/beta), '-', label=r'Gamma $p(\\mu)$');\n",
"plt.plot(mu, posterior_distribution(mu, data_N), '.', label=r'grid $p(\\mu|N)$');\n",
"plt.plot(mu, st.gamma.pdf(mu, alpha+data_N, scale=1.0/(beta+1.0)), '-', label=r'Gamma $p(\\mu|N)$');\n",
"plt.xlabel(r'$\\mu$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Choosing a prior\n",
"\n",
"We've put it off for long enough: it's time to talk about choosing a prior distribution.\n",
"\n",
"Recall that the prior represents what we know before accounting for the data that we're analyzing at the moment. Broadly speaking, depending on the circumstances we might choose a prior to\n",
"1. encode the results of some previous experiment,\n",
"2. encode an estimate of the uncertainty in the parameter (e.g. from theoretical considerations), or\n",
"3. be \"minimally informative\" or to have minimal influence on the posterior."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first of these in in some ways the most straightforward and least controversial. Suppose, hypothetically, we had acquired some data that measure the average density of matter in the Universe. Just to be difficult, cosmologists like to write this as $\\rho_\\mathrm{m} \\propto \\Omega_\\mathrm{m} h^2$, where $\\Omega_\\mathrm{m}$ is the ratio of the matter density to the critical density, and $h$ is the Hubble constant in dimensionless form. If we expect our measurement to have comparable uncertainty to what has been done previously, then it might make sense to combine the two results. That is, we would set our prior on $\\rho_\\mathrm{m}$ equal to the posterior distribution of a previous (independent) experiment and use Bayes' Law to update our knowledge in light of the new data.\n",
"\n",
"On the other hand, say our goal with the analysis is to use our data _in conjuction with_ someone else's constraints on $h$ in order to determine $\\Omega_\\mathrm{m}$. In this context, we would call $h$ a _nuisance parameter_, i.e. a parameter of the model that we have to care about (our data depend on it, in the generative sense), but which we are not interested in. We might then adopt the prior $h \\sim \\mathrm{Normal}(0.72, 0.08)$ on the basis of the Hubble Key Project (Freedman et al. 2001). Our posterior distribution for $h$ would simply reproduce this prior, but we would be able to obtain constraints on $\\Omega_\\mathrm{m}$. (In the previous case, where only $\\Omega_\\mathrm{m} h^2$ is constrained by the prior or data, we would have a constraint on that combination, but the marginal distribution of either $\\Omega_\\mathrm{m}$ or $h$ would extend to infinity.)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second option above is not functionally very different from the first, except that the prior information comes from theoretical considerations rather than a previous experiment. For example, in a cosmology analysis, we might require that the age of the Universe implied by the various parameters be greater than the age of the oldest stars in the Galaxy. (One could argue that this is still based on data, but it could nevertheless be a very hand-wavy bound.) Alternatively, a typical way to account for uncertainty in the gain of some instrument, if we're told by the designers that it's good to $\\approx5\\%$, would be to introduce a nuisance parameter representing this uncertainty, a place a $\\mathrm{Normal}(1.0, 0.05)$ prior on it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The third option is the one that people tend to have hang-ups about. What do we do if there is no previous measurement of our model parameters? What if there is no consensus about the theoretical prior expectation on a parameter? What if we want to show what our data accomplish, without that interpretation depending heavily on prior data or expectations? Even though there are arguably _always_ prior expectations of one kind or another, this latter case usually applies to any parameters our data can measure (i.e. not nuisance parameters) - philosophy aside, we want to see how constraining _our data_ are.\n",
"\n",
"In this case, the solution would be to choose what is incorrectly known as an uninformative prior.\n",
"\n",
"As a practical matter, we could call a prior uninformative if it is significantly wider than, and consistent with, the posterior distribution, as in the worked example above. But note two very important things about the solution above:\n",
"1. There is, in the information theoretic sense, **no such thing** as a prior distribution that contains no information. All PDFs are somewhat informative.\n",
"2. Consequently, there is still a choice to be made, since there is no uniquely zero-information prior.\n",
"\n",
"The good news is that, given our practical definition of uninformative, it often doesn't matter exactly what prior we choose. As long as the likelihood function is \"skinny\" compared with the prior, their product will largely resemble the likelihood.\n",
"\n",
"Things become stickier when the data simply aren't all that constraining compared with the reasonable range of parameter values. Of course, \"reasonable range\" is a prior expectation, but in this case, the _shape_ of the prior PDF over such a range could well make a noticeable difference to the posterior. (For a given value of \"noticeable\".) What to do then?\n",
"\n",
"Well, getting better data would be a good idea, if possible. But, in the meantime, all you can do is choose a prior PDF that you're willing to defend as reasonable, and see where that takes you, while acknowledging that it has some impact on the results. (Having \"some impact\" on the results is practically a tautology, but such statements have been known to mollify critics.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's unpack the statement that there are no truly uninformative PDFs a bit more. Let's consider the flux measurement example above, with the context that, once we account for the exposure time, distance, etc., the luminosity of the star we observed is $L=\\mu\\,L_\\odot$, with $L_\\odot$ the Solar luminosity (just to keep things simple). So, for $N=5$, we think the star is probably a few times brighter than the Sun.\n",
"\n",
"Now consider the following true statements about our uniform, semi-infinite prior:\n",
"* The a priori probability of $L$ being between 0 and 1$L_\\odot$ is the same as it being between 1 and 2$L_\\odot$.\n",
"* The a priori probability of $L$ being between 0 and 1$L_\\odot$ is the same as it being between 10 and 11$L_\\odot$.\n",
"* The a priori probability of $L$ being between 0 and 1$L_\\odot$ is the same as it being between 1000000 and 1000001$L_\\odot$.\n",
"* The a priori probability of $L$ being between 1 and 100$L_\\odot$ is 99 times larger than it being between 0 and 1$L_\\odot$.\n",
"\n",
"Now, I am far from an expert on stars, but I do know that faint stars are vastly more numerous than bright stars, and our Sun is vaguely near the average. Technically, we would say that the luminosity function (number as a function of luminosity) of stars is decreasing. So, absent any other information about the star we observed, all of these statements appear dubious. The target should be less likely to be in the 1-2 $L_\\odot$ range than 0-1 $L_\\odot$, and _much_ less likely to be in the 10-11 $L_\\odot$ range. I'm pretty sure 1000000$L_\\odot$ stars don't exist [citation needed]. And it's probably still more likely to be 0-1 $L_\\odot$ than 1-100$L_\\odot$, certainly not two orders of magnitude less likely.\n",
"\n",
"In situations like this, the uniform distribution (loosely translated as \"ignorance about the value of the parameter\") might be less appropriate than a prior that's uniform _in the log_ (\"ignorance about the order of magnitude of the parameter\"), that is $p(\\mu) \\propto \\mu^{-1}$ (you can derive this using [probability transformations](../tutorials/probability_transformations.ipynb)). We could make the following true statements about this prior:\n",
"* The a priori probability of $L$ being between 0.01 and 1$L_\\odot$ is $\\approx7$ times larger than it being between 1 and 2$L_\\odot$.\n",
"* The a priori probability of $L$ being between 0.01 and 1$L_\\odot$ is $\\approx48$ times larger than it being between 10 and 11$L_\\odot$.\n",
"* The a priori probability of $L$ being between 0.01 and 1$L_\\odot$ is $\\approx5$ million times larger than it being between 1000000 and 1000001$L_\\odot$.\n",
"* The a priori probability of $L$ being between 1 and 100$L_\\odot$ is the same as it being between 0.01 and 1$L_\\odot$.\n",
"\n",
"Qualitatively, this is a better match to our physical understanding of stars. You'll notice that it's become impossible to speak of zero luminosity, although that might not pose a problem (if we knew about the source ahead of time, it's likely that it occasionally emits a photon).\n",
"\n",
"Naturally, there's also a case to be made for using a theoretical or observed luminosity function as the basis of the prior, in this situation. But, as we're just illustrating a point, let's see how the uniform prior over $\\log \\mu$ changes our results. (Note that $1/x$ is also a limiting case of the Gamma distribution, so we can use that machinery.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# uniform prior\n",
"alpha = 1.0\n",
"beta = 0.0001 # putting in exactly zero here will get us into trouble\n",
"plt.plot(mu, 300*st.gamma.pdf(mu, alpha, scale=1.0/beta), '-', label=r'$p(\\mu)\\propto\\mathrm{const}$')\n",
"plt.plot(mu, st.gamma.pdf(mu, alpha+data_N, scale=1.0/(beta+1.0)), '-', label=r'$p(\\mu|N)$');\n",
"\n",
"# uniform-in-log prior\n",
"alpha = 0.0001 # putting in exactly zero here will (also) get us into trouble\n",
"beta = 0.0001 # putting in exactly zero here will get us into trouble\n",
"plt.plot(mu, 300*st.gamma.pdf(mu, alpha, scale=1.0/beta), '-', label=r'$p(\\mu)\\propto \\mu^{-1}$')\n",
"plt.plot(mu, st.gamma.pdf(mu, alpha+data_N, scale=1.0/(beta+1.0)), '-', label=r'$p(\\mu|N)$');\n",
"\n",
"plt.xlabel(r'$\\mu$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I multiplied both priors by a constant above so that it would be easier to see the difference in their shapes. The choice does make _some_ difference in this case. (You can play around with different priors or changing `data_N` to see how the picture varies.)\n",
"\n",
"In practice, the uniform and uniform-in-log distributions, with endpoints in the regime where the likelihood is essentially zero, are by far the most common choices when we want to be \"uninformed\". There are also strategies for choosing minimally informative priors, in some mathematical sense:\n",
"* The [Jeffreys prior](https://en.wikipedia.org/wiki/Jeffreys_prior) minimizes the Fisher information for a given problem.\n",
"* Priors with [maximal entropy](https://en.wikipedia.org/wiki/Maximum_entropy_probability_distribution) for a given problem can be defined.\n",
"\n",
"These are not identical, and in either case their function form depends on the form of the likelihood. Again, these are not often used, but you may run into them every so often.\n",
"\n",
"It's worth repeating that **there is no genuinely uninformative option**. Your job is not to magically produce a fully \"objective\" prior (whatever that means), nor is it even to choose a prior that has the smallest possible influence on the result (again, whatever that means). It's just to choose a sensible and defensible option. If the decision really isn't obvious, yet has a noticeable impact on the results, then say so.\n",
"\n",
"As we'll return to later, there are always some who interpret the need to make a decision about priors as some sort of disaster. It isn't. It simply means that, if the data are so inadequate that out choice of prior really matters, our job as analysts is to clearly state and defend our assumptions, and show their influence on the results. These are things that good scientists should be willing to do anyway."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bayes' Law as a model for accumulating information\n",
"\n",
"As alluded to in the discussion on prior distributions, above, Bayes' Law provides a seemless way to understand the refinement of knowledge as multiple data sets are accounted for. If we had not one data set but two ($x_1$ and $x_2$), we could write the posterior distribution for the parameters ($\\theta$) as\n",
"\n",
"$p(\\theta|x_1,x_2) = \\frac{p(\\theta)\\,p(x_1,x_2|\\theta)}{p(x_1,x_2)}$.\n",
"\n",
"If the two data sets are independent of one another, this factors to\n",
"\n",
"$p(\\theta|x_1,x_2) = \\frac{p(\\theta)\\,p(x_1|\\theta)\\,p(x_2|\\theta)}{p(x_1)\\,p(x_2)}$\n",
"\n",
"$\\quad = \\frac{p(\\theta)\\,p(x_1|\\theta)}{p(x_1)} \\, \\frac{p(x_2|\\theta)}{p(x_2)}$\n",
"\n",
"$\\quad = \\frac{p(\\theta|x_1) \\, p(x_2|\\theta)}{p(x_2)}$.\n",
"\n",
"In words, analyzing the two data sets in tandem is equivalent to computing the posterior $\\theta$ based on data $x_1$, and making that the new prior to be _updated_ by data $x_2$. This is one of those completely reasonable and intuitive things that happens seamlessly, thanks to the principled, mathematical basis we're working with.\n",
"\n",
"Just for fun, we can illustrate this with the flux measurement example from above, starting from a uniform prior and assuming a series of measurements: 5, 3, 6, 5, 5, 9, 6, 8, 5, 2. Notice how each subsequent posterior distribution becomes skinnier as they converge towards the value $\\mu=5.5$ (used to generate the data)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"data_Ns = [5, 3, 6, 5, 5, 9, 6, 8, 5, 2]\n",
"# uniform prior\n",
"alpha = 1.0\n",
"beta = 0.0001 # putting in exactly zero here will get us into trouble\n",
"plt.plot(mu, st.gamma.pdf(mu, alpha, scale=1.0/beta), '-');\n",
"# incorporate each datum\n",
"for data_N in data_Ns:\n",
" alpha = alpha + data_N\n",
" beta = beta + 1.0\n",
" plt.plot(mu, st.gamma.pdf(mu, alpha, scale=1.0/beta), '-');\n",
"plt.xlabel(r'$\\mu$', fontsize='x-large');\n",
"plt.ylabel(r'$p(\\mu|...)$', fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Posterior predictions\n",
"\n",
"A quick way to test whether our fit to the data makes sense is to look at _posterior predictions_, something we will come back to with more rigor much later. For now, we will just want to look at a posteriori predictions for our measurements, that is, preditions for what we might measure (based on the sampling distribution) for parameter values drawn from the posterior distribution. This may sound circular, and it is - that's the point! If the predictions we make from the posterior don't resemble the data that went into determining that posterior to begin with, that would be a good indication that our model assumptions have failed somewhere.\n",
"\n",
"Let's do this for the latest incarnation of the example, in which we have a posterior distribution based on a whole list of measured $N$'s. What we would do is draw many possible values of $\\mu$ from the posterior distribution, and, for each of those, draw one or more values of $N$ from the sampling distribution. From this we can build a histogram of predicted measurements (the posterior predictive distribution) to compare with the histogram of actual measurements, as below. (You may not be shocked to learn that, sometimes, the posterior predictive distribution is again a standard distribution with its own name. But we'll code up the random draws here anyway.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nmc = 10000 # number of posterior predictions to make\n",
"bins = np.arange(0, 18, 2);\n",
"plt.hist(st.poisson.rvs(st.gamma.rvs(alpha, scale=1.0/beta, size=nmc)), density=True, label='PPD', bins=bins);\n",
"plt.hist(data_Ns, bins=bins, density=True, histtype='step', label='data');\n",
"plt.xlabel(r'$N$', fontsize='x-large');\n",
"plt.legend(fontsize='x-large');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Qualitatievly, these two distributions look basically compatible. Making this kind of comparison quantitative is something we'll come back to, but, in the meantime, making simple comparisons of the data with posterior predictions is a very good habit to be in."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Just about everything is (or could be) an inference\n",
"\n",
"We've mostly focused on one task so far, constraining parameters within a model, but this framework applies any time we want to draw a conclusion from data. We've already mentioned the task of selecting among competing models, i.e. inferring which model better explains the data. In addition, there's\n",
"* source detection: a particular case of model selection (no source vs some source)\n",
"* interpolation and extrapolation: inference about the value of a function\n",
"* prediction: inference about something not yet measured\n",
"* and more\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next sets of notes will discuss how to quantitatively summarize posterior distributions, and put Bayesian analysis in context with other methodologies."
]
}
],
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| gpl-2.0 |
Obus/scikit-learn | my_playground/Untitled0.ipynb | 1 | 12095 | {
"metadata": {
"name": "",
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn\n",
"from sklearn import ensemble, datasets, cross_validation, metrics"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"boston = datasets.load_boston()\n",
"boston.data.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"(506, 13)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"friedman1 = datasets.make_friedman1(n_samples=10000, n_features=20, noise=1.)\n",
"friedman1\n",
"friedman1[0].shape\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"(10000, 20)"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = friedman1[0]\n",
"y = friedman1[1]\n",
"X_train, X_valid, y_train, y_valid = cross_validation.train_test_split(X, y, train_size=0.7)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_train.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"(7000, 20)"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def regression_report(y_true, y_pred):\n",
" print '\\t explained variance:', metrics.regression.explained_variance_score(y_true, y_pred)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_gbc = ensemble.GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
" max_depth=6, \n",
" max_features=None, \n",
" #max_leaf_nodes=15,\n",
" min_samples_leaf=20, min_samples_split=40,\n",
" min_weight_fraction_leaf=0.0, n_estimators=70,\n",
" random_state=None, subsample=0.8, tree_params_producer=None,\n",
" verbose=1, warm_start=False)\n",
"\n",
"model_gbc.fit(X_train, y_train)\n",
"\n",
"\n",
"print ' '\n",
"print 'Train:'\n",
"regression_report(y_train, model_gbc.predict(X_train))\n",
"print ' '\n",
"print 'Test:'\n",
"regression_report(y_valid, model_gbc.predict(X_valid))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Iter Train Loss OOB Improve Remaining Time \n",
" 1 21.1862 3.4779 4.98s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2 17.9907 3.0230 4.52s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 3 15.6662 2.4718 4.52s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 4 13.5447 1.9948 4.38s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 5 11.9471 1.5724 4.30s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 6 10.3941 1.3790 4.19s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 7 9.2391 1.1929 4.08s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 8 8.1028 0.9529 4.01s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 9 7.2339 0.7755 3.93s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 10 6.4155 0.7200 3.84s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 20 2.6969 0.1517 3.15s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 30 1.5622 0.0398 2.46s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 40 1.1068 0.0085 1.84s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 50 0.9194 0.0007 1.21s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 60 0.8084 0.0008 0.60s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 70 0.7397 -0.0002 0.00s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" \n",
"Train:\n",
"\t explained variance: 0.970261473808\n",
" \n",
"Test:\n",
"\t explained variance: 0.944339772202\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def linear_variable(vfrom, vto, N):\n",
" return lambda i: int(i * 1. / N * (vto - vfrom) + vfrom)\n",
"\n",
"\n",
"def tree_params_producer_variable_depth(depth_from, depth_to, n_estimators):\n",
" variable_depth_foo = linear_variable(depth_from, depth_to, n_estimators)\n",
" return lambda stage: {\n",
" 'max_depth': variable_depth_foo(stage),\n",
" 'min_samples_split': 2,\n",
" 'min_samples_leaf': 1,\n",
" 'min_weight_fraction_leaf': 0.0, \n",
" 'max_features': None,\n",
" 'max_leaf_nodes': None}\n",
"\n",
"\n",
"model_gbc = ensemble.GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
" #max_depth=8, \n",
" #max_features=None, \n",
" #max_leaf_nodes=15,\n",
" #min_samples_leaf=20, min_samples_split=40,\n",
" #min_weight_fraction_leaf=0.0, \n",
" n_estimators=80,\n",
" random_state=None, \n",
" subsample=0.8, \n",
" tree_params_producer=tree_params_producer_variable_depth(1, 10, 80),\n",
" verbose=1, warm_start=False)\n",
"\n",
"model_gbc.fit(X_train, y_train)\n",
"\n",
"\n",
"print ' '\n",
"print 'Train:'\n",
"regression_report(y_train, model_gbc.predict(X_train))\n",
"print ' '\n",
"print 'Test:'\n",
"regression_report(y_valid, model_gbc.predict(X_valid))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Iter Train Loss OOB Improve Remaining Time \n",
" 1 23.8465 1.2325 1.66s\n",
" 2 22.4922 1.0192 1.18s\n",
" 3 21.9183 0.8435 1.00s\n",
" 4 21.1896 0.6217 0.92s\n",
" 5 19.9862 0.7378 0.85s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 6 19.5505 0.6971 0.83s\n",
" 7 19.1929 0.6270 0.81s\n",
" 8 18.5405 0.5842 0.78s\n",
" 9 17.8962 0.5519 0.77s\n",
" 10 17.1822 0.9675 0.82s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 20 10.0114 0.5996 0.98s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 30 5.4769 0.3543 1.06s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 40 3.1294 0.1355 1.05s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 50 1.8813 0.0599 0.96s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 60 1.2367 0.0318 0.78s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 70 0.8239 0.0190 0.47s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 80 0.5909 0.0013 0.00s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" \n",
"Train:\n",
"\t explained variance: 0.976398815713\n",
" \n",
"Test:\n",
"\t explained variance: 0.946019133501\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"linear_variable(1, 8, 70)(30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"-2"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
azubiolo/itstep | it_step/ml_from_scratch/4_least-squares_continued/least-squares_empty.ipynb | 1 | 16603 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Ordinary Least Squares -- Part II\n",
"## Course recap\n",
"This lab consists in implementing the **Ordinary Least Squares** (OLS) algorithm, which is **a linear regression with a least-squares penalty**. Given a training set $ D = \\left\\{ \\left(x^{(i)}, y^{(i)}\\right), x^{(i)} \\in \\mathcal{X}, y^{(i)} \\in \\mathcal{Y}, i \\in \\{1, \\dots, n \\} \\right\\}$, recall (from lectures 1 and 2) OLS aims at minimizing the following cost function $J$:\n",
"$$J(\\theta) = \\dfrac{1}{2} \\sum_{i = 1}^{n} \\left( h\\left(x^{(i)}\\right) - y^{(i)} \\right)^2$$\n",
"where \n",
"$$h(x) = \\sum_{j = 0}^{d} \\theta_j x_j = \\theta^T x.$$\n",
"\n",
"For the sake of simplicity, we will be working on a small training set (the one we used in lectures 1 and 2):\n",
"\n",
"| living area (m$^2$) | price (1000's BGN)|\n",
"|--------------------:|------------------:|\n",
"| 50 | 30 |\n",
"| 76 | 48 |\n",
"| 26 | 12 |\n",
"| 102 | 90 |"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Defining the training set\n",
"**Exercise 1**: Define variables `X` and `Y` that will contain the features $\\mathcal{X}$ and labels $\\mathcal{Y}$ of the training set.\n",
"\n",
"**Hint**: Do not forget the intercept!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"In this simple example, the dimensionality is $d = 1$ (which means 2 features: don't forget the intercept!) and the number of samples is $n = 4$.\n",
"\n",
"**Remark**: `1.` is written instead of `1` in order to avoid *integers operations*. For example, in some languages (including Python 2), the result of `1/2` is `0` and not `0.5`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Instead, writing `1./2` forces a *float operation* and gives `0.5` as a result, which is what we want:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Prediction function\n",
"**Exercise**: Define a function `predict` that takes as parameter *the feature vector* $x$ and *the model* $\\theta$ and returns the predicted label:\n",
"$$ \\hat{y} = h(x) = \\theta^T x = \\sum_{j = 0}^d \\theta_j x_j$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Defining the cost function\n",
"### Cost function on a single sample\n",
"**Exercise**: Define a function `cost_function` that takes as parameter *the predicted label* $y$ and *the actual label* $\\hat{y}$ of a single sample and returns the value of the cost function for this pair. Recall from lectures 1 and 2 that it is given by:\n",
"$$ \\ell \\left( y, \\hat{y} \\right) = \\dfrac{1}{2}\\left( y - \\hat{y} \\right)^2$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Cost function on the whole training set\n",
"We are now able to compute the cost function for a single sample. We can easily compute the cost function for the whole training set by summing the cost function values for all the samples in the training set. Recall that the total cost function is given by:\n",
"$$J(\\theta) = \\dfrac{1}{2} \\sum_{i = 1}^{n} \\left( h\\left(x^{(i)}\\right) - y^{(i)} \\right)^2$$\n",
"where, for all $i \\in \\{ 1, \\dots, n \\}$\n",
"$$h\\left(x^{(i)}\\right) = \\sum_{j = 0}^{d} \\theta_j x^{(i)}_j = \\theta^T x^{(i)}$$\n",
"is the prediction of $x$ given the model $\\theta$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's now test the code written above and check the total cost function we would have when $\\theta = [0, 0]$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Note that this error is big, which is expectable because having $\\theta = [0, 0]$ means always predicting $\\hat{y} = 0$."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Defining the gradient of the cost function\n",
"### Gradient on a single sample\n",
"**Exercise**: Define a function `gradient` that implements the gradient of the cost function for a given sample $(x, y)$. Recall from the lectures 1 and 2 that the gradient is given by:\n",
"$$\\nabla J(\\theta) = \\left[ \\dfrac{\\partial}{\\partial \\theta_1} J(\\theta), \\dots, \\dfrac{\\partial}{\\partial \\theta_d} J(\\theta) \\right]^T$$\n",
"where, for all $j \\in \\{0, \\dots, d \\}$:\n",
"$$ \\dfrac{\\partial}{\\partial \\theta_j} J(\\theta) = \\left( h\\left(x\\right) - y \\right) x_j. $$\n",
"**Hint**: Recall that $d = 1$, hence the gradient is of size $2$ (one value for $j = 0$, and another one for $j = 1$). Its two values are given by:\n",
"$$ \\dfrac{\\partial}{\\partial \\theta_0} J(\\theta) = \\left( h\\left(x\\right) - y \\right) x_0 \\quad \\text{and} \\quad \n",
"\\dfrac{\\partial}{\\partial \\theta_1} J(\\theta) = \\left( h\\left(x\\right) - y \\right) x_1. $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"Let's try the `gradient` function on a simple example ($\\theta = [0, 0]$ on the first sample of the training set, *i.e.* $\\left(x^{(0)}, y^{(0)}\\right)$)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Gradient on the whole training set\n",
"Now we are able to compute the gradient of the cost function on a single sample, we can easily compute `gradient_total`, the gradient of the cost function on the whole training set by summing the gradients for all the samples in the training set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's now test the code written above and check the total gradient we would have when $\\theta = [0, 0]$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Question**: What is the sign of the gradient values? What would it mean if we had such a gradient when applying a gradient descent?\n",
"\n",
"**Hint**: Recall the gradient descent update:\n",
"$$\\theta_j := \\theta_j - \\alpha \\dfrac{\\partial}{\\partial \\theta_j} J(\\theta) \\quad \\text{for all } j \\in \\{0, \\dots, d \\}$$\n",
"\n",
"**Answer**: Both values are negative, which means this gradient step would increase the value of $\\theta$ due to fact we **substract** the gradient. This makes sense, because:\n",
"- we start with $\\theta = [0, 0]$,\n",
"- we expect $\\theta_0 > 0$ and $\\theta_1 > 0$ because otherwise we could predict a negative price.\n",
"\n",
"## Applying a gradient descent\n",
"### Gradient descent step implementation\n",
"We now have all the building blocs needed for the gradient descent algorithm, that is:\n",
"- The loss function\n",
"- The gradient\n",
"Indeed, the iterative update scheme of this algorithm is given by the following formula:\n",
"$$\\theta_j := \\theta_j - \\alpha \\dfrac{\\partial}{\\partial \\theta_j} J(\\theta)$$\n",
"for all $j \\in \\{0, \\dots, d \\}$. Recall that $\\alpha$ is a parameter called the *learning rate* (or *step size*).\n",
"\n",
"**Exercise**: Define a function called `gradient_descent_step` that performs an update on theta by applying the formula above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Run a few iterations manually. Play with the value of $\\alpha$ to see how it impacts the algorithm."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
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},
"source": [
"### Iterating gradient descent steps\n",
"The `gradient_descent_step` implements a single gradient step of the gradient descent algorithm. Implement a function called `gradient_descent` that starts from a given $\\theta$ (exaple $\\theta = [0, 0]$) and applies 100 gradient descent iterations. Display the total cost function $J(\\theta)$ at each iteration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Play with the code you've just run. Try different values of $\\alpha$ and see the impact it has."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Plot the loss over iterations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let us see what the trained regression looks like. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Question**: Looks at the evolution of the cost function over time. What comment can you make?\n",
"\n",
"**Answer**: The loss function constantly drops over time with this initial value of $\\theta$ and this $\\alpha$. It ends up reaching a plateau at around 186. It seems like the algorithm has converged to the optimal value of the cost function.\n",
"\n",
"**Question**: What does the value `theta_trained` represent?\n",
"\n",
"**Answer**: Recall that the model $\\theta$ has to values, $\\theta_0$ and $\\theta_1$. Hence, with the model `theta_trained` we've learnt, price prediction would be:\n",
"$$ \\text{price} = \\theta_0 + \\theta_1 \\times \\text{area}.$$\n",
"\n",
"## Batch gradient descent vs. stochastic gradient descent\n",
"As we have seen during the lecture 1, the gradient descent methods are often split into 2 different subfamilies:\n",
"- **Batch methods** update $\\theta$ after having computed the gradient on the whole training set\n",
"- **Stochastic methods** update $\\theta$ after having computed the gradient on a single sample\n",
"The gradient descent we have implemented above (`gradient_descent_step` and `gradient_descent`) corresponds to the batch version because it sums the gradient of all the samples in the training set. \n",
"\n",
"**Exercise**: Try to implement the stochastic version of the gradient descent algorithm. You will need to define a function `stochastic_gradient_descent_step` that compute a stochastic gradient step (on a single $(x, y)$ sample) and a function `stochastic_gradient_descent` iterates 100 stochastic gradient descent steps and returns the trained model $\\theta$."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Apply the algorithm with the same parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Plot the loss history over iterations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**: Plot the obtained regression."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Question**: Compare the results obtained when solving the OLS problem with stochastic gradient descent and batch gradient descent. Are the results the same? Why?\n",
"\n",
"**Hint**: Plot the loss function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
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"editable": true,
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}
],
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"file_extension": ".py",
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| mit |
greenelab/continuous_analysis | Shippable_Plotting.ipynb | 1 | 134544 | {
"cells": [
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import datetime\n",
"#%matplotlib inline\n",
"\n",
"output_folder = '/root/src/github.com/greenelab/continuous_analysis/shippable/output/'\n",
"abundances = pd.read_csv(output_folder + 'abundance.tsv', delimiter='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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161y72rIqSF/u1HP27Nlsj2f8MJB+FntOv+f26fzcuXPZHsv6xvHRRx9p0qRJsixLbdq0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\nwP8DFOB0Hor/kOIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f1fb2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 1\n",
"datetime_str = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n",
"generated_text = 'Figures generated at: ' + datetime_str\n",
"g = sns.distplot(abundances['eff_length'], kde=False, color=\"b\")\n",
"g.figure.suptitle(generated_text, fontsize=18, fontweight='bold')\n",
"g.figure.savefig(output_folder + 'fig1.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x11d64c050>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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WOk3H8qWk3t8AgF5TS/O8+bjq6h2ubN8o/aFtZyW0hRAjQCaTRsuasv2m2GtW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\nIkv8P71uUBXAcrOgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11dac1d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 2\n",
"sns.set(style=\"darkgrid\", color_codes=True)\n",
"g = sns.jointplot(\"length\", \"est_counts\", data=abundances, kind=\"reg\",\n",
" xlim=(800, 2400), ylim=(0, 1000), color=\"r\", size=7)\n",
"g.savefig(output_folder + 'fig2.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WVqZNmzZ57554PB5ZLBbl5uZqzJgxAZ4dAADobaYOJhkZGSw2BQDgMsIr6QEA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\nREQ8hgoTERER8Rj/Hybh9veebuWpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e9b0d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 3\n",
"f, ax = plt.subplots(figsize=(8, 10))\n",
"\n",
"sns.set_color_codes(\"pastel\")\n",
"sns.barplot(x=\"length\", y=\"target_id\", data=abundances,\n",
" label=\"Length\", color=\"b\")\n",
"\n",
"# Plot the crashes where alcohol was involved\n",
"sns.set_color_codes(\"muted\")\n",
"sns.barplot(x=\"eff_length\", y=\"target_id\", data=abundances,\n",
" label=\"Effective Length\", color=\"b\")\n",
"\n",
"\n",
"ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
"ax.set(ylabel=\"\",\n",
" xlabel=\"Length and Effective Length\")\n",
"sns.despine(left=True, bottom=True)\n",
"f.savefig(output_folder + 'fig3.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hEVgAAIDhEVgAAIDhEVgAAIDhEVgAAIDhEVgAAIDheSSw1NTUKCEhQQ0NDc62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\nKWERERERq/f/ACC436UM1xk4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ddb0190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 4\n",
"f, ax = plt.subplots(figsize=(8, 10))\n",
"\n",
"sns.set_color_codes(\"pastel\")\n",
"sns.barplot(x=\"tpm\", y=\"target_id\", data=abundances,\n",
" label=\"TPM\", color=\"b\")\n",
"\n",
"# Plot the crashes where alcohol was involved\n",
"sns.set_color_codes(\"muted\")\n",
"sns.barplot(x=\"est_counts\", y=\"target_id\", data=abundances,\n",
" label=\"Estimated Counts\", color=\"b\")\n",
"\n",
"\n",
"ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
"ax.set(ylabel=\"\",\n",
" xlabel=\"Counts and Transcripts Per Million\")\n",
"sns.despine(left=True, bottom=True)\n",
"f.savefig(output_folder + 'fig4.png', bbox_inches='tight')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
ercius/openNCEM | ncempy/notebooks/loading_data_examples.ipynb | 1 | 3621486 | null | gpl-3.0 |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_linear_model_patterns.ipynb | 1 | 4496 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Linear classifier on sensor data with plot patterns and filters\n\n\nDecoding, a.k.a MVPA or supervised machine learning applied to MEG and EEG\ndata in sensor space. Fit a linear classifier with the LinearModel object\nproviding topographical patterns which are more neurophysiologically\ninterpretable [1] than the classifier filters (weight vectors).\nThe patterns explain how the MEG and EEG data were generated from the\ndiscriminant neural sources which are extracted by the filters.\nNote patterns/filters in MEG data are more similar than EEG data\nbecause the noise is less spatially correlated in MEG than EEG.\n\n[1] Haufe, S., Meinecke, F., G\u00f6rgen, K., D\u00e4hne, S., Haynes, J.-D.,\nBlankertz, B., & Bie\u00dfmann, F. (2014). On the interpretation of\nweight vectors of linear models in multivariate neuroimaging.\nNeuroImage, 87, 96\u2013110. doi:10.1016/j.neuroimage.2013.10.067\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n# Romain Trachel <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\n\n# import a linear classifier from mne.decoding\nfrom mne.decoding import LinearModel\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\ntmin, tmax = -0.2, 0.5\nevent_id = dict(aud_l=1, vis_l=3)\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, preload=True)\nraw.filter(2, None, method='iir') # replace baselining with high-pass\nevents = mne.read_events(event_fname)\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n decim=4, baseline=None, preload=True)\n\nlabels = epochs.events[:, -1]\n\n# get MEG and EEG data\nmeg_epochs = epochs.copy().pick_types(meg=True, eeg=False)\nmeg_data = meg_epochs.get_data().reshape(len(labels), -1)\neeg_epochs = epochs.copy().pick_types(meg=False, eeg=True)\neeg_data = eeg_epochs.get_data().reshape(len(labels), -1)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Decoding in sensor space using a LogisticRegression classifier\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"clf = LogisticRegression()\nsc = StandardScaler()\n\n# create a linear model with LogisticRegression\nmodel = LinearModel(clf)\n\n# fit the classifier on MEG data\nX = sc.fit_transform(meg_data)\nmodel.fit(X, labels)\n# plot patterns and filters\nmodel.plot_patterns(meg_epochs.info, title='MEG Patterns')\nmodel.plot_filters(meg_epochs.info, title='MEG Filters')\n\n# fit the classifier on EEG data\nX = sc.fit_transform(eeg_data)\nmodel.fit(X, labels)\n# plot patterns and filters\nmodel.plot_patterns(eeg_epochs.info, title='EEG Patterns')\nmodel.plot_filters(eeg_epochs.info, title='EEG Filters')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.11",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
jwjohnson314/data-801 | notebooks/more_data_structures.ipynb | 1 | 42320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More on data structures\n",
"\n",
"#### Iterable vs. Iterators\n",
"\n",
"Lists are examples of iterable data structures, which means that you can iterate over the actual objects in these data structures."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BOB WAS HERE\n",
"SUE WAS HERE\n",
"MARY WAS HERE\n"
]
}
],
"source": [
"# iterating over a list by object\n",
"\n",
"x = ['bob', 'sue', 'mary']\n",
"\n",
"for name in x: \n",
" print(name.upper() + ' WAS HERE')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BOB WAS HERE\n",
"SUE WAS HERE\n",
"MARY WAS HERE\n"
]
}
],
"source": [
"# alternatively, you could iterate over position\n",
"\n",
"for i in range(len(x)):\n",
" print(x[i].upper() + ' WAS HERE')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__add__',\n",
" '__class__',\n",
" '__contains__',\n",
" '__delattr__',\n",
" '__delitem__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__iadd__',\n",
" '__imul__',\n",
" '__init__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__len__',\n",
" '__lt__',\n",
" '__mul__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__reversed__',\n",
" '__rmul__',\n",
" '__setattr__',\n",
" '__setitem__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" 'append',\n",
" 'clear',\n",
" 'copy',\n",
" 'count',\n",
" 'extend',\n",
" 'index',\n",
" 'insert',\n",
" 'pop',\n",
" 'remove',\n",
" 'reverse',\n",
" 'sort']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(x) # ignore the __ methods for now"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"generators return their contents 'lazily'. This leaves a minimal memory footprint, at the cost of making the generator nonreusable."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = (x*x for x in [1, 2, 3])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"generator"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(y)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__class__',\n",
" '__del__',\n",
" '__delattr__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__name__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__next__',\n",
" '__qualname__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" 'close',\n",
" 'gi_code',\n",
" 'gi_frame',\n",
" 'gi_running',\n",
" 'gi_yieldfrom',\n",
" 'send',\n",
" 'throw']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y.send??"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'generator' object is not subscriptable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-db3ecb40ccce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'generator' object is not subscriptable"
]
}
],
"source": [
"y[5]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(y)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.send(1)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "StopIteration",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-6f4987c48737>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# run this cell twice - what happens?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mStopIteration\u001b[0m: "
]
}
],
"source": [
"next(y) # run this cell twice - what happens?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'range' is something like a generator, but with special properties because of its intended use case (in 'for' loops or similar structures."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z = range(10, 5, -1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__class__',\n",
" '__contains__',\n",
" '__delattr__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__len__',\n",
" '__lt__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__reversed__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" 'count',\n",
" 'index',\n",
" 'start',\n",
" 'step',\n",
" 'stop']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(range)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['count', 'index', 'start', 'step', 'stop']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's filter that list a little\n",
"[x for x in dir(range) if not x.startswith('_')]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.start"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(z) # __ function - overloaded operator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the docs (https://docs.python.org/3/library/stdtypes.html#typesseq-range): The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start, stop and step values, calculating individual items and subranges as needed).\n",
"\n",
"Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range):"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"9\n",
"8\n",
"7\n",
"6\n"
]
}
],
"source": [
"for i in z:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"zips produced iterators from pairs: "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"GPA = zip(['bob', 'sue', 'mary'], [2.3, 4.0, 3.7])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"zip"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(GPA)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__class__',\n",
" '__delattr__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__next__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(GPA)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"('bob', 2.3)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(GPA)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4.0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(GPA)[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More on Dicts\n",
"\n",
"The dict data structure shows up all over Python."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dict?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from assignment:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"GPA_2 = dict(bob=2.0, sue=3.4, mary=4.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from iterator:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'bob': 3.2, 'lisa': 2.8, 'mary': 4.0, 'sue': 3.1}"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names = ['bob', 'mary', 'sue', 'lisa']\n",
"gpas = [3.2, 4.0, 3.1, 2.8]\n",
"\n",
"GPA_3 = dict(zip(names, gpas))\n",
"GPA_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In function definitions:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# explicitly named arguments are also positional\n",
"# Anything after * in a function is a positional argument - tuple\n",
"# Anything after ** is a named argument \n",
"# the latter are unpacked as dicts\n",
"\n",
"def arg_explainer(x, y, *args, **kwargs):\n",
" print('-'*30)\n",
" print('x is %d, even though you didn\\'t specify it, because of its position.' % x)\n",
" print('same with y, which is %d.' %y)\n",
" if args:\n",
" print('-'*30)\n",
" print('type(*args) = %s' % type(args))\n",
" print('these are the *args arguments: ')\n",
" for arg in args:\n",
" print(arg)\n",
" else:\n",
" print('-'*30)\n",
" print('no *args today!')\n",
" if kwargs:\n",
" print('-'*30)\n",
" print('type(**kwargs) == %s' % type(kwargs))\n",
" for key in kwargs:\n",
" print(key, kwargs[key])\n",
" else:\n",
" print('-'*30)\n",
" print('no **kwargs today!')\n",
" print('-'*30)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------------------\n",
"x is 2, even though you didn't specify it, because of its position.\n",
"same with y, which is 4.\n",
"------------------------------\n",
"type(*args) = <class 'tuple'>\n",
"these are the *args arguments: \n",
"3\n",
"7\n",
"8\n",
"9\n",
"10\n",
"------------------------------\n",
"type(**kwargs) == <class 'dict'>\n",
"rotate False\n",
"sharey True\n",
"plot True\n"
]
}
],
"source": [
"arg_explainer(2, 4, 3, 7, 8, 9, 10, plot=True, sharey=True, rotate=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In function calls:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------------------\n",
"x is 1, even though you didn't specify it, because of its position.\n",
"same with y, which is 2.\n",
"------------------------------\n",
"no *args today!\n",
"------------------------------\n",
"type(**kwargs) == <class 'dict'>\n",
"sharey True\n",
"plot False\n"
]
}
],
"source": [
"my_kwargs = {'plot': False, 'sharey': True}\n",
"\n",
"arg_explainer(1, 2, **my_kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows, for instance, matplotlibs plot function to accept a huge range of different plotting options, or few to none at all."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"?plt.plot"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qV69e4LkRI0bQK6+8QkR88t9///15ry1dupSaNm3qcF+5hp+VlZX33Ndff02N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\njUbjI/w/4zBq1fkosI0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bcd8f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-5, 5, 100)\n",
"y1 = np.sin(x)\n",
"y2 = np.cos(x)\n",
"\n",
"plt.plot(x, y1) # all of these arguments are *args\n",
"plt.plot(x, y2, color='red', label='just on the cosine, for no reason at all') # starting w/ color, **kwargs\n",
"plt.legend(loc='center');"
]
},
{
"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
}
| mit |
lexieheinle/jour407homework | StormData/nebraska-storm-analysis.ipynb | 1 | 37987 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import agate\n",
"text = agate.Text()\n",
"number = agate.Number()\n",
"tester = agate.TypeTester(force={\n",
" 'WFO': text,\n",
" 'DAMAGE_PROPERTY': text,\n",
" 'DAMAGE_CROPS': text,\n",
" 'EPISODE_ID': text,\n",
" 'INJURIES_INDIRECT': number,\n",
" 'DEATHS_DIRECT': number,\n",
" 'DEATHS_INDIRECT': number,\n",
" 'STATE_FIPS': text,\n",
" 'EVENT_ID': text,\n",
" },limit=300)\n",
"nebraskastorms = agate.Table.from_csv('clean-nebraska.csv', column_types=tester)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--------------------+---------------|\n",
"| column_names | column_types |\n",
"|--------------------+---------------|\n",
"| BEGIN_YEARMONTH | Number |\n",
"| BEGIN_DAY | Number |\n",
"| BEGIN_TIME | Number |\n",
"| END_YEARMONTH | Number |\n",
"| END_DAY | Number |\n",
"| END_TIME | Number |\n",
"| EPISODE_ID | Text |\n",
"| EVENT_ID | Text |\n",
"| STATE | Text |\n",
"| STATE_FIPS | Text |\n",
"| YEAR | Number |\n",
"| MONTH_NAME | Date |\n",
"| EVENT_TYPE | Text |\n",
"| CZ_TYPE | Text |\n",
"| CZ_FIPS | Number |\n",
"| CZ_NAME | Text |\n",
"| WFO | Text |\n",
"| BEGIN_DATE_TIME | DateTime |\n",
"| CZ_TIMEZONE | Text |\n",
"| END_DATE_TIME | DateTime |\n",
"| INJURIES_DIRECT | Number |\n",
"| INJURIES_INDIRECT | Number |\n",
"| DEATHS_DIRECT | Number |\n",
"| DEATHS_INDIRECT | Number |\n",
"| DAMAGE_PROPERTY | Text |\n",
"| DAMAGE_CROPS | Text |\n",
"|--------------------+---------------|\n",
"\n"
]
}
],
"source": [
"print(nebraskastorms)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--------+----------------------+--------|\n",
"| YEAR | EVENT_TYPE | count |\n",
"|--------+----------------------+--------|\n",
"| 1,951 | Tornado | 9 |\n",
"| 1,952 | Tornado | 11 |\n",
"| 1,953 | Tornado | 49 |\n",
"| 1,954 | Tornado | 19 |\n",
"| 1,955 | Tornado | 34 |\n",
"| 1,955 | Hail | 25 |\n",
"| 1,955 | Thunderstorm Wind | 9 |\n",
"| 1,956 | Thunderstorm Wind | 19 |\n",
"| 1,956 | Hail | 47 |\n",
"| 1,956 | Tornado | 36 |\n",
"| 1,957 | Hail | 50 |\n",
"| 1,957 | Tornado | 55 |\n",
"| 1,957 | Thunderstorm Wind | 15 |\n",
"| 1,958 | Hail | 90 |\n",
"| 1,958 | Tornado | 55 |\n",
"| 1,958 | Thunderstorm Wind | 17 |\n",
"| 1,959 | Tornado | 45 |\n",
"| 1,959 | Thunderstorm Wind | 29 |\n",
"| 1,959 | Hail | 39 |\n",
"| 1,960 | Thunderstorm Wind | 38 |\n",
"| 1,960 | Hail | 55 |\n",
"| 1,960 | Tornado | 43 |\n",
"| 1,961 | Thunderstorm Wind | 34 |\n",
"| 1,961 | Hail | 45 |\n",
"| 1,961 | Tornado | 12 |\n",
"| 1,962 | Tornado | 44 |\n",
"| 1,962 | Hail | 59 |\n",
"| 1,962 | Thunderstorm Wind | 52 |\n",
"| 1,963 | Tornado | 17 |\n",
"| 1,963 | Thunderstorm Wind | 37 |\n",
"| 1,963 | Hail | 35 |\n",
"| 1,964 | Tornado | 46 |\n",
"| 1,964 | Thunderstorm Wind | 51 |\n",
"| 1,964 | Hail | 55 |\n",
"| 1,965 | Thunderstorm Wind | 37 |\n",
"| 1,965 | Hail | 57 |\n",
"| 1,965 | Tornado | 48 |\n",
"| 1,966 | Thunderstorm Wind | 38 |\n",
"| 1,966 | Hail | 27 |\n",
"| 1,966 | Tornado | 10 |\n",
"| 1,967 | Tornado | 41 |\n",
"| 1,967 | Hail | 50 |\n",
"| 1,967 | Thunderstorm Wind | 40 |\n",
"| 1,968 | Hail | 64 |\n",
"| 1,968 | Thunderstorm Wind | 66 |\n",
"| 1,968 | Tornado | 21 |\n",
"| 1,969 | Thunderstorm Wind | 55 |\n",
"| 1,969 | Hail | 29 |\n",
"| 1,969 | Tornado | 20 |\n",
"| 1,970 | Thunderstorm Wind | 74 |\n",
"| 1,970 | Tornado | 14 |\n",
"| 1,970 | Hail | 51 |\n",
"| 1,971 | Tornado | 52 |\n",
"| 1,971 | Thunderstorm Wind | 37 |\n",
"| 1,971 | Hail | 52 |\n",
"| 1,972 | Hail | 36 |\n",
"| 1,972 | Thunderstorm Wind | 18 |\n",
"| 1,972 | Tornado | 30 |\n",
"| 1,973 | Thunderstorm Wind | 108 |\n",
"| 1,973 | Tornado | 19 |\n",
"| 1,973 | Hail | 74 |\n",
"| 1,974 | Thunderstorm Wind | 111 |\n",
"| 1,974 | Hail | 119 |\n",
"| 1,974 | Tornado | 36 |\n",
"| 1,975 | Tornado | 80 |\n",
"| 1,975 | Thunderstorm Wind | 86 |\n",
"| 1,975 | Hail | 77 |\n",
"| 1,976 | Hail | 66 |\n",
"| 1,976 | Thunderstorm Wind | 46 |\n",
"| 1,976 | Tornado | 26 |\n",
"| 1,977 | Hail | 96 |\n",
"| 1,977 | Tornado | 75 |\n",
"| 1,977 | Thunderstorm Wind | 85 |\n",
"| 1,978 | Thunderstorm Wind | 112 |\n",
"| 1,978 | Tornado | 51 |\n",
"| 1,978 | Hail | 102 |\n",
"| 1,979 | Hail | 167 |\n",
"| 1,979 | Thunderstorm Wind | 150 |\n",
"| 1,979 | Tornado | 22 |\n",
"| 1,980 | Hail | 210 |\n",
"| 1,980 | Thunderstorm Wind | 173 |\n",
"| 1,980 | Tornado | 51 |\n",
"| 1,981 | Thunderstorm Wind | 126 |\n",
"| 1,981 | Hail | 137 |\n",
"| 1,981 | Tornado | 19 |\n",
"| 1,982 | Thunderstorm Wind | 183 |\n",
"| 1,982 | Hail | 174 |\n",
"| 1,982 | Tornado | 37 |\n",
"| 1,983 | Hail | 86 |\n",
"| 1,983 | Thunderstorm Wind | 113 |\n",
"| 1,983 | Tornado | 16 |\n",
"| 1,984 | Hail | 130 |\n",
"| 1,984 | Thunderstorm Wind | 71 |\n",
"| 1,984 | Tornado | 52 |\n",
"| 1,985 | Hail | 128 |\n",
"| 1,985 | Thunderstorm Wind | 138 |\n",
"| 1,985 | Tornado | 55 |\n",
"| 1,986 | Thunderstorm Wind | 156 |\n",
"| 1,986 | Hail | 196 |\n",
"| 1,986 | Tornado | 57 |\n",
"| 1,987 | Tornado | 26 |\n",
"| 1,987 | Hail | 106 |\n",
"| 1,987 | Thunderstorm Wind | 87 |\n",
"| 1,988 | Hail | 61 |\n",
"| 1,988 | Thunderstorm Wind | 71 |\n",
"| 1,988 | Tornado | 23 |\n",
"| 1,989 | Hail | 107 |\n",
"| 1,989 | Thunderstorm Wind | 132 |\n",
"| 1,989 | Tornado | 41 |\n",
"| 1,990 | Hail | 121 |\n",
"| 1,990 | Tornado | 103 |\n",
"| 1,990 | Thunderstorm Wind | 89 |\n",
"| 1,991 | Thunderstorm Wind | 107 |\n",
"| 1,991 | Hail | 153 |\n",
"| 1,991 | Tornado | 67 |\n",
"| 1,992 | Thunderstorm Wind | 153 |\n",
"| 1,992 | Hail | 238 |\n",
"| 1,992 | Tornado | 77 |\n",
"| 1,993 | Thunderstorm Wind | 36 |\n",
"| 1,993 | Hail | 62 |\n",
"| 1,993 | Tornado | 21 |\n",
"| 1,994 | Thunderstorm Wind | 239 |\n",
"| 1,994 | Hail | 230 |\n",
"| 1,994 | Tornado | 54 |\n",
"| 1,995 | Thunderstorm Wind | 109 |\n",
"| 1,995 | Hail | 261 |\n",
"| 1,995 | Tornado | 26 |\n",
"| 1,996 | Blizzard | 118 |\n",
"| 1,996 | High Wind | 260 |\n",
"| 1,996 | Winter Storm | 171 |\n",
"| 1,996 | Cold/Wind Chill | 157 |\n",
"| 1,996 | Hail | 696 |\n",
"| 1,996 | Thunderstorm Wind | 156 |\n",
"| 1,996 | Tornado | 60 |\n",
"| 1,996 | Funnel Cloud | 16 |\n",
"| 1,996 | Flash Flood | 40 |\n",
"| 1,996 | Flood | 20 |\n",
"| 1,996 | Lightning | 37 |\n",
"| 1,996 | Ice Storm | 2 |\n",
"| 1,996 | Heavy Snow | 8 |\n",
"| 1,997 | Hail | 497 |\n",
"| 1,997 | Thunderstorm Wind | 202 |\n",
"| 1,997 | Tornado | 31 |\n",
"| 1,997 | Lightning | 19 |\n",
"| 1,997 | Flash Flood | 26 |\n",
"| 1,997 | High Wind | 164 |\n",
"| 1,997 | Funnel Cloud | 15 |\n",
"| 1,997 | Cold/Wind Chill | 202 |\n",
"| 1,997 | Heavy Rain | 2 |\n",
"| 1,997 | Flood | 39 |\n",
"| 1,997 | Winter Storm | 292 |\n",
"| 1,997 | Blizzard | 53 |\n",
"| 1,997 | Winter Weather | 6 |\n",
"| 1,997 | Heavy Snow | 29 |\n",
"| 1,997 | Ice Storm | 49 |\n",
"| 1,997 | Dense Fog | 1 |\n",
"| 1,998 | Cold/Wind Chill | 47 |\n",
"| 1,998 | Hail | 717 |\n",
"| 1,998 | Thunderstorm Wind | 289 |\n",
"| 1,998 | High Wind | 54 |\n",
"| 1,998 | Winter Weather | 5 |\n",
"| 1,998 | Winter Storm | 185 |\n",
"| 1,998 | Flash Flood | 26 |\n",
"| 1,998 | Tornado | 67 |\n",
"| 1,998 | Lightning | 18 |\n",
"| 1,998 | Heavy Snow | 5 |\n",
"| 1,998 | Flood | 20 |\n",
"| 1,998 | Heavy Rain | 3 |\n",
"| 1,998 | Funnel Cloud | 26 |\n",
"| 1,998 | Wildfire | 2 |\n",
"| 1,998 | Ice Storm | 3 |\n",
"| 1,998 | Blizzard | 10 |\n",
"| 1,999 | Cold/Wind Chill | 33 |\n",
"| 1,999 | Thunderstorm Wind | 214 |\n",
"| 1,999 | Hail | 547 |\n",
"| 1,999 | Winter Storm | 205 |\n",
"| 1,999 | Flood | 50 |\n",
"| 1,999 | High Wind | 111 |\n",
"| 1,999 | Tornado | 106 |\n",
"| 1,999 | Heavy Snow | 7 |\n",
"| 1,999 | Heat | 32 |\n",
"| 1,999 | Flash Flood | 57 |\n",
"| 1,999 | Lightning | 4 |\n",
"| 1,999 | Winter Weather | 3 |\n",
"| 1,999 | Ice Storm | 1 |\n",
"| 1,999 | Drought | 35 |\n",
"| 1,999 | Funnel Cloud | 1 |\n",
"| 1,999 | Dense Fog | 1 |\n",
"| 2,000 | Winter Storm | 211 |\n",
"| 2,000 | Drought | 42 |\n",
"| 2,000 | Hail | 662 |\n",
"| 2,000 | High Wind | 37 |\n",
"| 2,000 | Flood | 15 |\n",
"| 2,000 | Thunderstorm Wind | 293 |\n",
"| 2,000 | Tornado | 65 |\n",
"| 2,000 | Heavy Snow | 25 |\n",
"| 2,000 | Lightning | 14 |\n",
"| 2,000 | Flash Flood | 34 |\n",
"| 2,000 | Funnel Cloud | 16 |\n",
"| 2,000 | Extreme Cold/Wind... | 84 |\n",
"| 2,000 | Heavy Rain | 3 |\n",
"| 2,000 | Blizzard | 4 |\n",
"| 2,001 | Winter Storm | 296 |\n",
"| 2,001 | High Wind | 97 |\n",
"| 2,001 | Blizzard | 2 |\n",
"| 2,001 | Ice Storm | 21 |\n",
"| 2,001 | Heavy Snow | 12 |\n",
"| 2,001 | Flood | 18 |\n",
"| 2,001 | Hail | 838 |\n",
"| 2,001 | Thunderstorm Wind | 262 |\n",
"| 2,001 | Tornado | 70 |\n",
"| 2,001 | Lightning | 10 |\n",
"| 2,001 | Flash Flood | 18 |\n",
"| 2,001 | Strong Wind | 1 |\n",
"| 2,001 | Funnel Cloud | 7 |\n",
"| 2,001 | Heat | 35 |\n",
"| 2,001 | Heavy Rain | 1 |\n",
"| 2,002 | Drought | 33 |\n",
"| 2,002 | Heavy Snow | 5 |\n",
"| 2,002 | High Wind | 96 |\n",
"| 2,002 | Winter Storm | 178 |\n",
"| 2,002 | Thunderstorm Wind | 197 |\n",
"| 2,002 | Hail | 715 |\n",
"| 2,002 | Tornado | 29 |\n",
"| 2,002 | Lightning | 6 |\n",
"| 2,002 | Funnel Cloud | 3 |\n",
"| 2,002 | Heavy Rain | 6 |\n",
"| 2,002 | Flash Flood | 26 |\n",
"| 2,002 | Flood | 3 |\n",
"| 2,002 | Dust Storm | 1 |\n",
"| 2,003 | Thunderstorm Wind | 279 |\n",
"| 2,003 | Hail | 730 |\n",
"| 2,003 | Tornado | 82 |\n",
"| 2,003 | Funnel Cloud | 10 |\n",
"| 2,003 | High Wind | 34 |\n",
"| 2,003 | Strong Wind | 10 |\n",
"| 2,003 | Winter Weather | 7 |\n",
"| 2,003 | Winter Storm | 138 |\n",
"| 2,003 | Flash Flood | 30 |\n",
"| 2,003 | Flood | 8 |\n",
"| 2,003 | Extreme Cold/Wind... | 30 |\n",
"| 2,003 | Heavy Snow | 10 |\n",
"| 2,003 | Lightning | 10 |\n",
"| 2,003 | Heavy Rain | 3 |\n",
"| 2,004 | High Wind | 39 |\n",
"| 2,004 | Cold/Wind Chill | 89 |\n",
"| 2,004 | Winter Storm | 189 |\n",
"| 2,004 | Winter Weather | 6 |\n",
"| 2,004 | Hail | 844 |\n",
"| 2,004 | Blizzard | 14 |\n",
"| 2,004 | Thunderstorm Wind | 281 |\n",
"| 2,004 | Tornado | 116 |\n",
"| 2,004 | Heavy Snow | 16 |\n",
"| 2,004 | Flood | 16 |\n",
"| 2,004 | Flash Flood | 47 |\n",
"| 2,004 | Funnel Cloud | 20 |\n",
"| 2,004 | Lightning | 11 |\n",
"| 2,004 | Dust Storm | 1 |\n",
"| 2,004 | Strong Wind | 1 |\n",
"| 2,005 | Heavy Snow | 5 |\n",
"| 2,005 | Winter Storm | 114 |\n",
"| 2,005 | High Wind | 92 |\n",
"| 2,005 | Winter Weather | 52 |\n",
"| 2,005 | Dense Fog | 1 |\n",
"| 2,005 | Ice Storm | 6 |\n",
"| 2,005 | Hail | 1,111 |\n",
"| 2,005 | Flash Flood | 59 |\n",
"| 2,005 | Tornado | 40 |\n",
"| 2,005 | Thunderstorm Wind | 156 |\n",
"| 2,005 | Lightning | 5 |\n",
"| 2,005 | Funnel Cloud | 21 |\n",
"| 2,005 | Heavy Rain | 9 |\n",
"| 2,005 | Flood | 5 |\n",
"| 2,005 | Heat | 30 |\n",
"| 2,005 | Strong Wind | 2 |\n",
"| 2,005 | Blizzard | 52 |\n",
"| 2,005 | Cold/Wind Chill | 16 |\n",
"| 2,006 | Hail | 628 |\n",
"| 2,006 | Heavy Snow | 14 |\n",
"| 2,006 | Thunderstorm Wind | 343 |\n",
"| 2,006 | High Wind | 72 |\n",
"| 2,006 | Winter Storm | 139 |\n",
"| 2,006 | Cold/Wind Chill | 15 |\n",
"| 2,006 | Winter Weather | 31 |\n",
"| 2,006 | Funnel Cloud | 6 |\n",
"| 2,006 | Tornado | 23 |\n",
"| 2,006 | Strong Wind | 2 |\n",
"| 2,006 | Blizzard | 21 |\n",
"| 2,006 | Lightning | 10 |\n",
"| 2,006 | Flash Flood | 16 |\n",
"| 2,006 | Heavy Rain | 2 |\n",
"| 2,006 | Flood | 6 |\n",
"| 2,006 | Wildfire | 1 |\n",
"| 2,006 | Ice Storm | 36 |\n",
"| 2,006 | Frost/Freeze | 24 |\n",
"| 2,007 | Hail | 830 |\n",
"| 2,007 | High Wind | 24 |\n",
"| 2,007 | Flash Flood | 86 |\n",
"| 2,007 | Thunderstorm Wind | 297 |\n",
"| 2,007 | Flood | 68 |\n",
"| 2,007 | Lightning | 13 |\n",
"| 2,007 | Cold/Wind Chill | 79 |\n",
"| 2,007 | Heavy Rain | 23 |\n",
"| 2,007 | Tornado | 51 |\n",
"| 2,007 | Frost/Freeze | 24 |\n",
"| 2,007 | Ice Storm | 75 |\n",
"| 2,007 | Winter Weather | 32 |\n",
"| 2,007 | Winter Storm | 50 |\n",
"| 2,007 | Heavy Snow | 62 |\n",
"| 2,007 | Blizzard | 45 |\n",
"| 2,007 | Funnel Cloud | 7 |\n",
"| 2,008 | Winter Weather | 44 |\n",
"| 2,008 | Winter Storm | 89 |\n",
"| 2,008 | Ice Storm | 8 |\n",
"| 2,008 | Extreme Cold/Wind... | 55 |\n",
"| 2,008 | Heavy Snow | 19 |\n",
"| 2,008 | Hail | 989 |\n",
"| 2,008 | Blizzard | 8 |\n",
"| 2,008 | High Wind | 93 |\n",
"| 2,008 | Flood | 74 |\n",
"| 2,008 | Thunderstorm Wind | 275 |\n",
"| 2,008 | Tornado | 66 |\n",
"| 2,008 | Flash Flood | 114 |\n",
"| 2,008 | Funnel Cloud | 9 |\n",
"| 2,008 | Heavy Rain | 12 |\n",
"| 2,008 | Lightning | 2 |\n",
"| 2,008 | Cold/Wind Chill | 77 |\n",
"| 2,009 | Blizzard | 171 |\n",
"| 2,009 | High Wind | 91 |\n",
"| 2,009 | Heavy Snow | 70 |\n",
"| 2,009 | Cold/Wind Chill | 34 |\n",
"| 2,009 | Winter Weather | 35 |\n",
"| 2,009 | Winter Storm | 181 |\n",
"| 2,009 | Hail | 824 |\n",
"| 2,009 | Flood | 9 |\n",
"| 2,009 | Thunderstorm Wind | 255 |\n",
"| 2,009 | Lightning | 7 |\n",
"| 2,009 | Strong Wind | 1 |\n",
"| 2,009 | Tornado | 41 |\n",
"| 2,009 | Funnel Cloud | 28 |\n",
"| 2,009 | Heavy Rain | 11 |\n",
"| 2,009 | Flash Flood | 28 |\n",
"| 2,009 | Ice Storm | 4 |\n",
"| 2,009 | Extreme Cold/Wind... | 6 |\n",
"| 2,009 | Excessive Heat | 16 |\n",
"| 2,009 | Heat | 5 |\n",
"| 2,010 | High Wind | 90 |\n",
"| 2,010 | Heavy Snow | 16 |\n",
"| 2,010 | Extreme Cold/Wind... | 33 |\n",
"| 2,010 | Winter Weather | 98 |\n",
"| 2,010 | Ice Storm | 6 |\n",
"| 2,010 | Blizzard | 48 |\n",
"| 2,010 | Thunderstorm Wind | 462 |\n",
"| 2,010 | Tornado | 38 |\n",
"| 2,010 | Hail | 870 |\n",
"| 2,010 | Flash Flood | 71 |\n",
"| 2,010 | Strong Wind | 7 |\n",
"| 2,010 | Funnel Cloud | 33 |\n",
"| 2,010 | Heavy Rain | 44 |\n",
"| 2,010 | Flood | 114 |\n",
"| 2,010 | Cold/Wind Chill | 44 |\n",
"| 2,010 | Winter Storm | 63 |\n",
"| 2,010 | Lightning | 5 |\n",
"| 2,010 | Heat | 96 |\n",
"| 2,010 | Excessive Heat | 13 |\n",
"| 2,011 | Hail | 888 |\n",
"| 2,011 | Thunderstorm Wind | 460 |\n",
"| 2,011 | Flood | 111 |\n",
"| 2,011 | Flash Flood | 61 |\n",
"| 2,011 | Winter Weather | 135 |\n",
"| 2,011 | Funnel Cloud | 30 |\n",
"| 2,011 | Tornado | 56 |\n",
"| 2,011 | Heavy Snow | 39 |\n",
"| 2,011 | Heavy Rain | 95 |\n",
"| 2,011 | Excessive Heat | 69 |\n",
"| 2,011 | Heat | 100 |\n",
"| 2,011 | Dense Fog | 1 |\n",
"| 2,011 | High Wind | 120 |\n",
"| 2,011 | Winter Storm | 142 |\n",
"| 2,011 | Lightning | 7 |\n",
"| 2,011 | Cold/Wind Chill | 87 |\n",
"| 2,011 | Extreme Cold/Wind... | 58 |\n",
"| 2,011 | Ice Storm | 8 |\n",
"| 2,011 | Blizzard | 10 |\n",
"| 2,011 | Wildfire | 2 |\n",
"| 2,012 | Drought | 545 |\n",
"| 2,012 | Winter Storm | 78 |\n",
"| 2,012 | High Wind | 165 |\n",
"| 2,012 | Thunderstorm Wind | 150 |\n",
"| 2,012 | Hail | 670 |\n",
"| 2,012 | Winter Weather | 43 |\n",
"| 2,012 | Cold/Wind Chill | 12 |\n",
"| 2,012 | Lightning | 9 |\n",
"| 2,012 | Wildfire | 16 |\n",
"| 2,012 | Heavy Rain | 43 |\n",
"| 2,012 | Flash Flood | 27 |\n",
"| 2,012 | Tornado | 47 |\n",
"| 2,012 | Blizzard | 41 |\n",
"| 2,012 | Excessive Heat | 15 |\n",
"| 2,012 | Strong Wind | 7 |\n",
"| 2,012 | Heat | 75 |\n",
"| 2,012 | Funnel Cloud | 14 |\n",
"| 2,012 | Flood | 3 |\n",
"| 2,012 | Heavy Snow | 3 |\n",
"| 2,013 | Hail | 726 |\n",
"| 2,013 | Drought | 724 |\n",
"| 2,013 | Tornado | 51 |\n",
"| 2,013 | Thunderstorm Wind | 309 |\n",
"| 2,013 | Funnel Cloud | 14 |\n",
"| 2,013 | Heavy Rain | 63 |\n",
"| 2,013 | Winter Storm | 106 |\n",
"| 2,013 | Heavy Snow | 86 |\n",
"| 2,013 | High Wind | 25 |\n",
"| 2,013 | Flash Flood | 20 |\n",
"| 2,013 | Winter Weather | 75 |\n",
"| 2,013 | Cold/Wind Chill | 50 |\n",
"| 2,013 | Flood | 28 |\n",
"| 2,013 | Extreme Cold/Wind... | 24 |\n",
"| 2,013 | Heat | 16 |\n",
"| 2,013 | Blizzard | 45 |\n",
"| 2,013 | Lightning | 1 |\n",
"| 2,014 | Hail | 938 |\n",
"| 2,014 | Flash Flood | 41 |\n",
"| 2,014 | Thunderstorm Wind | 411 |\n",
"| 2,014 | Winter Storm | 63 |\n",
"| 2,014 | High Wind | 206 |\n",
"| 2,014 | Drought | 174 |\n",
"| 2,014 | Flood | 52 |\n",
"| 2,014 | Winter Weather | 78 |\n",
"| 2,014 | Cold/Wind Chill | 108 |\n",
"| 2,014 | Wildfire | 4 |\n",
"| 2,014 | Heavy Snow | 22 |\n",
"| 2,014 | Extreme Cold/Wind... | 80 |\n",
"| 2,014 | Dust Storm | 2 |\n",
"| 2,014 | Tornado | 74 |\n",
"| 2,014 | Funnel Cloud | 14 |\n",
"| 2,014 | Heavy Rain | 81 |\n",
"| 2,014 | Lightning | 3 |\n",
"| 2,014 | Dense Smoke | 1 |\n",
"| 2,015 | Hail | 741 |\n",
"| 2,015 | Thunderstorm Wind | 276 |\n",
"| 2,015 | Cold/Wind Chill | 28 |\n",
"| 2,015 | Blizzard | 4 |\n",
"| 2,015 | Winter Weather | 103 |\n",
"| 2,015 | Winter Storm | 146 |\n",
"| 2,015 | Tornado | 29 |\n",
"| 2,015 | Flash Flood | 44 |\n",
"| 2,015 | Flood | 50 |\n",
"| 2,015 | Heavy Rain | 26 |\n",
"| 2,015 | Funnel Cloud | 8 |\n",
"| 2,015 | Wildfire | 3 |\n",
"| 2,015 | High Wind | 7 |\n",
"| 2,015 | Heavy Snow | 14 |\n",
"| 2,015 | Lightning | 1 |\n",
"| 2,015 | Ice Storm | 1 |\n",
"|--------+----------------------+--------|\n"
]
}
],
"source": [
"years = nebraskastorms.group_by('YEAR').group_by('EVENT_TYPE')\n",
"year_counts = years.aggregate([\n",
" ('count', agate.Count())\n",
"])\n",
"year_counts.print_table()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tornados = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Tornado')\n",
"#tornados.print_table()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In 1951, there were 9 tornados, and in 2015, there were 29 tornados, a 2.222222222222222222222222222 percent change\n"
]
}
],
"source": [
"old_year = tornados.columns['YEAR'][0]\n",
"old_tornados = tornados.columns['count'][0]\n",
"newest_index = len((tornados.rows))\n",
"new_year = tornados.columns['YEAR'][64]\n",
"new_tornados = tornados.columns['count'][64]\n",
"pc_tornados = (new_tornados - old_tornados) / old_tornados\n",
"print(\"In {}, there were {} tornados, and in {}, there were {} tornados, a {} percent change\".format(old_year, old_tornados, new_year, new_tornados, pc_tornados))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hail = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Hail')\n",
"#hail.print_table()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"61\n",
"In 1955, there were 25 hail storms, and in 2015, there were 741 hail storms, a 28.64 percent change\n"
]
}
],
"source": [
"old_year = hail.columns['YEAR'][0]\n",
"old_hail = hail.columns['count'][0]\n",
"newest_index = len((hail.rows))\n",
"print(newest_index)\n",
"new_year = hail.columns['YEAR'][60]\n",
"new_hail = hail.columns['count'][60]\n",
"pc_hail = (new_hail - old_hail) / old_hail\n",
"print(\"In {}, there were {} hail storms, and in {}, there were {} hail storms, a {} percent change\".format(old_year, old_hail, new_year, new_hail, pc_hail))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"thunderstorms = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Thunderstorm Wind')\n",
"#thunderstorms.print_table()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"61\n",
"In 1955, there were 9 thunderstorms, and in 2015, there were 276 thunderstorms, a 29.66666666666666666666666667 percent change\n"
]
}
],
"source": [
"old_year = thunderstorms.columns['YEAR'][0]\n",
"old_thunderstorms = thunderstorms.columns['count'][0]\n",
"newest_index = len((thunderstorms.rows))\n",
"print(newest_index)\n",
"new_year = thunderstorms.columns['YEAR'][60]\n",
"new_thunderstorms = thunderstorms.columns['count'][60]\n",
"pc_thunderstorms = (new_thunderstorms - old_thunderstorms) / old_thunderstorms\n",
"print(\"In {}, there were {} thunderstorms, and in {}, there were {} thunderstorms, a {} percent change\".format(old_year, old_thunderstorms, new_year, new_thunderstorms, pc_thunderstorms))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--------+------------+--------|\n",
"| YEAR | EVENT_TYPE | count |\n",
"|--------+------------+--------|\n",
"| 1,996 | Blizzard | 118 |\n",
"| 1,997 | Blizzard | 53 |\n",
"| 1,998 | Blizzard | 10 |\n",
"| 2,000 | Blizzard | 4 |\n",
"| 2,001 | Blizzard | 2 |\n",
"| 2,004 | Blizzard | 14 |\n",
"| 2,005 | Blizzard | 52 |\n",
"| 2,006 | Blizzard | 21 |\n",
"| 2,007 | Blizzard | 45 |\n",
"| 2,008 | Blizzard | 8 |\n",
"| 2,009 | Blizzard | 171 |\n",
"| 2,010 | Blizzard | 48 |\n",
"| 2,011 | Blizzard | 10 |\n",
"| 2,012 | Blizzard | 41 |\n",
"| 2,013 | Blizzard | 45 |\n",
"| 2,015 | Blizzard | 4 |\n",
"|--------+------------+--------|\n",
"16\n"
]
}
],
"source": [
"blizzards = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Blizzard')\n",
"blizzards.print_table()\n",
"newest_index = len((blizzards.rows))\n",
"print(newest_index)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In 1996, there were 118 blizzards, and in 2015, there were 4 blizzards, a -0.9661016949152542372881355932 percent change\n"
]
}
],
"source": [
"old_year = blizzards.columns['YEAR'][0]\n",
"old_blizzards = blizzards.columns['count'][0]\n",
"new_year = blizzards.columns['YEAR'][15]\n",
"new_blizzards = blizzards.columns['count'][15]\n",
"pc_blizzards = (new_blizzards - old_blizzards) / old_blizzards\n",
"print(\"In {}, there were {} blizzards, and in {}, there were {} blizzards, a {} percent change\".format(old_year, old_blizzards, new_year, new_blizzards, pc_blizzards))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--------+------------+--------|\n",
"| YEAR | EVENT_TYPE | count |\n",
"|--------+------------+--------|\n",
"| 1,997 | Heavy Rain | 2 |\n",
"| 1,998 | Heavy Rain | 3 |\n",
"| 2,000 | Heavy Rain | 3 |\n",
"| 2,001 | Heavy Rain | 1 |\n",
"| 2,002 | Heavy Rain | 6 |\n",
"| 2,003 | Heavy Rain | 3 |\n",
"| 2,005 | Heavy Rain | 9 |\n",
"| 2,006 | Heavy Rain | 2 |\n",
"| 2,007 | Heavy Rain | 23 |\n",
"| 2,008 | Heavy Rain | 12 |\n",
"| 2,009 | Heavy Rain | 11 |\n",
"| 2,010 | Heavy Rain | 44 |\n",
"| 2,011 | Heavy Rain | 95 |\n",
"| 2,012 | Heavy Rain | 43 |\n",
"| 2,013 | Heavy Rain | 63 |\n",
"| 2,014 | Heavy Rain | 81 |\n",
"| 2,015 | Heavy Rain | 26 |\n",
"|--------+------------+--------|\n",
"17\n"
]
}
],
"source": [
"heavy_rain = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Heavy Rain')\n",
"heavy_rain.print_table()\n",
"newest_index = len((heavy_rain.rows))\n",
"print(newest_index)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In 1997, there were 2 heavy rain, and in 2015, there were 26 heavy rain, a 12 percent change\n"
]
}
],
"source": [
"old_year = heavy_rain.columns['YEAR'][0]\n",
"old_heavy_rain = heavy_rain.columns['count'][0]\n",
"new_year = heavy_rain.columns['YEAR'][16]\n",
"new_heavy_rain = heavy_rain.columns['count'][16]\n",
"pc_heavy_rain = (new_heavy_rain - old_heavy_rain) / old_heavy_rain\n",
"print(\"In {}, there were {} heavy rain, and in {}, there were {} heavy rain, a {} percent change\".format(old_year, old_heavy_rain, new_year, new_heavy_rain, pc_heavy_rain))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--------+------------+--------|\n",
"| YEAR | EVENT_TYPE | count |\n",
"|--------+------------+--------|\n",
"| 1,996 | Flood | 20 |\n",
"| 1,997 | Flood | 39 |\n",
"| 1,998 | Flood | 20 |\n",
"| 1,999 | Flood | 50 |\n",
"| 2,000 | Flood | 15 |\n",
"| 2,001 | Flood | 18 |\n",
"| 2,002 | Flood | 3 |\n",
"| 2,003 | Flood | 8 |\n",
"| 2,004 | Flood | 16 |\n",
"| 2,005 | Flood | 5 |\n",
"| 2,006 | Flood | 6 |\n",
"| 2,007 | Flood | 68 |\n",
"| 2,008 | Flood | 74 |\n",
"| 2,009 | Flood | 9 |\n",
"| 2,010 | Flood | 114 |\n",
"| 2,011 | Flood | 111 |\n",
"| 2,012 | Flood | 3 |\n",
"| 2,013 | Flood | 28 |\n",
"| 2,014 | Flood | 52 |\n",
"| 2,015 | Flood | 50 |\n",
"|--------+------------+--------|\n",
"20\n"
]
}
],
"source": [
"floods = year_counts.where(lambda row: row['EVENT_TYPE'] == 'Flood')\n",
"floods.print_table()\n",
"newest_index = len((floods.rows))\n",
"print(newest_index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"old_year = floods.columns['YEAR'][0]\n",
"old_heavy_floods = floods.columns['count'][0]\n",
"new_year = floods.columns['YEAR'][19]\n",
"new_heavy_floods = floods.columns['count'][19]\n",
"pc_heavy_floods = (new_heavy_rain - old_heavy_rain) / old_heavy_rain\n",
"print(\"In {}, there were {} heavy rain, and in {}, there were {} heavy rain, a {} percent change\".format(old_year, old_heavy_rain, new_year, new_heavy_rain, pc_heavy_rain))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/labs/sample_babyweight.ipynb | 1 | 84983 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating a Sampled Dataset\n",
"\n",
"**Learning Objectives**\n",
"\n",
"1. Setup up the environment\n",
"1. Sample the natality dataset to create train, eval, test sets\n",
"1. Preprocess the data in Pandas dataframe\n",
"\n",
"\n",
"## Introduction \n",
"In this notebook, we'll read data from BigQuery into our notebook to preprocess the data within a Pandas dataframe for a small, repeatable sample.\n",
"\n",
"We will set up the environment, sample the natality dataset to create train, eval, test splits, and preprocess the data in a Pandas dataframe.\n",
"\n",
"Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/tree/master/courses/machine_learning/deepdive2/end_to_end_ml/solutions/sample_babyweight.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hJ7ByvoXzpVI"
},
"source": [
"## Set up environment variables and load necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting google-cloud-bigquery==1.25.0\n",
"Downloading google_cloud_bigquery-1.25.0-py2.py3-none-any.whl (169 kB)\n",
"|████████████████████████████████| 169 kB 4.7 MB/s eta 0:00:01\n",
"Requirement already satisfied: six in /home/jupyter/.local/lib/python3.7/site-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: google-auth in /usr/local/lib/python3.7/site-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: google-resumable-media in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: google-cloud-core in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: google-api-core in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: cachetools in /usr/local/lib/python3.7/dist-packages(from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: rsa in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: pyasn1-modules in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: pyasn1 in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
"Installing collected packages: google-resumable-media, google-cloud-bigquery\n",
"\u001b[33mWARNING: You are using pip version 20.1; however, version 20.2.3 is available."
]
}
],
"source": [
"!pip install --user google-cloud-bigquery==1.25.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: Restart your kernel to use updated packages."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kindly ignore the deprecation warnings and incompatibility errors related to google-cloud-storage."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import necessary libraries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from google.cloud import bigquery\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hJ7ByvoXzpVI"
},
"source": [
"**Lab Task #1:** Set up environment variables so that we can use them throughout the notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"# TODO 1\n",
"# TODO -- Your code here.\n",
"echo \"Your current GCP Project Name is: \"$PROJECT"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"PROJECT = \"cloud-training-demos\" # Replace with your PROJECT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create ML datasets by sampling using BigQuery\n",
"\n",
"We'll begin by sampling the BigQuery data to create smaller datasets. Let's create a BigQuery client that we'll use throughout the lab."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"bq = bigquery.Client(project = PROJECT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to figure out the right way to divide our hash values to get our desired splits. To do that we need to define some values to hash within the module. Feel free to play around with these values to get the perfect combination."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"modulo_divisor = 100\n",
"train_percent = 80.0\n",
"eval_percent = 10.0\n",
"\n",
"train_buckets = int(modulo_divisor * train_percent / 100.0)\n",
"eval_buckets = int(modulo_divisor * eval_percent / 100.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make a series of queries to check if our bucketing values result in the correct sizes of each of our dataset splits and then adjust accordingly. Therefore, to make our code more compact and reusable, let's define a function to return the head of a dataframe produced from our queries up to a certain number of rows."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def display_dataframe_head_from_query(query, count=10):\n",
" \"\"\"Displays count rows from dataframe head from query.\n",
" \n",
" Args:\n",
" query: str, query to be run on BigQuery, results stored in dataframe.\n",
" count: int, number of results from head of dataframe to display.\n",
" Returns:\n",
" Dataframe head with count number of results.\n",
" \"\"\"\n",
" df = bq.query(\n",
" query + \" LIMIT {limit}\".format(\n",
" limit=count)).to_dataframe()\n",
"\n",
" return df.head(count)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For our first query, we're going to use the original query above to get our label, features, and columns to combine into our hash which we will use to perform our repeatable splitting. There are only a limited number of years, months, days, and states in the dataset. Let's see what the hash values are. We will need to include all of these extra columns to hash on to get a fairly uniform spread of the data. Feel free to try less or more in the hash and see how it changes your results."
]
},
{
"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>weight_pounds</th>\n",
" <th>is_male</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>date</th>\n",
" <th>state</th>\n",
" <th>mother_birth_state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.568469</td>\n",
" <td>True</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>46</td>\n",
" <td>2001</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>CA</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8.807467</td>\n",
" <td>True</td>\n",
" <td>39</td>\n",
" <td>1</td>\n",
" <td>42</td>\n",
" <td>2001</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>CA</td>\n",
" <td>Foreign</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.313632</td>\n",
" <td>True</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>2001</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>IL</td>\n",
" <td>IL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.000575</td>\n",
" <td>False</td>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>2001</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>IL</td>\n",
" <td>IL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.563162</td>\n",
" <td>False</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>2001</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>KY</td>\n",
" <td>IN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7.125340</td>\n",
" <td>False</td>\n",
" <td>34</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>2001</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>MD</td>\n",
" <td>MD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.438397</td>\n",
" <td>False</td>\n",
" <td>31</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>2001</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>MA</td>\n",
" <td>Foreign</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7.352416</td>\n",
" <td>True</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>37</td>\n",
" <td>2001</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>MI</td>\n",
" <td>MI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8.062305</td>\n",
" <td>True</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>2001</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>MN</td>\n",
" <td>MN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7.251004</td>\n",
" <td>True</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>2001</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>MS</td>\n",
" <td>MS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds is_male mother_age plurality gestation_weeks year \\\n",
"0 7.568469 True 22 1 46 2001 \n",
"1 8.807467 True 39 1 42 2001 \n",
"2 8.313632 True 23 1 35 2001 \n",
"3 8.000575 False 27 1 40 2001 \n",
"4 6.563162 False 29 1 39 2001 \n",
"5 7.125340 False 34 1 40 2001 \n",
"6 7.438397 False 31 1 38 2001 \n",
"7 7.352416 True 30 1 37 2001 \n",
"8 8.062305 True 16 1 40 2001 \n",
"9 7.251004 True 17 1 39 2001 \n",
"\n",
" month date state mother_birth_state \n",
"0 7 5 CA CA \n",
"1 8 3 CA Foreign \n",
"2 10 7 IL IL \n",
"3 6 7 IL IL \n",
"4 11 7 KY IN \n",
"5 12 7 MD MD \n",
"6 4 3 MA Foreign \n",
"7 5 7 MI MI \n",
"8 10 5 MN MN \n",
"9 2 5 MS MS "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get label, features, and columns to hash and split into buckets\n",
"hash_cols_fixed_query = \"\"\"\n",
"SELECT\n",
" weight_pounds,\n",
" is_male,\n",
" mother_age,\n",
" plurality,\n",
" gestation_weeks,\n",
" year,\n",
" month,\n",
" CASE\n",
" WHEN day IS NULL THEN\n",
" CASE\n",
" WHEN wday IS NULL THEN 0\n",
" ELSE wday\n",
" END\n",
" ELSE day\n",
" END AS date,\n",
" IFNULL(state, \"Unknown\") AS state,\n",
" IFNULL(mother_birth_state, \"Unknown\") AS mother_birth_state\n",
"FROM\n",
" publicdata.samples.natality\n",
"WHERE\n",
" year > 2000\n",
" AND weight_pounds > 0\n",
" AND mother_age > 0\n",
" AND plurality > 0\n",
" AND gestation_weeks > 0\n",
"\"\"\"\n",
"\n",
"display_dataframe_head_from_query(hash_cols_fixed_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `COALESCE` would provide the same result as the nested `CASE WHEN`. This is preferable when all we want is the first non-null instance. To be precise the `CASE WHEN` would become `COALESCE(wday, day, 0) AS date`. You can read more about it [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/conditional_expressions)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next query will combine our hash columns and will leave us just with our label, features, and our hash values."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .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>weight_pounds</th>\n",
" <th>is_male</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.109908</td>\n",
" <td>False</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>563561248331884029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.588311</td>\n",
" <td>False</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>3487851893553562338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.812691</td>\n",
" <td>True</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" <td>2669304657201106008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.251004</td>\n",
" <td>True</td>\n",
" <td>30</td>\n",
" <td>2</td>\n",
" <td>38</td>\n",
" <td>7076342771382320241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.206013</td>\n",
" <td>False</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>8828960867056723893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6.062712</td>\n",
" <td>False</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>4280252324912833683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.500126</td>\n",
" <td>False</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>6090508671071281093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7.687519</td>\n",
" <td>True</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" <td>8708360030053768340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8.875811</td>\n",
" <td>True</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>8530116731648975419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7.387690</td>\n",
" <td>False</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>1776323475383399588</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds is_male mother_age plurality gestation_weeks \\\n",
"0 7.109908 False 25 1 38 \n",
"1 7.588311 False 19 1 40 \n",
"2 4.812691 True 35 1 33 \n",
"3 7.251004 True 30 2 38 \n",
"4 6.206013 False 21 1 36 \n",
"5 6.062712 False 33 1 40 \n",
"6 7.500126 False 19 1 39 \n",
"7 7.687519 True 23 1 41 \n",
"8 8.875811 True 24 1 40 \n",
"9 7.387690 False 28 1 38 \n",
"\n",
" hash_values \n",
"0 563561248331884029 \n",
"1 3487851893553562338 \n",
"2 2669304657201106008 \n",
"3 7076342771382320241 \n",
"4 8828960867056723893 \n",
"5 4280252324912833683 \n",
"6 6090508671071281093 \n",
"7 8708360030053768340 \n",
"8 8530116731648975419 \n",
"9 1776323475383399588 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_query = \"\"\"\n",
"SELECT\n",
" weight_pounds,\n",
" is_male,\n",
" mother_age,\n",
" plurality,\n",
" gestation_weeks,\n",
" FARM_FINGERPRINT(\n",
" CONCAT(\n",
" CAST(year AS STRING),\n",
" CAST(month AS STRING),\n",
" CAST(date AS STRING),\n",
" CAST(state AS STRING),\n",
" CAST(mother_birth_state AS STRING)\n",
" )\n",
" ) AS hash_values\n",
"FROM\n",
" ({CTE_hash_cols_fixed})\n",
"\"\"\".format(CTE_hash_cols_fixed=hash_cols_fixed_query)\n",
"\n",
"display_dataframe_head_from_query(data_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next query is going to find the counts of each of the unique 657484 `hash_values`. This will be our first step at making actual hash buckets for our split via the `GROUP BY`."
]
},
{
"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>hash_values</th>\n",
" <th>num_records</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6001926139587584124</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6064126287360941757</td>\n",
" <td>758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6824828135709159935</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3363240092080644183</td>\n",
" <td>631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2666158614438147859</td>\n",
" <td>964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2958542686973584093</td>\n",
" <td>363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8332670353336108110</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1459116430691530322</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8084544908979932787</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2610866487448411172</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hash_values num_records\n",
"0 6001926139587584124 19\n",
"1 6064126287360941757 758\n",
"2 6824828135709159935 72\n",
"3 3363240092080644183 631\n",
"4 2666158614438147859 964\n",
"5 2958542686973584093 363\n",
"6 8332670353336108110 47\n",
"7 1459116430691530322 52\n",
"8 8084544908979932787 7\n",
"9 2610866487448411172 23"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the counts of each of the unique hash of our splitting column\n",
"first_bucketing_query = \"\"\"\n",
"SELECT\n",
" hash_values,\n",
" COUNT(*) AS num_records\n",
"FROM\n",
" ({CTE_data})\n",
"GROUP BY\n",
" hash_values\n",
"\"\"\".format(CTE_data=data_query)\n",
"\n",
"display_dataframe_head_from_query(first_bucketing_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The query below performs a second layer of bucketing where now for each of these bucket indices we count the number of records."
]
},
{
"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>bucket_index</th>\n",
" <th>num_records</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17</td>\n",
" <td>222562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46</td>\n",
" <td>281627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>270933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>85</td>\n",
" <td>368045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40</td>\n",
" <td>333712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>19</td>\n",
" <td>384793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>77</td>\n",
" <td>401941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>95</td>\n",
" <td>313544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>81</td>\n",
" <td>233538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>24</td>\n",
" <td>352559</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bucket_index num_records\n",
"0 17 222562\n",
"1 46 281627\n",
"2 7 270933\n",
"3 85 368045\n",
"4 40 333712\n",
"5 19 384793\n",
"6 77 401941\n",
"7 95 313544\n",
"8 81 233538\n",
"9 24 352559"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the number of records in each of the hash buckets\n",
"second_bucketing_query = \"\"\"\n",
"SELECT\n",
" ABS(MOD(hash_values, {modulo_divisor})) AS bucket_index,\n",
" SUM(num_records) AS num_records\n",
"FROM\n",
" ({CTE_first_bucketing})\n",
"GROUP BY\n",
" ABS(MOD(hash_values, {modulo_divisor}))\n",
"\"\"\".format(\n",
" CTE_first_bucketing=first_bucketing_query, modulo_divisor=modulo_divisor)\n",
"\n",
"display_dataframe_head_from_query(second_bucketing_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of records is hard for us to easily understand the split, so we will normalize the count into percentage of the data in each of the hash buckets in the next query."
]
},
{
"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>bucket_index</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>398118</td>\n",
" <td>0.012060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>92</td>\n",
" <td>336735</td>\n",
" <td>0.010201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>70</td>\n",
" <td>285539</td>\n",
" <td>0.008650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>78</td>\n",
" <td>326758</td>\n",
" <td>0.009898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16</td>\n",
" <td>172145</td>\n",
" <td>0.005215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>94</td>\n",
" <td>431001</td>\n",
" <td>0.013056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5</td>\n",
" <td>449280</td>\n",
" <td>0.013610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>62</td>\n",
" <td>426834</td>\n",
" <td>0.012930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30</td>\n",
" <td>333513</td>\n",
" <td>0.010103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>34</td>\n",
" <td>379000</td>\n",
" <td>0.011481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bucket_index num_records percent_records\n",
"0 4 398118 0.012060\n",
"1 92 336735 0.010201\n",
"2 70 285539 0.008650\n",
"3 78 326758 0.009898\n",
"4 16 172145 0.005215\n",
"5 94 431001 0.013056\n",
"6 5 449280 0.013610\n",
"7 62 426834 0.012930\n",
"8 30 333513 0.010103\n",
"9 34 379000 0.011481"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate the overall percentages\n",
"percentages_query = \"\"\"\n",
"SELECT\n",
" bucket_index,\n",
" num_records,\n",
" CAST(num_records AS FLOAT64) / (\n",
" SELECT\n",
" SUM(num_records)\n",
" FROM\n",
" ({CTE_second_bucketing})) AS percent_records\n",
"FROM\n",
" ({CTE_second_bucketing})\n",
"\"\"\".format(CTE_second_bucketing=second_bucketing_query)\n",
"\n",
"display_dataframe_head_from_query(percentages_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll now select the range of buckets to be used in training."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bucket_index</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" <th>dataset_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>52</td>\n",
" <td>204972</td>\n",
" <td>0.006209</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>33</td>\n",
" <td>410226</td>\n",
" <td>0.012427</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>23</td>\n",
" <td>559019</td>\n",
" <td>0.016934</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>28</td>\n",
" <td>449682</td>\n",
" <td>0.013622</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>62</td>\n",
" <td>426834</td>\n",
" <td>0.012930</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>73</td>\n",
" <td>411771</td>\n",
" <td>0.012474</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>38</td>\n",
" <td>338150</td>\n",
" <td>0.010243</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>35</td>\n",
" <td>250505</td>\n",
" <td>0.007588</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>65</td>\n",
" <td>289303</td>\n",
" <td>0.008764</td>\n",
" <td>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>61</td>\n",
" <td>453904</td>\n",
" <td>0.013750</td>\n",
" <td>train</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bucket_index num_records percent_records dataset_name\n",
"0 52 204972 0.006209 train\n",
"1 33 410226 0.012427 train\n",
"2 23 559019 0.016934 train\n",
"3 28 449682 0.013622 train\n",
"4 62 426834 0.012930 train\n",
"5 73 411771 0.012474 train\n",
"6 38 338150 0.010243 train\n",
"7 35 250505 0.007588 train\n",
"8 65 289303 0.008764 train\n",
"9 61 453904 0.013750 train"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose hash buckets for training and pull in their statistics\n",
"train_query = \"\"\"\n",
"SELECT\n",
" *,\n",
" \"train\" AS dataset_name\n",
"FROM\n",
" ({CTE_percentages})\n",
"WHERE\n",
" bucket_index >= 0\n",
" AND bucket_index < {train_buckets}\n",
"\"\"\".format(\n",
" CTE_percentages=percentages_query,\n",
" train_buckets=train_buckets)\n",
"\n",
"display_dataframe_head_from_query(train_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll do the same by selecting the range of buckets to be used evaluation."
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>bucket_index</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" <th>dataset_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80</td>\n",
" <td>312489</td>\n",
" <td>0.009466</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>83</td>\n",
" <td>411258</td>\n",
" <td>0.012458</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>85</td>\n",
" <td>368045</td>\n",
" <td>0.011149</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>82</td>\n",
" <td>468179</td>\n",
" <td>0.014182</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>87</td>\n",
" <td>523881</td>\n",
" <td>0.015870</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>88</td>\n",
" <td>423809</td>\n",
" <td>0.012838</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>86</td>\n",
" <td>274489</td>\n",
" <td>0.008315</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>89</td>\n",
" <td>256482</td>\n",
" <td>0.007770</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>81</td>\n",
" <td>233538</td>\n",
" <td>0.007074</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>84</td>\n",
" <td>341155</td>\n",
" <td>0.010334</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bucket_index num_records percent_records dataset_name\n",
"0 80 312489 0.009466 eval\n",
"1 83 411258 0.012458 eval\n",
"2 85 368045 0.011149 eval\n",
"3 82 468179 0.014182 eval\n",
"4 87 523881 0.015870 eval\n",
"5 88 423809 0.012838 eval\n",
"6 86 274489 0.008315 eval\n",
"7 89 256482 0.007770 eval\n",
"8 81 233538 0.007074 eval\n",
"9 84 341155 0.010334 eval"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose hash buckets for validation and pull in their statistics\n",
"eval_query = \"\"\"\n",
"SELECT\n",
" *,\n",
" \"eval\" AS dataset_name\n",
"FROM\n",
" ({CTE_percentages})\n",
"WHERE\n",
" bucket_index >= {train_buckets}\n",
" AND bucket_index < {cum_eval_buckets}\n",
"\"\"\".format(\n",
" CTE_percentages=percentages_query,\n",
" train_buckets=train_buckets,\n",
" cum_eval_buckets=train_buckets + eval_buckets)\n",
"\n",
"display_dataframe_head_from_query(eval_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly, we'll select the hash buckets to be used for the test split."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bucket_index</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" <th>dataset_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>92</td>\n",
" <td>336735</td>\n",
" <td>0.010201</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>91</td>\n",
" <td>333267</td>\n",
" <td>0.010096</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>90</td>\n",
" <td>286465</td>\n",
" <td>0.008678</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>94</td>\n",
" <td>431001</td>\n",
" <td>0.013056</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>93</td>\n",
" <td>215710</td>\n",
" <td>0.006534</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>99</td>\n",
" <td>223334</td>\n",
" <td>0.006765</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>95</td>\n",
" <td>313544</td>\n",
" <td>0.009498</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>97</td>\n",
" <td>480790</td>\n",
" <td>0.014564</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>96</td>\n",
" <td>529357</td>\n",
" <td>0.016036</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>98</td>\n",
" <td>374697</td>\n",
" <td>0.011351</td>\n",
" <td>test</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bucket_index num_records percent_records dataset_name\n",
"0 92 336735 0.010201 test\n",
"1 91 333267 0.010096 test\n",
"2 90 286465 0.008678 test\n",
"3 94 431001 0.013056 test\n",
"4 93 215710 0.006534 test\n",
"5 99 223334 0.006765 test\n",
"6 95 313544 0.009498 test\n",
"7 97 480790 0.014564 test\n",
"8 96 529357 0.016036 test\n",
"9 98 374697 0.011351 test"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose hash buckets for testing and pull in their statistics\n",
"test_query = \"\"\"\n",
"SELECT\n",
" *,\n",
" \"test\" AS dataset_name\n",
"FROM\n",
" ({CTE_percentages})\n",
"WHERE\n",
" bucket_index >= {cum_eval_buckets}\n",
" AND bucket_index < {modulo_divisor}\n",
"\"\"\".format(\n",
" CTE_percentages=percentages_query,\n",
" cum_eval_buckets=train_buckets + eval_buckets,\n",
" modulo_divisor=modulo_divisor)\n",
"\n",
"display_dataframe_head_from_query(test_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the below query, we'll `UNION ALL` all of the datasets together so that all three sets of hash buckets will be within one table. We added `dataset_id` so that we can sort on it in the query after."
]
},
{
"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>dataset_id</th>\n",
" <th>bucket_index</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" <th>dataset_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>368045</td>\n",
" <td>0.011149</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>88</td>\n",
" <td>423809</td>\n",
" <td>0.012838</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>256482</td>\n",
" <td>0.007770</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>80</td>\n",
" <td>312489</td>\n",
" <td>0.009466</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>81</td>\n",
" <td>233538</td>\n",
" <td>0.007074</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>83</td>\n",
" <td>411258</td>\n",
" <td>0.012458</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>82</td>\n",
" <td>468179</td>\n",
" <td>0.014182</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>84</td>\n",
" <td>341155</td>\n",
" <td>0.010334</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>87</td>\n",
" <td>523881</td>\n",
" <td>0.015870</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>86</td>\n",
" <td>274489</td>\n",
" <td>0.008315</td>\n",
" <td>eval</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_id bucket_index num_records percent_records dataset_name\n",
"0 1 85 368045 0.011149 eval\n",
"1 1 88 423809 0.012838 eval\n",
"2 1 89 256482 0.007770 eval\n",
"3 1 80 312489 0.009466 eval\n",
"4 1 81 233538 0.007074 eval\n",
"5 1 83 411258 0.012458 eval\n",
"6 1 82 468179 0.014182 eval\n",
"7 1 84 341155 0.010334 eval\n",
"8 1 87 523881 0.015870 eval\n",
"9 1 86 274489 0.008315 eval"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Union the training, validation, and testing dataset statistics\n",
"union_query = \"\"\"\n",
"SELECT\n",
" 0 AS dataset_id,\n",
" *\n",
"FROM\n",
" ({CTE_train})\n",
"UNION ALL\n",
"SELECT\n",
" 1 AS dataset_id,\n",
" *\n",
"FROM\n",
" ({CTE_eval})\n",
"UNION ALL\n",
"SELECT\n",
" 2 AS dataset_id,\n",
" *\n",
"FROM\n",
" ({CTE_test})\n",
"\"\"\".format(CTE_train=train_query, CTE_eval=eval_query, CTE_test=test_query)\n",
"\n",
"display_dataframe_head_from_query(union_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly, we'll show the final split between train, eval, and test sets. We can see both the number of records and percent of the total data. It is really close to that we were hoping to get."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_id</th>\n",
" <th>dataset_name</th>\n",
" <th>num_records</th>\n",
" <th>percent_records</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>train</td>\n",
" <td>25873134</td>\n",
" <td>0.783765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>eval</td>\n",
" <td>3613325</td>\n",
" <td>0.109457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>test</td>\n",
" <td>3524900</td>\n",
" <td>0.106778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_id dataset_name num_records percent_records\n",
"0 0 train 25873134 0.783765\n",
"1 1 eval 3613325 0.109457\n",
"2 2 test 3524900 0.106778"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show final splitting and associated statistics\n",
"split_query = \"\"\"\n",
"SELECT\n",
" dataset_id,\n",
" dataset_name,\n",
" SUM(num_records) AS num_records,\n",
" SUM(percent_records) AS percent_records\n",
"FROM\n",
" ({CTE_union})\n",
"GROUP BY\n",
" dataset_id,\n",
" dataset_name\n",
"ORDER BY\n",
" dataset_id\n",
"\"\"\".format(CTE_union=union_query)\n",
"\n",
"display_dataframe_head_from_query(split_query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we know that our splitting values produce a good global splitting on our data, here's a way to get a well-distributed portion of the data in such a way that the train, eval, test sets do not overlap and takes a subsample of our global splits."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Lab Task #2:** Sample the natality dataset"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 7733 examples in the train dataset.\n",
"There are 1037 examples in the validation dataset.\n",
"There are 561 examples in the test dataset.\n"
]
}
],
"source": [
"# TODO 2\n",
"# TODO -- Your code here.\n",
"# every_n allows us to subsample from each of the hash values\n",
"# This helps us get approximately the record counts we want\n",
"print(\"There are {} examples in the train dataset.\".format(len(train_df)))\n",
"print(\"There are {} examples in the validation dataset.\".format(len(eval_df)))\n",
"print(\"There are {} examples in the test dataset.\".format(len(test_df)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess data using Pandas\n",
"\n",
"We'll perform a few preprocessing steps to the data in our dataset. Let's add extra rows to simulate the lack of ultrasound. That is we'll duplicate some rows and make the `is_male` field be `Unknown`. Also, if there is more than child we'll change the `plurality` to `Multiple(2+)`. While we're at it, we'll also change the plurality column to be a string. We'll perform these operations below. \n",
"\n",
"Let's start by examining the training dataset as is."
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>weight_pounds</th>\n",
" <th>is_male</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>9.499719</td>\n",
" <td>True</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>505732274561700014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>6.027438</td>\n",
" <td>True</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>1409348435509100014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>6.124442</td>\n",
" <td>True</td>\n",
" <td>34</td>\n",
" <td>2</td>\n",
" <td>37</td>\n",
" <td>2620860165093800008</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>9.001474</td>\n",
" <td>True</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>1409348435509100014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>7.070225</td>\n",
" <td>False</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>4659354114038800077</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds is_male mother_age plurality gestation_weeks \\\n",
"0 9.499719 True 30 1 40 \n",
"1 6.027438 True 26 1 36 \n",
"2 6.124442 True 34 2 37 \n",
"3 9.001474 True 28 1 35 \n",
"4 7.070225 False 23 1 40 \n",
"\n",
" hash_values \n",
"0 505732274561700014 \n",
"1 1409348435509100014 \n",
"2 2620860165093800008 \n",
"3 1409348435509100014 \n",
"4 4659354114038800077 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, notice that there are some very important numeric fields that are missing in some rows (the count in Pandas doesn't count missing data)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>weight_pounds</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>7733.000000</td>\n",
" <td>7733.000000</td>\n",
" <td>7733.000000</td>\n",
" <td>7733.000000</td>\n",
" <td>7.733000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>7.264415</td>\n",
" <td>28.213371</td>\n",
" <td>1.035691</td>\n",
" <td>38.691064</td>\n",
" <td>4.983286e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>1.303220</td>\n",
" <td>6.134232</td>\n",
" <td>0.201568</td>\n",
" <td>2.531921</td>\n",
" <td>2.551244e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>0.562179</td>\n",
" <td>13.000000</td>\n",
" <td>1.000000</td>\n",
" <td>18.000000</td>\n",
" <td>5.826385e+15</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>6.624891</td>\n",
" <td>23.000000</td>\n",
" <td>1.000000</td>\n",
" <td>38.000000</td>\n",
" <td>3.153609e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>7.345803</td>\n",
" <td>28.000000</td>\n",
" <td>1.000000</td>\n",
" <td>39.000000</td>\n",
" <td>4.896699e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>8.062305</td>\n",
" <td>33.000000</td>\n",
" <td>1.000000</td>\n",
" <td>40.000000</td>\n",
" <td>6.784884e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>11.563246</td>\n",
" <td>48.000000</td>\n",
" <td>4.000000</td>\n",
" <td>47.000000</td>\n",
" <td>9.210618e+18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds mother_age plurality gestation_weeks hash_values\n",
"count 7733.000000 7733.000000 7733.000000 7733.000000 7.733000e+03\n",
"mean 7.264415 28.213371 1.035691 38.691064 4.983286e+18\n",
"std 1.303220 6.134232 0.201568 2.531921 2.551244e+18\n",
"min 0.562179 13.000000 1.000000 18.000000 5.826385e+15\n",
"25% 6.624891 23.000000 1.000000 38.000000 3.153609e+18\n",
"50% 7.345803 28.000000 1.000000 39.000000 4.896699e+18\n",
"75% 8.062305 33.000000 1.000000 40.000000 6.784884e+18\n",
"max 11.563246 48.000000 4.000000 47.000000 9.210618e+18"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is always crucial to clean raw data before using in machine learning, so we have a preprocessing step. We'll define a `preprocess` function below. Note that the mother's age is an input to our model so users will have to provide the mother's age; otherwise, our service won't work. The features we use for our model were chosen because they are such good predictors and because they are easy enough to collect."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hJ7ByvoXzpVI"
},
"source": [
"**Lab Task #3:** Preprocess the data in Pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
" # TODO 3\n",
" # TODO -- Your code here.\n",
"\n",
" # Modify plurality field to be a string\n",
" twins_etc = dict(zip([1,2,3,4,5],\n",
" [\"Single(1)\",\n",
" \"Twins(2)\",\n",
" \"Triplets(3)\",\n",
" \"Quadruplets(4)\",\n",
" \"Quintuplets(5)\"]))\n",
" df[\"plurality\"].replace(twins_etc, inplace=True)\n",
"\n",
" # Clone data and mask certain columns to simulate lack of ultrasound\n",
" no_ultrasound = df.copy(deep=True)\n",
"\n",
" # Modify is_male\n",
" no_ultrasound[\"is_male\"] = \"Unknown\"\n",
" \n",
" # Modify plurality\n",
" condition = no_ultrasound[\"plurality\"] != \"Single(1)\"\n",
" no_ultrasound.loc[condition, \"plurality\"] = \"Multiple(2+)\"\n",
"\n",
" # Concatenate both datasets together and shuffle\n",
" return pd.concat(\n",
" [df, no_ultrasound]).sample(frac=1).reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's process the train, eval, test set and see a small sample of the training data after our preprocessing:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"train_df = preprocess(train_df)\n",
"eval_df = preprocess(eval_df)\n",
"test_df = preprocess(test_df)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>weight_pounds</th>\n",
" <th>is_male</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>7.874912</td>\n",
" <td>Unknown</td>\n",
" <td>38</td>\n",
" <td>Single(1)</td>\n",
" <td>38</td>\n",
" <td>8717259940738900003</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>8.999270</td>\n",
" <td>Unknown</td>\n",
" <td>31</td>\n",
" <td>Single(1)</td>\n",
" <td>45</td>\n",
" <td>6781866293108400060</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>7.251004</td>\n",
" <td>True</td>\n",
" <td>24</td>\n",
" <td>Single(1)</td>\n",
" <td>40</td>\n",
" <td>1696737464106800060</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>8.562754</td>\n",
" <td>True</td>\n",
" <td>43</td>\n",
" <td>Single(1)</td>\n",
" <td>39</td>\n",
" <td>4614303140002600076</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>6.194990</td>\n",
" <td>True</td>\n",
" <td>23</td>\n",
" <td>Single(1)</td>\n",
" <td>41</td>\n",
" <td>780565305641800050</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds is_male mother_age plurality gestation_weeks \\\n",
"0 7.874912 Unknown 38 Single(1) 38 \n",
"1 8.999270 Unknown 31 Single(1) 45 \n",
"2 7.251004 True 24 Single(1) 40 \n",
"3 8.562754 True 43 Single(1) 39 \n",
"4 6.194990 True 23 Single(1) 41 \n",
"\n",
" hash_values \n",
"0 8717259940738900003 \n",
"1 6781866293108400060 \n",
"2 1696737464106800060 \n",
"3 4614303140002600076 \n",
"4 780565305641800050 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>weight_pounds</th>\n",
" <th>is_male</th>\n",
" <th>mother_age</th>\n",
" <th>plurality</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>15461</td>\n",
" <td>7.251004</td>\n",
" <td>True</td>\n",
" <td>32</td>\n",
" <td>Single(1)</td>\n",
" <td>39</td>\n",
" <td>8655151740159000017</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15462</td>\n",
" <td>8.811877</td>\n",
" <td>True</td>\n",
" <td>30</td>\n",
" <td>Single(1)</td>\n",
" <td>39</td>\n",
" <td>845203792559000058</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15463</td>\n",
" <td>7.248799</td>\n",
" <td>True</td>\n",
" <td>26</td>\n",
" <td>Single(1)</td>\n",
" <td>40</td>\n",
" <td>1409348435509100014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15464</td>\n",
" <td>7.625790</td>\n",
" <td>Unknown</td>\n",
" <td>22</td>\n",
" <td>Single(1)</td>\n",
" <td>40</td>\n",
" <td>2875790318525700041</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15465</td>\n",
" <td>6.499227</td>\n",
" <td>Unknown</td>\n",
" <td>22</td>\n",
" <td>Single(1)</td>\n",
" <td>38</td>\n",
" <td>8720767384765100051</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds is_male mother_age plurality gestation_weeks \\\n",
"15461 7.251004 True 32 Single(1) 39 \n",
"15462 8.811877 True 30 Single(1) 39 \n",
"15463 7.248799 True 26 Single(1) 40 \n",
"15464 7.625790 Unknown 22 Single(1) 40 \n",
"15465 6.499227 Unknown 22 Single(1) 38 \n",
"\n",
" hash_values \n",
"15461 8655151740159000017 \n",
"15462 845203792559000058 \n",
"15463 1409348435509100014 \n",
"15464 2875790318525700041 \n",
"15465 8720767384765100051 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look again at a summary of the dataset. Note that we only see numeric columns, so `plurality` does not show up."
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>weight_pounds</th>\n",
" <th>mother_age</th>\n",
" <th>gestation_weeks</th>\n",
" <th>hash_values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>15466.000000</td>\n",
" <td>15466.000000</td>\n",
" <td>15466.000000</td>\n",
" <td>1.546600e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>7.264415</td>\n",
" <td>28.213371</td>\n",
" <td>38.691064</td>\n",
" <td>4.983286e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>1.303178</td>\n",
" <td>6.134034</td>\n",
" <td>2.531839</td>\n",
" <td>2.551162e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>0.562179</td>\n",
" <td>13.000000</td>\n",
" <td>18.000000</td>\n",
" <td>5.826385e+15</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>6.624891</td>\n",
" <td>23.000000</td>\n",
" <td>38.000000</td>\n",
" <td>3.153609e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>7.345803</td>\n",
" <td>28.000000</td>\n",
" <td>39.000000</td>\n",
" <td>4.896699e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>8.062305</td>\n",
" <td>33.000000</td>\n",
" <td>40.000000</td>\n",
" <td>6.784884e+18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>11.563246</td>\n",
" <td>48.000000</td>\n",
" <td>47.000000</td>\n",
" <td>9.210618e+18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_pounds mother_age gestation_weeks hash_values\n",
"count 15466.000000 15466.000000 15466.000000 1.546600e+04\n",
"mean 7.264415 28.213371 38.691064 4.983286e+18\n",
"std 1.303178 6.134034 2.531839 2.551162e+18\n",
"min 0.562179 13.000000 18.000000 5.826385e+15\n",
"25% 6.624891 23.000000 38.000000 3.153609e+18\n",
"50% 7.345803 28.000000 39.000000 4.896699e+18\n",
"75% 8.062305 33.000000 40.000000 6.784884e+18\n",
"max 11.563246 48.000000 47.000000 9.210618e+18"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write to .csv files \n",
"\n",
"In the final versions, we want to read from files, not Pandas dataframes. So, we write the Pandas dataframes out as csv files. Using csv files gives us the advantage of shuffling during read. This is important for distributed training because some workers might be slower than others, and shuffling the data helps prevent the same data from being assigned to the slow workers."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"# Define columns\n",
"columns = [\"weight_pounds\",\n",
" \"is_male\",\n",
" \"mother_age\",\n",
" \"plurality\",\n",
" \"gestation_weeks\"]\n",
"\n",
"# Write out CSV files\n",
"train_df.to_csv(\n",
" path_or_buf=\"train.csv\", columns=columns, header=False, index=False)\n",
"eval_df.to_csv(\n",
" path_or_buf=\"eval.csv\", columns=columns, header=False, index=False)\n",
"test_df.to_csv(\n",
" path_or_buf=\"test.csv\", columns=columns, header=False, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2074 eval.csv\n",
" 1122 test.csv\n",
" 15466 train.csv\n",
" 18662 total\n"
]
}
],
"source": [
"%%bash\n",
"wc -l *.csv"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==> eval.csv <==\n",
"8.62448368944,Unknown,31,Single(1),42\n",
"6.9996768185,Unknown,32,Single(1),39\n",
"6.6248909731,False,30,Single(1),38\n",
"8.3114272774,False,19,Single(1),41\n",
"8.313631900019999,True,32,Single(1),37\n",
"7.06140625186,Unknown,34,Single(1),41\n",
"7.62578964258,Unknown,34,Single(1),39\n",
"7.3744626639,Unknown,20,Single(1),39\n",
"1.93786328298,False,32,Triplets(3),28\n",
"8.99926953484,True,34,Single(1),39\n",
"\n",
"==> test.csv <==\n",
"7.3744626639,Unknown,25,Single(1),44\n",
"6.93794738514,Unknown,24,Single(1),40\n",
"6.87621795178,True,30,Single(1),39\n",
"6.87621795178,Unknown,29,Single(1),39\n",
"7.0327461578,Unknown,36,Single(1),38\n",
"9.31232594688,False,25,Single(1),39\n",
"7.936641432,True,23,Single(1),37\n",
"4.7840310854,Unknown,34,Multiple(2+),38\n",
"7.31273323054,True,23,Single(1),39\n",
"8.24969784404,False,32,Single(1),39\n",
"\n",
"==> train.csv <==\n",
"7.87491199864,Unknown,38,Single(1),38\n",
"8.99926953484,Unknown,31,Single(1),45\n",
"7.25100379718,True,24,Single(1),40\n",
"8.56275425608,True,43,Single(1),39\n",
"6.1949895622,True,23,Single(1),41\n",
"9.0609989682,Unknown,24,Single(1),38\n",
"7.5618555866,True,26,Single(1),41\n",
"7.30611936268,False,31,Single(1),41\n",
"9.6672701887,True,29,Single(1),40\n",
"6.4992274837599995,True,22,Single(1),39\n"
]
}
],
"source": [
"%%bash\n",
"head *.csv"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==> eval.csv <==\n",
"7.43839671988,False,25,Single(1),37\n",
"7.06140625186,True,34,Single(1),41\n",
"7.43619209726,True,36,Single(1),40\n",
"3.56267015392,True,35,Twins(2),31\n",
"8.811876612139999,False,27,Single(1),36\n",
"8.0689187892,Unknown,36,Single(1),40\n",
"8.7633749145,Unknown,34,Single(1),39\n",
"7.43839671988,True,43,Single(1),40\n",
"4.62529825676,Unknown,38,Multiple(2+),35\n",
"6.1839664491,Unknown,20,Single(1),38\n",
"\n",
"==> test.csv <==\n",
"6.37576861704,Unknown,21,Single(1),39\n",
"7.5618555866,True,22,Single(1),39\n",
"8.99926953484,Unknown,28,Single(1),42\n",
"7.82420567838,Unknown,24,Single(1),39\n",
"9.25059651352,True,26,Single(1),40\n",
"8.62448368944,Unknown,28,Single(1),39\n",
"5.2580249487,False,18,Single(1),38\n",
"7.87491199864,True,25,Single(1),37\n",
"5.81138522632,Unknown,41,Single(1),36\n",
"6.93794738514,True,24,Single(1),40\n",
"\n",
"==> train.csv <==\n",
"7.81318256528,True,18,Single(1),43\n",
"7.31273323054,False,35,Single(1),34\n",
"6.75055446244,Unknown,37,Single(1),39\n",
"7.43839671988,True,32,Single(1),39\n",
"6.9666074791999995,True,20,Single(1),38\n",
"7.25100379718,True,32,Single(1),39\n",
"8.811876612139999,True,30,Single(1),39\n",
"7.24879917456,True,26,Single(1),40\n",
"7.62578964258,Unknown,22,Single(1),40\n",
"6.4992274837599995,Unknown,22,Single(1),38\n"
]
}
],
"source": [
"%%bash\n",
"tail *.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab Summary: \n",
"In this lab, we set up the environment, sampled the natality dataset to create train, eval, test splits, and preprocessed the data in a Pandas dataframe."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
]
},
{
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
elliekinz/Disease-ontology | wiki_pubmed_fuzzy/.ipynb_checkpoints/Wiki-PubMed-Fuzzy-checkpoint.ipynb | 1 | 15501 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('./../')\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from ontology import get_ontology\n",
"\n",
"ontology = get_ontology('../data/doid.obo')\n",
"name2doid = {term.name: term.id for term in ontology.get_terms()}\n",
"doid2name = {term.id: term.name for term in ontology.get_terms()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import re"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wiki links from obo descriptions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"example:'http://en.wikipedia.org/wiki/Abetalipoproteinemia'\n"
]
}
],
"source": [
"import wiki\n",
"lst = wiki.get_links_from_ontology(ontology)\n",
"print r'example:{:}'.format(repr(lst[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### urllib2 to read page in html"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'<!DOCTYPE html>\\n<html class=\"client-nojs\" lang=\"en\" dir=\"ltr\">\\n<head>\\n<meta charset=\"UTF-8\"/>\\n<title>Ameloblastoma - Wikipedia</title>\\n<script>document.documentElement.className = document.documentElement.className.replace( /(^|\\\\s)client-nojs(\\\\s|$)/, \"$1client-js$2\" );</script>\\n<script>(window.RLQ=window.RLQ||[]).push(function(){mw.config.set({\"wgCanonicalNamespace\":\"\",\"wgCanonicalSpecialPageName\":false,\"wgNamespaceNumber\":0,\"wgPageName\":\"Ameloblastoma\",\"wgTitle\":\"Ameloblastoma\",\"wgCurRevisionId\":766170591,\"wgRevisionId\":766170591,\"wgArticleId\":2020081,\"wgIsArticle\":true,\"wgIsRedirect\":false,\"wgAction\":\"view\",\"wgUserName\":null,\"wgUserGroups\":[\"*\"],\"wgCategories\":[\"All articles with dead external links\",\"Articles with dead external links from February 2017\",\"Articles with contributors link\",\"Articles needing additional references from March 2009\",\"All articles needing additional references\",\"Commons category with local link same as on Wikidata\",\"Odontogenic tumors\"],\"wgBreakFrames\":fals'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page = wiki.get_html(lst[101])\n",
"page[:1000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fuzzy logic"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import fuzzywuzzy.process as fuzzy_process\n",
"from fuzzywuzzy import fuzz"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('arrhythmogenic right ventricular cardiomyopathy', 67)\n"
]
}
],
"source": [
"string = \"ventricular arrhythmia\"\n",
"names = np.sort(name2doid.keys())\n",
"print fuzzy_process.extractOne(string, names, scorer=fuzz.token_set_ratio)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('hairy cell leukemia', 100)\n"
]
}
],
"source": [
"string = \"Complete remission of hairy cell leukemia variant (HCL-v) complicated by red cell aplasia post treatment with rituximab.\"\n",
"print fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wikipedia search engine: headers"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'Cardiac arrhythmia',\n",
" u'Re-entry ventricular arrhythmia',\n",
" u'Ventricular fibrillation']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"ventricular arrhythmia\"\n",
"\n",
"top = wiki.get_top_headers(query)\n",
"top"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('cardiac arrest', 75)\n",
"('arrhythmogenic right ventricular cardiomyopathy', 67)\n",
"('atrial fibrillation', 79)\n"
]
}
],
"source": [
"for header in top:\n",
" results = fuzzy_process.extractOne(header, names, scorer=fuzz.token_set_ratio)\n",
" print results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'The term cell growth is used in the contexts of biological cell development and cell division (reproduction). When used in the context of cell division, it refers to growth of cell populations, where a cell, known as the \"mother cell\", grows and divides to produce two \"daughter cells\" (M phase). When used in the context of cell development, the term refers to increase in cytoplasmic and organelle volume (G1 phase), as well as increase in genetic material (G2 phase) following the replication during S phase.'"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page = wikipedia.WikipediaPage(title='Cell_proliferation')\n",
"page.summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[name for name in names if len(re.split(' ', name)) > 3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pub-med"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1) Complete remission of hairy cell leukemia variant (HCL-v) complicated by red cell aplasia post treatment with rituximab.\n",
"('hairy cell leukemia', 100)\n",
"\n"
]
}
],
"source": [
"import pubmed\n",
"\n",
"query = 'hcl-v'\n",
"titles = pubmed.get(query)\n",
"titles_len = [len(title) for title in titles] \n",
"for i, string in enumerate(titles):\n",
" print(\"%d) %s\" % (i+1, string))\n",
" print fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)\n",
" print "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"def find_synonym(s_ref, s):\n",
" last = s_ref.find('(' + s + ')')\n",
" if last == -1:\n",
" return None\n",
" \n",
" n_upper = len(''.join([c for c in s if c.isupper()]))\n",
" first = [(i,c) for i, c in enumerate(s_ref[:last]) if c.isupper()][-n_upper][0]\n",
" return s_ref[first:last-1]\n",
"\n",
"print find_synonym('Wolff-Parkinson-White syndrome (WPW) and athletes: Darwin at play?',\n",
" 'WPW')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### synonyms"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wolff parkinson white \n",
"hairy cell leukemia variant \n"
]
}
],
"source": [
"import utils\n",
"\n",
"print utils.find_synonym('Wolff-Parkinson-White syndrome (WPW) and athletes: Darwin at play?', 'WPW')\n",
"print utils.find_synonym('Complete remission of hairy cell leukemia variant (HCL-v)...', 'hcl-v')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assymetric distance"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"s_ref = 'artery disease'\n",
"s = 'nonartery'\n",
"print utils.assym_dist(s, s_ref)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Length statistics"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean term name length: 27.5502935797\n",
"Mean article title length: 120.0\n"
]
}
],
"source": [
"print 'Mean term name length:', np.mean([len(term.name) for term in ontology.get_terms()])\n",
"print 'Mean article title length:', np.mean(titles_len)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unique words"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['(+)ssrna',\n",
" '(1p)',\n",
" '(atp',\n",
" '(perianal)',\n",
" ')ssrna',\n",
" '1.4mb',\n",
" '10q23',\n",
" '13q14',\n",
" '14q11',\n",
" '15q11.2']"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words = [re.split(' |-', term.name) for term in ontology.get_terms()]\n",
"words = np.unique([l for sublist in words for l in sublist if len(l) > 0])\n",
"words = [w for w in words if len(w) >= 4]\n",
"words[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Threading\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from threading import Thread\n",
"from time import sleep\n",
"\n",
"from ontology import get_ontology\n",
"\n",
"query_results = None\n",
"def fn_get_q(query):\n",
" global query_results\n",
" query_results = fuzzy_process.extractOne(query, names, scorer=fuzz.ratio)\n",
" return True\n",
"\n",
"wiki_results = None\n",
"def fn_get_wiki(query):\n",
" global wiki_results\n",
" header = wiki.get_top_headers(query, 1)[0]\n",
" wiki_results = fuzzy_process.extractOne(header, names, scorer=fuzz.ratio)\n",
" #sleep(0.1)\n",
" return True\n",
"\n",
"pubmed_results = None\n",
"def fn_get_pubmed(query):\n",
" global pubmed_results\n",
" string = pubmed.get(query, topK=1)\n",
"\n",
" if string is not None:\n",
" string = string[0]\n",
" print string\n",
" pubmed_results = fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)\n",
" return True\n",
" else:\n",
" return False\n",
"\n",
"'''main'''\n",
"## from bot\n",
"query = 'valve disease'\n",
"\n",
"def find_answer(query):\n",
" query = query.lower()\n",
" \n",
" # load ontology\n",
" ontology = get_ontology('../data/doid.obo')\n",
" name2doid = {term.name: term.id for term in ontology.get_terms()}\n",
" doid2name = {term.id: term.name for term in ontology.get_terms()}\n",
" \n",
" ## exact match\n",
" if query in name2doid.keys():\n",
" doid = name2doid[query]\n",
" else:\n",
" # exact match -- no\n",
" th_get_q = Thread(target = fn_get_q, args = (query,))\n",
" th_get_wiki = Thread(target = fn_get_wiki, args = (query,))\n",
" th_get_pubmed = Thread(target = fn_get_pubmed, args = (query,))\n",
"\n",
" th_get_q.start()\n",
" th_get_wiki.start()\n",
" th_get_pubmed.start()\n",
"\n",
"\n",
" ## search engine query --> vertices, p=100(NLP??); synonyms\n",
"\n",
" ## new thread for synonyms???\n",
"\n",
" ## synonyms NLP\n",
"\n",
" ## new thread for NLP\n",
"\n",
" ## tree search on vertices (returned + synonyms)\n",
"\n",
" ## sleep ?\n",
"\n",
" th_get_q.join()\n",
" print query_results\n",
"\n",
" th_get_wiki.join()\n",
" print wiki_results\n",
"\n",
" th_get_pubmed.join()\n",
" print pubmed_results\n",
"\n",
" ## final answer\n",
" ## draw graph\n",
"\n",
" doid = None\n",
" \n",
" graph = None\n",
" return doid, graph\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
jamesfolberth/NGC_STEM_camp_AWS | notebook_solutions/ML_morning_JTN/02_Logistic_Regression_and_Text_Models.ipynb | 2 | 60250 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 3: Logistic Regression and Text Models\n",
"***\n",
"\n",
"<img src=\"figs/logregwordcloud.png\",width=1000,height=50>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1: Logistic Regression for 2D Continuous Features \n",
"***\n",
"\n",
"In the video lecture you saw some examples of using logistic regression to do binary classification on text data (SPAM vs HAM) and on 1D continuous data. In this problem we'll look at logistic regression for 2D continuous data. The data we'll use are <a href=\"https://www.math.umd.edu/~petersd/666/html/iris_with_labels.jpg\">sepal</a> measurements from the ubiquitous *iris* dataset. \n",
"\n",
"\n",
"<p>\n",
"<img style=\"float:left; width:450px\" src=\"https://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg\">\n",
"</p>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two features of our model will be the **sepal length** and **sepal width**. Execute the following cell to see a plot of the data. The blue points correspond to the sepal measurements of the Iris Setosa (left) and the red points correspond to the sepal measurements of the Iris Versicolour (right). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"\n",
"from sklearn import datasets\n",
"\n",
"iris = datasets.load_iris()\n",
"X_train = iris.data[iris.target != 2, :2] # first two features and\n",
"y_train = iris.target[iris.target != 2] # first two labels only \n",
"\n",
"fig = plt.figure(figsize=(8,8))\n",
"mycolors = {\"blue\": \"steelblue\", \"red\": \"#a76c6e\", \"green\": \"#6a9373\"}\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], s=100, alpha=0.9, c=[mycolors[\"red\"] if yi==1 else mycolors[\"blue\"] for yi in y_train])\n",
"plt.xlabel('sepal length', fontsize=16)\n",
"plt.ylabel('sepal width', fontsize=16);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll train a logistic regression model of the form \n",
"\n",
"$$\n",
"p(y = 1 ~|~ {\\bf x}; {\\bf w}) = \\frac{1}{1 + \\textrm{exp}[-(w_0 + w_1x_1 + w_2x_2)]}\n",
"$$\n",
"\n",
"using **sklearn**'s logistic regression classifier as follows "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression # import from sklearn \n",
"logreg = LogisticRegression() # initialize classifier \n",
"logreg.fit(X_train, y_train); # train on training data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Determine the parameters ${\\bf w}$ fit by the model. It might be helpful to consult the documentation for the classifier on the <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">sklearn website</a>. **Hint**: The classifier stores the coefficients and bias term separately. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: In general, what does the Logistic Regression decision boundary look like for data with two features? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Modify the code below to plot the decision boundary along with the data. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"\n",
"fig = plt.figure(figsize=(8,8))\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], s=100, c=[mycolors[\"red\"] if yi==1 else mycolors[\"blue\"] for yi in y_train])\n",
"plt.xlabel('Sepal length')\n",
"plt.ylabel('Sepal width')\n",
"x_min, x_max = np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1\n",
"y_min, y_max = np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1\n",
"plt.xlim(x_min, x_max)\n",
"plt.ylim(y_min, y_max)\n",
"\n",
"x1 = np.linspace(x_min, x_max, 100)\n",
"w0 = logreg.intercept_\n",
"w1 = logreg.coef_[0][0]\n",
"w2 = logreg.coef_[0][1]\n",
"x2 = (-w0 - w1*x1)/w2#TODO \n",
"plt.plot(x1, x2, color=\"gray\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2: The Bag-of-Words Text Model \n",
"***\n",
"\n",
"The remainder of today's exercise will consider the problem of predicting the semantics of text. In particular, later we'll look at predicting whether movie reviews are positive or negative just based on their text. \n",
"\n",
"Before we can utilize text as features in a learning model, we need a concise mathematical way to represent things like words, phrases, sentences, etc. The most common text models are based on the so-called <a href=\"https://en.wikipedia.org/wiki/Vector_space_model\">Vector Space Model</a> (VSM) where individual words in a document are associated with entries of a vector: \n",
"\n",
"$$\n",
"\\textrm{\"The sky is blue\"} \\quad \\Rightarrow \\quad \n",
"\\left[\n",
"\\begin{array}{c}\n",
"0 \\\\\n",
"1 \\\\ \n",
"0 \\\\\n",
"0 \\\\\n",
"1\n",
"\\end{array}\n",
"\\right]\n",
"$$\n",
"\n",
"The first step in creating a VSM is to define a vocabulary, $V$, of words that you will include in your model. This vocabulary can be determined by looking at all (or most) of the words in the training set, or even by including a fixed vocabulary based on the english language. A vector representation of a document like a movie review is then a vector with length $|V|$ where each entry in the vector maps uniquely to a word in the vocabulary. A vector encoding of a document would then be a vector that is nonzero in positions corresponding to words present in the document and zero everywhere else. How you fill in the nonzero entries depends on the model you're using. Two simple conventions are the **Bag-of-Words** model and the **binary** model. \n",
"\n",
"In the binary model we simply set an entry of the vector to $1$ if the associate word appears at least once in the document. In the more common Bag-of-Words model we set an entry of the vector equal to the frequency with which the word appears in the document. Let's see if we can come up with a simple implementation of the Bag-of-Words model in Python, and then later we'll see how sklearn can do the heavy lifting for us. \n",
"\n",
"Consider a training set containing three documents, specified as follows \n",
"\n",
"$\\texttt{Training Set}:$\n",
"\n",
"$\\texttt{d1}: \\texttt{new york times}$\n",
"\n",
"$\\texttt{d2}: \\texttt{new york post}$\n",
"\n",
"$\\texttt{d3}: \\texttt{los angeles times}$\n",
"\n",
"\n",
"First we'll define the vocabulary based on the words in the test set. It is $V = \\{ \\texttt{angeles}, \\texttt{los}, \\texttt{new}, \\texttt{post}, \\texttt{times}, \\texttt{york}\\}$. \n",
"\n",
"\n",
"We need to define an association between the particular words in the vocabulary and the specific entries in our vectors. Let's define this association in the order that we've listed them above. We can store this mapping as a Python dictionary as follows: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"V = {\"angeles\": 0, \"los\": 1, \"new\": 2, \"post\": 3, \"times\": 4, \"york\": 5}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also store the documents in a list as follows: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"D = [\"the new york times\", \"the new york post\", \"the los angeles times\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"To be consistent with sklearn conventions, we'll encode the documents as *row-vectors* stored in a matrix. In this case, each row of the matrix corresponds to a document, and each column corresponds to a term in the vocabulary. For our example this gives us a matrix $M$ of shape $3 \\times 6$. The $(d,t)$-entry in $M$ is then the number of times the term $t$ appears in document $d$\n",
"\n",
"**Q**: Your first task is to write some simple Python code to construct the *term-frequency* matrix $M$ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"M = np.zeros((len(D),len(V)))\n",
"\n",
"for ii, doc in enumerate(D): \n",
" for term in doc.split(): \n",
" if(term in V): #only print if the term is in our dictionary\n",
" M[ii,V[term]] += 1 #TODO\n",
" \n",
"print(M)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully your code returns the matrix \n",
"\n",
"$$M = \n",
"\\left[\n",
"\\begin{array}{ccccccc}\n",
"0 & 0 & 1 & 0 & 1 & 1 \\\\\n",
"0 & 0 & 1 & 1 & 0 & 1 \\\\\n",
"1 & 1 & 0 & 0 & 1 & 0 \\\\\n",
"\\end{array}\n",
"\\right]$$. \n",
"\n",
"Note that the entry in the (2,0) position is $1$ because the first word (angeles) appears once in the third document. \n",
"\n",
"OK, let's see how we can construct the same term-frequency matrix in sklearn. We will use something called the <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\">CountVectorizer</a> to accomplish this. Let's see some code and then we'll explain how it functions. \n",
"\n",
"To avoid common words, such as \"the\", in our analysis, we will remove any word from a list of common english words in our analysis. We can do so by typing \n",
"\n",
" stop_words = 'english'\n",
"\n",
"in the CountVectorizer call."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics.pairwise import euclidean_distances\n",
"\n",
"from sklearn.feature_extraction.text import CountVectorizer # import CountVectorizer \n",
"vectorizer = CountVectorizer(stop_words = 'english') # initialize the vectorizer\n",
"X = vectorizer.fit_transform(D,) # fit to training data and transform to matrix "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The $\\texttt{fit_transform}$ method actually does two things. It fits the model to the training data by building a vocabulary. It then transforms the text in $D$ into matrix form. \n",
"\n",
"If we wish to see the vocabulary you can do it like so "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(vectorizer.vocabulary_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that this is the same vocabulary and indexing that we definfed ourselves (just in a different order). Hopefully that means we'll get the same term-frequency matrix. We can print $X$ and check "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(X.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yep, they're the same! Notice that we had to convert $X$ to a dense matrix for printing. This is because CountVectorizer actually returns a sparse matrix. This is a very good thing since most vectors in a text model will be **extremely** sparse, since most documents will only contain a handful of words from the vocabulary. \n",
"\n",
"OK, let's see how we can use the CountVectorizer to transform the test documents into their own term-frequency matrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get a sense of how different the vectors are\n",
"\n",
"for f in X:\n",
" print(euclidean_distances(X[0],f))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, now suppose that we have a query document not included in the training set that we want to vectorize. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d4 = [\"new york new tribune\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've already fit the CountVectorizer to the training set, so all we need to do is transform the test set documents into a term-frequency vector using the same conventions. Since we've already fit the model, we do the transformation with the $\\texttt{transform}$ method: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x4 = vectorizer.transform(d4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's print it and see what it looks like "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x4.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the query document included the word $\\texttt{new}$ twice, which corresponds to the entry in the $(0,2)$-position. \n",
"\n",
"**Q**: What's missing from $x4$ that we might expect to see from the query document? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"\n",
"### Problem 3: Term Frequency - Inverse Document Frequency \n",
"***\n",
"\n",
"The Bag-of-Words model for text classification is very popular, but let's see if we can do better. Currently we're weighting every word in the corpus by it's frequency. It turns out that in text classification there are often features that are not particularly useful predictors for the document class, either because they are too common or too uncommon. **Stop-words** are extremely common, low-information words like \"a\", \"the\", \"as\", etc. Removing these from documents is typically the first thing done in peparing data for document classification. \n",
"\n",
"**Q**: Can you think of a situation where it might be useful to keep stop words in the corpus? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other words that tend to be uninformative predictors are words that appear very very rarely. In particular, if they do not appear frequently enough in the training data then it is difficult for a classification algorithm to weight them heavily in the classification process. \n",
"\n",
"In general, the words that tend to be useful predictors are the words that appear frequently, but not too frequently. Consider the following frequency graph for a corpus. \n",
"\n",
"<img src=\"figs/feat_freq.png\",width=400,height=50>\n",
"\n",
"The features in column A appear too frequently to be very useful, and the features in column C appear too rarely. One first-pass method of feature selection in text classification would be to discard the words from columns A and C, and build a classifier with only features from column B.\n",
"\n",
"Another common model for identifying the useful terms in a document is the Term Frequency - Inverse Document Frequency (tf-idf) model. Here we won't throw away any terms, but we'll replace their Bag-of-Words frequency counts with tf-idf scores which we describe below. \n",
"\n",
"The tf-idf score is the product of two statistics, *term frequency* and *inverse document frequency*\n",
"\n",
"\n",
"$$\\texttt{tfidf(d,t)} = \\texttt{tf(d,t)} \\times \\texttt{idf(t)}$$\n",
"\n",
"The term frequency $\\texttt{tf(d,t)}$ is a measure of the frequency with which term $t$ appears in document $d$. The inverse document frequency $\\texttt{idf(t)}$ is a measure of how much information the word provides, that is, whether the term is common or rare across all documents. By multiplying the two quantities together, we obtain a representation of term $t$ in document $d$ that weighs how common the term is in the document with how common the word is in the entire corpus. You can imagine that the words that get the highest associated values are terms that appear many times in a small number of documents. \n",
"\n",
"\n",
"There are many ways to compute the composite terms $\\texttt{tf}$ and $\\texttt{idf}$. For simplicity, we'll define $\\texttt{tf(d,t)}$ to be the number of times term $t$ appears in document $d$ (i.e., Bag-of-Words). We will define the inverse document frequency as follows: \n",
"\n",
"$$\n",
"\\texttt{idf(t)} = \\ln ~ \\frac{\\textrm{total # documents}}{\\textrm{1 + # documents with term }t}\n",
" = \\ln ~ \\frac{|D|}{|d: ~ t \\in d |}\n",
"$$\n",
"\n",
"Note that we could have a potential problem if a term comes up that is not in any of the training documents, resulting in a divide by zero. This might happen if you use a canned vocabulary instead of constructing one from the training documents. To guard against this, many implementations will use add-one smoothing in the denominator (this is what sklearn does). \n",
"\n",
"$$\n",
"\\texttt{idf(t)} = \\ln ~ \\frac{\\textrm{total # documents}}{\\textrm{1 + # documents with term }t}\n",
" = \\ln ~ \\frac{|D|}{1 + |d: ~ t \\in d |}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Compute $\\texttt{idf(t)}$ (without smoothing) for each of the terms in the training documents from the previous problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Compute the td-ifd matrix for the training set "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idf = np.array([np.log(3), np.log(3), np.log(3./2), np.log(3), np.log(3./2), np.log(3./2)])\n",
"Xtfidf = np.dot(X.todense(), np.diag(idf))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully you got something like the following: \n",
"\n",
"$$\n",
"X_{tfidf} = \n",
"\\left[\n",
"\\begin{array}{ccccccccc}\n",
"0. & 0. & 0.40546511 & 0. & 0.40546511 & 0.40546511 \\\\\n",
"0. & 0. & 0.40546511 & 1.09861229 & 0. & 0.40546511 \\\\\n",
"1.09861229 & 1.09861229 & 0. & 0. & 0.40546511 & 0. \n",
"\\end{array}\n",
"\\right]\n",
"$$\n",
"\n",
"The final step in any VSM method is the normalization of the vectors. This is done so that very long documents to not completely overpower the small and medium length documents. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"row_norms = np.array([np.linalg.norm(row) for row in Xtfidf])\n",
"X_tfidf_n = np.dot(np.diag(1./row_norms), Xtfidf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(X_tfidf_n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what we get when we use sklearn. Sklearn has a vectorizer called TfidfVectorizer which is similar to CountVectorizer, but it computes tf-idf scores. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"tfidf = TfidfVectorizer()\n",
"Y = tfidf.fit_transform(D)\n",
"print(Y.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that these are not quite the same, because sklearn's implementation of tf-idf uses the add-one smoothing in the denominator for idf. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now let's see if we can use TFIDF analysis on real text documents!\n",
"\n",
"Run the following code to use this analysis on his inauguration speech from 2009. It will output what TFIDF thinks are the most important words from each paragraph\n",
"\n",
"**Q**: Is the analysis able to pick out the most important words correctly? Why does it sometimes pick the wrong words?\n",
"\n",
"**Q**: You can do the same analysis for his 2012 State of the Union Speech by replacing the first line of code with \"obama_SOU_2012.txt\". How does the analysis do here?\n",
"\n",
"**Q**: Find some other piece of text on your own and do the same analysis here by saving it in .txt file and entering the name of this file in the first line of code. You can find a big source of speeches [http://www.americanrhetoric.com/newtop100speeches.htm](here)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#load in text\n",
"ObamaText = open(\"data/obama_SOU_2012.txt\").readlines()\n",
"\n",
"#create TFIDF matrix\n",
"X = vectorizer.fit_transform(ObamaText)\n",
"D_tot = X.shape[0]\n",
"Xtfidf = np.zeros(X.shape)\n",
"\n",
"for i,col in enumerate(X.T): #loop over rows of X (i.e. paragraphs of text)\n",
" \n",
" #number of lines the word appears in (no need for smoothing here)\n",
" freq = np.count_nonzero(col.todense()) \n",
" #compute theidf\n",
" idf = math.log(D_tot/(freq))\n",
" #calculate the tf-idf\n",
" Xtfidf[:,i:i+1] = X[:,i].todense()*idf\n",
"\n",
"#normalize Xtfidf matrix\n",
"row_norms = np.array([np.linalg.norm(row) for row in Xtfidf])\n",
"Xtfidf_norm = np.dot(np.diag(1./row_norms),Xtfidf)\n",
"\n",
"#create a list from the dictionary\n",
"V_words, V_nums = vectorizer.vocabulary_.keys(), vectorizer.vocabulary_.values()\n",
"V_reverse = zip(V_nums,V_words)\n",
"V_reverse_dict = dict(V_reverse)\n",
"\n",
"#loop through the paragraphs of the text and print most important word\n",
"for i,row in enumerate(Xtfidf_norm):\n",
" row_str = \" \"\n",
" row_str = row_str + V_reverse_dict[np.argmax(row)]\n",
" #top_words_ind = np.argsort(row)[-5:]\n",
" #for ii in top_words_ind:\n",
" # row_str = row_str + V_reverse_dict[ii] + \" \"\n",
" \n",
" print(\"The top word in paragraph \" + str(i) + \" is \" + row_str)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"\n",
"### Problem 4: Classifying Semantics in Movie Reviews\n",
"***\n",
"> The data for this problem was taken from the <a href=\"https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words\">Bag of Words Meets Bag of Popcorn</a> Kaggle competition\n",
"\n",
"In this problem you will use the text from movie reviews to predict whether the reviewer felt positively or negatively about the movie using Bag-of-Words and tf-idf. I've partially cleaned the data and stored it in files called $\\texttt{labeledTrainData.tsv}$ and $\\texttt{labeledTestData.tsv}$ in the data directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import csv \n",
"\n",
"def read_and_clean_data(fname, remove_stops=True):\n",
" \n",
" with open('data/stopwords.txt', 'rt') as f:\n",
" stops = [line.rstrip('\\n') for line in f]\n",
" \n",
" with open(fname,'rt') as tsvin:\n",
" reader = csv.reader(tsvin, delimiter='\\t')\n",
" labels = []; text = [] \n",
" for ii, row in enumerate(reader):\n",
" labels.append(int(row[0]))\n",
" words = row[1].lower().split()\n",
" words = [w for w in words if not w in stops] if remove_stops else words \n",
" text.append(\" \".join(words))\n",
" \n",
" return text, labels\n",
"\n",
"text_train, labels_train = read_and_clean_data('data/labeledTrainData.tsv', remove_stops=True)\n",
"text_test, labels_test = read_and_clean_data('data/labeledTestData.tsv', remove_stops=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The current parameters are set to not remove stop words from the text so that it's a bit easier to explore. \n",
"\n",
" Look at a few of the reviews stored in $\\texttt{text_train}$ as well as their associated labels in $\\texttt{labels_train}$. Can you figure out which label refers to a positive review and which refers to a negative review? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labels_train[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first review is labeled $1$ and has the following text: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fourth review is labeled $0$ and has the following text: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully it's obvious that label 1 corresponds to positive reviews and label 0 to negative reviews! \n",
"\n",
"OK, the first thing we'll do is train a logistic regression classifier using the Bag-of-Words model, and see what kind of accuracy we can get. To get started, we need to vectorize the text into mathematical features that we can use. We'll use CountVectorizer to do the job. (Before starting, I'm going to reload the data and remove the stop words this time)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train, labels_train = read_and_clean_data('data/labeledTrainData.tsv', remove_stops=True)\n",
"text_test, labels_test = read_and_clean_data('data/labeledTestData.tsv', remove_stops=True)\n",
"\n",
"cvec = CountVectorizer()\n",
"X_bw_train = cvec.fit_transform(text_train)\n",
"y_train = np.array(labels_train)\n",
"X_bw_test = cvec.transform(text_test)\n",
"y_test = np.array(labels_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: How many different words are in the vocabulary? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, now we'll train a logistic regression classifier on the training set, and test the accuracy on the test set. To do this we'll need to load some kind of accuracy metric from sklearn. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"bwLR = LogisticRegression()\n",
"bwLR.fit(X_bw_train, y_train)\n",
"pred_bwLR = bwLR.predict(X_bw_test)\n",
"\n",
"print(\"Logistic Regression accuracy with Bag-of-Words: \" + str(accuracy_score(y_test, pred_bwLR)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, so we got an accuracy of around 81% using Bag-of-Words. Now lets do the same tests but this time with tf-idf features. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tvec = TfidfVectorizer()\n",
"X_tf_train = tvec.fit_transform(text_train)\n",
"X_tf_test = tvec.transform(text_test)\n",
"\n",
"tfLR = LogisticRegression()\n",
"tfLR.fit(X_tf_train, y_train)\n",
"pred_tfLR = tfLR.predict(X_tf_test)\n",
"\n",
"print(\"Logistic Regression accuracy with tf-idf: \" + str(accuracy_score(y_test, pred_tfLR)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**WOOHOO**! With tf-idf features we got around 85% accuracy, which is a 4% improvement. (If you're scoffing at this, wait until you get some more experience working with real-world data. 4% improvement is pretty awesome). \n",
"\n",
"**Q**: Which words are the strongest predictors for a positive review and which words are the strongest predictors for negative reviews? I'm not going to give you the answer to this one because it's the same question we'll ask on the next homework assignment. But if you figure this out you'll have a great head start! \n",
"\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"## Notebook Solutions\n",
"***\n",
"<br><br><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1: Logistic Regression for 2D Continuous Features \n",
"***\n",
"\n",
"In the video lecture you saw some examples of using logistic regression to do binary classification on text data (SPAM vs HAM) and on 1D continuous data. In this problem we'll look at logistic regression for 2D continuous data. The data we'll use are <a href=\"https://www.math.umd.edu/~petersd/666/html/iris_with_labels.jpg\">sepal</a> measurements from the ubiquitous *iris* dataset. \n",
"\n",
"\n",
"<!---\n",
"<img style=\"float:left; width:450px\" src=\"https://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg\",width=300,height=50>\n",
"-->\n",
"\n",
"<img style=\"float:left; width:450px\" src=\"http://www.twofrog.com/images/iris38a.jpg\",width=300,height=50>\n",
"\n",
"<!---\n",
"<img style=\"float:right; width:490px\" src=\"https://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg\",width=300,height=50>\n",
"-->\n",
"\n",
"<img style=\"float:right; width:490px\" src=\"http://blazingstargardens.com/wp-content/uploads/2016/02/Iris-versicolor-Blue-Flag-Iris1.jpg\",width=300,height=62>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two features of our model will be the **sepal length** and **sepal width**. Execute the following cell to see a plot of the data. The blue points correspond to the sepal measurements of the Iris Setosa (left) and the red points correspond to the sepal measurements of the Iris Versicolour (right). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"\n",
"from sklearn import datasets\n",
"\n",
"iris = datasets.load_iris()\n",
"X_train = iris.data[iris.target != 2, :2] # first two features and\n",
"y_train = iris.target[iris.target != 2] # first two labels only \n",
"\n",
"fig = plt.figure(figsize=(8,8))\n",
"mycolors = {\"blue\": \"steelblue\", \"red\": \"#a76c6e\", \"green\": \"#6a9373\"}\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], s=100, alpha=0.9, c=[mycolors[\"red\"] if yi==1 else mycolors[\"blue\"] for yi in y_train])\n",
"plt.xlabel('sepal length', fontsize=16)\n",
"plt.ylabel('sepal width', fontsize=16);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll train a logistic regression model of the form \n",
"\n",
"$$\n",
"p(y = 1 ~|~ {\\bf x}; {\\bf w}) = \\frac{1}{1 + \\textrm{exp}[-(w_0 + w_1x_1 + w_2x_2)]}\n",
"$$\n",
"\n",
"using **sklearn**'s logistic regression classifier as follows "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression # import from sklearn \n",
"logreg = LogisticRegression() # initialize classifier \n",
"logreg.fit(X_train, y_train); # train on training data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Determine the parameters ${\\bf w}$ fit by the model. It might be helpful to consult the documentation for the classifier on the <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">sklearn website</a>. **Hint**: The classifier stores the coefficients and bias term separately. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: The bias term is stored in logreg.intercept\\_ . The remaining coefficients are stored in logreg.coef\\_ . For this problem we have \n",
"\n",
"$$\n",
"w_0 =-0.599, \\quad w_1 = 2.217, \\quad \\textrm{and} \\quad w_2 = -3.692\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: In general, what does the Logistic Regression decision boundary look like for data with two features? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: The decision boundary for Logistic Regresion for data with two features is a line. To see this, remember that the decision boundary is made up of $(x_1, x_2)$ points such that $\\textrm{sigm}({\\bf w}^T{\\bf x}) = 0.5$. We then have \n",
"\n",
"$$\n",
"\\frac{1}{1 + \\textrm{exp}[-(w_0 + w_1x_1 + w_2x_2)]} = \\frac{1}{2} ~~\\Rightarrow ~~ w_0 + w_1x_1 + w_2x_2 = 0 ~~\\Rightarrow~~ x_2 = -\\frac{w_1}{w_2}x_1 - \\frac{w_0}{w_2}\n",
"$$\n",
"\n",
"So the decision boundary is a line with slope $-w_1/w_2$ and intercept $-w_0/w_2$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Modify the code below to plot the decision boundary along with the data. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"fig = plt.figure(figsize=(8,8))\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], s=100, c=[mycolors[\"red\"] if yi==1 else mycolors[\"blue\"] for yi in y_train])\n",
"plt.xlabel('Sepal length')\n",
"plt.ylabel('Sepal width')\n",
"x_min, x_max = np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1\n",
"y_min, y_max = np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1\n",
"plt.xlim(x_min, x_max)\n",
"plt.ylim(y_min, y_max)\n",
"\n",
"x1 = np.linspace(x_min, x_max, 100)\n",
"w0 = logreg.intercept_\n",
"w1 = logreg.coef_[0][0]\n",
"w2 = logreg.coef_[0][1]\n",
"x2 = -(w0/w2) - (w1/w2)*x1 #TODO \n",
"plt.plot(x1, x2, color=\"gray\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2: The Bag-of-Words Text Model \n",
"***\n",
"\n",
"The remainder of today's exercise will consider the problem of predicting the semantics of text. In particular, later we'll look at predicting whether movie reviews are positive or negative just based on their text. \n",
"\n",
"Before we can utilize text as features in a learning model, we need a concise mathematical way to represent things like words, phrases, sentences, etc. The most common text models are based on the so-called <a href=\"https://en.wikipedia.org/wiki/Vector_space_model\">Vector Space Model</a> (VSM) where individual words in a document are associated with entries of a vector: \n",
"\n",
"$$\n",
"\\textrm{\"The sky is blue\"} \\quad \\Rightarrow \\quad \n",
"\\left[\n",
"\\begin{array}{c}\n",
"0 \\\\\n",
"1 \\\\ \n",
"0 \\\\\n",
"0 \\\\\n",
"1\n",
"\\end{array}\n",
"\\right]\n",
"$$\n",
"\n",
"The first step in creating a VSM is to define a vocabulary, $V$, of words that you will include in your model. This vocabulary can be determined by looking at all (or most) of the words in the training set, or even by including a fixed vocabulary based on the english language. A vector representation of a document like a movie review is then a vector with length $|V|$ where each entry in the vector maps uniquely to a word in the vocabulary. A vector encoding of a document would then be a vector that is nonzero in positions corresponding to words present in the document and zero everywhere else. How you fill in the nonzero entries depends on the model you're using. Two simple conventions are the **Bag-of-Words** model and the **binary** model. \n",
"\n",
"In the binary model we simply set an entry of the vector to $1$ if the associate word appears at least once in the document. In the more common Bag-of-Words model we set an entry of the vector equal to the frequency with which the word appears in the document. Let's see if we can come up with a simple implementation of the Bag-of-Words model in Python, and then later we'll see how sklearn can do the heavy lifting for us. \n",
"\n",
"Consider a training set containing three documents, specified as follows \n",
"\n",
"$\\texttt{Training Set}:$\n",
"\n",
"$\\texttt{d1}: \\texttt{new york times}$\n",
"\n",
"$\\texttt{d2}: \\texttt{new york post}$\n",
"\n",
"$\\texttt{d3}: \\texttt{los angeles times}$\n",
"\n",
"\n",
"First we'll define the vocabulary based on the words in the test set. It is $V = \\{ \\texttt{angeles}, \\texttt{los}, \\texttt{new}, \\texttt{post}, \\texttt{times}, \\texttt{york}\\}$. \n",
"\n",
"\n",
"We need to define an association between the particular words in the vocabulary and the specific entries in our vectors. Let's define this association in the order that we've listed them above. We can store this mapping as a Python dictionary as follows: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"V = {\"angeles\": 0, \"los\": 1, \"new\": 2, \"post\": 3, \"times\": 4, \"york\": 5}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also store the documents in a list as follows: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"D = [\"new york times\", \"new york post\", \"los angeles times\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be consistent with sklearn conventions, we'll encode the documents as *row-vectors* stored in a matrix. In this case, each row of the matrix corresponds to a document, and each column corresponds to a term in the vocabulary. For our example this gives us a matrix $M$ of shape $3 \\times 6$. The $(d,t)$-entry in $M$ is then the number of times the term $t$ appears in document $d$\n",
"\n",
"**Q**: Your first task is to write some simple Python code to construct the *term-frequency* matrix $M$ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"M = np.zeros((len(D),len(V)))\n",
"\n",
"for ii, doc in enumerate(D): \n",
" for term in doc.split(): \n",
" M[ii, V[term]] += 1\n",
" \n",
"print(M)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully your code returns the matrix \n",
"\n",
"$$M = \n",
"\\left[\n",
"\\begin{array}{ccccccc}\n",
"0 & 0 & 1 & 0 & 1 & 1 \\\\\n",
"0 & 0 & 1 & 1 & 0 & 1 \\\\\n",
"1 & 1 & 0 & 0 & 1 & 0 \\\\\n",
"\\end{array}\n",
"\\right]$$. \n",
"\n",
"Note that the entry in the (2,0) position is $1$ because the first word (angeles) appears once in the third document. \n",
"\n",
"OK, let's see how we can construct the same term-frequency matrix in sklearn. We will use something called the <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\">CountVectorizer</a> to accomplish this. Let's see some code and then we'll explain how it functions. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer # import CountVectorizer \n",
"vectorizer = CountVectorizer() # initialize the vectorizer\n",
"X = vectorizer.fit_transform(D) # fit to training data and transform to matrix "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The $\\texttt{fit_transform}$ method actually does two things. It fits the model to the training data by building a vocabulary. It then transforms the text in $D$ into matrix form. \n",
"\n",
"If we wish to see the vocabulary you can do it like so "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(vectorizer.vocabulary_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that this is the same vocabulary and indexing that we definfed ourselves. Hopefully that means we'll get the same term-frequency matrix. We can print $X$ and check "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(X.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yep, they're the same! Notice that we had to convert $X$ to a dense matrix for printing. This is because CountVectorizer actually returns a sparse matrix. This is a very good thing since most vectors in a text model will be **extremely** sparse, since most documents will only contain a handful of words from the vocabulary. \n",
"\n",
"\n",
"OK, now suppose that we have a query document not included in the training set that we want to vectorize. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d4 = [\"new york new tribune\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've already fit the CountVectorizer to the training set, so all we need to do is transform the test set documents into a term-frequency vector using the same conventions. Since we've already fit the model, we do the transformation with the $\\texttt{transform}$ method: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x4 = vectorizer.transform(d4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's print it and see what it looks like "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x4.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the query document included the word $\\texttt{new}$ twice, which corresponds to the entry in the $(0,2)$-position. \n",
"\n",
"**Q**: What's missing from $x4$ that we might expect to see from the query document? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: The word $\\texttt{tribune}$ do not appear in vector $x4$ at all. This is because it did not occur in the training set, which means it is not present in the VSM vocabulary. This should not bother us too much. Most reasonable text data sets will have most of the important words present in the training set and thus in the vocabulary. On the other hand, the throw-away words that are present only in the test set are probably useless anyway, since the learning model is trained based on the text in the training set, and thus won't be able to do anything intelligent with words the model hasn't seen yet. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"\n",
"### Problem 3: Term Frequency - Inverse Document Frequency \n",
"***\n",
"\n",
"The Bag-of-Words model for text classification is very popular, but let's see if we can do better. Currently we're weighting every word in the corpus by it's frequency. It turns out that in text classification there are often features that are not particularly useful predictors for the document class, either because they are too common or too uncommon. **Stop-words** are extremely common, low-information words like \"a\", \"the\", \"as\", etc. Removing these from documents is typically the first thing done in peparing data for document classification. \n",
"\n",
"**Q**: Can you think of a situation where it might be useful to keep stop words in the corpus? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: If you plan to use bi-grams or tri-grams as features. Bi-grams are pairs of words that appear side-by-side in a document, e.g. \"he went\", \"went to\", \"to the\", \"the store\". "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Other words that tend to be uninformative predictors are words that appear very very rarely. In particular, if they do not appear frequently enough in the training data then it is difficult for a classification algorithm to weight them heavily in the classification process. \n",
"\n",
"In general, the words that tend to be useful predictors are the words that appear frequently, but not too frequently. Consider the following frequency graph for a corpus. \n",
"\n",
"<img src=\"figs/feat_freq.png\",width=400,height=50>\n",
"\n",
"The features in column A appear too frequently to be very useful, and the features in column C appear too rarely. One first-pass method of feature selection in text classification would be to discard the words from columns A and C, and build a classifier with only features from column B.\n",
"\n",
"Another common model for identifying the useful terms in a document is the Term Frequency - Inverse Document Frequency (tf-idf) model. Here we won't throw away any terms, but we'll replace their Bag-of-Words frequency counts with tf-idf scores which we describe below. \n",
"\n",
"The tf-idf score is the product of two statistics, *term frequency* and *inverse document frequency*\n",
"\n",
"\n",
"$$\\texttt{tfidf(d,t)} = \\texttt{tf(d,t)} \\times \\texttt{idf(t)}$$\n",
"\n",
"The term frequency $\\texttt{tf(d,t)}$ is a measure of the frequency with which term $t$ appears in document $d$. The inverse document frequency $\\texttt{idf(t)}$ is a measure of how much information the word provides, that is, whether the term is common or rare across all documents. By multiplying the two quantities together, we obtain a representation of term $t$ in document $d$ that weighs how common the term is in the document with how common the word is in the entire corpus. You can imagine that the words that get the highest associated values are terms that appear many times in a small number of documents. \n",
"\n",
"\n",
"There are many ways to compute the composite terms $\\texttt{tf}$ and $\\texttt{idf}$. For simplicity, we'll define $\\texttt{tf(d,t)}$ to be the number of times term $t$ appears in document $d$ (i.e., Bag-of-Words). We will define the inverse document frequency as follows: \n",
"\n",
"$$\n",
"\\texttt{idf(t)} = \\ln ~ \\frac{\\textrm{total # documents}}{\\textrm{# documents with term }t}\n",
" = \\ln ~ \\frac{|D|}{|d: ~ t \\in d |}\n",
"$$\n",
"\n",
"Note that we could have a potential problem if a term comes up that is not in any of the training documents, resulting in a divide by zero. This might happen if you use a canned vocabulary instead of constructing one from the training documents. To guard against this, many implementations will use add-one smoothing in the denominator (this is what sklearn does). \n",
"\n",
"$$\n",
"\\texttt{idf(t)} = \\ln ~ \\frac{\\textrm{total # documents}}{\\textrm{1 + # documents with term }t}\n",
" = \\ln ~ \\frac{|D|}{1 + |d: ~ t \\in d |}\n",
"$$\n",
"\n",
"**Q**: Compute $\\texttt{idf(t)}$ (without smoothing) for each of the terms in the training documents from the previous problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: \n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{angeles}) = \\ln ~ \\frac{3}{1} = \\ln ~ \\frac{3}{1} = 1.10\n",
"$\n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{los}) = \\ln ~ \\frac{3}{1} = \\ln ~ \\frac{3}{1} = 1.10\n",
"$\n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{new}) = \\ln ~ \\frac{3}{2} = \\ln ~ \\frac{3}{2} = 0.41\n",
"$\n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{post}) = \\ln ~ \\frac{3}{1} = \\ln ~ \\frac{3}{1} = 1.10\n",
"$\n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{times}) = \\ln ~ \\frac{3}{2} = \\ln ~ \\frac{3}{2} = 0.41\n",
"$\n",
"\n",
"$\n",
"\\texttt{idf}(\\texttt{york}) = \\ln ~ \\frac{3}{2} = \\ln ~ \\frac{3}{2} = 0.41\n",
"$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Compute the td-ifd matrix for the training set "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: There are several ways to do this. One way would be to multiply the term-frequency matrix on the right with a diagonal matrix with the idf-values on the main diagonal "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idf = np.array([np.log(3), np.log(3), np.log(3./2), np.log(3), np.log(3./2), np.log(3./2)])\n",
"Xtfidf = np.dot(X.todense(), np.diag(idf))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(Xtfidf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully you got something like the following: \n",
"\n",
"$$\n",
"X_{tfidf} = \n",
"\\left[\n",
"\\begin{array}{ccccccccc}\n",
"0. & 0. & 0.40546511 & 0. & 0.40546511 & 0.40546511 \\\\\n",
"0. & 0. & 0.40546511 & 1.09861229 & 0. & 0.40546511 \\\\\n",
"1.09861229 & 1.09861229 & 0. & 0. & 0.40546511 & 0. \n",
"\\end{array}\n",
"\\right]\n",
"$$\n",
"\n",
"The final step in any VSM method is the normalization of the vectors. This is done so that very long documents to not completely overpower the small and medium length documents. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"row_norms = np.array([np.linalg.norm(row) for row in Xtfidf])\n",
"X_tfidf_n = np.dot(np.diag(1./row_norms), Xtfidf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(X_tfidf_n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what we get when we use sklearn. Sklearn has a vectorizer called TfidfVectorizer which is similar to CountVectorizer, but it computes tf-idf scores. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"tfidf = TfidfVectorizer()\n",
"Y = tfidf.fit_transform(D)\n",
"print(Y.todense())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that these are not quite the same, becuase sklearn's implementation of tf-idf uses the add-one smoothing in the denominator for idf. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"\n",
"### Problem 4: Classifying Semantics in Movie Reviews\n",
"***\n",
"> The data for this problem was taken from the <a href=\"https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words\">Bag of Words Meets Bag of Popcorn</a> Kaggle competition\n",
"\n",
"In this problem you will use the text from movie reviews to predict whether the reviewer felt positively or negatively about the movie using Bag-of-Words and tf-idf. I've partially cleaned the data and stored it in files called $\\texttt{labeledTrainData.tsv}$ and $\\texttt{labeledTestData.tsv}$ in the data directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import csv \n",
"\n",
"def read_and_clean_data(fname, remove_stops=True):\n",
" \n",
" with open('data/stopwords.txt', 'r') as f:\n",
" stops = [line.rstrip('\\n') for line in f]\n",
" \n",
" with open(fname,'r') as tsvin:\n",
" reader = csv.reader(tsvin, delimiter='\\t')\n",
" labels = []; text = [] \n",
" for ii, row in enumerate(reader):\n",
" labels.append(int(row[0]))\n",
" words = row[1].lower().split()\n",
" words = [w for w in words if not w in stops] if remove_stops else words \n",
" text.append(\" \".join(words))\n",
" \n",
" return text, labels\n",
"\n",
"text_train, labels_train = read_and_clean_data('data/labeledTrainData.tsv', remove_stops=False)\n",
"text_test, labels_test = read_and_clean_data('data/labeledTestData.tsv', remove_stops=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The current parameters are set to not remove stop words from the text so that it's a bit easier to explore. \n",
"\n",
"**Q**: Look at a few of the reviews stored in $\\texttt{text_train}$ as well as their associated labels in $\\texttt{labels_train}$. Can you figure out which label refers to a positive review and which refers to a negative review? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labels_train[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first review is labeled $1$ and has the following text: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fourth review is labeled $0$ and has the following text: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hopefully it's obvious that label 1 corresponds to positive reviews and label 0 to negative reviews! \n",
"\n",
"OK, the first thing we'll do is train a logistic regression classifier using the Bag-of-Words model, and see what kind of accuracy we can get. To get started, we need to vectorize the text into mathematical features that we can use. We'll use CountVectorizer to do the job. (Before starting, I'm going to reload the data and remove the stop words this time)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_train, labels_train = read_and_clean_data('data/labeledTrainData.tsv', remove_stops=True)\n",
"text_test, labels_test = read_and_clean_data('data/labeledTestData.tsv', remove_stops=True)\n",
"\n",
"cvec = CountVectorizer()\n",
"X_bw_train = cvec.fit_transform(text_train)\n",
"y_train = np.array(labels_train)\n",
"X_bw_test = cvec.transform(text_test)\n",
"y_test = np.array(labels_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: How many different words are in the vocabulary? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_bw_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A**: It looks like around 17,800 distinct words "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, now we'll train a logistic regression classifier on the training set, and test the accuracy on the test set. To do this we'll need to load some kind of accuracy metric from sklearn. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"bwLR = LogisticRegression()\n",
"bwLR.fit(X_bw_train, y_train)\n",
"pred_bwLR = bwLR.predict(X_bw_test)\n",
"\n",
"print(\"Logistic Regression accuracy with Bag-of-Words: \", accuracy_score(y_test, pred_bwLR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, so we got an accuracy of around 81% using Bag-of-Words. Now lets do the same tests but this time with tf-idf features. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tvec = TfidfVectorizer()\n",
"X_tf_train = tvec.fit_transform(text_train)\n",
"X_tf_test = tvec.transform(text_test)\n",
"\n",
"tfLR = LogisticRegression()\n",
"tfLR.fit(X_tf_train, y_train)\n",
"pred_tfLR = tfLR.predict(X_tf_test)\n",
"\n",
"print(\"Logistic Regression accuracy with tf-idf: \", accuracy_score(y_test, pred_tfLR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**WOOHOO**! With tf-idf features we got around 85% accuracy, which is a 4% improvement. (If you're scoffing at this, wait until you get some more experience working with real-world data. 4% improvement is pretty awesome). \n",
"\n",
"**Q**: Which words are the strongest predictors for a positive review and which words are the strongest predictors for negative reviews? I'm not going to give you the answer to this one because it's the same question we'll ask on the next homework assignment. But if you figure this out you'll have a great head start! \n",
"\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>\n",
"<br><br><br>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.display import HTML\n",
"HTML(\"\"\"\n",
"<style>\n",
".MathJax nobr>span.math>span{border-left-width:0 !important};\n",
"</style>\n",
"\"\"\")"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Discrete_Fourier_Transform.ipynb | 2 | 148643 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.date(2017, 11, 2)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import date\n",
"date.today()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"author = \"kyubyong. https://github.com/Kyubyong/numpy_exercises\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.13.1'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Complex Numbers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q1. Return the angle of `a` in radian."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.785398163397\n"
]
}
],
"source": [
"a = 1+1j\n",
"output = ...\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q2. Return the real part and imaginary part of `a`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"real part= [ 1. 3. 5.]\n",
"imaginary part= [ 2. 4. 6.]\n"
]
}
],
"source": [
"a = np.array([1+2j, 3+4j, 5+6j])\n",
"real = ...\n",
"imag = ...\n",
"print(\"real part=\", real)\n",
"print(\"imaginary part=\", imag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q3. Replace the real part of a with `9`, the imaginary part with `[5, 7, 9]`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 9.+5.j 9.+7.j 9.+9.j]\n"
]
}
],
"source": [
"a = np.array([1+2j, 3+4j, 5+6j])\n",
"...\n",
"...\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q4. Return the complex conjugate of `a`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1-2j)\n"
]
}
],
"source": [
"a = 1+2j\n",
"output = ...\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discrete Fourier Transform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q5. Compuete the one-dimensional DFT of `a`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 8.00000000e+00 -6.85802208e-15j 2.36524713e-15 +9.79717439e-16j\n",
" 9.79717439e-16 +9.79717439e-16j 4.05812251e-16 +9.79717439e-16j\n",
" 0.00000000e+00 +9.79717439e-16j -4.05812251e-16 +9.79717439e-16j\n",
" -9.79717439e-16 +9.79717439e-16j -2.36524713e-15 +9.79717439e-16j]\n"
]
}
],
"source": [
"a = np.exp(2j * np.pi * np.arange(8))\n",
"output = ...\n",
"print(output)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q6. Compute the one-dimensional inverse DFT of the `output` in the above question."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a= [ 1. +0.00000000e+00j 1. -2.44929360e-16j 1. -4.89858720e-16j\n",
" 1. -7.34788079e-16j 1. -9.79717439e-16j 1. -1.22464680e-15j\n",
" 1. -1.46957616e-15j 1. -1.71450552e-15j]\n",
"inversed= [ 1. +0.00000000e+00j 1. -2.44929360e-16j 1. -4.89858720e-16j\n",
" 1. -7.34788079e-16j 1. -9.79717439e-16j 1. -1.22464680e-15j\n",
" 1. -1.46957616e-15j 1. -1.71450552e-15j]\n"
]
}
],
"source": [
"print(\"a=\", a)\n",
"inversed = ...\n",
"print(\"inversed=\", a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q7. Compute the one-dimensional discrete Fourier Transform for real input `a`."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.+0.j 0.-1.j -1.+0.j]\n",
"[ 1.+0.j 0.-1.j -1.+0.j 0.+1.j]\n"
]
}
],
"source": [
"a = [0, 1, 0, 0]\n",
"output = ...\n",
"print(output)\n",
"assert output.size==len(a)//2+1 if len(a)%2==0 else (len(a)+1)//2\n",
"\n",
"# cf.\n",
"output2 = np.fft.fft(a)\n",
"print(output2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q8. Compute the one-dimensional inverse DFT of the output in the above question."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inversed= [0, 1, 0, 0]\n"
]
}
],
"source": [
"inversed = ...\n",
"print(\"inversed=\", a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q9. Return the DFT sample frequencies of `a`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0.125 0.25 0.375 -0.5 -0.375 -0.25 -0.125]\n"
]
}
],
"source": [
"signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=np.float32)\n",
"fourier = np.fft.fft(signal)\n",
"n = signal.size\n",
"freq = ...\n",
"print(freq)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Window Functions"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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jK+i5HnZPgt+/gWunoUsYmNk9MKZCTw+L4GDM27cnMzwc/R/mMHz9Bfo7WPOT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PlV3mEzu/9sfBx9mLN7DgVlFhpUCSFEEyCDFiGEaOT2Xt3LhpMbGN95PIN9B1daU5aWZjgyFBSE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\nf9alElAadvRbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf30fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(19, 10))\n",
"\n",
"# Hamming window\n",
"window = np.hamming(51)\n",
"plt.plot(np.bartlett(51), label=\"Bartlett window\")\n",
"plt.plot(np.blackman(51), label=\"Blackman window\")\n",
"plt.plot(np.hamming(51), label=\"Hamming window\")\n",
"plt.plot(np.hanning(51), label=\"Hanning window\")\n",
"plt.plot(np.kaiser(51, 14), label=\"Kaiser window\")\n",
"plt.xlabel(\"sample\")\n",
"plt.ylabel(\"amplitude\")\n",
"plt.legend()\n",
"plt.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
aje/POT | notebooks/plot_otda_classes.ipynb | 1 | 227907 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# OT for domain adaptation\n",
"\n",
"\n",
"This example introduces a domain adaptation in a 2D setting and the 4 OTDA\n",
"approaches currently supported in POT.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Remi Flamary <[email protected]>\n",
"# Stanislas Chambon <[email protected]>\n",
"#\n",
"# License: MIT License\n",
"\n",
"import matplotlib.pylab as pl\n",
"import ot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate data\n",
"-------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_source_samples = 150\n",
"n_target_samples = 150\n",
"\n",
"Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\n",
"Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate the different transport algorithms and fit them\n",
"-----------------------------------------------------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"It. |Loss |Delta loss\n",
"--------------------------------\n",
" 0|1.017912e+01|0.000000e+00\n",
" 1|2.096083e+00|-3.856258e+00\n",
" 2|1.842979e+00|-1.373343e-01\n",
" 3|1.781632e+00|-3.443301e-02\n",
" 4|1.760919e+00|-1.176281e-02\n",
" 5|1.750958e+00|-5.688541e-03\n",
" 6|1.746386e+00|-2.618021e-03\n",
" 7|1.741793e+00|-2.636854e-03\n",
" 8|1.739054e+00|-1.575065e-03\n",
" 9|1.736474e+00|-1.486027e-03\n",
" 10|1.734361e+00|-1.218441e-03\n",
" 11|1.734259e+00|-5.863179e-05\n",
" 12|1.733704e+00|-3.201643e-04\n",
" 13|1.733018e+00|-3.957711e-04\n",
" 14|1.731842e+00|-6.791025e-04\n",
" 15|1.730974e+00|-5.012271e-04\n",
" 16|1.730584e+00|-2.257722e-04\n",
" 17|1.730492e+00|-5.272976e-05\n",
" 18|1.730153e+00|-1.961758e-04\n",
" 19|1.729837e+00|-1.828284e-04\n",
"It. |Loss |Delta loss\n",
"--------------------------------\n",
" 20|1.729361e+00|-2.749072e-04\n"
]
}
],
"source": [
"# EMD Transport\n",
"ot_emd = ot.da.EMDTransport()\n",
"ot_emd.fit(Xs=Xs, Xt=Xt)\n",
"\n",
"# Sinkhorn Transport\n",
"ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n",
"ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n",
"\n",
"# Sinkhorn Transport with Group lasso regularization\n",
"ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n",
"ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n",
"\n",
"# Sinkhorn Transport with Group lasso regularization l1l2\n",
"ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n",
" verbose=True)\n",
"ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n",
"\n",
"# transport source samples onto target samples\n",
"transp_Xs_emd = ot_emd.transform(Xs=Xs)\n",
"transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n",
"transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\n",
"transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fig 1 : plots source and target samples\n",
"---------------------------------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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bhBX799X5fKcKC7l84TyyHYV4EHC7WXckFdbD+NN7Bipsvyb16s2H11zHm1s3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\n3c2/GSgWjfHgheNT5wfXh3nw309y3cSLG445HA7+cPCO/OHgxFbHV4+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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdef9077e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pl.figure(1, figsize=(10, 5))\n",
"pl.subplot(1, 2, 1)\n",
"pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.legend(loc=0)\n",
"pl.title('Source samples')\n",
"\n",
"pl.subplot(1, 2, 2)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.legend(loc=0)\n",
"pl.title('Target samples')\n",
"pl.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fig 2 : plot optimal couplings and transported samples\n",
"------------------------------------------------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/rflamary/.local/lib/python3.5/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.\n",
" warnings.warn(message, mplDeprecation, stacklevel=1)\n"
]
},
{
"data": {
"image/png": 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KLor601Yv0Hs6f+MpDonqAb+iHY0z7mW8r3MalwPp4414/ww1ty7nHQDa0z7S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GyVy+hCNrDFZsAmNrewcXrVnHsz1nKKXeym2ZLNdu2jzpsxd0r2FTSyvXb92GJ8KzZ07z/SNH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ozTisMVNlxeB017UwQbO1BtKgWl+Zl4YXdZmyKpuLVoyS/xM+/qORkxvr2baskv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdef6f3c3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n",
"\n",
"pl.figure(2, figsize=(15, 8))\n",
"pl.subplot(2, 4, 1)\n",
"pl.imshow(ot_emd.coupling_, **param_img)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Optimal coupling\\nEMDTransport')\n",
"\n",
"pl.subplot(2, 4, 2)\n",
"pl.imshow(ot_sinkhorn.coupling_, **param_img)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Optimal coupling\\nSinkhornTransport')\n",
"\n",
"pl.subplot(2, 4, 3)\n",
"pl.imshow(ot_lpl1.coupling_, **param_img)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n",
"\n",
"pl.subplot(2, 4, 4)\n",
"pl.imshow(ot_l1l2.coupling_, **param_img)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Optimal coupling\\nSinkhornL1l2Transport')\n",
"\n",
"pl.subplot(2, 4, 5)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=0.3)\n",
"pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n",
" marker='+', label='Transp samples', s=30)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Transported samples\\nEmdTransport')\n",
"pl.legend(loc=\"lower left\")\n",
"\n",
"pl.subplot(2, 4, 6)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=0.3)\n",
"pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n",
" marker='+', label='Transp samples', s=30)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Transported samples\\nSinkhornTransport')\n",
"\n",
"pl.subplot(2, 4, 7)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=0.3)\n",
"pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n",
" marker='+', label='Transp samples', s=30)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Transported samples\\nSinkhornLpl1Transport')\n",
"\n",
"pl.subplot(2, 4, 8)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=0.3)\n",
"pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n",
" marker='+', label='Transp samples', s=30)\n",
"pl.xticks([])\n",
"pl.yticks([])\n",
"pl.title('Transported samples\\nSinkhornL1l2Transport')\n",
"pl.tight_layout()\n",
"\n",
"pl.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
pmorissette/bt | examples/Strategy_Combination.ipynb | 1 | 214276 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook creates a parent strategy(combined) with 2 child strategies(Equal Weight, Inv Vol).\n",
"\n",
"Alternatively, it creates the 2 child strategies, runs the backtest, combines the results, and creates a parent strategy using both of the backtests. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import ffn\n",
"import bt "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create fake data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d62c1f46a0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rf = 0.04\n",
"np.random.seed(1)\n",
"mus = np.random.normal(loc=0.05,scale=0.02,size=5) + rf\n",
"sigmas = (mus - rf)/0.3 + np.random.normal(loc=0.,scale=0.01,size=5)\n",
"\n",
"num_years = 10\n",
"num_months_per_year = 12\n",
"num_days_per_month = 21\n",
"num_days_per_year = num_months_per_year*num_days_per_month\n",
"\n",
"rdf = pd.DataFrame(\n",
" index = pd.date_range(\n",
" start=\"2008-01-02\",\n",
" periods=num_years*num_months_per_year*num_days_per_month,\n",
" freq=\"B\"\n",
" ),\n",
" columns=['foo','bar','baz','fake1','fake2']\n",
")\n",
"\n",
"for i,mu in enumerate(mus):\n",
" sigma = sigmas[i]\n",
" rdf.iloc[:,i] = np.random.normal(\n",
" loc=mu/num_days_per_year,\n",
" scale=sigma/np.sqrt(num_days_per_year),\n",
" size=rdf.shape[0]\n",
" )\n",
"pdf = np.cumprod(1+rdf)*100\n",
"pdf.iloc[0,:] = 100\n",
"\n",
"pdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"strategy_names = np.array(\n",
" [\n",
" 'Equal Weight',\n",
" 'Inv Vol'\n",
" ]\n",
")\n",
"\n",
"runMonthlyAlgo = bt.algos.RunMonthly(\n",
" run_on_first_date=True,\n",
" run_on_end_of_period=True\n",
")\n",
"selectAllAlgo = bt.algos.SelectAll()\n",
"rebalanceAlgo = bt.algos.Rebalance()\n",
"\n",
"strats = []\n",
"tests = []\n",
"\n",
"for i,s in enumerate(strategy_names):\n",
" if s == \"Equal Weight\":\n",
" wAlgo = bt.algos.WeighEqually()\n",
" elif s == \"Inv Vol\":\n",
" wAlgo = bt.algos.WeighInvVol()\n",
" \n",
" strat = bt.Strategy(\n",
" s,\n",
" [\n",
" runMonthlyAlgo,\n",
" selectAllAlgo,\n",
" wAlgo,\n",
" rebalanceAlgo\n",
" ]\n",
" )\n",
" strats.append(strat)\n",
" \n",
" t = bt.Backtest(\n",
" strat,\n",
" pdf,\n",
" integer_positions = False,\n",
" progress_bar=False\n",
" )\n",
" tests.append(t)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2054: RuntimeWarning: invalid value encountered in minimum\n",
" negative_returns = np.minimum(returns, 0.)\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2056: RuntimeWarning: divide by zero encountered in true_divide\n",
" res = np.divide(er.mean(), std)\n"
]
}
],
"source": [
"combined_strategy = bt.Strategy(\n",
" 'Combined',\n",
" algos = [\n",
" runMonthlyAlgo,\n",
" selectAllAlgo,\n",
" bt.algos.WeighEqually(),\n",
" rebalanceAlgo\n",
" ],\n",
" children = [x.strategy for x in tests]\n",
")\n",
"\n",
"combined_test = bt.Backtest(\n",
" combined_strategy,\n",
" pdf,\n",
" integer_positions = False,\n",
" progress_bar = False\n",
")\n",
"\n",
"res = bt.run(combined_test)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d62e54a358>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res.prices.plot()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d62f61d048>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res.get_security_weights().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get the weights of each strategy, you can run each strategy, get the prices for each strategy, combine them into one price dataframe, run the combined strategy on the new data set. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2054: RuntimeWarning: invalid value encountered in minimum\n",
" negative_returns = np.minimum(returns, 0.)\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2056: RuntimeWarning: divide by zero encountered in true_divide\n",
" res = np.divide(er.mean(), std)\n"
]
}
],
"source": [
"strategy_names = np.array(\n",
" [\n",
" 'Equal Weight',\n",
" 'Inv Vol'\n",
" ]\n",
")\n",
"\n",
"runMonthlyAlgo = bt.algos.RunMonthly(\n",
" run_on_first_date=True,\n",
" run_on_end_of_period=True\n",
")\n",
"selectAllAlgo = bt.algos.SelectAll()\n",
"rebalanceAlgo = bt.algos.Rebalance()\n",
"\n",
"strats = []\n",
"tests = []\n",
"results = []\n",
"\n",
"for i,s in enumerate(strategy_names):\n",
" if s == \"Equal Weight\":\n",
" wAlgo = bt.algos.WeighEqually()\n",
" elif s == \"Inv Vol\":\n",
" wAlgo = bt.algos.WeighInvVol()\n",
" \n",
" strat = bt.Strategy(\n",
" s,\n",
" [\n",
" runMonthlyAlgo,\n",
" selectAllAlgo,\n",
" wAlgo,\n",
" rebalanceAlgo\n",
" ]\n",
" )\n",
" strats.append(strat)\n",
" \n",
" t = bt.Backtest(\n",
" strat,\n",
" pdf,\n",
" integer_positions = False,\n",
" progress_bar=False\n",
" )\n",
" tests.append(t)\n",
" \n",
" res = bt.run(t)\n",
" results.append(res)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=1,ncols=1)\n",
"for i,r in enumerate(results):\n",
" r.plot(ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2054: RuntimeWarning: invalid value encountered in minimum\n",
" negative_returns = np.minimum(returns, 0.)\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2056: RuntimeWarning: divide by zero encountered in true_divide\n",
" res = np.divide(er.mean(), std)\n"
]
}
],
"source": [
"merged_prices_df = bt.merge(results[0].prices,results[1].prices)\n",
"\n",
"combined_strategy = bt.Strategy(\n",
" 'Combined',\n",
" algos = [\n",
" runMonthlyAlgo,\n",
" selectAllAlgo,\n",
" bt.algos.WeighEqually(),\n",
" rebalanceAlgo\n",
" ]\n",
")\n",
"\n",
"combined_test = bt.Backtest(\n",
" combined_strategy,\n",
" merged_prices_df,\n",
" integer_positions = False,\n",
" progress_bar = False\n",
")\n",
"\n",
"res = bt.run(combined_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d62f7d0748>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res.plot()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d62f9fb0f0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res.get_security_weights().plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
WomensCodingCircle/CodingCirclePython | Lesson08_Dictionaries/Dictionary.ipynb | 2 | 12320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dictionaries\n",
"\n",
"A dictionary is datatype that contains a series of key-value pairs. It is similar to a list except for that the indices of the values can be strings, tuples, etc. not just integers. It is also different in that it is unordered. You cannot expect to get the keys in the same order out as you put them in.\n",
"\n",
"To create a dictionary:\n",
"\n",
" my_dict = { key1: value1, key2: value2 }\n",
"\n",
"Creating an empty dictionary\n",
"\n",
" my_dict = {} \n",
" my_dict = dict()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fruit_season = {\n",
" 'raspberry': 'May',\n",
" 'apple' : 'September',\n",
" 'peach' : 'July',\n",
" 'grape' : 'August'\n",
"} \n",
"\n",
"print(type(fruit_season))\n",
"print(fruit_season)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To access a value, you index into it similarly to a list using square brackets.\n",
"\n",
" value_of_key1 = my_dict['key1'] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raspberry_season = fruit_season['raspberry']\n",
"print(raspberry_season)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trying to access a key not in the dictionary throws an error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(fruit_season['mangos'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To add an item to the dictionary set the value equal to the indexed keys\n",
"\n",
" dict['new_key'] = value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fruit_season['strawberry'] = 'May'\n",
"print(fruit_season)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To delete a key, use the del keyword\n",
"\n",
" del dict['key to delete']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"del fruit_season['strawberry']\n",
"print(fruit_season)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rules on keys\n",
"Keys in dictionary must be unique. If you try to make a duplicate key, the data will be overwritten\n",
"\n",
"Keys must be hashable. What this means is they must come from immutable values and be comparable. You can use strings, numbers, tuples, sets, (most) objects. You cannot use lists or dictionaries as keys."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"duplicate_fruit_season = {\n",
" 'raspberry': 'May',\n",
" 'raspberry': 'June',\n",
"} \n",
"print(duplicate_fruit_season)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable_key = {\n",
" ['watermelon', 'cantaloupe', 'honeydew']: 'July'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The solution is to use a tuple instead\n",
"immutable_key = {\n",
" ('watermelon', 'cantelope', 'honeydew'): 'July'\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TRY IT\n",
"Create a dictionary called vegetable_season with Eggplant-> July and Onion -> May"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dictionary Operators\n",
"\n",
"The in operator returns a boolean for whether the key is in the dictionary or not.\n",
"\n",
" key in dictionary "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('raspberry' in fruit_season)\n",
"print('mangos' in fruit_season)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use this in if statement"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if 'pineapple' in fruit_season:\n",
" print('Lets eat tropical fruit')\n",
"else:\n",
" print(\"Temperate fruit it is.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TRY IT\n",
"Check if 'broccoli' is in vegetable_season. If so, print 'Yum, little trees!'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dictionaries and Loops\n",
"\n",
"You can use a for in loop to loop through dictionaries\n",
"\n",
" for key in dictionary:\n",
" print key"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for fruit in fruit_season:\n",
" print(\"{0} is best in {1} (at least in Virginia)\".format(fruit.title(), fruit_season[fruit]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dictionary Methods\n",
"\n",
"You can use the `keys`, `values`, or `items` methods to return lists of keys, values, or key-value tuples respectively.\n",
"\n",
"You can then use these for sorting or for looping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(list(fruit_season.keys()))\n",
"print(list(fruit_season.values()))\n",
"print(list(fruit_season.items()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, value in list(fruit_season.items()):\n",
" print(\"In {0} eat a {1}\".format(value, key))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(sorted(fruit_season.keys()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TRY IT\n",
"Loop through the sorted keys of the vegetable_season dictionary. For each key, print the month it is in season"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More complex dictionaries\n",
"\n",
"Dictionary keys and values can be almost anything. The keys must be hashable which means it cannot change. That means that lists and dictionaries cannot be keys (but strings, tuples, and integers can).\n",
"\n",
"Values can be just about anything, though.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_complicated_dictionary = {\n",
" (1, 2, 3): 6,\n",
" 'weevil': {\n",
" 'e': 2,\n",
" 'i': 1,\n",
" 'l': 1,\n",
" 'v': 1,\n",
" 'w': 1,\n",
" },\n",
" 9: [3, 3]\n",
"}\n",
"print(my_complicated_dictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use this to create a more realistic fruit season dictionary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"true_fruit_season = {\n",
" 'raspberry': ['May', 'June'],\n",
" 'apple': ['September', 'October', 'November', 'December'],\n",
" 'peach': ['July', 'August'],\n",
" 'grape': ['August', 'September', 'October']\n",
"} \n",
"\n",
"print(true_fruit_season)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
"\n",
"for month in months:\n",
" print(('It is {0}'.format(month)))\n",
" for fruit, season in list(true_fruit_season.items()):\n",
" if month in season:\n",
" print((\"\\tEat {0}\".format(fruit)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TRY IT\n",
"Add a key to the true_fruit_season for 'watermelons' the season is July, August, and September"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Project: Acrostic\n",
"\n",
"Create an acrostic poem generator.\n",
"\n",
"You will create a function that takes a name and generates an acrostic poem\n",
"\n",
"1. Create a dictionary that has each of the capital letters as keys and an adjective that start with the letter as the value and store in variable named adjectives. (Reference: http://www.enchantedlearning.com/wordlist/adjectives.shtml)\n",
"2. Create a function called acrostic that takes one parameter name.\n",
"3. In the acrostic function capitalize the name (use the upper method)\n",
"4. For each letter in the name\n",
"5. Get the adjective corresponding to that letter and store in a variable called current_adj\n",
"6. Print out Letter-current_adj\n",
"\n",
"** Challenge ** instead of just one adjective have each letter's value be a list of adjectives. Use the random module to select a random adjective instead of always selecting the same one.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bonus Material\n",
"\n",
"Auto generating the dictionary for the acrostic:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# If you have a list of adjectives\n",
"my_dict = {}\n",
"\n",
"# Imaging this is the full alphabet\n",
"for i in ['A', 'B', 'C']:\n",
" my_dict[i] = []\n",
" \n",
" \n",
"for i in ['Adoreable', 'Acceptable', 'Bad', 'Cute', 'Basic', 'Dumb']:\n",
" first_char = i[0]\n",
" if first_char in my_dict:\n",
" my_dict[first_char].append(i)\n",
"print(my_dict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Generating from a file\n",
"my_dict = {}\n",
"\n",
"for i in ['A', 'B', 'C']:\n",
" my_dict[i] = []\n",
" \n",
"# adjectives.txt has one adjective per line\n",
"with open('adjectives.txt') as fh:\n",
" for line in fh:\n",
" word = line.rstrip().title()\n",
" first_char = word[0]\n",
" if first_char in my_dict:\n",
" my_dict[first_char].append(word)\n",
" \n",
"print(my_dict['A'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
qutip/qutip-notebooks | docs/guide/Eseries.ipynb | 2 | 40174 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Eseries Class\n",
"\n",
"###Contents\n",
"- [Exponential-Series Representation of Quantum Objects](#exponential)\n",
"- [Applications of Exponential-Series](#applications)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from pylab import *\n",
"from qutip import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='exponential'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Exponential-Series Representation of Time-Dependent Quantum Objects\n",
"\n",
"The `eseries` object in QuTiP is a representation of an exponential-series expansion of time-dependent quantum objects (a concept borrowed from the quantum optics toolbox). \n",
"\n",
"An exponential series is parameterized by its amplitude coefficients $c_i$ and rates $r_i$, so that the series takes the form \n",
"$E(t) = \\sum_i c_i e^{r_i t}$. The coefficients are typically quantum objects (i.e. states, operators, etc.), so that the value of the eseries also is a quantum object, and the rates can be either real or complex numbers (describing decay rates and oscillation frequencies, respectively). Note that all amplitude coefficients in an exponential series must be of the same dimensions and composition. \n",
"\n",
"In QuTiP, an exponential series object is constructed by creating an instance of the class `eseries`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"es1 = eseries(sigmax(), 1j)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the first argument is the amplitude coefficient (here, the sigma-X operator), and the second argument is the rate. The eseries in this example represents the time-dependent operator $\\sigma_x e^{i t}$. To add more terms to an `eseries` object we simply add objects using the ``+`` operator:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"omega = 1.0\n",
"es2 = (eseries(0.5 * sigmax(), 1j * omega) + eseries(0.5 * sigmax(), -1j * omega))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `eseries` in this example represents the operator $0.5 \\sigma_x e^{i\\omega t} + 0.5 \\sigma_x e^{-i\\omega t}$, which is the exponential series representation of $\\sigma_x \\cos(\\omega t)$. Alternatively, we can also specify a list of amplitudes and rates when the `eseries` is created:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"es2 = eseries([0.5 * sigmax(), 0.5 * sigmax()], [1j * omega, -1j * omega])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can inspect the structure of an `eseries` object by printing it to the standard output console:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"ESERIES object: 2 terms\n",
"Hilbert space dimensions: [[2], [2]]\n",
"Exponent #0 = -1j\n",
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 0.5]\n",
" [ 0.5 0. ]]\n",
"Exponent #1 = 1j\n",
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 0.5]\n",
" [ 0.5 0. ]]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and we can evaluate it at time $t$ by using the `esval` function or the `value` method:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/latex": [
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 1.0\\\\1.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
],
"text/plain": [
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" esval(es2, 0.0) # equivalent to es2.value(0.0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 1.0\\\\1.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
],
"text/plain": [
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es2.value(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or for a list of times ``[0.0, 1.0 * pi, 2.0 * pi]``:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]],\n",
" Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. -1.]\n",
" [-1. 0.]],\n",
" Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]]], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"times = [0.0, 1.0 * np.pi, 2.0 * np.pi]\n",
"esval(es2, times)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]],\n",
" Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. -1.]\n",
" [-1. 0.]],\n",
" Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 1.]\n",
" [ 1. 0.]]], dtype=object)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es2.value(times)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate the expectation value of an time-dependent operator represented by an `eseries`, we use the `expect` function. For example, consider the operator $\\sigma_x \\cos(\\omega t) + \\sigma_z\\sin(\\omega t)$, and say we would like to know the expectation value of this operator for a spin in its excited state (``rho = fock_dm(2,1)`` produce this state):"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ESERIES object: 2 terms\n",
"Hilbert space dimensions: [[1, 1]]\n",
"Exponent #0 = (-0-1j)\n",
"(-0.5+0j)\n",
"Exponent #1 = 1j\n",
"(-0.5+0j)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es3 = (eseries([0.5*sigmaz(), 0.5*sigmaz()], [1j, -1j]) + \n",
" eseries([-0.5j*sigmax(), 0.5j*sigmax()], [1j, -1j]))\n",
"\n",
"rho = fock_dm(2, 1)\n",
"es3_expect = expect(rho, es3)\n",
"es3_expect"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the expectation value of the `eseries` object, ``expect(rho, es3)``, itself is an `eseries`, but with amplitude coefficients that are c-numbers instead of quantum operators. To evaluate the c-number `eseries` at the times `times` we use ``es3_expect.value(times)`` or equivalently ``esval(es3_expect, times)``."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ -1.00000000e+00, -6.12323400e-17])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es3_expect.value([0.0, pi/2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='applications'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Applications of Exponential Series\n",
"\n",
"The exponential series formalism can be useful for the time-evolution of quantum systems. One approach to calculating the time evolution of a quantum system is to diagonalize its Hamiltonian (or Liouvillian, for dissipative systems) and to express the propagator (e.g., $\\exp(-iHt) \\rho \\exp(iHt)$) as an exponential series. \n",
"\n",
"The QuTiP function `ode2es` and `essolve` use this method to evolve quantum systems in time. The exponential series approach is particularly suitable for cases when the same system is to be evolved for many different initial states, since the diagonalization only needs to be performed once (as opposed to e.g. the ode solver that would need to be ran independently for each initial state).\n",
"\n",
"As an example, consider a spin-1/2 with a Hamiltonian pointing in the $\\sigma_z$ direction, and that is subject to noise causing relaxation. For a spin originally is in the up state, we can create an `eseries` object describing its dynamics by using the `es2ode` function:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"psi0 = basis(2,1)\n",
"H = sigmaz()\n",
"L = liouvillian(H, [sqrt(1.0) * destroy(2)])\n",
"es = ode2es(L, psi0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `ode2es` function diagonalizes the Liouvillian $L$ and creates an exponential series with the correct eigenfrequencies and amplitudes for the initial state \n",
"$\\psi_0$ (`psi0`).\n",
"\n",
"We can examine the resulting `eseries` object by printing a text representation:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ESERIES object: 2 terms\n",
"Hilbert space dimensions: [[2], [2]]\n",
"Exponent #0 = (-1+0j)\n",
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[-1. 0.]\n",
" [ 0. 1.]]\n",
"Exponent #1 = 0j\n",
"Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 1. 0.]\n",
" [ 0. 0.]]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or by evaluating it and arbitrary points in time (here at 0.0 and 1.0):"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0. 0.]\n",
" [ 0. 1.]],\n",
" Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isherm = True\n",
"Qobj data =\n",
"[[ 0.63212056 0. ]\n",
" [ 0. 0.36787944]]], dtype=object)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es.value([0.0, 1.0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the expectation value of the exponential series can be calculated using the `expect` function:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" es_expect = expect(sigmaz(), es)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result `es_expect` is now an exponential series with c-numbers as amplitudes, which easily can be evaluated at arbitrary times:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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xmJm1q5RpvABZEeEPO4pqdPl/2e3pTh5m1s+8H0ihn8vzgLnABsBOEczodgxmZp3yjoTV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e979080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"times = linspace(0.0, 10.0, 100)\n",
"sz_expect = es_expect.value(times)\n",
"plot(times, sz_expect, lw=2)\n",
"xlabel(\"Time\", fontsize=14)\n",
"ylabel(\"Expectation value of sigma-z\", fontsize=14)\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
"\n",
"body {\n",
" -webkit-font-smoothing: antialiased;\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, Verdana, sans-serif;\n",
"}\n",
"\n",
"\n",
"div.cell{\n",
" width:768px;\n",
" margin-left:10% !important;\n",
" margin-right:auto;\n",
"}\n",
"h1 {\n",
" font-family: \"Source Sans Pro\", ,Helvetica, Arial, serif;\n",
"\n",
"}\n",
"\n",
"h4{\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
"\tmargin-top:12px;\n",
" margin-bottom: 3px;\n",
" }\n",
"\n",
"\n",
"div.text_cell_render{\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, sans-serif;\n",
" line-height: 125%;\n",
" font-size: 110%;\n",
" width:768px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"div.cell.code_cell { /* area that contains code + output */\n",
" background: #fff;\n",
" border: none;\n",
" border-radius: 10px;\n",
" padding-top: 1ex;\n",
"}\n",
"\n",
"div.input_area { /* box around box with code */\n",
" border: none;\n",
" background: #f5f5f5;\n",
" border: 1px solid #ccc;\n",
" border-radius: 10px;\n",
" padding-top: 0.5ex;\n",
" padding-bottom: 0.5ex;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
"div.prompt { /* remove In/Out prompt */\n",
" display: none;\n",
"}\n",
"\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" /* draw border around running cells */\n",
" border: 3px dotted #f33;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: \"Source Code Pro\", Consolas, monospace;\n",
"}\n",
".prompt{\n",
" display: None;\n",
"}\n",
".text_cell_render h5 {\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
" font-size: 20pt;\n",
" color: #0001E0;\n",
" margin-bottom: .5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\n",
"\n",
".warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
"\n",
"a {\n",
" color: #0080FF;\n",
" text-decoration: none;\n",
" -webkit-transition: color 0.2s ease-in-out;\n",
" -moz-transition: color 0.2s ease-in-out;\n",
" -o-transition: color 0.2s ease-in-out;\n",
" -ms-transition: color 0.2s ease-in-out;\n",
" transition: color 0.2s ease-in-out;\n",
"}\n",
"a:hover {\n",
" color: #8C0028;\n",
"}\n",
"\n",
"li li{\n",
" font-size:14px;\n",
"}\n",
"\n",
"div.danger { \n",
" background-color: #F7A7AA;\n",
" border-color: #F1595F;\n",
" border: 2px solid #F1595F;\n",
" border-radius: 5px;\n",
" padding-top: 0.5ex;\n",
" padding-bottom: 0.5ex;\n",
" padding-left: 0.5em;\n",
" }\n",
"\n",
"div.warn { \n",
"background-color: #FBD1A7;\n",
"border-color: #F9A65A;\n",
"border: 2px solid #F9A65A;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"div.info { \n",
"background-color: #A6CBE9;\n",
"border-color: #599AD3;\n",
"border: 2px solid #599AD3;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"div.success { \n",
"background-color: #B9E0B0;\n",
"border-color: #79C36A;\n",
"border: 2px solid #79C36A;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"table a:link {\n",
" color: #666;\n",
" font-weight: bold;\n",
" text-decoration:none;\n",
"}\n",
"table a:visited {\n",
" color: #999999;\n",
" font-weight:bold;\n",
" text-decoration:none;\n",
"}\n",
"table a:active,\n",
"table a:hover {\n",
" color: #bd5a35;\n",
" text-decoration:underline;\n",
"}\n",
"table {\n",
" font-family:\"Source Sans Pro\", Helvetica, Arial, serif;\n",
" color:#666;\n",
" font-size:14px;\n",
" text-shadow: 1px 1px 0px #fff;\n",
" background:#eaebec;\n",
" margin:20px;\n",
" border:#ccc 1px solid;\n",
" border-spacing: 0;\n",
" -moz-border-radius:3px;\n",
" -webkit-border-radius:3px;\n",
" border-radius:3px;\n",
"\n",
" -moz-box-shadow: 0 1px 2px #d1d1d1;\n",
" -webkit-box-shadow: 0 1px 2px #d1d1d1;\n",
" box-shadow: 0 1px 2px #d1d1d1;\n",
"}\n",
"table th {\n",
" padding:21px 25px 22px 25px;\n",
" border-top:1px solid #fafafa;\n",
" border-bottom:1px solid #e0e0e0;\n",
"\n",
" background: #ededed;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#ededed), to(#ebebeb));\n",
" background: -moz-linear-gradient(top, #ededed, #ebebeb);\n",
"}\n",
"table th:first-child{\n",
" text-align: left;\n",
" padding-left:20px;\n",
"}\n",
"table tr:first-child th:first-child{\n",
" -moz-border-radius-topleft:3px;\n",
" -webkit-border-top-left-radius:3px;\n",
" border-top-left-radius:3px;\n",
"}\n",
"table tr:first-child th:last-child{\n",
" -moz-border-radius-topright:3px;\n",
" -webkit-border-top-right-radius:3px;\n",
" border-top-right-radius:3px;\n",
"}\n",
"table tr{\n",
" text-align: center;\n",
" padding-left:20px;\n",
"}\n",
"table tr td:first-child{\n",
" text-align: left;\n",
" padding-left:20px;\n",
" border-left: 0;\n",
"}\n",
"table tr td {\n",
" padding:18px;\n",
" border-top: 1px solid #ffffff;\n",
" border-bottom:1px solid #e0e0e0;\n",
" border-left: 1px solid #e0e0e0;\n",
"\n",
" background: #fafafa;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#fbfbfb), to(#fafafa));\n",
" background: -moz-linear-gradient(top, #fbfbfb, #fafafa);\n",
"}\n",
"\n",
"table tr:nth-child(2n) td {\n",
" background: #f6f6f6;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#f8f8f8), to(#f6f6f6));\n",
" background: -moz-linear-gradient(top, #f8f8f8, #f6f6f6);\n",
"}\n",
"\n",
"table tr:last-child td{\n",
" border-bottom:0;\n",
"}\n",
"table tr:last-child td:first-child{\n",
" -moz-border-radius-bottomleft:3px;\n",
" -webkit-border-bottom-left-radius:3px;\n",
" border-bottom-left-radius:3px;\n",
"}\n",
"table tr:last-child td:last-child{\n",
" -moz-border-radius-bottomright:3px;\n",
" -webkit-border-bottom-right-radius:3px;\n",
" border-bottom-right-radius:3px;\n",
"}\n",
"table tr:hover td{\n",
" background: #f2f2f2;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#f2f2f2), to(#f0f0f0));\n",
" background: -moz-linear-gradient(top, #f2f2f2, #f0f0f0);\t\n",
"}\n",
"\n",
"\n",
"caption {\n",
" display: table-caption;\n",
" font-weight: 700;\n",
"}\n",
"\n",
"figure {\n",
" display: inline-block;\n",
" position: relative;\n",
" margin: 1em 0 2em;\n",
"}\n",
"figcaption {\n",
" font-style: italic;\n",
" text-align: center;\n",
" background: white;\n",
" color: #666;\n",
" position: absolute;\n",
" left: 0;\n",
" bottom: -24px;\n",
" width: 98%;\n",
" padding: 1%;\n",
" -webkit-transition: all 0.2s ease-in-out;\n",
" -moz-transition: all 0.2s ease-in-out;\n",
" -o-transition: all 0.2s ease-in-out;\n",
" -ms-transition: all 0.2s ease-in-out;\n",
" transition: all 0.2s ease-in-out;\n",
"}\n",
"\n",
".prompt.input_prompt {\n",
" color: rgba(0,0,0,0.5);\n",
"}\n",
"\n",
".cell.command_mode.selected {\n",
" border-color: rgba(0,0,0,0.1);\n",
"}\n",
"\n",
".cell.edit_mode.selected {\n",
" border-color: rgba(0,0,0,0.15);\n",
" box-shadow: 0px 0px 5px #f0f0f0;\n",
" -webkit-box-shadow: 0px 0px 5px #f0f0f0;\n",
"}\n",
"\n",
"div.output_scroll {\n",
" -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
" box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
" border-radious: 2px;\n",
"}\n",
"\n",
"#menubar .navbar-inner {\n",
" background: #fff;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
" border-radius: 0;\n",
" border: none;\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
" font-weight: 400;\n",
"}\n",
"\n",
".navbar-fixed-top .navbar-inner,\n",
".navbar-static-top .navbar-inner {\n",
" box-shadow: none;\n",
" -webkit-box-shadow: none;\n",
" border: none;\n",
"}\n",
"\n",
"div#notebook_panel {\n",
" box-shadow: none;\n",
" -webkit-box-shadow: none;\n",
" border-top: none;\n",
"}\n",
"\n",
"div#notebook {\n",
" border-top: 1px solid rgba(0,0,0,0.15);\n",
"}\n",
"\n",
"#menubar .navbar .navbar-inner,\n",
".toolbar-inner {\n",
" padding-left: 0;\n",
" padding-right: 0;\n",
"}\n",
"\n",
"#checkpoint_status,\n",
"#autosave_status {\n",
" color: rgba(0,0,0,0.5);\n",
"}\n",
"\n",
"#header {\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
"}\n",
"\n",
"#notebook_name {\n",
" font-weight: 200;\n",
"}\n",
"\n",
"/* \n",
" This is a lazy fix, we *should* fix the \n",
" background for each Bootstrap button type\n",
"*/\n",
"#site * .btn {\n",
" background: #fafafa;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
"}\n",
"\n",
"</style>\n",
"\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {equationNumbers: { autoNumber: \"AMS\" }, \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 object>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"../styles/guide.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
}
],
"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
}
| lgpl-3.0 |
xesscorp/myhdlpeek | examples/peeker_simple_mux.ipynb | 1 | 21415 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#A-Simple-Multiplexer-Example\" data-toc-modified-id=\"A-Simple-Multiplexer-Example-1\"><span class=\"toc-item-num\">1 </span>A Simple Multiplexer Example</a></span></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Simple Multiplexer Example\n",
"\n",
"This example shows how to use the `myhdlpeek` module to monitor the inputs and output of a simple, two-input multiplexer."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T21:27:46.840188Z",
"start_time": "2020-12-15T21:27:46.342040Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t z a b sel\n",
"1 3 2 3 False\n",
"2 1 6 1 False\n",
"3 2 0 2 False\n",
"4 2 2 4 1\n",
"5 1 6 1 0\n",
"6 4 4 1 1\n",
"7 2 2 7 1\n",
"8 1 6 1 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<class 'myhdl.StopSimulation'>: No more events\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 252x144 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from myhdl import *\n",
"from myhdlpeek import * # Import the myhdlpeeker module.\n",
"\n",
"def mux(z, a, b, sel):\n",
" \"\"\"A simple multiplexer.\"\"\"\n",
"\n",
" @always_comb\n",
" def mux_logic():\n",
" if sel == 1:\n",
" z.next = a # Signal a sent to mux output when sel is high.\n",
" else:\n",
" z.next = b # Signal b sent to mux output when sel is low.\n",
" \n",
" return mux_logic\n",
"\n",
"# Create some signals to attach to the multiplexer.\n",
"a, b, z = [Signal(0) for _ in range(3)] # Integer signals for the inputs & output.\n",
"sel = Signal(bool(0)) # Binary signal for the selector.\n",
"\n",
"# Create some Peekers to monitor the multiplexer I/Os.\n",
"Peeker.clear() # Clear any existing Peekers. (Start with a clean slate.)\n",
"Peeker(a, 'a') # Add a Peeker to the a input.\n",
"Peeker(b, 'b') # Add a Peeker to the b input.\n",
"Peeker(z, 'z') # Add a peeker to the z output.\n",
"Peeker(sel, 'select') # Add a Peeker to the select input. The Peeker label doesn't have to match the signal name.\n",
"\n",
"# Instantiate mux.\n",
"mux_1 = mux(z, a, b, sel)\n",
"\n",
"# Create a simple testbed to apply random patterns to the multiplexer.\n",
"from random import randrange\n",
"def test():\n",
" '''Simple testbed generator that applies random inputs to the multiplexer.'''\n",
" print(\"t z a b sel\")\n",
" for _ in range(8):\n",
" a.next, b.next, sel.next = randrange(8), randrange(8), randrange(2)\n",
" yield delay(1)\n",
" print(\"%d %s %s %s %s\" % (now(), z, a, b, sel))\n",
"\n",
"# Simulate the multiplexer, testbed and the peekers.\n",
"sim = Simulation(mux_1, test(), *Peeker.instances()).run()\n",
"\n",
"setup(use_jupyter=True, use_wavedrom=False)\n",
"\n",
"# Display the complete waveforms captured by all the Peekers. \n",
"# clear_traces()\n",
"show_waveforms()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
AbhiK24/Introductory_Machine_Learning | Data_Visualisation/Deep Learning - TSNE.ipynb | 1 | 429657 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/user/anaconda/envs/tensorflow/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"# These are all the modules we'll be using later. Make sure you can import them\n",
"# before proceeding further.\n",
"%matplotlib inline\n",
"from __future__ import print_function\n",
"import collections\n",
"import math\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"import tensorflow as tf\n",
"import zipfile\n",
"from matplotlib import pylab\n",
"from six.moves import range\n",
"from six.moves.urllib.request import urlretrieve\n",
"from sklearn.manifold import TSNE"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found and verified text8.zip\n"
]
}
],
"source": [
"url = 'http://mattmahoney.net/dc/'\n",
"\n",
"def maybe_download(filename, expected_bytes):\n",
" \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
" if not os.path.exists(filename):\n",
" filename, _ = urlretrieve(url + filename, filename)\n",
" statinfo = os.stat(filename)\n",
" if statinfo.st_size == expected_bytes:\n",
" print('Found and verified %s' % filename)\n",
" else:\n",
" print(statinfo.st_size)\n",
" raise Exception(\n",
" 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
" return filename\n",
"\n",
"filename = maybe_download('text8.zip', 31344016)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data size 17005207\n"
]
}
],
"source": [
"def read_data(filename):\n",
" \"\"\"Extract the first file enclosed in a zip file as a list of words\"\"\"\n",
" with zipfile.ZipFile(filename) as f:\n",
" data = tf.compat.as_str(f.read(f.namelist()[0])).split()\n",
" return data\n",
" \n",
"words = read_data(filename)\n",
"print('Data size %d' % len(words))\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n",
"Sample data [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n"
]
}
],
"source": [
"vocabulary_size = 50000\n",
"\n",
"def build_dataset(words):\n",
" count = [['UNK', -1]]\n",
" count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
" dictionary = dict()\n",
" for word, _ in count:\n",
" dictionary[word] = len(dictionary)\n",
" data = list()\n",
" unk_count = 0\n",
" for word in words:\n",
" if word in dictionary:\n",
" index = dictionary[word]\n",
" else:\n",
" index = 0 # dictionary['UNK']\n",
" unk_count = unk_count + 1\n",
" data.append(index)\n",
" count[0][1] = unk_count\n",
" reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
" return data, count, dictionary, reverse_dictionary\n",
"\n",
"data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
"print('Most common words (+UNK)', count[:5])\n",
"print('Sample data', data[:10])\n",
"del words # Hint to reduce memory."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
"\n",
"with num_skips = 2 and skip_window = 1:\n",
" batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term']\n",
" labels: ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'of', 'a']\n",
"\n",
"with num_skips = 4 and skip_window = 2:\n",
" batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a']\n",
" labels: ['a', 'originated', 'anarchism', 'term', 'term', 'of', 'originated', 'as']\n"
]
}
],
"source": [
"data_index = 0\n",
"\n",
"def generate_batch(batch_size, num_skips, skip_window):\n",
" global data_index\n",
" assert batch_size % num_skips == 0\n",
" assert num_skips <= 2 * skip_window\n",
" batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
" labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
" span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
" buffer = collections.deque(maxlen=span)\n",
" for _ in range(span):\n",
" buffer.append(data[data_index])\n",
" data_index = (data_index + 1) % len(data)\n",
" for i in range(batch_size // num_skips):\n",
" target = skip_window # target label at the center of the buffer\n",
" targets_to_avoid = [ skip_window ]\n",
" for j in range(num_skips):\n",
" while target in targets_to_avoid:\n",
" target = random.randint(0, span - 1)\n",
" targets_to_avoid.append(target)\n",
" batch[i * num_skips + j] = buffer[skip_window]\n",
" labels[i * num_skips + j, 0] = buffer[target]\n",
" buffer.append(data[data_index])\n",
" data_index = (data_index + 1) % len(data)\n",
" return batch, labels\n",
"\n",
"print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
"\n",
"for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
" data_index = 0\n",
" batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)\n",
" print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
" print(' batch:', [reverse_dictionary[bi] for bi in batch])\n",
" print(' labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"embedding_size = 128 # Dimension of the embedding vector.\n",
"skip_window = 1 # How many words to consider left and right.\n",
"num_skips = 2 # How many times to reuse an input to generate a label.\n",
"# We pick a random validation set to sample nearest neighbors. here we limit the\n",
"# validation samples to the words that have a low numeric ID, which by\n",
"# construction are also the most frequent. \n",
"valid_size = 16 # Random set of words to evaluate similarity on.\n",
"valid_window = 100 # Only pick dev samples in the head of the distribution.\n",
"valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
"num_sampled = 64 # Number of negative examples to sample.\n",
"\n",
"graph = tf.Graph()\n",
"\n",
"with graph.as_default(), tf.device('/cpu:0'):\n",
"\n",
" # Input data.\n",
" train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
" train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
" valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
" \n",
" # Variables.\n",
" embeddings = tf.Variable(\n",
" tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
" softmax_weights = tf.Variable(\n",
" tf.truncated_normal([vocabulary_size, embedding_size],\n",
" stddev=1.0 / math.sqrt(embedding_size)))\n",
" softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
" \n",
" # Model.\n",
" # Look up embeddings for inputs.\n",
" embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
" # Compute the softmax loss, using a sample of the negative labels each time.\n",
" loss = tf.reduce_mean(\n",
" tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,\n",
" train_labels, num_sampled, vocabulary_size))\n",
"\n",
" # Optimizer.\n",
" # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
" # This is because the embeddings are defined as a variable quantity and the\n",
" # optimizer's `minimize` method will by default modify all variable quantities \n",
" # that contribute to the tensor it is passed.\n",
" # See docs on `tf.train.Optimizer.minimize()` for more details.\n",
" optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n",
" \n",
" # Compute the similarity between minibatch examples and all embeddings.\n",
" # We use the cosine distance:\n",
" norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
" normalized_embeddings = embeddings / norm\n",
" valid_embeddings = tf.nn.embedding_lookup(\n",
" normalized_embeddings, valid_dataset)\n",
" similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Average loss at step 0: 7.182359\n",
"Nearest to that: zilog, dominating, breakdown, milken, meer, imposing, maoist, yorker,\n",
"Nearest to world: niklaus, employed, sherbrooke, wipes, thermoelectric, dory, ay, original,\n",
"Nearest to while: slot, critic, fracturing, erdrich, rapidity, riddles, scorer, exponential,\n",
"Nearest to i: monarchist, smuts, portable, polidori, aloes, pledged, belisarius, electra,\n",
"Nearest to up: externalization, chs, diabetes, slur, yorkist, anytime, cyanide, lehigh,\n",
"Nearest to be: fractured, carne, alternatively, flexible, cretan, integrin, enabling, coeur,\n",
"Nearest to known: bryant, scam, plethora, falling, vindicated, critic, produces, coat,\n",
"Nearest to had: spur, interpolation, hatching, fibre, chosenness, suspension, leucippus, accommodation,\n",
"Nearest to as: swimmers, intercourse, cage, procedures, hamad, goggles, alleys, serie,\n",
"Nearest to five: pepe, sram, septic, walther, bucket, amphitheatre, flint, clarinetists,\n",
"Nearest to first: nara, chadian, foreshadowed, premium, ced, convoy, meow, lancers,\n",
"Nearest to of: rpg, transitivity, equipping, homozygous, fulfilment, demonstrating, secures, cryptozoology,\n",
"Nearest to by: paolo, jeff, unsurpassed, advertise, fertilisation, consolidation, livestock, grub,\n",
"Nearest to at: hurry, gadget, ate, happenings, unintelligible, competitiveness, plugins, submits,\n",
"Nearest to its: swinburne, screenshots, riverview, moses, ramzi, pathfinder, shelton, pseudopods,\n",
"Nearest to however: missionary, psychosocial, logo, pollute, juliet, hyenas, ach, norms,\n",
"Average loss at step 2000: 4.349140\n",
"Average loss at step 4000: 3.866727\n",
"Average loss at step 6000: 3.789125\n",
"Average loss at step 8000: 3.682475\n",
"Average loss at step 10000: 3.610805\n",
"Nearest to that: what, which, catenary, initially, it, spacey, launched, renegades,\n",
"Nearest to world: niklaus, original, sherbrooke, employed, lebrun, lothair, confrontational, during,\n",
"Nearest to while: slot, through, cupid, obituary, riddles, taking, exponential, critic,\n",
"Nearest to i: they, intuitionism, aloes, battlecruiser, ahmet, essence, wren, electra,\n",
"Nearest to up: externalization, diabetes, slur, reunified, fiennes, chs, bolton, yorkist,\n",
"Nearest to be: have, was, is, by, become, enabling, directive, stillborn,\n",
"Nearest to known: scam, vindicated, bryant, banchan, exist, nchez, smoky, such,\n",
"Nearest to had: has, have, was, were, hatching, became, dancing, autistic,\n",
"Nearest to as: serie, aiming, ucr, inc, by, cooking, ric, algemeen,\n",
"Nearest to five: six, nine, seven, eight, three, four, zero, two,\n",
"Nearest to first: nara, gassing, same, second, ced, last, daleks, microorganism,\n",
"Nearest to of: in, second, equipping, secures, sabina, for, walkie, lol,\n",
"Nearest to by: was, on, be, with, at, as, lemons, boron,\n",
"Nearest to at: nola, by, in, during, of, eat, voters, gadget,\n",
"Nearest to its: the, his, their, ramzi, allotment, pathfinder, swinburne, candidacy,\n",
"Nearest to however: logo, pollute, referees, psychosocial, ach, whose, homages, riordan,\n",
"Average loss at step 12000: 3.603191\n",
"Average loss at step 14000: 3.568690\n",
"Average loss at step 16000: 3.410434\n",
"Average loss at step 18000: 3.455742\n",
"Average loss at step 20000: 3.537034\n",
"Nearest to that: which, but, what, catenary, when, mulligan, accusation, launched,\n",
"Nearest to world: lebrun, original, lothair, sherbrooke, niklaus, ibsen, laszlo, regimens,\n",
"Nearest to while: slot, through, critic, reproduce, obituary, cupid, rounds, riddles,\n",
"Nearest to i: hypothermia, unicellular, teimanim, ii, monolith, repent, battlecruiser, mojito,\n",
"Nearest to up: him, slur, externalization, reunified, diabetes, fiennes, them, bolton,\n",
"Nearest to be: have, is, by, been, was, enabling, were, become,\n",
"Nearest to known: used, vindicated, scam, such, banchan, carlist, interchange, bryant,\n",
"Nearest to had: has, have, was, were, became, disarm, prejudices, shrub,\n",
"Nearest to as: serie, ric, electromechanical, gumby, incompatibility, inc, acceptance, lowlands,\n",
"Nearest to five: four, six, three, two, zero, seven, eight, nine,\n",
"Nearest to first: second, nara, last, same, gassing, daleks, ced, grampus,\n",
"Nearest to of: for, asuras, condominiums, at, sabina, jail, abolitionist, merriam,\n",
"Nearest to by: with, be, in, for, were, was, at, across,\n",
"Nearest to at: in, on, from, eat, liberties, during, and, is,\n",
"Nearest to its: their, his, the, our, allotment, ethereal, kenyon, swinburne,\n",
"Nearest to however: but, logo, whitish, labeled, iverson, aeolus, repeatedly, when,\n",
"Average loss at step 22000: 3.504483\n",
"Average loss at step 24000: 3.492695\n",
"Average loss at step 26000: 3.481749\n",
"Average loss at step 28000: 3.481588\n",
"Average loss at step 30000: 3.503363\n",
"Nearest to that: which, what, this, zilog, catenary, but, shallower, appel,\n",
"Nearest to world: lebrun, lothair, niklaus, original, dory, ibsen, laszlo, league,\n",
"Nearest to while: slot, through, but, though, although, reproduce, usta, is,\n",
"Nearest to i: unicellular, ii, iii, sasl, repent, hymnal, camcorders, mimeograph,\n",
"Nearest to up: him, them, slur, diabetes, noticeably, chs, bolton, man,\n",
"Nearest to be: been, macros, have, enabling, were, is, behold, refer,\n",
"Nearest to known: used, such, written, scam, possible, vindicated, banchan, carlist,\n",
"Nearest to had: has, have, was, were, is, before, became, would,\n",
"Nearest to as: serie, ric, incompatibility, gumby, by, with, inc, behold,\n",
"Nearest to five: four, seven, six, three, eight, zero, two, nine,\n",
"Nearest to first: second, last, same, nara, gassing, syndicalists, nepotism, parties,\n",
"Nearest to of: in, and, from, jutes, for, predecessor, collation, competent,\n",
"Nearest to by: were, in, with, apec, dilated, instruction, alpina, curiosity,\n",
"Nearest to at: in, during, johore, eat, pythagoreans, liberties, gnu, respite,\n",
"Nearest to its: their, his, the, bingo, our, kenyon, curry, ethereal,\n",
"Nearest to however: but, logo, when, although, would, therefore, regiments, and,\n",
"Average loss at step 32000: 3.503823\n",
"Average loss at step 34000: 3.490536\n",
"Average loss at step 36000: 3.451427\n",
"Average loss at step 38000: 3.301047\n",
"Average loss at step 40000: 3.427026\n",
"Nearest to that: which, what, this, where, however, renegades, catenary, when,\n",
"Nearest to world: lothair, farr, laszlo, league, undertook, dory, lebrun, civil,\n",
"Nearest to while: although, though, are, lordship, through, when, were, obituary,\n",
"Nearest to i: ii, we, he, you, meagre, lemnos, battlecruiser, mater,\n",
"Nearest to up: him, them, out, back, slur, fandom, regard, man,\n",
"Nearest to be: have, been, refer, were, being, macros, become, kajang,\n",
"Nearest to known: used, such, possible, scam, regarded, carlist, hennessy, lenin,\n",
"Nearest to had: has, have, was, were, became, began, rimet, penfield,\n",
"Nearest to as: when, gumby, incompatibility, suffices, quadrants, manuel, untreated, ts,\n",
"Nearest to five: seven, six, four, three, eight, two, zero, nine,\n",
"Nearest to first: second, last, same, nara, nepotism, next, best, speeding,\n",
"Nearest to of: in, sells, for, merriam, borg, from, exe, dale,\n",
"Nearest to by: alpina, dalton, dispersive, ramgoolam, be, with, dilated, paolo,\n",
"Nearest to at: during, in, peckinpah, quark, on, liberties, eclipsing, timorese,\n",
"Nearest to its: their, his, the, our, her, cheese, pye, kenyon,\n",
"Nearest to however: but, although, would, quijote, that, hardly, regiments, though,\n",
"Average loss at step 42000: 3.435784\n",
"Average loss at step 44000: 3.454770\n",
"Average loss at step 46000: 3.454720\n",
"Average loss at step 48000: 3.350677\n",
"Average loss at step 50000: 3.382323\n",
"Nearest to that: which, however, what, where, zilog, reunification, shallower, but,\n",
"Nearest to world: civil, laszlo, undertook, mulligan, lothair, lebrun, sport, floor,\n",
"Nearest to while: although, when, though, and, after, however, but, where,\n",
"Nearest to i: we, ii, lippincott, lemnos, he, t, extremist, you,\n",
"Nearest to up: him, them, out, off, back, down, rydberg, fandom,\n",
"Nearest to be: have, been, was, refer, were, being, is, become,\n",
"Nearest to known: used, possible, such, regarded, written, carlist, scam, hennessy,\n",
"Nearest to had: has, have, was, were, having, penfield, artois, since,\n",
"Nearest to as: sermon, catching, specificity, incompatibility, artcyclopedia, is, when, burg,\n",
"Nearest to five: four, six, seven, eight, three, nine, two, zero,\n",
"Nearest to first: second, last, same, next, nepotism, original, third, nara,\n",
"Nearest to of: in, and, gleichschaltung, for, including, bangui, vernacular, ichij,\n",
"Nearest to by: was, with, attenuated, baluchistan, burgundian, pallas, alpina, reminding,\n",
"Nearest to at: during, lifestyle, pythagoreans, timorese, insurgencies, in, vytautas, on,\n",
"Nearest to its: their, his, the, our, her, whose, exploitative, herbicides,\n",
"Nearest to however: but, although, that, though, while, when, and, hardly,\n",
"Average loss at step 52000: 3.434542\n",
"Average loss at step 54000: 3.425755\n",
"Average loss at step 56000: 3.439364\n",
"Average loss at step 58000: 3.394515\n",
"Average loss at step 60000: 3.395599\n",
"Nearest to that: which, what, however, this, renegades, there, shallower, where,\n",
"Nearest to world: civil, mulligan, laszlo, dory, abbotsford, lebrun, bosniaks, floor,\n",
"Nearest to while: although, when, though, after, identifiers, lehigh, if, lordship,\n",
"Nearest to i: we, ii, lippincott, you, they, t, lemnos, magnate,\n",
"Nearest to up: out, him, them, off, down, back, tamil, together,\n",
"Nearest to be: been, refer, was, have, become, kajang, is, macros,\n",
"Nearest to known: used, possible, such, written, regarded, carlist, called, described,\n",
"Nearest to had: has, have, was, were, having, been, transcribing, ve,\n",
"Nearest to as: artcyclopedia, gumby, ric, denouncing, hulme, became, in, panhandle,\n",
"Nearest to five: four, six, three, eight, seven, zero, nine, two,\n",
"Nearest to first: second, last, same, best, third, next, fourth, original,\n",
"Nearest to of: in, for, augustan, same, luce, although, dimmu, physical,\n",
"Nearest to by: with, alpina, without, dispersive, cairn, activate, baluchistan, was,\n",
"Nearest to at: dollar, insurgencies, during, namco, in, timorese, exe, troposphere,\n",
"Nearest to its: their, his, the, her, our, whose, instants, vulgar,\n",
"Nearest to however: but, although, though, that, highwayman, when, which, there,\n",
"Average loss at step 62000: 3.241005\n",
"Average loss at step 64000: 3.261436\n",
"Average loss at step 66000: 3.406607\n",
"Average loss at step 68000: 3.394912\n",
"Average loss at step 70000: 3.356614\n",
"Nearest to that: which, what, however, but, this, where, carnivores, shallower,\n",
"Nearest to world: abbotsford, mulligan, bosniaks, laszlo, floor, civil, lebrun, sun,\n",
"Nearest to while: although, where, though, when, including, and, however, if,\n",
"Nearest to i: we, ii, g, lippincott, you, mimeograph, moller, kulak,\n",
"Nearest to up: them, off, out, him, down, back, tamil, rydberg,\n",
"Nearest to be: been, is, refer, were, being, become, have, are,\n",
"Nearest to known: used, such, possible, regarded, written, defined, described, seen,\n",
"Nearest to had: has, have, was, were, having, been, penfield, began,\n",
"Nearest to as: burkert, before, cardiomyopathy, by, when, in, expand, artcyclopedia,\n",
"Nearest to five: four, six, three, seven, eight, nine, zero, two,\n",
"Nearest to first: second, last, same, next, original, best, fourth, synthesizing,\n",
"Nearest to of: including, augustan, barbecue, firstborn, in, amphibian, shia, nicely,\n",
"Nearest to by: through, using, from, baluchistan, in, be, alpina, dowager,\n",
"Nearest to at: during, namco, on, heh, lifestyle, in, after, transplanted,\n",
"Nearest to its: their, his, her, our, the, ashcroft, whose, vulgar,\n",
"Nearest to however: but, although, though, when, that, while, where, highwayman,\n",
"Average loss at step 72000: 3.372038\n",
"Average loss at step 74000: 3.348384\n",
"Average loss at step 76000: 3.310601\n",
"Average loss at step 78000: 3.347736\n",
"Average loss at step 80000: 3.375984\n",
"Nearest to that: which, however, where, what, renegades, but, zilog, shallower,\n",
"Nearest to world: bosniaks, laszlo, sun, mulligan, lebrun, country, floor, abbotsford,\n",
"Nearest to while: although, though, when, after, but, and, however, or,\n",
"Nearest to i: we, ii, g, you, t, lippincott, fianc, kabbalists,\n",
"Nearest to up: out, off, him, them, down, back, tamil, together,\n",
"Nearest to be: been, have, being, refer, become, macros, remain, were,\n",
"Nearest to known: used, regarded, such, possible, defined, described, written, called,\n",
"Nearest to had: have, has, was, were, having, began, been, artois,\n",
"Nearest to as: untreated, when, selenium, bins, like, arbitrarily, encompasses, ediacaran,\n",
"Nearest to five: six, four, seven, eight, three, nine, two, zero,\n",
"Nearest to first: second, last, third, same, next, best, final, fourth,\n",
"Nearest to of: pikes, including, during, entire, following, diner, collation, original,\n",
"Nearest to by: when, through, alpina, using, without, baluchistan, curiosity, after,\n",
"Nearest to at: in, during, on, namco, peckinpah, ovaries, alonso, exe,\n",
"Nearest to its: their, his, her, our, the, your, my, whose,\n",
"Nearest to however: although, but, that, though, highwayman, while, when, where,\n",
"Average loss at step 82000: 3.408579\n",
"Average loss at step 84000: 3.410099\n",
"Average loss at step 86000: 3.388013\n",
"Average loss at step 88000: 3.349884\n",
"Average loss at step 90000: 3.368032\n",
"Nearest to that: which, however, what, renegades, where, piloting, but, dominating,\n",
"Nearest to world: lebrun, bosniaks, mulligan, floor, lawful, civil, abbotsford, ruling,\n",
"Nearest to while: although, when, though, after, before, but, where, were,\n",
"Nearest to i: g, we, ii, you, t, iv, betty, lippincott,\n",
"Nearest to up: off, out, them, him, back, down, together, tamil,\n",
"Nearest to be: been, refer, become, being, was, is, have, remain,\n",
"Nearest to known: used, such, regarded, possible, described, seen, opposed, defined,\n",
"Nearest to had: has, have, was, were, having, began, continued, been,\n",
"Nearest to as: whip, havelock, gumby, denouncing, macron, artcyclopedia, brookline, serie,\n",
"Nearest to five: four, seven, eight, three, six, two, nine, zero,\n",
"Nearest to first: second, last, same, original, next, fourth, third, best,\n",
"Nearest to of: in, including, for, vernacular, barbecue, pikes, asuras, same,\n",
"Nearest to by: without, through, with, under, when, alpina, from, for,\n",
"Nearest to at: during, under, on, pythagoreans, without, troposphere, once, johore,\n",
"Nearest to its: their, his, her, the, our, your, whose, vulgar,\n",
"Nearest to however: but, although, that, though, highwayman, while, russel, variously,\n",
"Average loss at step 92000: 3.397000\n",
"Average loss at step 94000: 3.256320\n",
"Average loss at step 96000: 3.356558\n",
"Average loss at step 98000: 3.237762\n",
"Average loss at step 100000: 3.356719\n",
"Nearest to that: which, what, however, where, but, who, shallower, lineman,\n",
"Nearest to world: bosniaks, lebrun, country, ruling, issue, mercury, eddington, mulligan,\n",
"Nearest to while: although, when, though, where, but, before, after, however,\n",
"Nearest to i: we, ii, you, g, lippincott, fianc, betty, they,\n",
"Nearest to up: off, out, them, him, back, down, together, fandom,\n",
"Nearest to be: been, being, are, become, refer, is, macros, have,\n",
"Nearest to known: such, possible, used, regarded, seen, defined, described, opposed,\n",
"Nearest to had: has, have, was, were, having, since, would, artois,\n",
"Nearest to as: when, cardiomyopathy, bins, meshech, like, suffices, arbitrarily, toleration,\n",
"Nearest to five: seven, four, six, eight, three, two, nine, zero,\n",
"Nearest to first: last, second, next, third, fourth, final, same, original,\n",
"Nearest to of: in, including, original, sabina, and, sedentary, during, firstborn,\n",
"Nearest to by: through, without, when, under, using, resolvers, kitab, with,\n",
"Nearest to at: during, on, in, allusions, timorese, after, observational, pythagoreans,\n",
"Nearest to its: their, his, our, her, the, vulgar, your, instants,\n",
"Nearest to however: but, although, that, though, and, while, when, where,\n"
]
}
],
"source": [
"\n",
"\n",
"num_steps = 100001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.initialize_all_variables().run()\n",
" print('Initialized')\n",
" average_loss = 0\n",
" for step in range(num_steps):\n",
" batch_data, batch_labels = generate_batch(\n",
" batch_size, num_skips, skip_window)\n",
" feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
" _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
" average_loss += l\n",
" if step % 2000 == 0:\n",
" if step > 0:\n",
" average_loss = average_loss / 2000\n",
" # The average loss is an estimate of the loss over the last 2000 batches.\n",
" print('Average loss at step %d: %f' % (step, average_loss))\n",
" average_loss = 0\n",
" # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
" if step % 10000 == 0:\n",
" sim = similarity.eval()\n",
" for i in range(valid_size):\n",
" valid_word = reverse_dictionary[valid_examples[i]]\n",
" top_k = 8 # number of nearest neighbors\n",
" nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
" log = 'Nearest to %s:' % valid_word\n",
" for k in range(top_k):\n",
" close_word = reverse_dictionary[nearest[k]]\n",
" log = '%s %s,' % (log, close_word)\n",
" print(log)\n",
" final_embeddings = normalized_embeddings.eval()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_points = 400\n",
"\n",
"tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
"two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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lqM7Z+HN6r86bN4/f/va3dO7cGSgYGr53zzdJkk4WwfH2TXMQBI2B7Ozs7J/1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\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EWlpaszVGyMvLg4vLQJiamsLV1RUmJiZwcRnIOr/fB3l5eQwbNgxbt27F9u3bYWZmBisr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PEGPcGUL4AvATEsnfR8AjJDYAkCRJkiRJklrUXi3mL0mSJEmSJLVXndJdgCRJkiRJktQa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\nVOMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10efea2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot(embeddings, labels):\n",
" assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
" pylab.figure(figsize=(15,15)) # in inches\n",
" for i, label in enumerate(labels):\n",
" x, y = embeddings[i,:]\n",
" pylab.scatter(x, y)\n",
" pylab.annotate(label, xy=(x, y), xytext=(10, 2), textcoords='offset points',\n",
" ha='right', va='bottom')\n",
" pylab.show()\n",
"\n",
"words = [reverse_dictionary[i] for i in range(1, num_points+1)]\n",
"plot(two_d_embeddings, words)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:tensorflow]",
"language": "python",
"name": "conda-env-tensorflow-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
Michaelt293/ANZSMS-Programming-workshop | Python_tutorial.ipynb | 1 | 38981 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#ANZSMS 2015 Python Programming Workshop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Print Function "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The print function prints output to the command-line interface (terminal in OS X/Linux, powershell in Windows). In the following example, we print the text string \"Hello, world!\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, world!\n"
]
}
],
"source": [
"print(\"Hello, world!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Variables, numbers and math basic operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take the following statement, x = 33. In this statement, x is a variable with a value of 33 (integer literal). As its name suggests, variables can be reassigned when executing a program.\n",
"\n",
"Simple data types in Python include: integers, floating point numbers, strings and Boolean (True/False) values. In the following examples, the variables Ag and Au are assigned to integer values. The variables are then reassigned to floating point numbers and some simple math operations demonstrated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Au and Ag variables assigned to integer values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Integers are whole numbers, i.e., 1, 0, -20 etc."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Ag = 107"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Au = 197"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Ag)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Au)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Au and Ag variables reassigned to floating point values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Floats use a decimal point or exponential notation. Note that values such as 20.0, 1.0, 0.0 etc. are floats, not integers. Also, floating point values may not be a true representation of the number since computer use a binary (base-2) number system. This leads to floating point errors when working with floating point values."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Ag = 106.9"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Au = 197.0"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Ag)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Au)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Math operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python 3, math operations behave as you would expect. In Python 2, division is the same as floor division when working with integers!"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"303.9"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au + Ag # addition (Note: You can add comments to your code by using the # symbol)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"90.1"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au - Ag # subtraction"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"985.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au * 5 # multiplication"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11427.61"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag ** 2 # exponential - mass of silver squared in this case"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.842843779232928"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au / Ag # division"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au // Ag # floor division"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Type conversion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can convert a float to an integer or an integer to a float. Conversions of string representations of numbers to actual numbers (integers and floats) is also common in Python programming."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"integer_Ag = int(Ag)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"106"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"integer_Ag"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(integer_Ag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The modulo operator (%) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The modulo (%) operator gives the remainder from division. This is commonly used to check whether a number is odd or even. In mass spectrometry, we could use this to test whether an ion has an odd number of nitrogen atoms. For example, \n",
"mz = 114; if mz % 2 == 0:; print(\"Ion has an odd number of nitrogens\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"modulo = integer_Ag % 2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ion is even\n"
]
}
],
"source": [
"if modulo == 0:\n",
" print(\"Ion is even\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Strings and indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following example, we have a file name represented as a string. From this string, we can use to indexing to select certain characters or substrings from the file name string."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"String are simply strings of characters (letters, numbers, symbols etc.). Strings are indicated using either single quotes ('This is a string') or double quotes (\"This is another string\"). Multiline strings can be made using triple quotes (i.e. \"\"\"Multiline string\"\"\")."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"MS2_spectrum = \"Liver_MS2_406.raw\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Liver_MS2_406.raw'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS2_spectrum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Single characters are indexed using square brackets after the variable name. In Python, the first character is 0. Characters may also be index from the end of the string (starting at -1). "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'L'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS2_spectrum[0]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'w'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS2_spectrum[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Substrings can be indexed using string[start:end]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sample = MS2_spectrum[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Liver'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample # Note: The character at position 5 is not included in the substring."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ion = MS2_spectrum[10:13] "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'406'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ion"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"file_format = MS2_spectrum[-3:]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'raw'"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Type conversion - string to float"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(ion)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"406.0"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(ion)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Collection data types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Lists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists are mutable (i.e., modifiable), ordered collections of items. Lists are created by enclosing a collection of items with square brackets. An empty list may also be created simply by assigning [] to a variable, i.e., empty_list = []."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"MS_files = [\"MS_spectrum\", \"MS2_405\", \"MS2_471\", \"MS2_495\"]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['MS_spectrum', 'MS2_405', 'MS2_471', 'MS2_495']"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS_files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Indexing in lists is the same as for strings"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'MS2_471'"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS_files[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Several list 'methods' exist for manipulating lists"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"MS_files.remove(\"MS2_405\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['MS_spectrum', 'MS2_471', 'MS2_495']"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS_files"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"MS_files.append(\"MS3_225\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['MS_spectrum', 'MS2_471', 'MS2_495', 'MS3_225']"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MS_files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Tuples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tuples are immutable (i.e., can't modified after their creation), ordered collections of items and are the simplist collection data type. Tuples are created by enclosing a collection of items by parentheses). "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Fe_isotopes = (53.9, 55.9, 56.9, 57.9)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(53.9, 55.9, 56.9, 57.9)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fe_isotopes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Indexing"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"53.9"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fe_isotopes[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dictionaries are mutable, unordered collections of key: value pairs. Dictionaries are created created by enclosing key: value pairs with curly brackets. Importantly, keys must be hashable. This means, for example, that lists can't be used as keys since the items inside a list may be modified."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"carbon_isotopes = {\"12\": 0.9893, \"13\": 0.0107}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Fetching the value for a certain key"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9893"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carbon_isotopes[\"12\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Dictionary methods"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['13', '12'])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carbon_isotopes.keys()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_values([0.0107, 0.9893])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carbon_isotopes.values()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_items([('13', 0.0107), ('12', 0.9893)])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carbon_isotopes.items()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Sets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sets are another data type which are like an unordered list with no dublicates. They are especially useful for finding all the unique items from a list as shown below."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"phospholipids = [\"PA(16:0/18:1)\", \"PA(16:0/18:2)\", \"PC(14:0/16:0)\", \"PC(16:0/16:1)\", \"PC(16:1/16:2)\"]\n",
"# Lets assume we apply a function that finds the type of phospholipid name to \n",
"phospholipid_fatty_acids = [\"16:0\", \"18:1\", \"16:0\", \"18:2\", \"14:0\", \"16:0\", \"16:0\", \"16:1\", \"16:1\", \"16:2\"]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_fatty_acids = set(phospholipid_fatty_acids)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'14:0', '16:0', '16:1', '16:2', '18:1', '18:2'}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_fatty_acids "
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_unique_fa = len(unique_fatty_acids)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_unique_fa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Boolean operators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Boolean operators asses the truth or falseness of a statement. "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag > Au"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag < Au"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag == 106.9"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Au >= 100"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag <= Au and Ag > 200"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ag <= Au or Ag > 200"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Conditional statements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Code is only executed if the conditional statement is evaluated as True. In the following example, Ag has a value of greater than 100 and therefore only the \"Ag is greater than 100 Da\" string is printed. A colon follows the conditional statement and the following code block is indented by 4 spaces (always use 4 spaces rather than tabs - errors will resulting when mixing tabs with spaces!). Note, the elif and else statements are optional."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ag is greater than 100 Da.\n"
]
}
],
"source": [
"if Ag < 100:\n",
" print(\"Ag is less than 100 Da\")\n",
"elif Ag > 100:\n",
" print(\"Ag is greater than 100 Da.\")\n",
"else:\n",
" print(\"Ag is equal to 100 Da.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# While loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While loops repeat the execution of a code block while a condition is evaulated as True. When using while loops, be careful not to make an infinite loop where the conditional statement never evaluates as False. (Note: You could, however, use 'break' to break from an infinite loop.)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ask for money\n",
"Number of mass spectrometers equals 1\n",
"Ask for money\n",
"Number of mass spectrometers equals 2\n",
"Ask for money\n",
"Number of mass spectrometers equals 3\n",
"Ask for money\n",
"Number of mass spectrometers equals 4\n",
"Ask for money\n",
"Number of mass spectrometers equals 5\n",
"\n",
"Now we need more lab space\n"
]
}
],
"source": [
"mass_spectrometers = 0\n",
"while mass_spectrometers < 5:\n",
" print(\"Ask for money\")\n",
" mass_spectrometers = mass_spectrometers + 1\n",
" # Comment: This can be written as mass_spectrometers += 1\n",
" print(\"Number of mass spectrometers equals\", mass_spectrometers)\n",
" \n",
"print(\"\\nNow we need more lab space\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#For loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For loops iterate over each item of collection data types (lists, tuples, dictionaries and sets). For loops can also be used to loop over the characters of a string. In fact, this fact will be utilised later to evaluate each amino acid residue of a peptide string."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lipid_masses = [674.5, 688.6, 690.6, 745.7]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Na = 23.0\n",
"\n",
"lipid_Na_adducts = []\n",
"for mass in lipid_masses:\n",
" lipid_Na_adducts.append(mass + Na)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[697.5, 711.6, 713.6, 768.7]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lipid_Na_adducts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###List comprehension "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following is a list comprehension which performs the same operation of the for loop above but in less lines of code."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adducts_comp = [mass + Na for mass in lipid_masses]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[697.5, 711.6, 713.6, 768.7]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adducts_comp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could also add a predicate to a list comprehension. Here, we calculate the mass of lipids less than 700 Da."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"adducts_comp = [mass + Na for mass in lipid_masses if mass < 700]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[697.5, 711.6, 713.6]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adducts_comp "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#While and for loops with conditional statements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both while and for loops can be combined with conditional statements for greater control of flow within a program."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of mass spectrometers equals 1\n",
"Woohoo, the first of many!\n",
"Number of mass spectrometers equals 2\n",
"More!!\n",
"Number of mass spectrometers equals 3\n",
"More!!\n",
"Number of mass spectrometers equals 4\n",
"More!!\n",
"Number of mass spectrometers equals 5\n",
"That'll do for now.\n"
]
}
],
"source": [
"mass_spectrometers = 0\n",
"while mass_spectrometers < 5:\n",
" mass_spectrometers += 1\n",
" print(\"Number of mass spectrometers equals\", mass_spectrometers)\n",
" if mass_spectrometers == 1:\n",
" print(\"Woohoo, the first of many!\")\n",
" elif mass_spectrometers == 5:\n",
" print(\"That'll do for now.\")\n",
" else:\n",
" print(\"More!!\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MS file: MS_spectrum\n",
"MS2 file: MS2_471\n",
"MS2 file: MS2_495\n",
"MS3 file: MS3_225\n"
]
}
],
"source": [
"for MS_file in MS_files:\n",
" if \"spectrum\" in MS_file:\n",
" print(\"MS file:\", MS_file)\n",
" elif \"MS2\" in MS_file:\n",
" print(\"MS2 file:\", MS_file)\n",
" else:\n",
" print(\"MS3 file:\", MS_file)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#Exercise: Calculate peptide masses "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following example, we will calculate the mass of a peptide from a string containing one letter amino acid residue codes. For example, peptide = \"GASPV\". To do this, we will first need a dictionary containing the one letter codes as keys and the masses of the amino acid residues as values. We will then need to create a variable to store the mass of the peptide and use a for loop to iterate over each amino acid residue in the peptide."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"amino_dict = {\n",
" 'G': 57.02147,\n",
" 'A': 71.03712,\n",
" 'S': 87.03203,\n",
" 'P': 97.05277,\n",
" 'V': 99.06842,\n",
" 'T': 101.04768,\n",
" 'C': 103.00919,\n",
" 'I': 113.08407,\n",
" 'L': 113.08407,\n",
" 'N': 114.04293,\n",
" 'D': 115.02695,\n",
" 'Q': 128.05858,\n",
" 'K': 128.09497,\n",
" 'E': 129.0426,\n",
" 'M': 131.04049,\n",
" 'H': 137.05891,\n",
" 'F': 147.06842,\n",
" 'R': 156.10112,\n",
" 'Y': 163.06333,\n",
" 'W': 186.07932,\n",
" }\n",
"\n",
"# Data modified from http://www.its.caltech.edu/~ppmal/sample_prep/work3.html"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"peptide_name = \"SCIENCE\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mass = 18.010565\n",
"for amino_acid in peptide_name:\n",
" mass += amino_dict[amino_acid]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"796.2731749999999"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions perform a specified task when called during the execution of a program. Functions reduce the amount of code that needs to be written and greatly improves code readability. (Note: readability matters!) The for loop created above is better placed in a function so that the for loop doesn't need to be re-written everytime we wish to calculate the mass of a peptide. Pay careful attention to the syntax below."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def peptide_mass(peptide):\n",
" mass = 18.010565\n",
" for amino_acid in peptide:\n",
" mass += amino_dict[amino_acid]\n",
" return mass"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"796.2731749999999"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peptide_mass(peptide_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#User input"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A simple means to gather user inputted data is to use input. This will prompt the user to enter data which may be used within the program. In the example below, we prompt the user to enter a peptide name. The peptide name is then used for the function call to calculate the peptide's mass."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter peptide name: WHATTHEWTF\n"
]
}
],
"source": [
"user_peptide = input(\"Enter peptide name: \")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1314.5782049999998"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peptide_mass(user_peptide)"
]
}
],
"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
}
| cc0-1.0 |
edwardd1/phys202-2015-work | assignments/assignment06/LaTeXEx01.ipynb | 1 | 13209 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# LaTeX Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The images of the equations on this page were taken from the Wikipedia pages referenced for each equation."
]
},
{
"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": [
"## Typesetting equations"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the probability density of the normal distribution $f(x, \\mu, \\sigma)$, which can be found [here](http://en.wikipedia.org/wiki/Normal_distribution). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAN0AAAAvBAMAAACVsNI7AAAAMFBMVEX///+KiorMzMyenp4WFhZA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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='normaldist.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "19b733e9b9c40a9d640d0ff730227a31",
"grade": true,
"grade_id": "latexex01a",
"points": 2,
"solution": true
}
},
"source": [
"\\begin{equation*}\n",
"f(x, \\mu, \\sigma) = \\frac{1}{\\sigma \\sqrt{2 \\pi}} e ^{- \\frac{(x-\\mu)^{2}}{2 \\sigma ^ {2}}}\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the time-dependent Schrodinger equation for non-relativistic particles shown [here](http://en.wikipedia.org/wiki/Schr%C3%B6dinger_equation#Time-dependent_equation) (use the version that includes the Laplacian and potential energy). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVYAAAAwBAMAAAC4W09DAAAAMFBMVEX///8WFhZ0dHSKiooiIiIM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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='tdseqn.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "4d858b55aeb9117b8cfa6f706ab5b617",
"grade": true,
"grade_id": "latexex01b",
"points": 4,
"solution": true
}
},
"source": [
"\\begin{equation*}\n",
"i \\hbar \\frac{\\partial}{\\partial t} \\Psi(\\textbf{r}, t) = \\left[\\frac{-\\hbar ^{2}}{2 \\mu} \\nabla ^{2} + V(\\textbf{r}, t)\\right]\\Psi\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the Laplacian squared ($\\Delta=\\nabla^2$) acting on a scalar field $f(r,\\theta,\\phi)$ in spherical polar coordinates found [here](http://en.wikipedia.org/wiki/Laplace_operator#Two_dimensions). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
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"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='delsquared.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "625624933082a6695c8fd5512a808b77",
"grade": true,
"grade_id": "latexex01c",
"points": 4,
"solution": true
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"source": [
"\\begin{equation*}\n",
"\\Delta f = \\frac{1}{r ^{2}} \\frac{\\partial}{\\partial r} \\left(r ^{2} \\frac{\\partial f}{\\partial r} \\right) + \\frac{1}{r^{2} sin \\theta} \\frac{\\partial}{\\partial \\theta} \\left(sin \\theta \\frac{\\partial f}{\\partial \\theta} + \\frac{1}{r^{2}sin \\theta}\\frac{\\partial^{2} f}{\\partial \\psi^{2}} \\right)\n",
"\\end{equation*}"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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"file_extension": ".py",
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| mit |
CartoDB/cartoframes | docs/examples/data_visualization/legends/color_category_legend.ipynb | 1 | 58159 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Color category legend\n",
"The `color-category` type draws a categorical legend on your map that is based on your data's geometry using either the `color` (default) or `strokeColor` property of your visualization.\n",
"\n",
"To view available legend parameters, run `help(color_category_legend)`\n",
"\n",
"In this example, the category legend draws the top 3 tree species types in San Francisco."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from cartoframes.auth import set_default_credentials\n",
"\n",
"set_default_credentials('cartoframes')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" style=\"\n",
" border: 1px solid #cfcfcf;\n",
" width: 100%;\n",
" height: 632px;\n",
" \"\n",
" srcDoc=\"\n",
" <!DOCTYPE html>\n",
"<html lang="en">\n",
"<head>\n",
" <title>None</title>\n",
" <meta name="description" content="None">\n",
" <meta name="viewport" content="width=device-width, initial-scale=1.0">\n",
" <meta charset="UTF-8">\n",
" <!-- Include CARTO VL JS -->\n",
" <script src="https://libs.cartocdn.com/carto-vl/v1.4/carto-vl.min.js"></script>\n",
" <!-- Include Mapbox GL JS -->\n",
" <script src="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.js"></script>\n",
" <!-- Include Mapbox GL CSS -->\n",
" <link href="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.css" rel="stylesheet" />\n",
"\n",
" <!-- Include Airship -->\n",
" <script nomodule="" src="https://libs.cartocdn.com/airship-components/v2.3/airship.js"></script>\n",
" <script type="module" src="https://libs.cartocdn.com/airship-components/v2.3/airship/airship.esm.js"></script>\n",
" <script src="https://libs.cartocdn.com/airship-bridge/v2.3/asbridge.min.js"></script>\n",
" <link href="https://libs.cartocdn.com/airship-style/v2.3/airship.min.css" rel="stylesheet">\n",
" <link href="https://libs.cartocdn.com/airship-icons/v2.3/icons.css" rel="stylesheet">\n",
"\n",
" <link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet" type="text/css">\n",
"\n",
" <!-- External libraries -->\n",
"\n",
" <!-- pako -->\n",
" <script src="https://libs.cartocdn.com/cartoframes/dependencies/pako_inflate.min.js"></script>\n",
" \n",
" <!-- html2canvas -->\n",
" \n",
"\n",
" \n",
" <style>\n",
" body {\n",
" margin: 0;\n",
" padding: 0;\n",
" }\n",
"\n",
" aside.as-sidebar {\n",
" min-width: 300px;\n",
" }\n",
"\n",
" .map-image {\n",
" display: none;\n",
" max-width: 100%;\n",
" height: auto;\n",
" }\n",
"\n",
" as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n",
" font-size: 14px;\n",
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" }\n",
"</style>\n",
" <style>\n",
" .map {\n",
" position: absolute;\n",
" height: 100%;\n",
" width: 100%;\n",
" }\n",
"\n",
" .map-info {\n",
" position: absolute;\n",
" bottom: 0;\n",
" padding: 0 5px;\n",
" background-color: rgba(255, 255, 255, 0.5);\n",
" margin: 0;\n",
" color: rgba(0, 0, 0, 0.75);\n",
" font-size: 12px;\n",
" width: auto;\n",
" height: 18px;\n",
" font-family: 'Open Sans';\n",
" }\n",
"\n",
" .map-footer {\n",
" background: #F2F6F9;\n",
" font-family: Roboto;\n",
" font-size: 12px;\n",
" line-height: 24px;\n",
" color: #162945;\n",
" text-align: center;\n",
" z-index: 2;\n",
" }\n",
"\n",
" .map-footer a {\n",
" text-decoration: none;\n",
" }\n",
"\n",
" .map-footer a:hover {\n",
" text-decoration: underline;\n",
" }\n",
"</style>\n",
" <style>\n",
" #error-container {\n",
" position: absolute;\n",
" width: 100%;\n",
" height: 100%;\n",
" background-color: white;\n",
" visibility: hidden;\n",
" padding: 1em;\n",
" font-family: "Courier New", Courier, monospace;\n",
" margin: 0 auto;\n",
" font-size: 14px;\n",
" overflow: auto;\n",
" z-index: 1000;\n",
" color: black;\n",
" }\n",
"\n",
" .error-section {\n",
" padding: 1em;\n",
" border-radius: 5px;\n",
" background-color: #fee;\n",
" }\n",
"\n",
" #error-container #error-highlight {\n",
" font-weight: bold;\n",
" color: inherit;\n",
" }\n",
"\n",
" #error-container #error-type {\n",
" color: #008000;\n",
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"\n",
" #error-container #error-name {\n",
" color: #ba2121;\n",
" }\n",
"\n",
" #error-container #error-content {\n",
" margin-top: 0.4em;\n",
" }\n",
"\n",
" .error-details {\n",
" margin-top: 1em;\n",
" }\n",
"\n",
" #error-stacktrace {\n",
" list-style: none;\n",
" }\n",
"</style>\n",
" <style>\n",
" .popup-content {\n",
" display: flex;\n",
" flex-direction: column;\n",
" padding: 8px;\n",
" }\n",
"\n",
" .popup-name {\n",
" font-size: 12px;\n",
" font-weight: 400;\n",
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"\n",
" .popup-value {\n",
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"\n",
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" }\n",
"</style>\n",
" <style>\n",
" as-widget-header .as-widget-header__header {\n",
" margin-bottom: 8px;\n",
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"\n",
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"\n",
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" max-height: 250px;\n",
" }\n",
"</style>\n",
"</head>\n",
"\n",
"<body class="as-app-body as-app">\n",
" <img id="map-image" class="map-image" alt='Static map image' />\n",
" <as-responsive-content id="main-container">\n",
" \n",
" <main class="as-main">\n",
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" \n",
" <span slot="footer">Data: City of SF</span>\n",
" \n",
" </as-legend>\n",
" \n",
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" </div>\n",
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" \n",
" </as-layer-selector>\n",
"</div>\n",
" </div> <!-- as-panel -->\n",
" </div> <!-- as-map-panels -->\n",
" \n",
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" </main> <!-- as-main -->\n",
" </as-responsive-content>\n",
"\n",
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"</div>\n",
"</body>\n",
"\n",
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" var init = (function () {\n",
" 'use strict';\n",
"\n",
" const BASEMAPS = {\n",
" DarkMatter: carto.basemaps.darkmatter,\n",
" Voyager: carto.basemaps.voyager,\n",
" Positron: carto.basemaps.positron\n",
" };\n",
"\n",
" const attributionControl = new mapboxgl.AttributionControl({\n",
" compact: false\n",
" });\n",
"\n",
" const FIT_BOUNDS_SETTINGS = { animate: false, padding: 50, maxZoom: 16 };\n",
"\n",
" /** From https://github.com/errwischt/stacktrace-parser/blob/master/src/stack-trace-parser.js */\n",
"\n",
" /**\n",
" * This parses the different stack traces and puts them into one format\n",
" * This borrows heavily from TraceKit (https://github.com/csnover/TraceKit)\n",
" */\n",
"\n",
" const UNKNOWN_FUNCTION = '<unknown>';\n",
" const chromeRe = /^\\s*at (.*?) ?\\(((?:file|https?|blob|chrome-extension|native|eval|webpack|<anonymous>|\\/).*?)(?::(\\d+))?(?::(\\d+))?\\)?\\s*$/i;\n",
" const chromeEvalRe = /\\((\\S*)(?::(\\d+))(?::(\\d+))\\)/;\n",
" const winjsRe = /^\\s*at (?:((?:\\[object object\\])?.+) )?\\(?((?:file|ms-appx|https?|webpack|blob):.*?):(\\d+)(?::(\\d+))?\\)?\\s*$/i;\n",
" const geckoRe = /^\\s*(.*?)(?:\\((.*?)\\))?(?:^|@)((?:file|https?|blob|chrome|webpack|resource|\\[native).*?|[^@]*bundle)(?::(\\d+))?(?::(\\d+))?\\s*$/i;\n",
" const geckoEvalRe = /(\\S+) line (\\d+)(?: > eval line \\d+)* > eval/i;\n",
"\n",
" function parse(stackString) {\n",
" const lines = stackString.split('\\n');\n",
"\n",
" return lines.reduce((stack, line) => {\n",
" const parseResult =\n",
" parseChrome(line) ||\n",
" parseWinjs(line) ||\n",
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"\n",
" if (parseResult) {\n",
" stack.push(parseResult);\n",
" }\n",
"\n",
" return stack;\n",
" }, []);\n",
" }\n",
"\n",
" function parseChrome(line) {\n",
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"\n",
" if (!parts) {\n",
" return null;\n",
" }\n",
"\n",
" const isNative = parts[2] && parts[2].indexOf('native') === 0; // start of line\n",
" const isEval = parts[2] && parts[2].indexOf('eval') === 0; // start of line\n",
"\n",
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" if (isEval && submatch != null) {\n",
" // throw out eval line/column and use top-most line/column number\n",
" parts[2] = submatch[1]; // url\n",
" parts[3] = submatch[2]; // line\n",
" parts[4] = submatch[3]; // column\n",
" }\n",
"\n",
" return {\n",
" file: !isNative ? parts[2] : null,\n",
" methodName: parts[1] || UNKNOWN_FUNCTION,\n",
" arguments: isNative ? [parts[2]] : [],\n",
" lineNumber: parts[3] ? +parts[3] : null,\n",
" column: parts[4] ? +parts[4] : null,\n",
" };\n",
" }\n",
"\n",
" function parseWinjs(line) {\n",
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"\n",
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"\n",
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" column: parts[4] ? +parts[4] : null,\n",
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"\n",
" function parseGecko(line) {\n",
" const parts = geckoRe.exec(line);\n",
"\n",
" if (!parts) {\n",
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"\n",
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"\n",
" const submatch = geckoEvalRe.exec(parts[3]);\n",
" if (isEval && submatch != null) {\n",
" // throw out eval line/column and use top-most line number\n",
" parts[3] = submatch[1];\n",
" parts[4] = submatch[2];\n",
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" }\n",
"\n",
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" lineNumber: parts[4] ? +parts[4] : null,\n",
" column: parts[5] ? +parts[5] : null,\n",
" };\n",
" }\n",
"\n",
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" const error$ = document.getElementById('error-container');\n",
" const errors$ = error$.getElementsByClassName('errors');\n",
" const stacktrace$ = document.getElementById('error-stacktrace');\n",
"\n",
" errors$[0].innerHTML = e.name;\n",
" errors$[1].innerHTML = e.type;\n",
" errors$[2].innerHTML = e.message.replace(e.type, '');\n",
"\n",
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"\n",
" const stack = parse(e.stack);\n",
" const list = stack.map(item => {\n",
" return `<li>\n",
" at <span class="stacktrace-method">${item.methodName}:</span>\n",
" (${item.file}:${item.lineNumber}:${item.column})\n",
" </li>`;\n",
" });\n",
"\n",
" stacktrace$.innerHTML = list.join('\\n');\n",
" }\n",
"\n",
" // Computes the decimal coefficient and exponent of the specified number x with\n",
" // significant digits p, where x is positive and p is in [1, 21] or undefined.\n",
" // For example, formatDecimal(1.23) returns ["123", 0].\n",
" function formatDecimal(x, p) {\n",
" if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n",
" var i, coefficient = x.slice(0, i);\n",
"\n",
" // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n",
" // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n",
" return [\n",
" coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n",
" +x.slice(i + 1)\n",
" ];\n",
" }\n",
"\n",
" function exponent(x) {\n",
" return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n",
" }\n",
"\n",
" function formatGroup(grouping, thousands) {\n",
" return function(value, width) {\n",
" var i = value.length,\n",
" t = [],\n",
" j = 0,\n",
" g = grouping[0],\n",
" length = 0;\n",
"\n",
" while (i > 0 && g > 0) {\n",
" if (length + g + 1 > width) g = Math.max(1, width - length);\n",
" t.push(value.substring(i -= g, i + g));\n",
" if ((length += g + 1) > width) break;\n",
" g = grouping[j = (j + 1) % grouping.length];\n",
" }\n",
"\n",
" return t.reverse().join(thousands);\n",
" };\n",
" }\n",
"\n",
" function formatNumerals(numerals) {\n",
" return function(value) {\n",
" return value.replace(/[0-9]/g, function(i) {\n",
" return numerals[+i];\n",
" });\n",
" };\n",
" }\n",
"\n",
" // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n",
" var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n",
"\n",
" function formatSpecifier(specifier) {\n",
" if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n",
" var match;\n",
" return new FormatSpecifier({\n",
" fill: match[1],\n",
" align: match[2],\n",
" sign: match[3],\n",
" symbol: match[4],\n",
" zero: match[5],\n",
" width: match[6],\n",
" comma: match[7],\n",
" precision: match[8] && match[8].slice(1),\n",
" trim: match[9],\n",
" type: match[10]\n",
" });\n",
" }\n",
"\n",
" formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n",
"\n",
" function FormatSpecifier(specifier) {\n",
" this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n",
" this.align = specifier.align === undefined ? ">" : specifier.align + "";\n",
" this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n",
" this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n",
" this.zero = !!specifier.zero;\n",
" this.width = specifier.width === undefined ? undefined : +specifier.width;\n",
" this.comma = !!specifier.comma;\n",
" this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n",
" this.trim = !!specifier.trim;\n",
" this.type = specifier.type === undefined ? "" : specifier.type + "";\n",
" }\n",
"\n",
" FormatSpecifier.prototype.toString = function() {\n",
" return this.fill\n",
" + this.align\n",
" + this.sign\n",
" + this.symbol\n",
" + (this.zero ? "0" : "")\n",
" + (this.width === undefined ? "" : Math.max(1, this.width | 0))\n",
" + (this.comma ? "," : "")\n",
" + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0))\n",
" + (this.trim ? "~" : "")\n",
" + this.type;\n",
" };\n",
"\n",
" // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n",
" function formatTrim(s) {\n",
" out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n",
" switch (s[i]) {\n",
" case ".": i0 = i1 = i; break;\n",
" case "0": if (i0 === 0) i0 = i; i1 = i; break;\n",
" default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n",
" }\n",
" }\n",
" return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n",
" }\n",
"\n",
" var prefixExponent;\n",
"\n",
" function formatPrefixAuto(x, p) {\n",
" var d = formatDecimal(x, p);\n",
" if (!d) return x + "";\n",
" var coefficient = d[0],\n",
" exponent = d[1],\n",
" i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n",
" n = coefficient.length;\n",
" return i === n ? coefficient\n",
" : i > n ? coefficient + new Array(i - n + 1).join("0")\n",
" : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n",
" : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n",
" }\n",
"\n",
" function formatRounded(x, p) {\n",
" var d = formatDecimal(x, p);\n",
" if (!d) return x + "";\n",
" var coefficient = d[0],\n",
" exponent = d[1];\n",
" return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n",
" : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n",
" : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n",
" }\n",
"\n",
" var formatTypes = {\n",
" "%": function(x, p) { return (x * 100).toFixed(p); },\n",
" "b": function(x) { return Math.round(x).toString(2); },\n",
" "c": function(x) { return x + ""; },\n",
" "d": function(x) { return Math.round(x).toString(10); },\n",
" "e": function(x, p) { return x.toExponential(p); },\n",
" "f": function(x, p) { return x.toFixed(p); },\n",
" "g": function(x, p) { return x.toPrecision(p); },\n",
" "o": function(x) { return Math.round(x).toString(8); },\n",
" "p": function(x, p) { return formatRounded(x * 100, p); },\n",
" "r": formatRounded,\n",
" "s": formatPrefixAuto,\n",
" "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n",
" "x": function(x) { return Math.round(x).toString(16); }\n",
" };\n",
"\n",
" function identity(x) {\n",
" return x;\n",
" }\n",
"\n",
" var map = Array.prototype.map,\n",
" prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n",
"\n",
" function formatLocale(locale) {\n",
" var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n",
" currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n",
" currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n",
" decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n",
" numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n",
" percent = locale.percent === undefined ? "%" : locale.percent + "",\n",
" minus = locale.minus === undefined ? "-" : locale.minus + "",\n",
" nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n",
"\n",
" function newFormat(specifier) {\n",
" specifier = formatSpecifier(specifier);\n",
"\n",
" var fill = specifier.fill,\n",
" align = specifier.align,\n",
" sign = specifier.sign,\n",
" symbol = specifier.symbol,\n",
" zero = specifier.zero,\n",
" width = specifier.width,\n",
" comma = specifier.comma,\n",
" precision = specifier.precision,\n",
" trim = specifier.trim,\n",
" type = specifier.type;\n",
"\n",
" // The "n" type is an alias for ",g".\n",
" if (type === "n") comma = true, type = "g";\n",
"\n",
" // The "" type, and any invalid type, is an alias for ".12~g".\n",
" else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n",
"\n",
" // If zero fill is specified, padding goes after sign and before digits.\n",
" if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n",
"\n",
" // Compute the prefix and suffix.\n",
" // For SI-prefix, the suffix is lazily computed.\n",
" var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n",
" suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n",
"\n",
" // What format function should we use?\n",
" // Is this an integer type?\n",
" // Can this type generate exponential notation?\n",
" var formatType = formatTypes[type],\n",
" maybeSuffix = /[defgprs%]/.test(type);\n",
"\n",
" // Set the default precision if not specified,\n",
" // or clamp the specified precision to the supported range.\n",
" // For significant precision, it must be in [1, 21].\n",
" // For fixed precision, it must be in [0, 20].\n",
" precision = precision === undefined ? 6\n",
" : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n",
" : Math.max(0, Math.min(20, precision));\n",
"\n",
" function format(value) {\n",
" var valuePrefix = prefix,\n",
" valueSuffix = suffix,\n",
" i, n, c;\n",
"\n",
" if (type === "c") {\n",
" valueSuffix = formatType(value) + valueSuffix;\n",
" value = "";\n",
" } else {\n",
" value = +value;\n",
"\n",
" // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n",
" var valueNegative = value < 0 || 1 / value < 0;\n",
"\n",
" // Perform the initial formatting.\n",
" value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n",
"\n",
" // Trim insignificant zeros.\n",
" if (trim) value = formatTrim(value);\n",
"\n",
" // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n",
" if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n",
"\n",
" // Compute the prefix and suffix.\n",
" valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix;\n",
" valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n",
"\n",
" // Break the formatted value into the integer “value” part that can be\n",
" // grouped, and fractional or exponential “suffix” part that is not.\n",
" if (maybeSuffix) {\n",
" i = -1, n = value.length;\n",
" while (++i < n) {\n",
" if (c = value.charCodeAt(i), 48 > c || c > 57) {\n",
" valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n",
" value = value.slice(0, i);\n",
" break;\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" // If the fill character is not "0", grouping is applied before padding.\n",
" if (comma && !zero) value = group(value, Infinity);\n",
"\n",
" // Compute the padding.\n",
" var length = valuePrefix.length + value.length + valueSuffix.length,\n",
" padding = length < width ? new Array(width - length + 1).join(fill) : "";\n",
"\n",
" // If the fill character is "0", grouping is applied after padding.\n",
" if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n",
"\n",
" // Reconstruct the final output based on the desired alignment.\n",
" switch (align) {\n",
" case "<": value = valuePrefix + value + valueSuffix + padding; break;\n",
" case "=": value = valuePrefix + padding + value + valueSuffix; break;\n",
" case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n",
" default: value = padding + valuePrefix + value + valueSuffix; break;\n",
" }\n",
"\n",
" return numerals(value);\n",
" }\n",
"\n",
" format.toString = function() {\n",
" return specifier + "";\n",
" };\n",
"\n",
" return format;\n",
" }\n",
"\n",
" function formatPrefix(specifier, value) {\n",
" var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n",
" e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n",
" k = Math.pow(10, -e),\n",
" prefix = prefixes[8 + e / 3];\n",
" return function(value) {\n",
" return f(k * value) + prefix;\n",
" };\n",
" }\n",
"\n",
" return {\n",
" format: newFormat,\n",
" formatPrefix: formatPrefix\n",
" };\n",
" }\n",
"\n",
" var locale;\n",
" var format;\n",
" var formatPrefix;\n",
"\n",
" defaultLocale({\n",
" decimal: ".",\n",
" thousands: ",",\n",
" grouping: [3],\n",
" currency: ["$", ""],\n",
" minus: "-"\n",
" });\n",
"\n",
" function defaultLocale(definition) {\n",
" locale = formatLocale(definition);\n",
" format = locale.format;\n",
" formatPrefix = locale.formatPrefix;\n",
" return locale;\n",
" }\n",
"\n",
" function formatter(value, specifier) {\n",
" const formatFunc = specifier ? format(specifier) : formatValue;\n",
"\n",
" if (Array.isArray(value)) {\n",
" const [first, second] = value;\n",
" if (first === -Infinity) {\n",
" return `< ${formatFunc(second)}`;\n",
" }\n",
" if (second === Infinity) {\n",
" return `> ${formatFunc(first)}`;\n",
" }\n",
" return `${formatFunc(first)} - ${formatFunc(second)}`;\n",
" }\n",
" return formatFunc(value);\n",
" }\n",
"\n",
" function formatValue(value) {\n",
" if (typeof value === 'number') {\n",
" return formatNumber(value);\n",
" }\n",
" return value;\n",
" }\n",
"\n",
" function formatNumber(value) {\n",
" if (!Number.isInteger(value)) {\n",
" return value.toLocaleString(undefined, {\n",
" minimumFractionDigits: 2,\n",
" maximumFractionDigits: 3\n",
" });\n",
" }\n",
" return value.toLocaleString();\n",
" }\n",
"\n",
" function updateViewport(id, map) {\n",
" function updateMapInfo() {\n",
" const mapInfo$ = document.getElementById(id);\n",
" const center = map.getCenter();\n",
" const lat = center.lat.toFixed(6);\n",
" const lng = center.lng.toFixed(6);\n",
" const zoom = map.getZoom().toFixed(2);\n",
"\n",
" mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n",
" }\n",
"\n",
" updateMapInfo();\n",
"\n",
" map.on('zoom', updateMapInfo);\n",
" map.on('move', updateMapInfo);\n",
" }\n",
"\n",
" function getBasecolorSettings(basecolor) {\n",
" return {\n",
" 'version': 8,\n",
" 'sources': {},\n",
" 'layers': [{\n",
" 'id': 'background',\n",
" 'type': 'background',\n",
" 'paint': {\n",
" 'background-color': basecolor\n",
" }\n",
" }]\n",
" };\n",
" }\n",
"\n",
" function getImageElement(mapIndex) {\n",
" const id = mapIndex !== undefined ? `map-image-${mapIndex}` : 'map-image';\n",
" return document.getElementById(id);\n",
" }\n",
"\n",
" function getContainerElement(mapIndex) {\n",
" const id = mapIndex !== undefined ? `main-container-${mapIndex}` : 'main-container';\n",
" return document.getElementById(id);\n",
" }\n",
"\n",
" function saveImage(mapIndex) {\n",
" const img = getImageElement(mapIndex);\n",
" const container = getContainerElement(mapIndex);\n",
"\n",
" html2canvas(container)\n",
" .then((canvas) => setMapImage(canvas, img, container));\n",
" }\n",
"\n",
" function setMapImage(canvas, img, container) {\n",
" const src = canvas.toDataURL();\n",
" img.setAttribute('src', src);\n",
" img.style.display = 'block';\n",
" container.style.display = 'none';\n",
" }\n",
"\n",
" function resetPopupClick(interactivity) {\n",
" interactivity.off('featureClick');\n",
" }\n",
"\n",
" function resetPopupHover(interactivity) {\n",
" interactivity.off('featureHover');\n",
" }\n",
"\n",
" function setPopupsClick(map, clickPopup, hoverPopup, interactivity, attrs) {\n",
" interactivity.on('featureClick', (event) => {\n",
" updatePopup(map, clickPopup, event, attrs);\n",
" hoverPopup.remove();\n",
" });\n",
" }\n",
"\n",
" function setPopupsHover(map, hoverPopup, interactivity, attrs) {\n",
" interactivity.on('featureHover', (event) => {\n",
" updatePopup(map, hoverPopup, event, attrs);\n",
" });\n",
" }\n",
"\n",
" function updatePopup(map, popup, event, attrs) {\n",
" if (event.features.length > 0) {\n",
" let popupHTML = '';\n",
" const layerIDs = [];\n",
"\n",
" for (const feature of event.features) {\n",
" if (layerIDs.includes(feature.layerId)) {\n",
" continue;\n",
" }\n",
" // Track layers to add only one feature per layer\n",
" layerIDs.push(feature.layerId);\n",
"\n",
" for (const item of attrs) {\n",
" const variable = feature.variables[item.name];\n",
" if (variable) {\n",
" let value = variable.value;\n",
" value = formatter(value, item.format);\n",
"\n",
" popupHTML = `\n",
" <span class="popup-name">${item.title}</span>\n",
" <span class="popup-value">${value}</span>\n",
" ` + popupHTML;\n",
" }\n",
" }\n",
" }\n",
"\n",
" if (popupHTML) {\n",
" popup\n",
" .setLngLat([event.coordinates.lng, event.coordinates.lat])\n",
" .setHTML(`<div class="popup-content">${popupHTML}</div>`);\n",
"\n",
" if (!popup.isOpen()) {\n",
" popup.addTo(map);\n",
" }\n",
" } else {\n",
" popup.remove();\n",
" }\n",
" } else {\n",
" popup.remove();\n",
" }\n",
" }\n",
"\n",
" function setInteractivity(map, interactiveLayers, interactiveMapLayers) {\n",
" const interactivity = new carto.Interactivity(interactiveMapLayers);\n",
"\n",
" const clickPopup = new mapboxgl.Popup({\n",
" closeButton: true,\n",
" closeOnClick: false\n",
" });\n",
"\n",
" const hoverPopup = new mapboxgl.Popup({\n",
" closeButton: false,\n",
" closeOnClick: false\n",
" });\n",
"\n",
" const { clickAttrs, hoverAttrs } = _setInteractivityAttrs(interactiveLayers);\n",
"\n",
" resetPopupClick(map);\n",
" resetPopupHover(map);\n",
"\n",
" if (clickAttrs.length > 0) {\n",
" setPopupsClick(map, clickPopup, hoverPopup, interactivity, clickAttrs);\n",
" }\n",
"\n",
" if (hoverAttrs.length > 0) {\n",
" setPopupsHover(map, hoverPopup, interactivity, hoverAttrs);\n",
" }\n",
" }\n",
"\n",
" function _setInteractivityAttrs(interactiveLayers) {\n",
" let clickAttrs = [];\n",
" let hoverAttrs = [];\n",
"\n",
" interactiveLayers.forEach((interactiveLayer) => {\n",
" interactiveLayer.interactivity.forEach((interactivityDef) => {\n",
" if (interactivityDef.event === 'click') {\n",
" clickAttrs = clickAttrs.concat(interactivityDef.attrs);\n",
" } else if (interactivityDef.event === 'hover') {\n",
" hoverAttrs = hoverAttrs.concat(interactivityDef.attrs);\n",
" }\n",
" });\n",
" });\n",
"\n",
" return { clickAttrs, hoverAttrs };\n",
" }\n",
"\n",
" function renderWidget(widget, value) {\n",
" widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n",
"\n",
" if (value && widget.element) {\n",
" widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n",
" }\n",
" }\n",
"\n",
" function renderBridge(bridge, widget, mapLayer) {\n",
" widget.element = widget.element || document.querySelector(`#${widget.id}`);\n",
"\n",
" switch (widget.type) {\n",
" case 'histogram':\n",
" const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n",
" const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n",
" bridge[histogram](widget.element, widget.value, widget.options);\n",
" break;\n",
" case 'category':\n",
" bridge.category(widget.element, widget.value, widget.options);\n",
" break;\n",
" case 'animation':\n",
" widget.options.propertyName = widget.prop;\n",
" bridge.animationControls(widget.element, widget.value, widget.options);\n",
" break;\n",
" case 'time-series':\n",
" widget.options.propertyName = widget.prop;\n",
" bridge.timeSeries(widget.element, widget.value, widget.options);\n",
" break;\n",
" }\n",
" }\n",
"\n",
" function bridgeLayerWidgets(map, mapLayer, mapSource, widgets) {\n",
" const bridge = new AsBridge.VL.Bridge({\n",
" carto: carto,\n",
" layer: mapLayer,\n",
" source: mapSource,\n",
" map: map\n",
" });\n",
"\n",
" widgets\n",
" .filter((widget) => widget.has_bridge)\n",
" .forEach((widget) => renderBridge(bridge, widget, mapLayer));\n",
"\n",
" bridge.build();\n",
" }\n",
"\n",
" function _getWidgetType(layer, property, value) {\n",
" return layer.metadata && layer.metadata.properties[value] ?\n",
" layer.metadata.properties[value].type\n",
" : _getWidgetPropertyType(layer, property);\n",
" }\n",
"\n",
" function _getWidgetPropertyType(layer, property) {\n",
" return layer.metadata && layer.metadata.properties[property] ?\n",
" layer.metadata.properties[property].type\n",
" : null;\n",
" }\n",
"\n",
" function createLegends(layer, legends, layerIndex, mapIndex=0) {\n",
" if (legends.length) {\n",
" legends.forEach((legend, legendIndex) => _createLegend(layer, legend, layerIndex, legendIndex, mapIndex));\n",
" } else {\n",
" _createLegend(layer, legends, layerIndex, 0, mapIndex);\n",
" }\n",
" }\n",
"\n",
" function _createLegend(layer, legend, layerIndex, legendIndex, mapIndex=0) {\n",
" const element = document.querySelector(`#layer${layerIndex}_map${mapIndex}_legend${legendIndex}`);\n",
"\n",
" if (legend.prop) {\n",
" const othersLabel = 'Others'; // TODO: i18n\n",
" const prop = legend.prop;\n",
" const dynamic = legend.dynamic;\n",
" const order = legend.ascending ? 'ASC' : 'DESC';\n",
" const variable = legend.variable;\n",
" const config = { othersLabel, variable, order };\n",
" const formatFunc = (value) => formatter(value, legend.format);\n",
" const options = { format: formatFunc, config, dynamic };\n",
"\n",
" if (legend.type.startsWith('size-continuous')) {\n",
" config.samples = 4;\n",
" }\n",
"\n",
" AsBridge.VL.Legends.rampLegend(element, layer, prop, options);\n",
" }\n",
" }\n",
"\n",
" function SourceFactory() {\n",
" const sourceTypes = { GeoJSON, Query, MVT };\n",
"\n",
" this.createSource = (layer) => {\n",
" return sourceTypes[layer.type](layer);\n",
" };\n",
" }\n",
"\n",
" function GeoJSON(layer) {\n",
" const options = JSON.parse(JSON.stringify(layer.options));\n",
" const data = _decodeJSONData(layer.data, layer.encode_data);\n",
"\n",
" return new carto.source.GeoJSON(data, options);\n",
" }\n",
"\n",
" function Query(layer) {\n",
" const auth = {\n",
" username: layer.credentials.username,\n",
" apiKey: layer.credentials.api_key || 'default_public'\n",
" };\n",
"\n",
" const config = {\n",
" serverURL: layer.credentials.base_url || `https://${layer.credentials.username}.carto.com/`\n",
" };\n",
"\n",
" return new carto.source.SQL(layer.data, auth, config);\n",
" }\n",
"\n",
" function MVT(layer) {\n",
" return new carto.source.MVT(layer.data.file, JSON.parse(layer.data.metadata));\n",
" }\n",
"\n",
" function _decodeJSONData(data, encodeData) {\n",
" try {\n",
" if (encodeData) {\n",
" const decodedJSON = pako.inflate(atob(data), { to: 'string' });\n",
" return JSON.parse(decodedJSON);\n",
" } else {\n",
" return JSON.parse(data);\n",
" }\n",
" } catch(error) {\n",
" throw new Error(`\n",
" Error: "${error}". CARTOframes is not able to parse your local data because it is too large.\n",
" Please, disable the data compresion with encode_data=False in your Layer class.\n",
" `);\n",
" }\n",
" }\n",
"\n",
" const factory = new SourceFactory();\n",
"\n",
" function initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex) {\n",
" const mapSource = factory.createSource(layer);\n",
" const mapViz = new carto.Viz(layer.viz);\n",
" const mapLayer = new carto.Layer(`layer${layerIndex}`, mapSource, mapViz);\n",
" const mapLayerIndex = numLayers - layerIndex - 1;\n",
"\n",
" try {\n",
" mapLayer._updateLayer.catch(displayError);\n",
" } catch (e) {\n",
" throw e;\n",
" }\n",
"\n",
" mapLayer.addTo(map);\n",
"\n",
" setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends);\n",
" setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource);\n",
"\n",
" return mapLayer;\n",
" }\n",
"\n",
" function getInteractiveLayers(layers, mapLayers) {\n",
" const interactiveLayers = [];\n",
" const interactiveMapLayers = [];\n",
"\n",
" layers.forEach((layer, index) => {\n",
" if (layer.interactivity) {\n",
" interactiveLayers.push(layer);\n",
" interactiveMapLayers.push(mapLayers[index]);\n",
" }\n",
" });\n",
"\n",
" return { interactiveLayers, interactiveMapLayers };\n",
" }\n",
"\n",
" function setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends) {\n",
" if (hasLegends && layer.legends) {\n",
" createLegends(mapLayer, layer.legends, mapLayerIndex, mapIndex);\n",
" }\n",
" }\n",
"\n",
" function setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource) {\n",
" if (layer.widgets.length) {\n",
" initLayerWidgets(layer.widgets, mapLayerIndex);\n",
" updateLayerWidgets(layer.widgets, mapLayer);\n",
" bridgeLayerWidgets(map, mapLayer, mapSource, layer.widgets);\n",
" }\n",
" }\n",
"\n",
" function initLayerWidgets(widgets, mapLayerIndex) {\n",
" widgets.forEach((widget, widgetIndex) => {\n",
" const id = `layer${mapLayerIndex}_widget${widgetIndex}`;\n",
" widget.id = id;\n",
" });\n",
" }\n",
"\n",
" function updateLayerWidgets(widgets, mapLayer) {\n",
" mapLayer.on('updated', () => renderLayerWidgets(widgets, mapLayer));\n",
" }\n",
"\n",
" function renderLayerWidgets(widgets, mapLayer) {\n",
" const variables = mapLayer.viz.variables;\n",
"\n",
" widgets\n",
" .filter((widget) => !widget.has_bridge)\n",
" .forEach((widget) => {\n",
" const name = widget.variable_name;\n",
" const value = getWidgetValue(name, variables);\n",
" renderWidget(widget, value);\n",
" });\n",
" }\n",
"\n",
" function getWidgetValue(name, variables) {\n",
" return name && variables[name] ? variables[name].value : null;\n",
" }\n",
"\n",
" function setReady(settings) {\n",
" try {\n",
" return settings.maps ? initMaps(settings.maps) : initMap(settings);\n",
" } catch (e) {\n",
" displayError(e);\n",
" }\n",
" }\n",
"\n",
" function initMaps(maps) {\n",
" return maps.map((mapSettings, mapIndex) => {\n",
" return initMap(mapSettings, mapIndex);\n",
" });\n",
" }\n",
"\n",
" function initMap(settings, mapIndex) {\n",
" const basecolor = getBasecolorSettings(settings.basecolor);\n",
" const basemapStyle = BASEMAPS[settings.basemap] || settings.basemap || basecolor;\n",
" const container = mapIndex !== undefined ? `map-${mapIndex}` : 'map';\n",
" const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n",
"\n",
" if (settings.show_info) {\n",
" const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n",
" updateViewport(id, map);\n",
" }\n",
"\n",
" if (settings.camera) {\n",
" map.flyTo(settings.camera);\n",
" }\n",
"\n",
" return initLayers(map, settings, mapIndex);\n",
" }\n",
"\n",
" function initLayers(map, settings, mapIndex) {\n",
" const numLayers = settings.layers.length;\n",
" const hasLegends = settings.has_legends;\n",
" const isStatic = settings.is_static;\n",
" const layers = settings.layers;\n",
" const mapLayers = getMapLayers(\n",
" layers,\n",
" numLayers,\n",
" hasLegends,\n",
" map,\n",
" mapIndex\n",
" );\n",
"\n",
" if (settings.layer_selector) {\n",
" addLayersSelector(layers.reverse(), mapLayers.reverse(), mapIndex);\n",
" }\n",
"\n",
" setInteractiveLayers(map, layers, mapLayers);\n",
"\n",
" return waitForMapLayersLoad(isStatic, mapIndex, mapLayers);\n",
" }\n",
"\n",
" function waitForMapLayersLoad(isStatic, mapIndex, mapLayers) {\n",
" return new Promise((resolve) => {\n",
" carto.on('loaded', mapLayers, onMapLayersLoaded.bind(\n",
" this, isStatic, mapIndex, mapLayers, resolve)\n",
" );\n",
" });\n",
" }\n",
"\n",
" function onMapLayersLoaded(isStatic, mapIndex, mapLayers, resolve) {\n",
" if (isStatic) {\n",
" saveImage(mapIndex);\n",
" }\n",
"\n",
" resolve(mapLayers);\n",
" }\n",
"\n",
" function getMapLayers(layers, numLayers, hasLegends, map, mapIndex) {\n",
" return layers.map((layer, layerIndex) => {\n",
" return initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex);\n",
" });\n",
" }\n",
"\n",
" function setInteractiveLayers(map, layers, mapLayers) {\n",
" const { interactiveLayers, interactiveMapLayers } = getInteractiveLayers(layers, mapLayers);\n",
"\n",
" if (interactiveLayers && interactiveLayers.length > 0) {\n",
" setInteractivity(map, interactiveLayers, interactiveMapLayers);\n",
" }\n",
" }\n",
"\n",
" function addLayersSelector(layers, mapLayers, mapIndex) {\n",
" const layerSelectorId = mapIndex !== undefined ? `#layer-selector-${mapIndex}` : '#layer-selector';\n",
" const layerSelector$ = document.querySelector(layerSelectorId);\n",
" const layersInfo = mapLayers.map((layer, index) => {\n",
" return {\n",
" title: layers[index].title || `Layer ${index}`,\n",
" id: layer.id,\n",
" checked: true\n",
" };\n",
" });\n",
"\n",
" const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n",
"\n",
" layerSelector.build();\n",
" }\n",
"\n",
" function createMap(container, basemapStyle, bounds, accessToken) {\n",
" const map = createMapboxGLMap(container, basemapStyle, accessToken);\n",
"\n",
" map.addControl(attributionControl);\n",
" map.fitBounds(bounds, FIT_BOUNDS_SETTINGS);\n",
"\n",
" return map;\n",
" }\n",
"\n",
" function createMapboxGLMap(container, style, accessToken) {\n",
" if (accessToken) {\n",
" mapboxgl.accessToken = accessToken;\n",
" }\n",
"\n",
" return new mapboxgl.Map({\n",
" container,\n",
" style,\n",
" zoom: 9,\n",
" dragRotate: false,\n",
" attributionControl: false\n",
" });\n",
" }\n",
"\n",
" function init(settings) {\n",
" setReady(settings);\n",
" }\n",
"\n",
" return init;\n",
"\n",
"}());\n",
"</script>\n",
"<script>\n",
" document\n",
" .querySelector('as-responsive-content')\n",
" .addEventListener('ready', () => {\n",
" const basecolor = '';\n",
" const basemap = 'Positron';\n",
" const bounds = [[-122.5085209, 37.70792829], [-122.3724441, 37.80902219]];\n",
" const camera = null;\n",
" const has_legends = 'true' === 'true';\n",
" const is_static = 'None' === 'true';\n",
" const layer_selector = 'False' === 'true';\n",
" const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"trees_sf\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vd1e609", "title": "common_species"}, "event": "hover"}], "legends": [{"ascending": true, "description": "Top 3 species", "dynamic": true, "footer": "Data: City of SF", "format": null, "prop": "color", "title": "Trees in San Francisco", "type": "color-category", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"trees_sf\\"", "title": null, "type": "Query", "viz": "@vd1e609: prop(\\u0027common_species\\u0027)\\ncolor: opacity(ramp(top(prop(\\u0027common_species\\u0027), 3), bold),1)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n",
" const mapboxtoken = '';\n",
" const show_info = 'None' === 'true';\n",
"\n",
" init({\n",
" basecolor,\n",
" basemap,\n",
" bounds,\n",
" camera,\n",
" has_legends,\n",
" is_static,\n",
" layer_selector,\n",
" layers,\n",
" mapboxtoken,\n",
" show_info\n",
" });\n",
"});\n",
"</script>\n",
"</html>\n",
"\">\n",
"\n",
"</iframe>"
],
"text/plain": [
"<cartoframes.viz.layer.Layer at 0x7f3b0a84e880>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from cartoframes.viz import Layer, color_category_style, color_category_legend\n",
"\n",
"Layer(\n",
" 'trees_sf',\n",
" color_category_style('common_species', top=3),\n",
" legends=color_category_legend(\n",
" title='Trees in San Francisco',\n",
" description='Top 3 species',\n",
" footer='Data: City of SF'\n",
" )\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
ChristinaLK/xd | misc/plotting.ipynb | 1 | 10837 | {
"metadata": {
"name": "",
"signature": "sha256:67bc813e70642aa32e2996e280d8a18755558387cb9df4e1882009281b950285"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"%matplotlib"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using matplotlib backend: Qt4Agg\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv(\"madison_PI_2014-04-30_to_2015-04-30_aggregate.csv\")\n",
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"Index([u'PI', u'CPU Hours: Total', u'Job Size: Per Job (Core Count)'], dtype='object')"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(df[\"PI\"], df[\"CPU Hours: Total\"], 'ro')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: Halzen, Francis ",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-d9194ca4bc8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"PI\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"CPU Hours: Total\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ro'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2985\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2986\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2987\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2988\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2989\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 4136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4137\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4138\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4139\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36madd_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m 1495\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1497\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1498\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_label\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[0;32m 1499\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_line%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36m_update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m 1506\u001b[0m \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1507\u001b[0m \"\"\"\n\u001b[1;32m-> 1508\u001b[1;33m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\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 1509\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\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[0;32m 1510\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\lines.pyc\u001b[0m in \u001b[0;36mget_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 741\u001b[0m \"\"\"\n\u001b[0;32m 742\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 743\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecache\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 744\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 745\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\matplotlib\\lines.pyc\u001b[0m in \u001b[0;36mrecache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 419\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 421\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 422\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\numpy\\core\\numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \"\"\"\n\u001b[1;32m--> 462\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: Halzen, Francis "
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
UWSEDS/LectureNotes | week_4/Exceptions.ipynb | 1 | 16840 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An exception is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions.\n",
"\n",
"You've already seen some exceptions in the **Debugging** lesson.\n",
"* "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many programs want to know about exceptions when they occur. For example, if the input to a program is a file path. If the user inputs an invalid or non-existent path, the program generates an exception. It may be desired to provide a response to the user in this case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It may also be that programs will *generate* exceptions. This is a way of indicating that there is an error in the inputs provided. In general, this is the preferred style for dealing with invalid inputs or states inside a python function rather than having an error return."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Catching Exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python provides a way to detect when an exception occurs. This is done by the use of a block of code surrounded by a \"try\" and \"except\" statement."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def divide(numerator, denominator):\n",
" result = numerator/denominator\n",
" print(\"result = %f\" % result)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "ZeroDivisionError",
"evalue": "float division by zero",
"output_type": "error",
"traceback": [
"\u001b[0;31m--------------------------------------------------------------\u001b[0m",
"\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-ebcb36a8aa31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdivide\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-4-2d5d8a17c73f>\u001b[0m in \u001b[0;36mdivide\u001b[0;34m(numerator, denominator)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdivide\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdenominator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumerator\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdenominator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"result = %f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
]
}
],
"source": [
"divide(1.0, 0)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def divide1(numerator, denominator):\n",
" try:\n",
" GARBAGE\n",
" result = numerator/denominator\n",
" print(\"result = %f\" % result)\n",
" except (ZeroDivisionError, NameError) as err:\n",
" import pdb; pdb.set_trace()\n",
" print(\"You can't divide by 0! or use GARBAGE.\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> <ipython-input-18-5b38ede8ee30>(8)divide1()\n",
"-> print(\"You can't divide by 0! or use GARBAGE.\")\n",
"(Pdb) type(err)\n",
"<class 'NameError'>\n",
"(Pdb) isinstance(err, Exception)\n",
"True\n",
"(Pdb) exit()\n"
]
},
{
"ename": "BdbQuit",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m--------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-5b38ede8ee30>\u001b[0m in \u001b[0;36mdivide1\u001b[0;34m(numerator, denominator)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mGARBAGE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumerator\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdenominator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'GARBAGE' is not defined",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mBdbQuit\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-a9166f13178f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdivide1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-18-5b38ede8ee30>\u001b[0m in \u001b[0;36mdivide1\u001b[0;34m(numerator, denominator)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mZeroDivisionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNameError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You can't divide by 0! or use GARBAGE.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-18-5b38ede8ee30>\u001b[0m in \u001b[0;36mdivide1\u001b[0;34m(numerator, denominator)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mZeroDivisionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNameError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You can't divide by 0! or use GARBAGE.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mtrace_dispatch\u001b[0;34m(self, frame, event, arg)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;31m# None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'line'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'call'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mdispatch_line\u001b[0;34m(self, frame)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_here\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbreak_here\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquitting\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mBdbQuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_dispatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mBdbQuit\u001b[0m: "
]
}
],
"source": [
"divide1(1.0, 'a')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'err' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m--------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-8f0ccd491a61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'err' is not defined"
]
}
],
"source": [
"print(err)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"result = 0.500000\n"
]
}
],
"source": [
"divide1(1.0, 2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You can't divide by 0!\n"
]
}
],
"source": [
"divide1(\"x\", 2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def divide2(numerator, denominator):\n",
" try:\n",
" result = numerator / denominator\n",
" print(\"result = %f\" % result)\n",
" except (ZeroDivisionError, TypeError) as err:\n",
" print(\"Got an exception: %s\" % err)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got an exception: unsupported operand type(s) for /: 'int' and 'str'\n"
]
}
],
"source": [
"divide2(1, \"X\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#divide2(\"x, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Why didn't we catch this `SyntaxError`?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Handle division by 0 by using a small number\n",
"SMALL_NUMBER = 1e-3\n",
"def divide3(numerator, denominator):\n",
" try:\n",
" result = numerator/denominator\n",
" except ZeroDivisionError:\n",
" result = numerator/SMALL_NUMBER\n",
" print(\"result = %f\" % result)\n",
" except Exception as err:\n",
" print(\"Different error than division by zero:\", err)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"result = 1000.000000\n"
]
}
],
"source": [
"divide3(1,0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Different error than division by zero: unsupported operand type(s) for /: 'str' and 'int'\n"
]
}
],
"source": [
"divide3(\"1\",0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What do you do when you get an exception?\n",
"\n",
"First, you can feel relieved that you caught a problematic element of your software! Yes, relieved. Silent fails are much worse. (Again, another plug for testing.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating Exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Why *generate* exceptions? (Don't I have enough unintentional errors?)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"def validateDF(df):\n",
" \"\"\"\"\n",
" :param pd.DataFrame df: should have a column named \"hours\"\n",
" \"\"\"\n",
" if not \"hours\" in df.columns:\n",
" raise ValueError(\"DataFrame should have a column named 'hours'.\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'hours': range(10) })\n",
"validateDF(df)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"class SeattleCrimeError(Exception):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"ename": "SeattleCrimeError",
"evalue": "There's been a crime!",
"output_type": "error",
"traceback": [
"\u001b[0;31m--------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSeattleCrimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-bd30635a6cac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mSeattleCrimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"There's been a crime!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mSeattleCrimeError\u001b[0m: There's been a crime!"
]
}
],
"source": [
"b = False\n",
"if not b:\n",
" raise SeattleCrimeError(\"There's been a crime!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class exercise\n",
"For the ``entropy`` function, create a new functions that\n",
"throws an exception if the argument is not a valid probability distribution."
]
}
],
"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
}
| bsd-2-clause |
chicago-justice-project/article-tagging | lib/notebooks/senteval_budgeting.ipynb | 1 | 217157 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import json\n",
"sys.path.append('..')\n",
"import tagnews\n",
"import matplotlib.pyplot as plt\n",
"import datetime as dt\n",
"import numpy as np\n",
"import pandas as pd\n",
"pd.set_option('display.width', 150)\n",
"pd.set_option('max.columns', 15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Determine the costs of processing existing articles\n",
"\n",
"Based on complete data files from through 2019-09-07.\n",
"\n",
"Each 1000 words of an article submitted is one \"unit\", rounded up.\n",
"\n",
"1,496,665 units total = $2487 to process at once, or 300 months in free batches of 5k..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/rachel/Code/civic_data/article-tagging/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
"../tagnews/utils/load_data.py:236: RuntimeWarning: 5 location strings were not found in the bodytext.\n",
" RuntimeWarning)\n"
]
}
],
"source": [
"crimetags = tagnews.CrimeTags()\n",
"\n",
"df_all = tagnews.load_data()\n",
"df_all['read_date'] = df_all['created'].str.slice(0, 10)\n",
"### Limiting it to last two years because the data volume is unstable before that\n",
"df = df_all.loc[df_all['read_date'] >= '2017-01-01']\n",
"del df_all\n",
"### Number of units to process title and article through Google Cloud API\n",
"df['n_chars'] = df['title'].str.len() + df['bodytext'].str.len()\n",
"df['n_units'] = np.ceil(df['n_chars']/1000.)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def calculate_google_nlp_price(total_units, verbose=True):\n",
" '''Cost to run entity sentiment analysis on a given number of \n",
" units in a single month through in Google Cloud API.\n",
" https://cloud.google.com/natural-language/#natural-language-api-pricing\n",
" \n",
" First 5000 = free\n",
" 5k-1M = $2 per 1000 units\n",
" 1M-5M = $1 per 1000 units\n",
" 5M-20M = $0.5 per 1000 units\n",
" '''\n",
" free_units = min(5e3, total_units)\n",
" first_tier_units = min(1e6-5e3, total_units-free_units)\n",
" second_tier_units = min(5e6-1e6, total_units-free_units-first_tier_units)\n",
" third_tier_units = max(0, total_units-free_units-first_tier_units-second_tier_units)\n",
" units = [free_units, first_tier_units, second_tier_units, third_tier_units]\n",
" costs = [0, 2., 1., 0.5]\n",
" total_cost = sum([c*np.ceil(u/1e3) for (c, u) in zip(costs, units)])\n",
" if verbose:\n",
" print('{:.0f} units: {:.0f}*0 + {:.0f}*$2 + {:.0f}*$1 + {:.0f}*$0.50 = ${:.2f}'\n",
" .format(total_units,\n",
" np.ceil(free_units/1e3),\n",
" np.ceil(first_tier_units/1e3),\n",
" np.ceil(second_tier_units/1e3),\n",
" np.ceil(third_tier_units/1e3),\n",
" total_cost))\n",
" return total_cost"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1496665 units: 5*0 + 995*$2 + 497*$1 + 0*$0.50 = $2487.00\n"
]
}
],
"source": [
"units = df['n_units'].sum()\n",
"cost = calculate_google_nlp_price(units)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-01-01 2019-09-07\n"
]
}
],
"source": [
"units_per_day = (df\n",
" .groupby('read_date')\n",
" .agg({'url': 'count',\n",
" 'n_units': 'sum'})\n",
" )\n",
"print(units_per_day.index.min(), units_per_day.index.max())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fd00ff57358>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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b6yGEtIADIVlq5FmTsy5eK40wsviLrzyIR5IkG6R3mUlNtV7GNIn/09a4dm6WndSiE6Yo5ppy564TeOF7bsUtO44t9qmQRaaTbJYGwOcAPGat/YDafqHa7TcBPJL8fD2A1xpj+owxTwNwOYAHAGwCcLkx5mnGmAriJCnXz81lEEI6gQMhWSrkJVpw2SxX+Gu8/9QEvrf9CP7iKw8u9qmQNgTLzB1C2uVDB87g/r2n3HbtOtlOwHZzTyZqgff7Mruds0Iypj544PQinwlZbDrJZvliAH8I4GFjzNZk2zsA/J4x5nmIvWCeBPDnAGCtfdQY83XEiU0CAG+y1oYAYIz5CwA3ASgCuNpa++gcXgshpB0cCMkSweQkWhARt9JfY7l+08xMQnqG5SbmhFseO4YT41V8500vBpCpMxfMXZ25rJsl475TCkn7D3lPVjydZLO8G+lijObGFp95D4D35Gy/sdXnCFkIakGEggFKxa7y/ywL5mteMVkL8N++/CDe/es/gR85Z3B+voSsSHzLHAHSCS2lXO8zkwLZvYxeP6jWU6Glyw3Uwgj/8v3H8fQNw/jtn7mk4RjdJEBpdLMEbtlxDC+4bD3WDJa7OPPlh0xhmOGTrLzZLFnx/Pjf/Dt++5P3LvZpLArz5WZ5y2PH8cOdJ/DPNz0+L8cnKw+XaEFtszTNAUgFLg1zvc9ytcwB/rVp0RqEET7xwyfwlm9sy/1cNwlQJqq+m+WpiSpe/4XN+LMvbu7ybJcfzjK3vNYLyAygmCMrkm2HVmbigPkSc7QUkDkneZny3tkVnwBF2hvVXM+z3LJZah1WD/MzWLazvNW7SAozVc9Y5pKPPn5ktONjLFeKhbj9MxaeUMwRsgJw8Ufz1OfLYFLg5JLMMXlulit97iITZ7a23me5ZbOMPGucEnCRdeNMu2yV3QjcbMyc3M/R6QDv/ffHV3QMnYy3FHOEYo6QFYArwDxvlrn43wJnlz3Lkycn8MC+kcU+jY5x2Sz1RnpZAkgny1w76X2Wm2VOu0jWvaQnEVb1xWkYsta0LN3EEWbdLPX9/NQdTzSIvZVEoSBulsvrHSPdQzG3QFx25Q1405eZRposDsat4M3P8dMYHs4ue5X//L4f4jWfvm+xT6NjUmuyyma54mVcTN1Z5tjeep3lFjPX1M0yilxCkqwAy6LvSTvLWjYBStbSOd7mu5YzRVrmSALF3AJyw8NHFvsUVjwr1SVjvi1zkYvhab/vc9/1A3zoll3zch5k+eG5WYplboW2Y4GWuaXDcstmqceQbJzc6v5YzLWzlmlB1k7sTmasfNl4vLHpeusTXsZINkta5gjFHFlRVIPlNbB2Sp6VYy5xda86sBScnarjQ7fsnpfzIMuPvHd2pU9dUjFHNTdX/OW1D+GWHcfm/LhLfaL9zS2HcHBk0v0eZkoQCEEUYc1AZ2JOC7JamzG5nWVudHruLXNj03WcnqjN+XHnGmazJALFHFlR1FZorycia97mFUyVvmJ57Mgovv/I0Tk/bt47m1rm5vzrlhR1JkCZc7699Sm8/gtzl+5e+sKFcrPcfWwMk7W5FTZhZPE/v7ENv/mJe9Jt2jKXSYYy3FeCMWh7Hr5Fr/WYnHXZzFrmxudBzP38e2/D8//+5jk/7lzDbJZEoJgjK4pqfWWKuVZp3ueCNJtl6/1WunvccuSVH74Lb/zSljk/rrMmq20r8e0JwqhhchzQzXJOmQ9XSHk0C5HNMggj/NrH7saX7z8wp8etBrFV7OR4aqXSfXgYWZfdsh5GKBcLGKqUMFFNrWn7T03gqw/456XvSTvLXNbNMptQZmxeLHNLIw4vtcytxJ6RaCjmyIpCBqf5YLoeth2YFgsXMzdPp9epm+VySwZA5h8vAcoKXAx445e24Nl/e5O3rUYxN6fMh/u9uMAuRDbLiVqI6XqEkxPVrj9rrcWHb9ntuVIKeYufWeEgNeOCyKJcNBisFDFVT8XQb37iXrz9Ww9jVMW26XvS7t63c7NcyTFzLpvlCuwXiQ/FHFlRzGfM3LP+9/fxKx+5a96OPxvMPFvmXGmCnB7lqw8cwMa9pwAsvzTdZP5YijrlwQOnseOpuS1mfMtjxxu2STtiXce5YTqx/hTnsLZKapmb/z5PLLeT1e4XK/efmsQHb9mFN3yx0bqeF5aQ3STvYj2IUCoWMFgpepa5kST2TItFzzI3SzfLpWJFmw8km+VKXOQiPhRzZEUx35azPcfH5/X4M0UsZvPV54ducG6cDL39Ww/jd6+6H8D8uhyNVwO86uP3YOfRsXn7juXAUhn4TU7a7V4/89/6xL3ztqCj+y6XAGVevmnlIYt85eLc39GFEHMinmZSc03a11ROnFueZS67ICjvYt1Z5kq553FwZEp9pvOYuewCbHb/sTkuTaD7x3bnttgwmyURKObIimKlZrMU5qtOV91ZCvztWbfW+bTMHRyZxLaDZ7D90Jl5+47lwHxNLuctU2pOApSViHY3c5NMWubmhFTMzd2UyCVAWQBBINarmSRAkUWTvKaVF5bQ4GaZ9OlBGKFUKGCor5h7Hp5lrkk2y7OTjS6TkbX4hR/fgFc85wL3PZp2bpYT1QD/8L0dzvraii37T+Npb7/R+2wvw2yWRKCYIyuKagcd+nIkdbNs/Nv1257CC95zy6xWIcXilnX7Oj7qx3DU59EyN5U826kV+ow7Zb4Eddb9abbIm+QfdeWquQk1QZZ7ve3gGbz4vbctGWtrryIT/b7SHIq55A1eEMucuFnOwDJXaOGCrxc/JdFJdj/p+4PQolQ0GKiUMJFnmTutyxs0JkC5/fHjeO67f+Bc8t3xQ4tK0eAN/+nHAHTvZvmZu/bis3fvw7/e+2TL/QDg/sx393pBcrkTzGZJKObIimKlWuZaFQ1/8uQEToxVZxRvIbhU6RlDwfExX8zN1B3kG5sP4i+vfajlPtPJBCIbME985svVdc5dknImmSt5zjLpibn0Xh8+M7Wi70u3PHTgNN7xbw97Arhby9ytjx3DodONCUM8nGVuAWLmnJvlDCxzLVzw9XgpCUwa3CyDJGYukmyWxQaLVl+pgAPKMlfPqVW3ef8IAGDjvhHvs5G1KBaMWyjsNgGKfFUn48LqpE6eMDGLMXEhkEdBN0tCMUdWFCtWzLUIlJZV6dlk+mwWw3NibBoAUElWvGc6sfnrb27Ht7c+1XIfscjNZHV6JTFfA/98xaNSqMToiWXW2rPcVubv2XPSy344l/zmJ+7FVzYe8Cz44rFR6dAy96fXbMavf+yelvtIXxguQGmC2VjmXAmQXDGXHu904gLZNJtlaFEqxDFzWYH1o+cMel4a2lVS+o2BchEA8L3tT+E7Ww+n+0Yi5uSz6ff3lwttrWeVJA6yk8WmSiZmstctc2KbW27tn3QPxRxZUfRq6YD5JrXMNf5NBO5shK4MlNnji2VudX/J2w+Y+xgrmZx1Ehuxkplrd8j0uHPbtvKiwXp5yhLN8+q4drPM9mO9fF+6ZXS6jj/47Eb8+RfmvnahTlClH5f0fZUOLHPynkuWxmaISJqv9qaZnIVXQqtuWI8Jcr152SyttUlpgjibZbaQ97rBSq6bMAAcORsv+PUnYm7XsXG8+dqt7u9RZFEwqWVOPvvVP3sRXvRj57R1sxRrayf9U1YM93rM3HKyzP33rz6EG7Yf8bbNd5+6nKCYIyuK+aoz1/MxK+KyltM5yj2ZjQiS1dKsxUBWYwcq8UCtB52ZTnKstbmDl0xkGDPXmvka+Ofa6u2yWarz7eVmNl9Wf0mXr92gsxPT5bQyL33Jw4fPzvmxdRIO3Q6k7+vEzVKeQ7vcM+K+OB/t7W3f3I43feVB97uIjokZuFnKu5M3hulFgzOTtdz96mHk+vJy0WCwr9gQM7d+qOIJoyCKsHawjMFK0WUfbnbvgyi2+LmEMoklsFgwWNVf7kLMtX8OS07MJf8u9fYfhBG+u+0p751+5PBZ/Ng7bsTdu08u4pktHSjmFoCen+j3CO/+7g589Nbd8/od8zXh6vVi2C0tc0n66f2nJmdcgFUGyqxL0bHReNVVJgV6QJ2p6PrkHU/g6e+4scEFZroH3CzDyOI9N+zA8eS6e5G5sqAdG53G095+w5wfN4v1fu7ddjaTeKVOKCViTk/Us/3NchpipK+YDy8K7bqZGzPXgZulPAdxC2yGs8zNg5vl1zYf9KwYc1GaoJXXBpC6HGYLVNfDyAmsUrGAoUqp4Tjrhipefx0mVrzLz1+FXcdiMddsMTGMLAqFRstcsQAM9zW6dGaRZ9qZZc5vw73uZimPYgE8eeeVUzlW7nv2xCLujl2NdTZJIxRzC0CPz/N7hqvv2Yf337xrXr9jvrJZ9no9mlYxczJgv/4Lm/GrH717RseX689OMqWTFquZDl6fqSXw65sOAkCDYOqFbJYb957CZ+7ah7det33RzqEdc2UpuHv3SU9EtCv+O1OWSmmC+XrvxLKgJ+pZobPUV+Y1TszNw/ukrTh5bpZ9HVjmxFrTTswJ4YK4WaYxc90uHst9yFso0ePlaHLv8koTiMCKY+Ya78v6wQqm65GLlauHFuWCwTPPH3Zirln7iWxsmXMJUEKxzBWwur/U3jJX6DxmbulZ5pJF1CXe/mXRV7cpmUuU5rBcyHKGd2kBWA7+zMuF2jzFbElGr16lVWkC7Xq6/1SbDG1NEJGWfddPJ64500Gj2JupBU1iK6YzBW2nalHy7yK6WRo5l9519ZxpNsuRiRouu/IGXL8tTkRTzBQVnOs24BIz9LA1TjNfsZqlJClD1k1Ns5yGmFo4+/t4w/YjeP01mxu2Z61DgnOzLLWv2ycuhAM5oiWP+gI8HLEWhpHtWgSnbpaNf9PHkji4htIEYSrSysVCbnmHtYNxlsjJ5D4HYYRSsYBnnDeMk+M1nJ2qN/TnNTVmeAlQkvtZNAar+kuoBpHb11qL9920E9sOprVGu4ldlH77N59/MQDklljoJZZLzJyEY6xR2URFfJezxWtJLhRzC8ByWjVd6lTVgDGX/d98WSXmilalCebC9bTeJGbudGKZqwURosh6mci04PnutqfwnL/9fkcxjX0i5jL7OsvcIg7ApULcpfba4KoXLmbqEizJI76Q1GsqZAbZTtrA2765HT/5zpu6+l59ur11V31kMaEd1lqcHK+23zFB3im9+JEVzktxjAnCCJueHGnYPhf90Zu+8iBueexYQzsca+dmOYeWOfnuhchmqeMpu+3/nJjL+ZseL+XeNWaztMqKYhr6BQAY6otdL+Xe1aO0Jh0QLyhmF0PE2hglYs4Y38JWSNws9bndvvM4Pnb7Hvyfm3a648i5ddI/TdZCXHbOID74u89DuWh6380y+Xcptn/NsSTrtRZzMlegZa4zeJcWgCXezpYVeqKQzazYzQQriz5W0IPCziWTyBNz9e7O9+jZaew9Me5tc9ksGyxz6eRpOgg9i4J2q/nHGx/DRC3EyfHWGeIAoD9Z+c26wLiYuUV0sxQrykKsxneDnoDNtDyEHEMma8VMBohOYpy+tvkgxjqcILlX1asz11v3VdOpm+Udu07gin+4Bc94x434QAdu5XLNE03qzMX7dHGiPcInf/gEXv2p+xqKRM9lrNzoVNz/7Dk+htt3Hm/hZpmUJkgmjl/ffBC37DiWe0wn5tpY5qS9zGedubx3o1trknTJrerM9ZXSEgDZrq0eRMqKUnAxnpqsmAvCCOVCwVnbrG20bMv3BZFF0TSWJpAEKHrfz9/zJADgGecNu+PIc6h38F5N1kInMIf6Sr3vZmllwWDpdABhZHEiU3/2WGKZW9WfxlvKO1Uq0jLXCRRzC0CzVZP7955aUo2wF9j05Mis4tO05Uff+y9tPIAr/uEW7E7897tFn1MvWumkO2xXS6gTXvRPt+Il77/D25aXzTIII5ydqmNd4mITx0zkW+YkHqKT+BJxs8ymv5bjTS+iZW4ha0t1g3YxmqllTvoxEXHZBdOFSIDSy3Qq5iQ+JIgs7tx1ou3+Im68bJYNCVCWyl1KeTJx6dbFpIG5FXNnEjH30g/ciT/5/Cavz5D3eevBM/jAD2JRLcky3vrN7Xj9FxrdNIHUQtrWMmfzvRXmEhFb2mo72aUAaZXNUsaGc1ClZqUAACAASURBVIf7UjfLzPUEUaSsKAbFQuO0crgv6bOTdzhMrG3Sl4SRbRBzktQlakiAksTMJW6WQBoLeXCkMUxAzq3TBChDiUivFAs9HwsvLKV55L/e+yRe8J5b8IRaED6u+kRBxqwl2LUtChRzC0CemNuy/zRee9X9+PAt85vwYznxyOGzePWn7sM///vjMz6GtszpjuOuZFL1RMbi1Cm60+/W0rUQtLTMzYmbZWPMnEykLlwzACCe7DazzMnqWyfxXP3luNvKWnhc0fD64q2mLsRq/EzQGfVmKjSdmEuWyAszsMx1g0wuI88yN6dfMad06t4m/c7P/Oi6jrLHVpO25VnmGhKgdHqWvcNAJW7H2Um8Xgz7vavun1WtqbNT/v09o36XtvqaT92XjgUdfNV4B5a5KLLuXZ1PTw2xHGl3wF/+4J1dtUXb4tJrQYRyMRZNLgFKphHWQpta5oqFhkUeAC7DpXOzDC3KyiUzjCym6iFW95fwWz99sXdNUprAiTnlISCWOclSKhYfPR47y1yHpQnkuRrT+1ki3bNbQu1/z/F4wfyaxF0fSOvR6r5gLkomrSQo5haA/HTw8Qu6cV9jzADJR1xm8uoPfen+/fjhzvYpbPUgpwdZZxmaYeetB4r5Kn+gsda6Ff5OaJ0AZe7EnBbIUpfoorX9AOJOWYsc7cJSdBnHZmGZczFzizcCy/X3WqmKuudePDs3S3mXGhKgdNF4OllJzvGy7Gn0pEMLkCdOjOMl7/uhc+OWNrBhuA9nJluLOWttrmWuMQHKErlJCrFsZS2auo++b++pWXk6SB8kaMuN3DL9HndyHyc7KE3geSjMcV+gLWhikRubDjwXtclagEcOn8VlV96Qa63ShC0tcxH6SkWs7i9jvBq/qw2WuUyduTzLXEPMXJIApagWGafrEZ62YRi//8If8fYNbWyZc3XmmljmJqqBczHV44wsZHXyHk3VQpeNs2BMzydfWorZLAfK8TO7Q3kliHDXbV+e/0LMp5YDFHMLQF4nOZh0bidmEae1nOjETagvscjkdcp/8+1H8Mef39T2GLpj0BNKGdBn2il6bpYL0Pl85YED+Nl/vBWPHx3t6nP5ljl/MqWfxfGxaXz01t1tV8dlwqKtPiMTGctcLfQmNqfVREviLDpKgJK4QjWrMzc1i3pf7/zOI/jNT9wz4887d9Mec8/R932mLjkNlrkZJEBx+3ZjOfB+7t1JixYl2hL66TuewN6TE/jBo3EMljyLDav6cGaq7trbd7Yexpb9/uKefm6SJACIrSGapSjmZFEmu/iSfTdmc2limZP+Zb8SNnLP1g9VGra1Qtz/+luIOX2cubbS6zFMrLVnp+r4j5ef67aHkcW1mw4AAG57vPUiZ8sEKEGIvlIBw/2lpjFzQWjTOnM5MXMFo8RcLcDn79mHe584hVoQpeNuYpnrLxUahF8klrmCcd8HSMxcvO/4dODFvHuWuSZult/ZehjfTTLzCpP1AIOJFbFgTM9bvNM6cz1+ogrxRtCLuTJ26wUxed9omesMirkFIK+dyYQqGwi6UulsIphM+Gfhxuj5ZKufZaCYaeyJ52bZZQxaO85O1huKmV6/NR6EDo5MdXSMljFzmfupJwt//Y3teP/Nu/DIU43WUI2zzIWNYu3CxDJXDULvPp1WhULFMpq3Cpd9JvJrVsyJm9tUvftaS8I19+3HQwfOtN+xCTKp6TXLXK2Je3E3yKOT1fTZuFl21EZkorIE3Sy19VPuSyVZhBChv2FVH8LIOve1N1+7Fb/9yfu8Y+p7+tiRUffZhsWCHr4vzehvkpU2OxbMxuqw98QEakHkUuPXgshZeMJcMed//qo7n8BlV97gva95STGm6yGu23Io10NhrvsCPbmdqMZ93dnJOi5dP4i//42fABBfh45Ha4V1lrnGv9WCKBZzfWk9t+zxasoyVyqahn6hYAyGVMyclDZ5+PBZN+5G1qJaj10cnUumq03qJ0CRhRLtZjk2XffmUvWc+58Vc2++div++1cf8rZNZd0se7nDgSpN0OPnqZH3aEJ5Gsg7rcd/2a8Xw1Z6EYq5BSCvQ5Bt7QperhQ6mQhKZzwbtxttOdLJNiRh0kytFjWVKnyu3QKe++4f4CXv8xOOjCRC6PRk++yPgKrZ1UHMnH4nxZUz69KYRQZzff9ErF3kLHOR9/cRbZlLHkDeKtyjT6XWR2utO0a27YhlJLKN1/TYkVEcPdu5W+pM6dWYuWxiGs3xsWn8zbcf9trggwdO47oth7z9pP1J/GU2m2U79828VPAt92/4ocfFnHp39T2W/qrs4kLjizh3uA9AoyugRp7Jcy9di+l6hCdOTABonJi267ZGJmq48rrtPZWdTyzs2VjD7LvRakL9e1fdj7d/a7u3Tb9nH751N6781nasHUwFm6Q/tzliTvcvAPC+m3Y1nJOIDDmvL2/cj2f97+/jLd/Yhrt2x65joZdwaG7HA12PbbIWYKoeohZGWDtQcYInSlwT9Xk2Qy63WTx1pVTAqv5Sx3XmGi1zxpUQmKgG+ImL1gAA/um3flKdb3xd/aWiE34T1cBZnIqFgioanmxTxx2bDjwxp9ufPM9sHbs8JqohBsvKzXIB+pvLrrwBf/+9HTP6rJzeUkqAIvGNU/XQnfdUjpgTq3N2sYfkQzG3AOR1kkup8S0EXYm5WYileo4vPYCGTFndH1db5uZ+JeloJj5OXEqOdxg3ZyADe+PfslYSPeGTiejxHAuyHjCDnBXpB54cQaVUwMXrYjGnY+b6ywWcnkjjhUQYZFfhHjl8Fr/60bvd79amq64Nlrl640qf/PzKD9+FP/js/Q3XMNf0bMycflaZc3vXd3fgS/cf8NyxfusT9+It39jm7SftrlkN11qbQVc/n5m6WfYy+p3TC06y0FPMuIltWBWLudOT9ab3Q47zMz+yDgCw/dCZZHt3bpbv/ffHcO2mg7i5Sbr99FwjvOJDd3rxLK148uQEfu6fbsWRs515CGjklBsSoGTdLFu8KvftPYWvPnDQ25ZNzX/vnlMuoy4ArE6sOdIMSl7MnP/9eQuHrv5Z8vlP/vAJ9zfxlNCWEt3eosjiDz+3Ed9/5Ejzi2qDvl+TtdDFXa4dLHsxaMUWSa80zkWviddGX6mI4f7mlrlA15krGBQzqeSNieMLjUkEmrU4d7iC33vhj3jWwymxzCUCbbwapAXCC7r4dxIzVzColOIi5WPVwIWsnLeqD4dOT+Hjt++Btem5tUtQFCXnICEwC2mZ+9zd+2b0OZckqsfGm1boRdhsnLvnZknLXFdQzC0Aef2B7hCXYlrpuaYTa5sMsrNxY9T3Xf8sq5idrN7lMV9ulvocTyWD1ZnJmqvfJvVZ2mHUiq3GWtsgPrVIqjsx1yga9TPLWuYOjkzi2w8dxh+96EexNlkJj+vMxX8/b1W/sy4C6UR3rFr3RGLWDTmy1v19PJMJUJck0Km6b3r0KADgqTPzb5kL3H3orQHIr4PovwMyyckTabpvqqlJFND4LumFEmstPnLrbm+xQVt3O2kjqfuXOm7bTy0eerKo77HcN5mUBFHs6icWodOTtYasi+6zSdt81gWrAACHTk8lx+8uAcquY51l6X3qzBQePzqG//VvD3e0/xfv348jZ6cbYo86IcisygtZMaeF0Tv+7WG8/Vutzy2bIfQnL1njBBwArEvuu/RVuv+ztjFFPuALShfLpWJIf/25F6G/XHBlFrQ1Tj+rqXqIu3afxBu/9GDLa2iFtlRM1gL37qwdKKtEXlbFo7U+nkuAAuArGw/gqjtTcVoNQvSVC1jdX0YtjFANwoZ3LXazTGLmcixzUvB7qBLH3UXWOuu+zmY5XQ/RX47FWalgnPCT/ZxlLkrvOxAXDp+oxpa5ggHOW92Hhw+fxf+5aSeeODHu+uJ2sVfyHgwoy1yvayRnmVtCc0jdPiczcXFBpMb3Ki1z3UAxtwC0s8ydbpPRbCUwG8tcN1ZO391Mu1nGA0OntaKanVve+c0G7UYpWTxHp9JJcdZi1wwZXvMm4NnXU4u5MxO+aNQrgPo6s7EiT52ZQmSBX3zWeSrRQVqaYMOqvkwClLgr+quvbcPvf3aj2y5xRoJFKhpu33nCpTkG4mcnAfF64L49sTg956LVmG9czFyPuVnWW7h96bTiWUaVAHOWuSZiTov7hw+fxQdu3oW/+vpWt23MC3jv3M1SN+9eXfg6PVHDTTuOut89MScptoN0wlIuFJy16EwrMZfc075yAcak199t0fD9p2L3zFMTrd2y5d3IexfycIXkTRNzbcvPxt+VtZjINf/5L/wYgPQ9qwYhvrLxAL76wIEGq7wm6359yboB6NN7yTPPaziuENn8yaOeLEubkGchcWWXrht0mSN1E9Pj0+Qc1MDUbWeimlrm1gyW07YZtS5Ho0nT21u8498exj/emJb+qYURKsWCEziT1TA3AYr0LxWVoVKQd6NSiuu2RZGqVanOcaoeor9cjIVfItC0xS91s4y8zxYKBpG1GK8GGKqU0FfSiWlM00WD9Pqtu1YgdYfW7W2+mPXxnYvs7M9loRidDrCqz4+LnK6HKgmaL+ZomesMirkFQHfmMhnW29rFIy0nPn/PPtyeU0KgEwFUdZY5f99uxJO2mOhJrWRTnmnmJO329OD+0zM6Rh6nxtPJ166koLmeNGfdLL90/358/5GjyCIDe3bsyLOQyPt4dqruJuDiZqkHxLyaffJ+y+tdMMbFxlSDNFB+w3BfrmUOAB5Q5Tqy5xdZ6z23v/3Oo+7nWhC5Ffi8QOpWE8B2jE7X8fl79rUdfF1Nox6zzAUtLHPye551/KxaaJJ2VmzyLul2mBfXOOZZ5rpws9RiruNPLSy37zyOgyNTeO0LLgWQdbMUy0CavKRYMFiXxHGdnqh7Yu6t39zmVq/ls32lOGbIFaLOPMNWr+XodN0tGJ5qkz1ZxEbWutKMbIbTbmhnmbto7YD3HVtUv5rN0Kjbpdw7WQiy1h9vn50s6shHqkGEX3zmBjz/R9a6FPlZtCA6nFhHpYnXkriyS9cP4qBYTqPGvhHwx5c8b4dO8N0sA5ydivvRNQNlV+Mtstb93G6xU64tv4RShL5ywQmcMBNTCMQLC4GzzJmGd0F+LRYMwsjGpQYy5U3CyKJaj9zCX7lYQD2y3mKBS4ASirUuOYaJjyvFxfW7Wy4aLy6rVcx4Gp+XJnjSu287eMZL2jUXzFaESXbfpeJmaa3F2HQdF6yJk6JNVIP4/YmsS1JUDaLEUpv0mzlzlHv3nMSh061Lbqw02oo5Y8ylxpjbjTE7jDGPGmPenGxfb4y52RizO/l3XbLdGGM+YozZY4zZboz5aXWs1yX77zbGvG7+Lqu30B1CkCPmZmoN6paRiVpDVsSF5l3f3YE/ySkh0MnkTjrxrHjrxq3Rt1DYhu0ztsypc/rIbXtwz56T7vcfPHq0I7eliWqAq+/e53XMevIlIktW5Vf1lRrcLP/m24/gjV/a0vQ7squ0efddRI9OGCKJUJrFPcnPMomRQbNg/EyhIqbPWx3X2LJtJoPZlWxr/Ymsnw7euuB5fV1yjKyY+/jte/C7n77PtYlWk563fXM73vXdHdh6sHWmyyAnEUwvoMVFY8xLczckbT2VY+gkCxptLSrkWAXGc6x8rXAWAy8DStuPLQryHoo7pJ7My32T+1sPLUpFg9UDacHjUSXmvr75EL54//74s0FqKSuYdPI3kenHW1lftKuyXhzKQ55RqUvL3EzEnHw2m5SlFkQomNQ6KLdy4950kefRp856/aS2IMvPX/2zF2HdYBmRtQhtLFBve8t/cu+vc7Osx2JMJu9544l8Vz2MXHyg3HMn5tYN4NDIZEMSlWb91c6jqVdBN2SzWaYxc5XUzdLaBktWM6KcdibW0ukgRH+p6GrHhVGemLMujrlcNC6ZlSD9vxNdKjmL/FsPLWph5CyAxUJ8z+W+lxJXTSBtW6noiq8htNbF0bnvNqllztp8j4Abth9xIrPxuOm1vurj9+A1n76v4fOzYbbjxFLLZjldjxd0tZiTNiFJiqbroV88POeZ/f5nN+KX3n9Hw/aVTCc9dgDgLdbaZwN4EYA3GWOeDeBKALdaay8HcGvyOwC8EsDlyX9vAPBJIBZ/AN4J4GcBvBDAO0UALnd0hyA/B4sg5n7672/2kkn0Ep3EzOWlfQa6tczlD7Ju9XyGbjDNXNestXjDF7fgyxsPtD3G7TuP493f24HH1SB/Uq0EikuCHPuCNX7cWSuaFQ3PE3NZ8bOqv+QsgNolSk965Prl/jrLnHKPiSw8y1wtjNw1NRVz1UYxVw8jvPBp6/Hrz73Iu/4wsq5GUFW1KWlf2jL05MkJ/J+bdmLjvhE8dSaenGUnyBp5Ju0mrfOZAOWpM1MzXoENmixiAOkz0cJZrvNVH78H39l6GECjm2Ury5ygm4UUHQY6jJlDOgnrdWQxR5In1HV227rEzMXXHEZx3SwXexhZl+FNkHdS2rqIjWZZkFuJOd1GTraxzIklvlLsTJzNRszJe5ddZKmFsTjSViYgvo51g2UMVoqIIj/WV1tMRBwO95XcPbPW4qcuWYMf2zDc4CZcC+MkHzJ5z7fMxf8eOTOtsj/G/0ph7QvWDGAsmZx644xqBLr/nKlHTjabZV7MXBTZdBGhzfjo6sypV0ishhPVEEN9JWftChIxptHZLEsq66QgvxcLsbCyNt0mr40sqPUntWRLhYKXWKVYSC1z0pcVtJtlZBHZ+HjaMhcv/mkvjUZ35rd8Yxs+etvuXMtcem/if3cf7yz2tFNmm2BFPt1ri4fNkPt/wepYzE3WQjfnktj6ahD5CcwyMaJ7kmfAYuI+bcWctfaItfbB5OcxAI8BuBjAqwBck+x2DYDfSH5+FYAv2Jj7Aaw1xlwI4OUAbrbWjlhrTwO4GcAr5vRqehTdzpw7mmrE7bIszSV7k9TWvUYngqzZPt006iCyzu3PWz1PjpEV1h+8eRe+vsnPlpZ7bhm3J/Hb15acdtcowkVP7EaSyVe5aNyAJxO8NQNxUHonBaqbJUCp5iwkyD7yXl6wuh/j1bSGm/usdrOUYtmyCgplmVMr4bLfeauTTH7JJCw7GZRrzFqSoyQ7WblocP7qPhwbnYZNJmthZF2qan1uU0qcyqCsJ7UyOWo1uRLLZLtYr9BZJlvu1jX7T03g5997Gz7xwz1t9z02Oo1f/ehdLvMhkM1m6V9DkBO7NFhJ404+dcdeAI3vbyvLnPys95m5m2V6jF4tGi7CRGpkaTdbue5pZb0Wy1exELtOZmPmpJ92NeqKqeUojCwma6GXsKbVXE6sceuHKt7iUB4ihLq2zM0iZi7b7qr1EJVioSHma7IWYqBcRDGxtui+aMv+024yLu/xYCWOv4qSe5YKCD+zb7UeF8Y2yeQ9z0It53BQuXZJvyPiU5ee0JNr3W70Oc/U7VsvhNRDizNTdZSLBoOVokpOpN1722dxBHyjt3h8jFcDDPWVUnfI0BdzA+Vi4maZWNCKxsU/C9qlMkrujdtm/MRjYo0tiGXOWctSkZhNxFRMYuai5BnrdzdU2SwB4MlTk7nzrU1PjqTCUb3LWeE+18x20U8exVJxsxSrubPM1QL37FM3y9AbH7Rl7s+/uAUv/QAtcnl0FTNnjLkMwPMBbARwvrVW8useBXB+8vPFAPTs91Cyrdn27He8wRiz2Riz+cSJztIj9zq68wszk15g5VS4byVmmv3t7t0n3QSj3sRVTDd8vY/4Z2vCKPXLz8s4l52sf/jW3XjrdX4dozzqmfOXCei9T5xy29q5uMoKlJ70npqooWDi+BEpsimpzsVNSyYIreK5pDRBdp9WRbrFUjXcX3LbJmv5Yk7uX9YyZ4zxVsLDJJOfxAvJJDYrDERsTWbahmSzLBUKOH91P6brEUan00D5XDfLeupGKfdKT56l/bWqwSXX3e4ZzldJAolZvH1n+z7xnd95FI8cHsUP1b6ee3GTmDk90dRi7mnnDgJIn7Hsn73UvDgx/VinmywENCN1s2zc1mvItQ8m75/uD2RxRrtZljMuYmczSbAePhQnO6p6lrl40ibiR4Qj0Lrti2Xux88fbhszJwKjU0ubuHcVZmCZk7aStTLG4qiorEzx9ukkdX0xiYPS7+tbvrENr/3M/XE2SokzLKf3LIyUa19mYasaxHFhBlLvLCcBSnKukuBk/VAFkU2tXxLTCMSZL3U/oPtMfc6yPYqse96doM8vsvH7MNxXivtatXDmFijbpeRPTlWPwallLsBQpehcJ4Mo8rJj9pcLCK11ixflYqHh3RFRXioYhNavgZe6WfoCrdEyl4pwXWcOQBJLmmbwLBe1GPOF9Ws+fR9+/7P3N7SXU+O1ND7Pi5mT+dr8WIFm7WaZ9I5Lxc1S+pfzxDJXDV2bWDMgbpapZW6oUvTGCh2+Qnw6FnPGmGEA1wH4S2vtqP6bjd/4OXmbrLVXWWuvsNZesWHDhrk45KKjOw7pFPRKykK5WS42rQaVPDF3+MwU/uvnNuLN18YZ8XSj1qua+rN6oPvGlkP4yb/7AfaeSF0jgtA6V4681dNmz6Jd/Tn5u0yC5dB6kM7WP8oi90cL0JPjNawfqmC4r5RrmdOf00I0OyHpxs3SBYwnxx3uKzVY64DW2SxdSmmTullaG8e1lQrGnbvEe2QHtePJynD2nbFIJsNF4waE46PTboCXCa5evc5zbTqjJs/ODbOJmNNttd3CixZKc5kJTVasO6mDePNjcS2x1f3pZL+VZU6uSd8niV0BgB89ZwhA+rzlWWWvr6ZdC3Msc/5qaydulmg4Rq9OWeTeSMFhaQf1MHKTdrnPYWRdLa6Cia0VWcvcWGJF9mPmYiueiEMRjkDr+zIyEbelZ56/qiFmbqIaeEXLRVidnazjljY16YC0bXQv5VTMXC3w3qVqkh0y62Y5WQswWCm52KtsW3xg30g8EUzu90C5mFptrG3MoCgxc4G4WZrcUi1Auohw8PQkigWDi9b2e9avShLTCPhJQoYqRW9M8fqipL/56qYD+LWP3Y0f5iQGy0P38xLrJTFtOla13Zgm5Ln6HR+tIkoswJ5lLrGWvfBp63HlK5+FcrHgeVyUCjkxc8mvhYJBGEWxmFOul0CjmCuY+D7KMyoY48Yw6b+0OE/dLI2XiTWKbMMC20MHzjSMN0+dmXLbSk5ops99vrITz9aillrm5uBkFgDxIhKXyomajplLLHP10L3jawbK3ljRqcfASqSjO2OMKSMWcl+21n4r2XwscZ9E8q/0RIcBXKo+fkmyrdn2ZY9ur7KCEnYxQewlHj86ihu2z6zgaauYpLyYORFCtySTUz0h1W5y+rN6oPtBUl9MskAC8SSrPzPhAvJdUvQEY08bX3k5t6v/+AUA0gFy+6EzboCabONWkxfbNTpVx5qBsqvRo79LJuvyOS1wdeIKIJ1s1YII//Tvjzn3Ruko/+fLfhwfeM1zk3OP95VJ6FCl5LZN1Rtd5WpB5O57dqKv3Sy1VW1N0nHLJDYrUsQS1ZAAJUrc1AoFnJ8UXT42WnUDvBScrXpxJSHOS/YdVZk6BTn3Zpa5E+pda5da3HunZliAPg+ZYHTijpxXP6tZdj0gfd/0temV3v6Sb+10C1IdWOb0RNH7eyf3Rixz6nt6dQG6HkYoFw3KiQu3XJ92IZT3rB5GKBeUm2WOmAPi51hXlp84VXr6vLRlrlXszamJGob7Slg/1OfFc0WRxe9edR+e9+6bcd2WQwDSNrDz2Bhe/4XNbS0HMsediUU6XfjJWPmDNCGJvjbnZpncs7zFwdCmFrv+ctHVCotsmv0wdd+Mf68GsZulTN5lDNALGs7NcmQKF63tR6lQQKSEX1+54AkeuW+r+su+ZU79LPdaCo0/cri9dc5aix1PxWvpQ5UiIhtvc4JJi7mwcUxrdswsx8eqqWdGJmYujCz+wwWr8Mb/9HSUCiYpTZAsOpSax8yVCpJ10reqAUrMuX0LCENVjLxo1L7Ws/5pwV4owHPzDK113lCaembb6HTgxpy8mLn58riYrUUtb8Grl5HbKCWEJmuhaxNpPdrIeSmtGax4fUOnWXZXIp1kszQAPgfgMWvtB9SfrgfwuuTn1wH4jtr+R0lWyxcBOJu4Y94E4GXGmHVJ4pOXJduWPZ6bZdQo5paSZe4VH7oLb/rKzAqetpoI501S9eB2+MyU6/D7ywVced12NwjplRs9cEmnrjuDUMfM5RT6nmriEtNuoJWYOcmkFVqLkYkanjo7jZ992noA7S1zLnZLiYrR6TpWD5Qx2Fd0908GIrFu5bkAZhOjyATmhzuP49N37MU7r3/UnScAXHHZerz02bGntF4JB2I3yzQepXHCLpZEY9J7KtpBW+YkfqFUMFg74LtZZieNzs0yc89iQRhnAzw/scwdVZa5wYybpU0mdxKjJ/f2TI6bZbOYOS2M27VVXfpipgXo83A1FtuIIK/It7acKqtZ1noolp5mmUrlfZBzSF1p/Wc2nfN5vUvNs8x1ETPX8Z4z5/uPHO1oMt2MWhChXCw4kSb3eDKnbwoj66wXxURsTAcRMnPgWOTURcylViZpb7JwAbRemR+ZiK37OlX96HQdf/m1rXjkcCwM9p6MF6uycVxt09onf+8kbjeLnmRnLf55MXPiZllKEmlIn/3R33s+/ua//Ad3vlKzqlyMBXAap9XoZhlFcY00nWBG7vmFa/vTc1Uxc5euG0QhEdbaMpeWf0kzIw73l5qOKSLmzkmKmLerAQgA9z1xCl/bHEerDFRKScZHNFi6PDfLtn1W47bJWuDc+mPLXOrNot0kxeVVxqRyobFouI5VlM/Lu17MLFJp90tt4dSlCQA/rk2OG0ax9dVzs0xcXqWmIxDHn+f1o/I+yflKvKVc93ww61g3MQ4sGTEXn2dfqYhy0WC8Gjjhpi1zVWeZK6EWRrNK0qr8zwAAIABJREFUtLRS6MQy92IAfwjgJcaYrcl/vwLgvQB+2RizG8BLk98B4EYAewHsAfAZAP8NAKy1IwD+HsCm5L93J9uWPXqgdanL5ygByuuv2YxP3/FE2/16odhuq3gjV8NKNdZHnkonV8dGp1EL4tTF7/y152DTk6fxw11xTFAtR5QBcBMmneks0DFzOXF3OnNSNnatFfWkuGpRDegiAi47N3ZTa2eZk8meToAyOlXH6v6yK6Iq3wWkMXMieCZU5sfTE/5Kv9zVgcQN1KXXzhFd8qpM5rlZenFPcr7xea0frDRM9I1JXTythRNizs0yqZGUXSmV92GyFuDCNf34pWfFhX4t4AShrO5NVAMXszGccbOcrkewFjh/VTwxE8E2mhFz1lp8I7FOZNH3tV1b9d4pda+2HTyD721/quVnW9GsLEcWvy0oMdfEMqdr/02p9lkLIlyc1PnSxZH155vFOf7TjY/hv3/1oYZ9PDHXScxcTjbL+UqA8sYvbZlVpt+6JMEo+ZYGPyubWOZStzhjUlFxzlC84CALQpG1TgwOVIpObEi/pOMa22WzXD9U8WJXb3vsOK7f9hR+8uI1iUiM980uaLRb8Rcr7Wwsc4Avel02y4wFbbIWYrBSdNkLZWHp/NX9TkBESSxdmuJeuVkWfMHjx7ylyVKk7xCRBaRt4ODIVCLmjOfKGGffTBetUstcyXMjlfch3p7+DDQuwOUh1qOL1vSjUowFT6QtczkJUNrHzDU+uzCyTtgP9RX9bJZKGLvYtlZ15pJZZqmoShNkhHVNuWnKv9rCmc2Sqb9DnkWYiEzPzdLGcdoSow0kicNy+p/sHESXJpi3mLmce392qo7/8bWtudb6LPLpHpjedUQafgEMVkqYrAZpNkspTaAtc8k8Qbuba5aSV9t800k2y7uttcZa+1PW2ucl/91orT1lrf0la+3l1tqXijBLsli+yVr7dGvtT1prN6tjXW2tfUby3+fn88J6iXalCWbzQm56cgR3dxAUOtcrSzM5XivLnMTYSGf+5MkJ3PfEKfzI+jj5grXWxUr9zs9cgvNX9+FrD8QrlH7MXPqznKF2kwvCJtksw8aBT4u5dgNiPYg8V5AoSlfVZVLQecycXzNp9UAZQxVtmfPFnEvgoS1zk/kTA0mEIuJUd67ZxAATtQCVUgGVUiF1s8xM+IFUGK0bqrjBSfY3MF6MShBZlIoF9JcLqBQLTS1z2rVqzUAZ//mZG9z2IIyzAepJmXx+MJPNUsSnWObEqnFmsuYmfNNBhEefGm0oRCzoRQj9Hty1+4RL2y9oq5cWLK+96n78xVce8uKTukEmS+3EXF5SmuzntPXwgzfvcj9nLXOv+IkLXLZFfQy5xuwEQuIcP33nXrdNP9ZaELl41Y5KE1j51zZs6zVqoUW5WHDeAPWc/kTaaRBFznogLoORja0HD//dy/BXL/1xAImYq6bCTcRGOsnWCVCan9up8RrOGUprkEXKGnv1H78ApUQcAY2WuXZiTl6xmcQU6fcw269Iwhd9DuJmKZY57Q6phdR0PUJf0rZTN8s0Zk67WaaWz0JibUtLE6xVAiCM4md5cryKS9cPuMyi0say4lPux3BfKald578P5w73ucU5mQt0Iubk+Xz7L17speQ3GXHku1m27jPynnFkrVfiIbX4Ra6eG5C+v3Ununwxt36ogg/97vOSczNJaQPflRHIiZnLiLliAZ7l2hNzBSh3Uz9mL0zGnFUDqWVu9UA5N/Y4ew4G8x8zl3fcq+/eh289dBjX3Ptk28/3an/YDDnfQsFgqFLERC1NgCKLGkEYuYVQEeEyBmcXCrIlXVYyjCZcAHSDc6vac+BmaW28enZgZLLtvnPt8z2Tc+4kAYqsvHz6zr0oFgze/EuXA4gHU8lyVi4W4uyOSQOvemIu/Q4RGbporhczl1dnTg18emIz3WbyGcfMFLwSAPKZbIfUjLxslrFlroTBSqNlLutmqWO+pKTByfEqLrvyBuw9OZFcX7zv6ayYK/jukED8vOJJpBZ42jKXiLmkQ9WWORczp7KQxZOcyBWAXTNYds8oO7jqJCyDlaIbySMbTxzKReNNytLJU+JmWU8tewCwIbHMSZKTs1N1nJ8IvGo9dOLCGHhuOvF9Ta9ZLAibnxzBH37uAZecR9DtTFtzZeL9na0zs87JxKxdAhS/2GrjswL89/7apOzGucN93mKLto7IJblsli5mLj3OOUMVnBirNngAZLPODpRj95puakP6ljm9vXdmMuIaWHGJauJz0+9Vts4c0Gg5WtVfTq1MNm3bA+WiExuyIHHhmtQNsBPLnIgNbXVZ1V9y5wDkibnW1+1ccGdguairg0+2cLN0fYFks0wWGNLYuEJqkUpE3kDFt3yGUY7gUa6aceZL42Wz1K55kbU4lJQluHT9oDuudoM16rhyX1b3p330nbtO4P0370I5KRifjYHuRsyt6iu752aTWDEA3jOWNr/jyGhLYZD36oRRKua8OnOh79YZC+vI69e1m+U1f/JC/MyPrnf7ysKb7OISoIibpTqutIt4v/R9iPdLz1US4qRullnLnPXqJq5VYu5Xf+pCvPUVz4zPISdLpnz/vLlZ5tz8asZC2Ipe6gPb8d++vAX/698eBhA/v0qp4MXbS1mhuA3G27Kx7uWsmJuaWXmP5QjF3AKgTenSKXhFw2vdD4QAXDD74dNTbWMW6k1W6WdKO2GSh7YcNWbCS900gDgD26XrBt2ERVxaKsXGjrZZHR8ZHLWYi2Pmiu7n7Pd7dYB0Xaw2q5uxwCh4wkUmBecMJ5a5TAHsnUfH8IL33IKjZ/2C3DJZk3im1QNlDCUxc3Fdo/i8UzHXmMBCBOG+k35dQRGMpyflO+LteW6WE9UQQ5WSW4UG4KU2l8mpdKjrhspeUgM5rha4gYoXWjNQbprN0ib379REDYOVUjp4O0GYrtxbmwbK95XilftsDOS5yTOQ389M1V3M3XQ9dOf73EvWNkxg8yxzDx44jTy0teGQqkl18brYZXHTkzPzLA86dLPU72meZa6/XEhrASaLQW/6xafj8vOG3fvq4ogk5ijTzrLlJ4A4vqgWRl6W0Hgfv41VSgX0lYodulnKv/kTlrmaXzXrO6frIW7vMMOguFm6FO7OMpdYeQbK7prryfsLSFyOLzakvcT15IJYyCWLLXG8W/w+vvXlz8LPP/0cAM3jCq2NY3fXD1c8F7zx6QDFgnEp9eUWNIuZe+s3t+GyK29oOL4bz2ZimVOfyV1IKPj9kSzsFAsGYZha5vqT2nNAkgCllrpZyjgRRdbFDGqLvktgoouGB6ET1kJkretLL0ncLGPLXHwOnptllPZHaaKHAH909QPx+ZaKGO5r9LQ43YmYmw5QMImANSrVvwgQ7UKq2thHbt3ddOKfFRSVYgFhlL4LwznZLOVeyjup+/Ws1Sz9OU6W4sXcZbJZukUOE++rSxPE34ec70jbUKHgi0kZc/T+awcr7vte/pwLnDt5Y0bNdNybtwQoOceVviO7qJjH0pFywP5Tk3jyVDwmGhPXAwyiyM25BpMQCb3IsiEZo8WDJ5vNspVl7rEjo/jYbbvn9iJ6GIq5BSAvAYpY5vrLhbZWH+GRw2dx/bZ0ZV8m7EFkcSQRBM3Qg+1MY/T0pGd6BgI0O2BrnJhTSUu8FVdr3YQJSBIHRP5nAd+yJhPLkyoddxDGNYUAf2W4plZHn0wGbV0ioJ0rbD2IXadk8LJq1Xb9UL5l7v0/2IkTY1Vs3BfXonNZKaXIcD2OZ5KYuSCKXWdkFVNWffPqpIkFKWvJybrc5LpZupXwIInVSRckpFSCvmfOMjdUgbVJAV91XJMIOhlYJUnEmoGy66Szg2VoLf7LR+7CY0dGXbxQfL5p3J12GwvUQNxXKrhJmo77A9L2d3aqjnOH+1AsGEzXI3fN2kohiDWyYNK2oxdg/GLZaXD/odNTbnszC2SniDUsG1uYxbfM6Ri1MLk3RXevqkGUFFovY1C78Uap65ikzgdax8xdsDqeEB0f8+uYZbNZittuZ26WjaJRmxHmalV6uomw/OfvP44/+fwmbD14JvfvGpfNsuj3LdPKXci5WYZ+ApQwiq0rWbFhraSGF2ES34ux6SCx7pTwhl/4MQDNLXNj1QC1MErcLONtUWJ1kdpkWrBnxZzc469vPuT9LriYuQ7e6+Nj0/ivn92I7z9yJPls/piU52bpYuEqcTIO7WbZn4hdIEmAEoTO+0LGCR0zp/uSVMwVABjnetlXKrh+Lr5ncY2r/nIBz7lodeLal4rBSsbtW9qM9Dv6+ibr8SJZ6mkR79ssLnvfyQl84OZdbvFF15RzbpbJvtrNX4+LpyZqbiKdJSsoVg+UYzfLWmqZk2sLoiQ2TSxoLgGKn6FV0D+Ltc1aPykKkMbMaZGnYw+zpRfyYuakDfmWuXTxTxgoF10ZFb0Am42Z0+1iJgl+OiGv3abCtv30fAkZ5rx+vJhYcOtqUUb6OXGVBpRlzom5rGWuuZh75Yfvwvt+sGtOjBdLAYq5BUAPgFnL3HBf2QWAtuPqu/c5MzXgi412rpbaDea57/6BZzXoFJ0BcLLevWVOJwDJigpZ4ZQ+eroeuto/QJo5TDpqowSGnhjqyawkIJECqEDiZimWuYzl4teeexEKBi5bmLjkFUx7MVcLI69wbGjT1SVxs8xa5rYnpRfOW5VaiIBUpItIWj1QcmnIJ6ph6mY52NzNUiYP2RVzfa/qYeRZ0PQkR75rUCVeAGK3zYuSLG//eOPjuOnRo2nMXHKdv/3Je93+Ym0oJscIwsgNlms9MdcoMp84MZFcQ+QmK7GbZdRgBZX2VC4a9JWL7jpdeQXnwhHvNzoVWzz7SwVM1UO3wlksxKuxus3Ke3vucJ8T3NqCqwPVw8hi3WAFawbKXhuTezTT2AuZ7LTLZqnbVTXzfveV4ppQ19y3H7c/fty9Z8N9RfSrelg1NcHVyTHkeO4a1KXIO6HbGuBnyhP3ub5Sobtslup79N2bq8XyZl4Gx0bjaznYgRu7y2ZZ9N3GXEHcwbJ7NtpSIPdXT5BdHxIlVqYk0YlYIMam61jVX04m9Knwy2MkWchaP9TnxZWNV0MnNOJ3Pv78ZKaPyk70sxZVeVfqHTyMbQfP4u49J/HGLz2IehghjNLFOb3QJ8mk9H2QBc84Pi6xoCX3c6BSVHG5cd8nYs65WVqba/mUfqKiY+YSMfj/vvhpeOb5q9w9u/Xx4/h/nnGuKnngJ0DR2TdTy1zaR4vbZhhZDPWlpWbkGM3a9muvug8fuXU3Tk/WnZgD/HjLVOTAnUN24aiZV4B+dYyJ+4PYFTedZKdiLvLEWDGJXxQBLtsEnXVS9s1zs3RCSh3Xi5nLtA0vGYq4WSb3oZyJmQsj3zKn702lpEseLIZlrnGbtKVOvnEJaTmvj5J6gEEYYbIWW5vFmq5rSJ6ftcw1xMy1n4d2aixZ6lDMLQC6H8iuag/3FTuOPzsxXsXYdKCyHqYvcjsxl51ESq2abtBuIDOx7k16VgP/82ldquTvQZTEMcS/u9W/YrpC50oT5MTMTddDN0HQrl9BZJ3ry1Tdn+xeum4AP3HxGpeiXCxkG1b1NYjPw2emcI9KPBOv5hbVanI6sR6sFNFfLjRMGo8mk8U09Xa8v4h0EQCr+8suc91ENWgsGu4SoMT/rh+qqOD65pa5E2NVJbrguUMCqVuTDGrWWpwYqzorDABct+UQRqfrDW5J2n1T/o1svAotrhLazTL7fkZqwN9+6Gw6aU32jeMz0vOVzxcLvliQunirMmJuvBpgVX8J/eVi4mYZb5fBQk9w9H2ddJa59FlqMScT9UvXDzjLnE7/P9Pac52uDOuBy7fMxZMtmUB+6NbdqRtVfwmD5aJr0zp+Nc/NMs8yd+GaxDI36lvm9OJBfA5Fz3Laijw3S1/YzZFlromXwZqkfMaZDrLK1UIRc35yJc8yF+gEKCqmK0oLHgPw+pCJWoDBcinZHrfDiWqgrHX+AkwWsfZkE6CMV+veMWRhLNtHZY+brcUoz7GTmKLRzKJHEFlXK1N/b2STGF7lFirvZuxmGVvmXMxcJpPkdJBmLPbcLDNWHV0gXBbirI3LeJSLBgOVIq78lWcBiPvKQ6en8HNPPxdAmrZef14nQJHrWT8sXhkhLliT9ptDfcWGGOjsIpJwLGlTkbUYnw4w3J++D2Lpkr7QxRnaNAHK6v4SBspF7Dw61nBsOa7QXyq6sgA6AYpYibLWK8k6qcdmbVHScW7FgnHxhE4MZoWUhFFkxFxaIzA9liAJUKT8hHbFk6RbXrkCJebKxbQ4vVhIU5fV9N7MV8ycPq48e1kI6iQxnn5fsmUOdh4d6ygjZjdYZXVuxx9d/QB+55P3puenztWY2MoWRBYTycKS9sKSdiWWObmOYsZa2Wxc1IuKKyXjJcXcAqBf/mydueH+UudibixNagH4iTL2N3GhELKT5XOG+zr6Ts1ICzFnrcXeE42FtV/+wTvxP762teEz2cmcTJLT2nGRSxcNSHYu61b/pAMHMkXDk+OKSLhoTT+qQeTF+0iqZRFN0uFXSoUkXiA+sNzfc4b6vEny6YkaXvze2/AHn92oBGXogujlOrQb0FCl5MUMvuu7j7qfnXhqapkrO8vSZC10VpqhJDmJ3NfJWhDXcBssKzdL/7nrju3keNW9m3Fsm+9yNVkPkni1VOCcHK9iw6r03XnmBaswOhVgdX+pYcCMjxv/niYiSDP5DVSKKsOfxWuuuAQ73v3y5PPAT12yFgDw4mec6yUXkIyY+rzE8lwqGvSXiw1ulkMquDpOkhBP+GIxFzmRoF2lhMlqgMFKEUN9JScOdZvViwVhFCcCuGTtoLPoTNVD9xxmapnr1D3Tq/WmyxQkrmPCcy5arSZrZQxoy5zK0Oe7WUoCD3/hBQAuWBO/E6cmMmIuE8MnMXOdTVT8f+Of84XdbGjW/0rdo7MdZCDNxszJ85brXDtQ9t0sM+5kevFCT2oma6GrnSgxXaGFc2nT7SIP6bPXazEXwU2g4uOm1tepeuhlDcyKC+1d8Kf/usl5F3Tyfur4FklMIa7ie06Mu35MUu3rmFiXCCapMyeJTsrFePLuuVnWQgyU/XFCZ2D03CxVAhOZvIfWNliCZFFiWD0LqyxzfSV/4VEmnxclMd+TtQAXrE77zaG+tDSBvnet5slBGLs++s8tXxxFibXsNVdcgu1/93L0lwtNn5H+zv5ykvQoccUVi4mzoLlFsw4tcxkrXZARc/KuNRYNbyxNINes/3X3IUqTCPkxc3CWubve+osuM7NesEpdPdMxBIgzMbsEP/PkZqnFnCySTXch5jRZ6+HLP3QnXv2pe5vsPTNe+oE78MJ/vLWjfe/cdQKb96ex5fpaC8agXIjfybHpAKv6y17yHqkVKa7O0p6ycYTN2svjR9KFi5mEBC1FKOYWAP3CZd0sByuljq1cEvslok4sRwXT3hUom23MmPhcPnjzLty841jD/lsPnmk4pifmMh3N1zYdxEvefwc27j3ltk3XQ+w8NoZvPRSnb9cTgWxHtftYLASdlSoRRzoIvhaELlucdv3TE0a5J2J1uCAZTMerca2fOJNcAav6Sw3WEl04Nv5MHQPlIob7St753pjEfOjvqQa+m2WUEXODfUXPhenOXSfctbiJVPIejNcCRJF1iUXibJZF933ihmSMwWC5CF1nbrBSxGCl2NTNstEyB3c/gdQdEohdrgYrqbWxHkUYmahhw3AaS7J+qOKStGhyLXORbXAxE4tAEMbiXVwtZGB+1gWr8C+/81NO1LsV1UJaRDbyBn2JmfMToGg3SxF6A+Ui+pKY1QbLnLqWiVqIwUq8uu1i5tR91BaHIIzP+/zVfa6d6oxbM50UaFHeKlbMuZ6Vi571W4SUcNGafrdoMNRX9KyZuhCydrOUc9h1bByvv2ZTw4p+9jzle+XZ1IIQfaW47WVjs1rR7HpnI+aiyGLTkyMYrwZNxZxkThuZ6MAyl7iQljOlCeR5rFYJULxkEUalzm8QG9bVVpPtsm9qiYn/bXYrRhJx7RUNt3Hs1ZATBZL0Jo7RHSyn9euydbD0gtStqpRHJ4sUuh24lPGJlenTd+zF/3/tQ+5vReVCGior3ECS7EQSJ8h7V8zsO6Bj5mycgVHulRNd1s9mKclovAQdyYfk+ozuz5QoqJR8QXk2WeC5wIm5EMPKc2G4UkItiNw91/elGTLxHWrpZpkKVV9gFZq6Cup2LMXpJePpYKWUJKtI+t/keuVelgrx4qfuX1q5WYaRX5ogFYmNLo7iOhlvR7Id3n7uuFaybMLr58LE5bVUKODS9YO4/LzhJImYtsz516aFptyZebPMqXsv75IsMrcq5STo5qm9cGRxZ9exxgX22fDEiQlnTOgWfa6FQmKZC63zNNCWeL3YWikV3BhrMsdsFius78WxsekVETdHMbcA6MmIS+udTGoHyp2tUoeRdQOzTBKl0T99w7DnZjk6XcfXNx30vzfHjW338TF8+Nbd+LMvbMbuY74Lxm98/B78x3+53dumTfaTtdB1cF+6fz8+cmucNUjO4+DIJF71sXu8z0+pODstKqLI4tGkQLhzs6xHscuH52apLHPaYpCIm4Fy0XU0Mkifm1ggx6cDN5iVCnFqaJnM6iB2bfEbr8YuLX3lgne+etVHnkUs5lSijihdYesvFxosc5FNB3q5Djlna2NBpy1zfW6yHLlkKwA8i8rIRA2rB8oYLJecm0/WzVK7vZ0cT90sdXYyuX5dpDfev4bIAucqy1wYWVfYXIt97b4ZH9+kq6TGH7CBNCmEs8Qmk5QNq/qSGJX4OOnqqR+fqIPGtRuf3BuX9tibGBbQX4pFj1yzuHF4lrlaPNgMqCQhcVxOvG/WzTKeIKS1+bRFopPYojy0O0krV03pS+KYvSk8sC+Ok6kGoXuHADh3PSBOcy7xWEBmgmrSCYceEG957LjXv7hC1znXJ++cLHisHih3VR/INvm5VTr+drz1uu149afuw7UPHGgaMyfvUDYOMA+pgaknJID//okbnU7IIG6WYZTGdPnueiEGEjdL55IZNcbXNbsXzs1yWFvmbEPslQhHACiryXD2cWbdLIVOiir7lrm4LxCXQQC474lT7vwKnht1OrF12Swjv56cE6rJqn4aM9dYZ057T+iFC3Gz9O9vfNys5UispHl15qyN+4SBcjF1ha+F3jOS85uuh56XSvY5+nMH69zDgcQV0SLjZpneh3iBIf6eUsF42UM1/nmlC5qBGm8bRJcSj84y5xZa02NrK68Wn1l3yXrG4ieWuTQxVbsEKGncqXbzFAtwViTK9+kF2LyYOXn/FyKbpXy/zEs68djSruatPJ8Wmjz3R/2eiTtsXfVFenE2jluNn+Pq/nS8yC4WNs3Qqr7+1Z+6D6/+9H3e30+MVfG1TQe6vq5ehmJuAdD9gLxkQTKpHUjcvNoxMlFzxxELnTT651y0GvtPpSno33X9Drz1uu1e+vSsRUAPkABwv7KoNWsgejX9gX0jePo7bsRdu0/g/T/YiaeSbJrS+X/x/v3YmQjEZ1+4GoD/fVpU7Ds1gYlaiFV9pdTalnFbFD/3clEPpvHnZQVyw6o+J+bknoo76Vi1nlpviollLpmE+64yKhlAImb6M4Jbxx6kYi62Oug4Lu3Co4WA/F1WO3WMmkywxqeDtDhwxe/o6mHk4gIGlBVu17ExXH7esLct2/lVm8bMpauRsm2iJiuz8f6SEOLc4T78yYsvc+cuK+FHVUbVrGUudbP001JbNVj6tbeyk6pkMuHcY/zJXvps44yNcp2yQi4uc5FNB8mBJJZxuh41iFrdBGKLZ8lbeJmuh7ggE5wNpC49kqRB/71SKrjV33ZsP3TGa9P6Obay5Mv5rR4o4fCZKbwmGcQkAcp1/9/Pxedp01pjQ31+kpuqJ+bS9tCYpCb9uZyxMvvnlFr8KsUCVveX2tYHauZOqX+ejZgT4TBRDZsupjkxl4kDfOd3HsHn7t7nbdOZdsXSBcC5C8nf8tK4x9ks0wmydp2cqvnxcWI5ylrxsrciCKO4LMF4Df3lgucuHdk0m6UcI4zS96qciTnSNLOotsuyCvgW7DSWKf0uqfsYJfciFUfWndtApegyKMo7re+D9EdpzFwqlvMsn7ov0G6sung1kE7m09itjGUuWQgE4rZ1dqqOtYNll259shYkRa2Bu9/2i6kFLfLH5qwFSN5TIH6mE9XAJcMqiri3jSInshbVjLWsqWVObdeWudCmrr/SN+fHzEWohdYtAmTj5PTPqUtxtl8Pvf1lX1eaINM2fDGXLnIUC37R8Pg9i9z5FwoSu63cLLMupEqwSz+0EHXm5N5KO+kkMZ5u97ptzrR2cae0yySsM7PK+6WtkLGbpUEQRhirBhjuL3txrxJmAwBrBkpuDG2sR5v//dmxYdvBM979+Z1P3Yu3XfdwR8mtlgoUcwuAfrF0wd1iwXiWlVZo07a2zBkD/IcLV2N0OnATV3nxdX21RjFnvc5i474001WzDEG6MdyXiL/rtz7lapYBcIPrZpU5SybSehKqBeyDiV/1/2Xv3YNuS676sF/vfc75nvc1c++8JHn0mtEDKWDzMGVeBgsbmzIYx04ghLLjd2IqWHFwmZi4bFIEHEMwVdghJLiMQ2KCUiYmGCGDCwUBkvEIPRASkkbDzGjGo3ncuc/vdc7Z3fmj9+r1W6t7f983F3miQberpu435+yzd+/evbvXWr/f+q03v/xcCb1Ljg/nN7CaJUvIiyN1cXdhnCsAhRJ4wyNzm4rMeZqlLNwrQvzE4Esp4SOfuo4vemUuhPqsIIGrUbCFaKGH65zT0XfKD5fGhYPjGLE/WA0l4ffG4ZrQIhvxXw5qBAkKtxoiPvHsTbzunrNGZt5HyORez2zO8NzNZZMOKZ+JsSSbW3GMtub4G3/k9aU/MWWTeAM/AAAgAElEQVQjxyuGtc7LRmsXmHasDqqgQUzH8bkVUqBWNlxBnmddwMZcaZaX95Y4szkr81IUAgFga6ECKLI5zaaQuZG+KuO6v1xXSlvSv1kXzDySzfnizuJUCAYAfN0P/Qq+4u+/U89Lv+PC7Skl/LEffBe+6//5MABFg8852qsIoEgB35hUrXV3c2aKgxelt15y5lDGjhuPkcxHT8sD1MGUoMuZzblR4m21KadtCqVrnyPhh//fT+CZ6zWyJpHemNJknU+ZQ59yv/+xdz+G/+5nPownr2rpCb82yThIoIMNFc6ZEwdtSFT4mQzyPaJZaoHxRgFs4/wmvPZvvR3f9TMfxpX9Fe4YVWa5VthNout1neSl1YV5vSriFPXrJIGed3382bJn6DhEk98kTAUZi47GTFgdW6OSZKEXujEbYjYEtxpj1tGY52N1bm7NewRoTq10S44tzpwz9JWm2dN5E64erHBuS4Wr9seasA/cdQYvv7Bt1CHNvuDWnf/kf/035f9Xw5QASrtoONMsswN8upw5Rfxq+qY4HCoSkqlyy/WAjb42JytnLrZpll58pFKzdM51m76ZbapKzXJgR97O6XmvQYM2MpfMd5/uZmrdulI/p0PmtLGGwr9vZ+4kAIIDYDfHdYWnXxcszXJ3w74/kmYD2BJGLTu21Vq+9y997FkA2S4WjYmThANfSu22M/ciNJ5wTCvru4Cdjf5EwwawjtmzN7Nxcf1wjd3FDK+4YxsAinFxdisv9p7+ZfpEdLP779zG+x7XWkrPT9S7aVFsfFFqUcz7wBPX8Fe/8jX4olfdUe7/cK2y9BwN/5e/8RRedn4Lr7/nLB07jPWD8jHCc19Q9E9zcWpkTqLqdxLNciAqx5nNWVn8BC2ZdaLeh3Leed8V9AbIdbRuHK7xJa/NqmbPVTTL/NtcK0VzOrrOLmYxJuM4rIZkKIw3DlckTkIGyLgRSfH0LCkf8dvP7WE1JLzunl2DArZofTJWjMyp8cJFoVMpVgzYvAam80ik9Tu/9o04vz3HvFfHgM+r+SjjZ2T0MjJnE/tRnnd+JuMzdLmTWprA0iwv7y1x586iiLuwMM3WyMk/WA0NARQdr73lgO2NmaNvRpzZnGNn0TeRuUBOcanDt7s4FYLRaqu1/o7Xiw8+cQ0ffuo6/vGvZKRI8uTObjpnbsUoRh4HQ7Ok5z5Fs6xqAdL/57p/bZplcebG9/fs1gzXD9enrhNnHDh2Wpw98ZMPfdLk//72c3v43rf/Fv7Kj7/XHLcec4/kfFOGj6C7n7yy31wT/9Vvfqr8LVRvwFLAD1cRmwtLv7aBi1rEoipNUGiWoXI2GJ3mvgDAP333YxYxNE4i0fVCfg8LlXFDqY/i+EmbQuZOoqF9y4/+mqm76OlvAHD3GaWdd4FyYpPmW+9szDDrVOK+J6cCyMbecohl3Q2NtUTWn8iI3+iMJfhnMd7f4JyY4EoT9Fb86tpY+kTYGplmSQ4lOapGAIXG0RuaqyHi5nJdlHlF8dEKilina2PGAYbmozH2ycasV8Qv6v0WNUvn8Agdkh1HboZmGdRBk3H1jpQRBmo5c+55yz0LosrlOvK92RxVCSoaAZSiZmnvLRwTyPp0NX7eisy9AJoldYuduX/fCo4nqWQyNZ3XWmlKs4w5QEG1DGNMOCLbyTpz9jnwOX/uQ5/CG//2z+FgOTT3FtnvGGg4STjwpdRuO3MvQuN5ZQzlkGuMXdlfVQma7/r4s3jl3/yXeHycbOKkLGYdLhPN8szmrBgRcm4x5FrI3F//6gfH6+ti8aqLO0aF7vm9doLrzaMBF0ekS5bLR7wz1/d47PIehpjwpvvOjQZe/u5wNeD8iBiIOuS1/RXe9fHn8HWfdx9mveZVrYZU1W3zvHwZV8nFubirohNiiJWcuaO1UTxkHrZS7AK45pKgGZvzvvRXDOlXXszR1WcLrXNoCKBwTkcwUdeYVJmJHetLBpnTfnEEfjXEQmnJwhVDoX6+7u6MzMn5WhHzjX4cq5u1AArnTomxFIpBY8dJ+i7GxLntOb759/+ekqMCoEwUE02X6Oto9ArNjBXLfB6R3H9JXDeOn95n3400S3Hmbh4Vh16MVhZTENRVxkHRUoqQH2VkruvYSM/1v7i8AiCGQ2dolrKZ3bGzcaoIb8twYETvGl3vp0ZxISBvoJwzx+1oUNqKPIubh1mtbtMgyjYPiOeDd9S4nzInhpRKroM0cRIE6T27OccQ07EJ/mnyf/hj+8Xf+L8+iL/4Tx8q/y/d8xs2GyIx2TITfE8yh1ICfpnKkMjcZ7YEO01iiAIY87dU/lyoY3NntDL1jKnae4ZmOUqDp2QMztxH7bcEnuZ9GFEu+w7dPMrzvQhpjO+QvBdvfcuDpWRATNZIFIPIz4XjBFBahl92apPJbypU1PH+eC0VZ/rC9kJl6+nexCnYK6g7UV5TnasrYyZCRluLvhjvhpIpCJrL6fICKMLAADSP+NxWrgUowknshHNdvCUFanj+yd4ve/b1wxVS9dwsUit9kGcp+6XQIVuN7ZPFWObB0yzlvPJO+OLeXmBJ2hSCVub6+BNP3+Q+5P/Px2l/7DWyyE3KjqjZZz3FFkWwRe5Xc+bstQKdi4MVpw1CnabxeZdDRmnl2Z1KAIXWQEOznPjtx5++ge/52Y+curzAVLt6cLzC79OEzBXVcLqk0iwFmSM1yyRq0/lBnNmcl4BSK11I2t9/x29hfzngk1f2m8icPMuPPKVluR57fq8+8CXabjtzL0JrInNjdFZodc86haC3PfQEAOB9n8wURDEc7j23WRa+m0cr7G7ODFdfzg0AT1EOk2xGAl0PFJW8a6yjJi+KOIuAfXkkz2Jr3heDw0esQ9DN+9z23NIhV0MxMsXZeur6AYaY8OaXnSuLp9zfJlFXYkJVZ67kdo3KbJfObODK/gqrIZIAitAsKWeuE6rXaJgIOjUaELIQCM1y0+RK5b5tL2aO1lnTQoX+Kf3ljXpIWm8tJo2kyXy4frgq/QjBOk88Dv0YaRTH9OLuAtuLmdaZaxhZBcW8oaUJ5HkqHVKpSUo1itofuk+OOMvGKhuedRJRoqcA50Woky2fx2QpmWH0CguKynW6DDJn1Syf31sWeWMx7PbJ4NuocuZGQ4/2jP1RzbIPRJ8b5c/PUtQQUPpsR8fKfN7d6E+p+lcbvxyR5Osx1e+9j17B4SoiBDX4pB2tBoPQxISSeM7RbImyA1YUAqgplGyIzMq7k4oB6e9HoveifHqcCEpyBlmrnWSPyPp2hUoLDDGZ4sns3APAOz/6DP7OT+eyIUfrAa+/5wzOb8/xrpGisyJ1TrtOJvNOSt8OR5olj29mZdhnEVNNPTtY5pIZWwvrhLPx7nPmlutY0Nl539n6auO/8jxkjoQRiZH34u6zG/jv/+SbxzFOuHFkha+AWoTnOPrww8/U9c1ayJzMcUXmdM27vLdECMCF7bkRx/C142TdM3XmxIFw6M4Q1emR0gLiLHuUVAKBjIhyXhq/QzHld1QCl5Kvxk64BI08MsfvmCBzr760C0ARG6bdqnME0z/ZT4ya5cTaw9ec97p28Zj5nLkZfT7EhNW6fu+lj/y3BO46NydXDvlUFE+cPJuP551ECSD2naJpgAY/TaAwpbKPsJqlOpS1kAuPnUeH3vPIZfzchz6FW2k89qt1Mtd5IeVbAMvYmPrtX/nx9+J//qVHboleyGsyBzBbjYGEr/kH78J7H7ti7jWEvIcL2rzr1CxZsEuQeKDBMDNzd0SP17G5Z8hPP/zUdbz2rl28+tJOCZj8bmi3nbkXobVqiQwjhUHybp52eRmyOcoEfe7mUqPa40TdXw7Y2ZgZQwFAkcDnc8r5VA5f6WaCBslGyA4av7TiNOX6YFP8+1SMhXNbc+N0Ha4izm1bZE7oM1yc+tBtsEBeSFZDwnxWI0g3RpheULjn95bFWVQBFK9mmeXRxTgCMMphW7rZYtZhc9aNtcioUO3cIYHrYcyb0P4eUXSJnVr5nnNj1JDSUgotmmWOBqvh2HWaXwbkzW57kZGpIaaqJAWQ59TZzRluHCn6x7Si7IyBrp3/ZsUx2UvLpl+MnPwMh1ifN41OXu+O5Xy3/DmqnJgKmXO0mTVt+htzldl/7uayOPQyF8XIF+njozXlzDlRGiBvkjsbvXmGB6vs4J3bmhvnazXEgiqIUyx93l7MToXMtYpU8+/4+2v7K7zpZVlg6IkrB4Xau+Gi5Muhpll6RcN83w2aZVmzptEYuecYU0GjpZUSIKO6njAHThJBkcb7sv1b/6dFAZccMO72D/z8x/BXfvzXy//7nLk//2MP4Z/86qMAcoBme9Hj9fecwaOjGA07fpzkzzlzHNEXMY46GGPfN6aeyTsjTpQIXkgwomW8x5Twkw99Eg9+59vx8DNZjnzRd9YxcQ7P9tzmlQlCubXoKViTiiIqQKVYaP2fdeFY+nBLHj0LwUTM+oAf/MbPGz+TfHJYZy5lhP381nxEvbOz0RI1kXdb9rmSMxd5HHTMxNkWZ8w71jK+Mtd9DrCnL8t5r+6vSuCy1ExjJ1ycucHTLHWMHn9+H2c3Z7hzXL9kXywUXQp6eefzqAS9rNPVarzWyfh61VQp5i1BMnWis5O4JLYIN6ZDGsTP5TrK/CkBvV6otDDXK/OezivBiGHsrw+e+3mSkl7PInPRXUvPxcEKv4Z/44+8p6Jyn7YZmuUwGIfntCWrpE0JoPBzFzvjE42awCe1qYBiq/nUobc9ZNXVJbfx+kFGmzUPdKRZrmMBHpjp4AXEeErLe7jvlGOlyTk+8tQNvOHes7j/jm08etuZu91eSOMJx5SlWReKI+UV01bOwH32Ri7W3NHEFmUrpo0A+lIzMqeLV1+O3S/IXHYgBKliI4Wj2jfEmZv3kxLVQ9SCqUIzkftngQ+JasuCtb2YjQnoWgdss4po+wgbyv2yM/fsjaOy6ZzdyjkWdc5c3mhvHq7J8VD+vYzZvA/FOD1aR1M7bmssTp3GPhtkLlqJ7NYmw461jKcoJDLNkg0bMYIWvd1wFAnTyO3BaphE5jRijTImfL5CbyFUcE2bnYiPlGKtLkK+pjHl83qaJUDRXkJd09gHVdn0kWGrHMjvy7zvsBwiYky4ss/InBYbBjLNctF3YyTPjoOM/fXDFa4frnHf+S2TQ5FLE/SGzy/PR6Lb+Tya77a96E/nzDWKVK8HzTdl5/HqwRJ37uR5PySVdN4gqqMEFhYOJWYRDHHOY7I1mDztlhvTtgJGmmVERbOU8REUQ3J6j8sV5itZARRG7PSYVu2j/UZ02tfUTGk6N0UU1e46s4lnxqANi0Zdpmv6OlsyVjJPeF6xcSkIRAsNEmpRQeY6fYc8ZTAm4P8eKbcfG1WE532Xg4bO4Zmq6SXjILXFAOBT1w7xd0dxHSBTjgHgaMjH/nd/4k343FecPxaZ+3jDmUspOzKzLuDrP+9lePWlHQp0JvSddbqe31uWwFyLrqfF2ht5T0mpmzwOopKp4zvtWNc5c/qucF4akJ2uA2Kh9F2HwQWymHY7hcw9dnkf99+5Uxz/o7V/bpSHXM7bdkyOU7Nku1foohIglPMer2ZpUyC4cf6anndapdhQ8BOVJuhD9f3Uef3aYXLmOr/GhWrMNICoNkarhMCnoxkBlHUya93pcuY46NimWbKTJ2vUx5954c4c9+faCcjcnnNEX3HHtlmvu5BLSMhxuxuKYnuRKFGvBTJC/i1ffD/e8x1/CIC9fwmm3RjpyL4JSvvk1QO85tIOLmwvmiyYl2q77cy9CK1V702KJwsSwwmjT1DCvUS7nrt5hIu7G4YTLtL5zNWXzwGHzFFdlXysvpycVwZY+tAVcuwKzXLRTybCpwTjzHXU35wzt8A9ZzdLpFui5wWZg0fm1FixEUg9795YD0425aP1YBxCETuxOXPZoMx0xtHx6EKhLQKEzIkzt4qmKLM41qshI1m+vyLikvsbwHtATBYFEmfu4u4G+i5kARRyMFgIpqKbxkQOacAWyWG3cuZKXkTSzUP2xjD2s5yv09pvq/KZ9itvuLW6nhgOTAcbIsyxsveX2nGdGh5Dasu1a505dj5taQIZk+sjtVacHaHjWDlypejkPliJfaFh3H/HNvpOVfiWQ8TWvMf57XklNNR3miMlG0jO5etOJYDSQuaWQ8KFkbbMaPnV/RXuHJ3VIUquQW+MnUxb0Zw5oQGuBop+UrBA3h0t1aG0W24srpOQyjvpjbprB6tS02sxsguAk2iW+veEDWocO6b1SLN1l8b6gM5AGsi555ajw1lR7a4zisBzHsvlvWVBm1mUiPNjD9f5ecicL0WMDbrSFt04pOeQP5c6aNZIzmOk9yGjMp+FkQGAsV/534IydfYd4lpu0gde53c3Zrh5pPmPQM6/PQmZu3qwLGIr95FipbwrwEilEpplssJLQrOUoEw/HttC5la0DspYyhrlHQhxYKXAONek8w6wX/tK0Guo8xfl/TzLyNygyFG+B81z53x5rgX39PVD3HNus6xJ8nyZNitOrafdeqeWAwy+MTo07znQZ8vIALVDKQjaqXPmBof4OUeK94D1EKvSBB5llu9MkITXjmiVo4VCWtQsO2X/cK1SwOXM0XM5rs7nC23svEkAUtoLoVmGMK1myUEzseceJmfum37kPfj6H/rlE691ROf8/p//6LEO3f5yXYLKQE7/4PnXBd3DAdjyK9FSkjlFZTUknN2alfWkRbPkQDg3yWUFsiI3ByoB4Pve8VF8y4/+m+p3L5V225l7ERqvoR984ur4WZ6sd+4s0HfBOF5f+vd+Ee8d5frn48Ly7I2jYuhLEDQnx3P9IP0cAK7sr6oaUaW4b8ob2WLWlQiiLAYsgHLF0Sx3N2bYnKuqn2+CzPVdGAtBMs0yowavvLiNR5+ztCWu86PGZG+isyYhm84ryBzncbFBuruplEpAc+aA0Zkbb6ULodAWARE16AvScLgeDM2yC3kxLfLUs74kdMeUDAWn7+ymGWmDiVENqZ2NHrsbM7MghUDGdsr5CV4GXU7dhVDoUwfLoa1mKVSloc5t6zvNG5Hz++g0Gy+eZlnoQx6Zo/P6Y4WyNTPUM08nsxFciVbLhrumjViMU6nHKDSlEFxpgnmfHbRoxw/QAIzkFrziju0y58TI3lrkd4eTwaWmUTHeo9LqZn13qtIErU1yPTrwZzdn5XopZQn0O4wzlwMIR64MxpLyN2XMWhFy+/4pzVLGh6XkOVCQUk0lBvJn1w5WxQCSouHAC6FZktOY0Py75cyx4/XU1UOsh1jR2TPFue6HjMPmrMelMxvYXw64ebQu7/995zbx2OV9vOo7fhbrmAMPPsACZCRva95VKLQK+OT3PybU1DMvAx90HQzuvUhJ83nlWc37rhRS5mPXLv9L0Aqu5eYDhD/4jZ+H+85vlqBTmSPzzkTOWy3GhAvbC/z6f/vV+PaveV05LxvZfdcVw13mpadZStBi1ql6oXc2VhV6xbX5xvGl8x6sFJkLIIEZzzRwyFwRS2kce1TWKF2jJWfOC7ZkMQ4dO0bmbhyucXZzXtZFT51UNUv7LAFmO5BzdIrSBLPOBvoK8ilqlk4ApdSZo5p/3EjfpuQcx1TTWD1aLIE3FUCxQT12EuVZpITKQPdBA9kbSgmZjtWaa4ddTsWo5q0qErca+4UrYogs+u6UAii5nd2c4+ZRO2fu5tEa7//kVfzbR58vZacYmXv3I5fxgSeunXgtdhCfvn6EX/zoM5PH7h1pHVYg3yc/lxCCqTEpzpkEHQTsAMY5NqK0mflig+bSxLZlvQFuQ0zl/s9uzY0NCQA/9IsP410ff67Ypi+1dtuZexEaT5gfe/djeOzyXpmsXRdwaXej0Cw9XUgW4+dujjTLoFHf/aPBRFEjfQ7o5P2p9z2Bb/uJ9wPQCZ9SGg2NvvCVZTG4dqDRfqZZ3jzKOXq8kLbu9drBCmc3VVihqFmOUepXXdwpCnPFiRmpPdkoYYdJo7McrWHKm+TMyTgMMUvbhpAXxd2NLHbCimQSNcpKhvK5pVkerbMRLhK5h6uBUMO+bHps2LBRzMgcR+vley5NIA74zsasIImaM6fOQUww+QliDBanrFOa5f5ymEbmQu0Eyr8S5QQ0jxAgRTemwjSiaHysP2/r2OVI2eLoaUo2ku2jp1ZAwgqgiIPGCLE8A5v32JfyCHXOXL6eOHP337ld5ud+yS3KOXOHKxXckZIjRUhFnlffYd53I4p7vDHQpFmOm9j57QWukXT1ch0L/UxQtaxwSnk4ST/nMYupzuGJEbg6Rm/PbWvOqxiCbLB5o0YiqHx7UiCcpdIZFZ9qjLrxe8NXNM5cg2bJKpWPPb+PT145MEGoM5uzkWZZvyMFzZx3uOusUOEPy9x52YWtciznGAKWUn04JvL3zsjuHYrXKqSseaC05o1ztbxDhNhLHrJQIRd9594hQUFcAGc8b0HmiN4ux4YQcOfOBn7lE8/hg09cNSI5s647ts6cGO937CyKASdoptzHnOqgifIko9uZZknIXFT1QrmH3F/rbMj+w04tB9wOlorM8drn152pOnMx1g6aPjeU/g4xjuggzHcS7JHGTvHNo6xWLWNWkLnOPjdTD27iGR+XM9cUQIkw/ZV/ZZ0zCNqILnJ9N2mGZtmrUysfS8kY/9xmo/M5NCjB/P8ylqy++RWvu1S+8/nY8txWQzKOLkCBgOKwq03FlHKft/U7aexg7y01gLuzISVzjt8r5GsutQQ4muXhGn/iH/4K/vQPvxtAXqcErbMquMc7j14nQdboGBP+xfufNGvA/nJdgowACtNGWhdsYLDQLAPnw6p4zzBo/v+870xagDSZf1PIXExOz6GzbKm7x3X+n5NC9Eup3XbmXoQmE+tbv/K1AFAMdVlEzm/PC7VKJOalpZQXquf3lri0u4AkJwOKzPk8nz0yYq7uL/F97/hY+X9Ps9xe9EUEQRaD64drvPauXSz6ztSRu3m0MqpD/pzSh2sH62JASzQ5jgv+5rzH/Xfu4PLeEtcPVyXSu0W1mA7IYeIIjDE+g1LYjtYxO3M0DmK8hhBGJHEgZI5lpHUzE+dT1gHJhxCH7GA1FKqB9LempaH0l+V1OVoPiJql9lfGITtzc0uzDLWapc+Z4/y6LXLmWlHERd+h75WiI+Op51NnJgTdYJduwy3R01gbE0OJ/ofqvP7YEnGm6GlRPXMR2XpzHp0NctTFWWEklvtwODr6QiOM5ID4d+mxy/u4Y2eBM5vz8p2I9myNOXOAUos1Zw7lPKKKKmjMSTW5ZC1g2tJyyGIRZ7fmxdkTOteFbaUzC4LHG3OV20NzRpFuPfby3hK7G7Py/glNTcZQmo/0M2UQyBL3klPIDo/k6U1RtYF2ORf/OW/Ygsxxvh5Hkj/xzM1iiEoThKfliAhtcWPWlZziZ24cFSPp/LYaKi0kRk65Ho3Gzs31eWcdP0ZBZIjVIEf5t6B4FaKqCLcU6Z31oSCm0q/cpxplGmJ25hZ9h1mvpSo4T/a7vv5zcPNojZ/70KfM85z3x9MsW0iiGGzeKWAV3ULvHmIufs506eholkIh9YyA8b1IzTHzzlwOFsSo55M+TJUmOO65yW8zsqBsHN8HFsSROc0CRTJGh86Ryohqez4odd2ig63GBv1sDIZxyRm5l1kXmrltIvV/Is1Sgn+0X8jnXiMgl4FBqY13nACKMAIkCHDvuS38m/8m51Qt19YBlvdtNcTyDlZ5hiaomK9hkbm2M3crJQv8GiY2ws7GbGT9HO84ikPF8v2ADVCxkwcAX/jKC+U6LHZ3ksKlrKd/9+s+B4Ciij/50CfxbT/xfvz4ex4rx+6N4nxf9fq78rGkAgzksZ0RMrdNecFi8+rakMdf5gjnpNv9IP+bS3i0nDndp89uzcHlgwBV922xPF4K7bYz9yI0mS/nRyVHiXQzPUNert+qnLnsyMWUC0qzktfhKqutMaULyHTIl53PkePn95aGu2xpljnv58ymNa6uj9StB+7eLTU51kPOF2shc6+8c6f8LS+MOnPBLEqb8x73j0XOH7+8TwIoWu7AUBkpipqSpd2xE7S7SXTTKHXfNEeIlR37PpjIjrz4fQgmJ1Fy0wrNchUNqiObyFHT+bRFw2tkTo2/mKgo7qLH7kaPvSNFDEPQTUw3f6vWNpCxrTl+Q5NaU5C5qA6PCo2o8y3n92gbRy7F6dJIa/53FZP5/xBU+TK4DblFCVJDSX5vHaFg5gEImevABnI+BuVfKckhCnaKUFpjQv7/U9cOcN/5TXOeEoCY90QZVGeu72x9RKHFCpp6UgK9OGmrISpNejQ+zpN6phx3fsylE8W8rgvGmTtaZYn7RaFZhkJ78Tkow7jecH4S05148+M8krvObOicSgnf8Htfhm97ywOldEOp60RS4KetdcTXtAIo+rdQapdrHTNBms5szPCJZ28Wh/R7/uSb8fNv/fISNJiK4kqeYSkfQ86c5H7l6+T5UNByMhKEsucpeCbnM1rq2ZTTxchRoDkt15FnLsyMeUHmYM7jHZ5+NKAOlmsqg5C/W9GxD9x9plA3mQo/646nD6fUEGyJY34pqS2uB6+im7+THG5hi/RhVDpMde7V0qErXQh1/hg5wAerAZsLypkbz1s9i1KWZeyfiKUc89x4fx9iNP2V559l/bXIuazHh6ts/O5sKDInzrqZD7G9pnq66anVLMe1y9Ms5dxVfnMfSrmORW9VbLk/pQ+js96783pKcX7GsQQFvQAKozqKWNfBP9/fsvbR3CtzvUojeGE5cycF6VqNT/XMDa37KrbiaQU6KmSO1v89RyO/tLtRrsOlbU6iF8r6Ik6PPJvHRieQRU+y8nmPH/iPR6XaZOdZ14US3ARAQW+lYOu73ZVcdUD2+Pw73kNkbb5+sEbrUWSm2ujMbc7LfsV9BmByWF9K7bYz9yI0byjmyWoRDmeXtp4AACAASURBVDGWPuacOTGuAOCOnUVZwMWA2FkoMifvyt5yKDSgq/sr48yJg5OjkusiEAKoepoUI3/DvWfxkadulHMCMAiYtFddVGdO6G1i5EoOGlMnZfNcDhH7qwHzPpgXVBTj2DmqaHejkSkLWEYodcw8rYwdl3mnOSwxWkeIk21FoWtBRrhQDTZn6jR4wQh5Fiyvy9QruS6jQEUufFQnZcfH1wHjmlbeIA1BNyNxJHxjARSPSMn84lwFeS6e9lUEUBrGDwuyyL+FllQ27Hxej8yFgBJ5r6hnlXx0nhucz9e7MWHKldAsW5FA7j9HZEuh+oLMSS2rziBzP/PBf4dHntsrNdeAXK8uF3kP5f0/KedCNvCU9Fgpy8EovuTOndtalDkjAY/7zisNkJFuHrMYa2EBT2kTZEOEGTggIcbL+//2V+Ous5sFkWKxiRYyp+jtscNQ2pQRyj5YKWY9jtlf+LGH8A9+4ePYmHV44O7d7MyNP7hzZ4EH7j6jtQ8b/chIV0bmiuLwjaOikPnNX3w/3vKGHHUu1GuqMyd9FvTIOxte7Mc6aNbp8u8b0ywBGzzisahz5vIvVhXKhILMlRpm8owGcWLUyBaWBZCf56wPOK5+You2KE6poCOzriuBALmO9IGFWXK/87G8hpacxAZ7wDuvHMjLpQmcwMxxY0ZIVUwwhcCPe27rQXLbQnXsctByHjJvpCzF7uaMcuakNIGdO/a8MOOgfZiuM8ev17wfmQ0uwADkOSsOJTuJLPN/XJO1dh21NAGQx/So6q99N70Qlkf2ZH/3NRX9XJec8HWsAwzLBvraRuba43gawRLfuPzQ09cPy/y/MCL/J5UAkP6d3ZwZ2jr3ZYgJLx/twe/+hjcZRfQnr5Azd/l4Z072kd0Nmav5c7EbheEFZKr3NtmmQ4w1zZKQuQ0X9GZkriimFvGzOmfumRuH5Z25QaJ23GJMJU9baJbsDEpA/dOpVvpittvO3IvQZL7MCRUbRqEEQA1ywEZK5LcqGa15WmWD29CcOeHfL9exvLxX9pcl2gooJTKORu3Woh+L2jLNcoWzm3O8/p4zeO7mEZ69cWRogBUyd5GRucxLFhqSODFCEWGlvRgtzUUMBisyos5BTGyY598LmnjGCMG4OiWhpuKxw6PKjVr7DBAjnI3PvPnPulAWlCHC5cyh3JtXW+Pg9ZCSUmtiwt5IcRJHyyfiy32nZGtayQZZ0EW+t9iuMyfoiGysMp7yezFSpN9Vrg0ZUDk63crLifa84/2zgeCRudpJpEjr2HdvKMm4CoIgSe35HmD6JO+Z1P6SfvF5PTLXopCyoXSeNt1v/T/el/sa9L6HlErO3OKUyNwh0QHFgJPCt+e25gWRu+aQuTLnQsBfe8uD+JLX3pnPJ86Gq3loIvqEmFzeWxYURCh4BZmjrq9HEYHyrndaS1Du/+zWHNcPrTPHKOBU46/4mvw5/81iAUfrAb/wkVyCYHvR47V37eLhZ/YI9bGBgDYlR9cQEUvaO1qXQNP2oscXvzqPr9Spa9XAjMkqM6pBzvTrNnrl3zdVW6yN95RU/fIm5cyx43cS4sdUOY+E8/qQHRAtSj3vu+Y6I80HLvNnTuSgFyRG71nmZlEZ7PVYmZN+LfEITwht9U5g3H9WlmYprISpIBLvPxJgZEeB+xtozPS8+hmgTIuthe7LgBrIZzZmpGYp644NGvD75imD3iBuNYPM9ZxPreeVc7ecRGmtnDluM+qbzC85V0t9E6j3BkVr9Lz5uaFZv+7IBU8Y3fbX8oXLZT4ALmduYv0+iRLZarIG3ntuC89cV5rlqZ258d+NmS17wzlz6zH4+3Wfex+++fffb4Q/xN7cXvR4zyNaT7LVjoodOjN9l/WGnbm95YCdRU8AhkPmQjDzxZRwGteGjt5tCWIDwKJ3dW5jwhd997/Gh57MLLKcM1f3f0iEzG3NyjwH8rssCOZtZ+52m2wyiSUKuR5qJS45xgugcF2szZnkaWn0dZecGHbyXn4hUxkzzVJfskKzjDB0syw7vcZqiNhfDji7Ncf9I33yqWsH5YXlHD1pr7q4be5VBFAAjW4VRGvemQ1dCpHLsYAaZhmZw3hvGI9BOTYmdeYszTLLihuKIyFzs94KirCio/CopXbZgvJH/OYvuQVtmqUm8suxcv00ImIsg7+/XGOb5HlVpcw6EiKrzEWHvZCJUUprbDDsoNb5MyiOlIydLJxr56ApZYU2QHq2cow9b+1AeIXKjDKhotW2zisb7hDj2NcWkqj3EhNKoWD+Tp1EfSb531qmnB1KQea4XMBHP3WjMtZEKCKP4/HIHIuXyHuzHudiLm+QP7tKydx90JISXRewmHX46jfcDUCDIwuD5tpSHyw2cfnmkaFZMnLJDtgqOhpWqJECKaquOTHkUE4Yl75NOX1MuWRFSk7U77uc8/b83lE5D6vViVPglfhW6yyLbtagaGuxyTM+LHk5NQqvYh6jcbnyxqXQ9RqiGw3jUhkKMN8l6PomRsl8dHr8u9kUCUkO2SjHNtDBmMp9bMxUGXeqtd5jeTfrnDmU//e5ujy+gsz59VHfTb1eq+YaMAZKTZ25Rk06GQcKbpkxawSyfH7drFPhjxaDYTXUNEs2kGVdLEXDqW81kihrqntufZh8j0xpgk7qRQparO8358zxvUlrqVly4/lnaJYB5rnzvxI0qJ05+n1X5+L5uqQ9B09SXa5A+sX3xO8xo3FTAii35MyNY3/f+U08feOwjIOsv1dPqOeGsqYF48B4ZI7XDHm+QEYDN+cd/uwfeCXe+dFn8NQ1CyZwO3A0S5k3AgIwMpvtGbJNR5qxtC4EM3e2FtZWi8kic4A6k1kAJdslLPwk7cZUzlzMtumsC6OSNdUDXQ2qlXCbZnm7TTWvlCfRhLKRETJXOXMpUWS9Lw6EUk9s0XAuPt2FvBi0kLkh5Zw5iYhk0Y11eTHPbs7AtNAbhWtsaZZ/6ctfXZy+fCyK0Amgxop3SKUP+yQNLadlJMEn4vNiHhOaTmZMY8Ffoc902TlYEU2Q83YspXBMkKZSDjIO65jzFDecQiXTLGWPKpFY2pw07ygfwwIoN8cC8NLfOG7S6vjQecnBYDELOa6n/raiTCKAAtS5beJ8DxPnk2vme8IoHsLGWv63Fm8Io+Ncb8hTxXCbFLGmwh9MkdHeGVXGoRzR4DI/3fxipwYQRxXuWDXsijLjwQoP3r0LIMs+l2BBVFqsGGUnRf7YKDDI3EiBkudwtFK0O0cvM5XFG60emTNlIpzQwxCl0PqGfRbizJHFsB5imTcA0Z3ovGdcmQ2mxR2LzLGaJV2TN2k2XhiZY0Nm72hNdEofCFCki9dIgKmpXQkQRAqWbc016lxRJ2k9FwRAqVwWZVLjpUavvBOi/W2UlKApJbXgZn1ngobFaG2gbeW8nX3fWuuuoM0ACaAc45i3nKPyzpecuW4MctK6E9rjW5QOG87RsuEAt5QoAWWnyFoQIOt2fawPZOl61qp15/rQcI6YncL7pUfmMs2yrWbZhZGxEu2c5nFgp2samdO/hXEiolDsNPVdVwug0Pcn0SwZmaOfVTXj+Lx6vfy9DwTKb6bo/r4UiOwBEvTi6/s8wxyAzNfgsZvKmTu6BZqlnFeQuRdMs4SULrJ9PFgNODM6Xb4sBjMHruyvcGF7gT/25nsRE0pJrFYTBsJuyZkTZy73Uc65XEeshmTEezhQA+kzwaubpAQs+XH+WcqaLO+DPHehR0q7TkrgX3D/BXzuy8+VoPf1Uc8hhGDGgcW4Pp11BF/MdtuZexGaGu+KirEAStehFLdkdSFAqH0W1RoIkdoxNYE0Qr2z0ePC9gLP7y9L3THuQxodLMlDyLXNViVf5+zIKc791/ocZzbnZiH+s3/glSYnTww/3vSEnpjvQSPdaYyMlvylYI3PBXGjW3kPMSVDR5H1fYipSIIDeaGLUfN+OGdODCm5vqBNLL3NtLCjsVZe7oPPmetLxEicEWOIRXHmxn6QAMr+0UCFMxt0H3Y+KYIuJRrk3CEEdRInkDnOW5qilrRpli4fpRGd9mqWgee4mxtKW5SIs1X+NKpn8my9k0g5EDO/OTeNYXGu2o6fRP/FaYjm3jAeq2Mm79M6phJN/dE/8wUmsCBIqjzvk505pgzmY1cxFQqUPJtCDe2D5ho0HDTZhEWgoIUqyDhcG1G0i7ttmiU3zvMC1EgwUulkyOb/l2MtIuAbX84IoNDnf/qHf7X8zcgcj9/BSIuW/vK9Fqc21sgcr1d8H+zkyVzxBi5LXsckiL81Lrm0hpYmyL8pKo7yDhaUSQWHGEn3YyQBvQChb8LcdwuN9zlHHRlhfB0ZhyNaH09TmsDPM69eOHPzpAuKsPs1ivOpKtSx8Sz8usWBFkbphWLP9MJ6fWBD3ws1SB+s88jiLhXyGfPatTW3eUg3CJkrapYuX01z5ho0ywZ1fbrOHCFzfZdZJKmmWc66UAn4MLpyYs4cPfuWA8f95UCJMC74HLK3A/oOtXIzPbInLKgh1cG/5aA56oBNueCcuSkEzkv3n6YVZ+78Jm4erYs9c2FnZHycImcuQAMG2pfBCJXwmtGRk3p1f4nz24ty7HGsERVAaaPI8ltZi7cXqnzuz9t3VgClOGhdrVTbu/lvlbwtpRTI67/c34/+mS/Ev/jWLx3XrezoiZ4DB93YIbyNzN1uk00mzMIgc/WifGU/q1YuKGIRkxonm2P9HxZA2SbhDzFUgWxsnN/OMuY7xGU2Sl6Ur7Yxz/Qtr/aT+69Q+rmtWbX4bpGzWCSNybBOSV/EDUez3F+usT3P/QtlAR43zr6ucWbrB1maJVPbbMFuLUQp/89GECchdyEbQC3Bhjg6iVuUbyWUTrm38nmqFyQxiAvdkxw0KTPBv48UGWX6Jn9eaKEJtQESE1rJ2kKNyuNa589Y43u6zpwWDa+j/+vB5VtQ9NQboitHCRJnw25A1rj0dKf1EM2GANTUKDUCW2p17pmMw8bR3ppyBTPnYgS++NV34MseuGSi/5LjqM7c9IYJWKNANtDVOpejEMoWYOeRbFYt0ZjDQk+hZ5xc7tW4hki9tjtIOTCltuO1GmqJcS+dz04m98krifnG37SS2QFVsAQyMidCToeriItj7T3pQ+5vY66Pc1KO//z7L5TzAcpkEARYjP+uU5qQR5ZZ8jrPNXVivbqeIOFmnpX3zTtd5IQXh30cLxoiFYPxolEYx6F+hzJ9s/EeO5RJnpsvGn6cEWhFQsZxcE5t3wesYjQqup4qN3PvbK5Th9Kv3N96LdF1S/vUd1l0YzUkV5rArtt17qB9bgb57PyY6bV8oEWO9ah5MZBL8JTULFsy+9GvkyhjY8YsTFNhee7Me7uW+Ny2o2OQOS4C3Wrs+Bl6dmevwd8vPSVzvASXIOka605xHNca8CrHJhFhse/Fcj24PmpQj4MVj19uS/j70ienabJOXByZEFLXN6NHp0HmUlFl5nVyfzmU+sHrWAdRZZ5lZG5uELSppgIo7Zw5pXlr7WBARHKsg8SBUPu55j9O0SyNUFpK2F9Ztc5hIEE4olun5FKAOkXX9wiZu50zd7tNtkKzlHycOJYmoIVriKlQLF9+hyrRGYriXAVQWMpe87/YCM/10Y5Wtv6LvNAqBKFoyDqq2k9G5vRYQezObM7N4huCRq9zHzwNqL4HRrr2l0PJFVOqlyIqnfvMR5cOiPbU0TgYNUtyrgBFMuRYpoBJ5E5rmljnkyl6hWZJ+SN6vTrfSgwVWXMLUjsuJrL4sXR8oAVY+hCjzTsTjrl3UMSR8G1O9z9Fh2TqqSIFDpnrGEGD+W4draFvxBtcPz31TJANKQIr/ZL759/y5sx5hACLFkgfNOLsI72shiljB2RjzR/LUW8OeFiDXPu7GoUlZqemWSpaLcbTOmYBFEZo2XAtNZxSLcjgVUjlnWzlMsl7Lhu2IKpyrT/3Ja8q/RyqZ1xH9PuurRYq0fTTNB6uqV/sL4ci2nK4GgqyyPfWFOUZ+7azMcOj3/u1+FOf/3IAuq74AM0+y/e7+ct5Wjp/EnzNNO6T0KOaCI/PVwvTOVI8lmxcGTpZ5aChnF+em95D/tcjUn2nRaIBYKPvTxRAaQVEvIM273zdS16jLCWTUU49b75Wm2Zp5798XvaOBSPWE3TT5v5jC5erI2XHTJQ6+bkp2mYRYE/72h3TB0LQII9XQvV06RBqVc9ZP11njoMqZ8bccwmmsSM160Op2+Yda8AGoVvNj3/5PNSfy1q5XCf3u/w3I3Ma0NT56/eW3jkAw9DKmatzgOV5rGPC2c0Z7j67Uco1+fY7yZkTG01zSTuc2ZidWJpAkDkJWEu7ebTG+RGB8vYBqzhe3V/iwvaCbJzpe9hfro295wMPMr/2RZl7Q9dJT13sQlswx6Cs7vlIbtyc7KyUUNEsmdLpGSI3D1dF0IqBDXHmck766falz7R225l7EZq8Y1xbxteniSnhuRs5KiPiJfm3RLOk+mAGmSMnhhEGqevCeSaqiqhGphzPuXFnNu15b1Ck0CBzIZQoPqBiHJ7axjlzjHTtG5plPgdHQTUPwTkdncsV66x8/9F6qChSvKkzNXVw14sJBpljJ7EuBA5DswQsBcfnu8gYyZjn88LKghdVQIrKE/oak6VDCTIXaOECsiHW4n9vkXjDcsx7YjokK/zxM2gZdmL8VKhCjG5jrJUOpxETzeliWlM+r42QiwOxHmpHau0QiD7UKqEexVO0NF/P0NSKo6oUUulXneMn7xlKztzi1DTLiLPjhiPvjRSfLpTBpOqss66zhl0ZG+mvviPSt+SOnXrGgl7JO/K5rziHH/5PP7/cBz1iQ5HxuUHFAaZxP55mqd/xcd7/k+P2jta4UJy5WHJO8vhY58iL8iSzXuXfiDKkLQSecLCMBckp5/XUPop8x3ENmMr/EnqURVe806Xv5hRizZFl+TuLCKF63+pnrDR/r6jZolkyfXtj3hUaIbf3PX4Fv/HEtdyPWAdPavqby5nriGbp3k125ir6ZkP90D93+Vscpg2X3+2RZYCVRVHOW7EvXGCIqZ7rEvRyBqpz5ioBlBFFmHcdCe3QnhJtcEr60SyBMfG+xZTzm77jj74e/+Hve3k5lpFEOYdHoV8IzZKVL1toXItOuaTcKUCfoUfmphyAFhKutQQ1UAPABNjLsbJnx0xzf+O9Z/HhT6czlzRoDLBwDXB+e4Gr+8vJ3wKaM8f5X0BmUsl6uBrsHOF85av7K5w7JTL3kadu4DWXdjXgOR57owSP8v1rCpAic359yEXo6/kiQUkWqBG7WexgDtpKDjw3GxTS8w6jfaz5ynq8iEZd2F7cplnebtNNJpYYc3nhiWYTGGLC5b2MzL3igiJzVb7ZuJkyMqdODKFaXaDEa+0LOzw+GjxElm6dG0TqxqFVAZLWdwE7GzP80rd/JYBabU1QJtmItijHT5C1rblVs1zTxlmcgwk57VYkNyXUdebI+J13VEIg+Zy5Mdds0IWDHb9DchJl8/fS2brJMgqlG7X02Qug7JKqZ8lhcYaC0qxoQUtoOklDagug7G7oM1gP3ukSI0X/30tuc80fr17IBiMb+owgVlSYhiy1d1D9PGAURHPmPM1ymkLqkYJJZC5alDn31yK5APR5k+HNz2Deay7jScVlj1axqGSKgbAcFTFZLXSI2ZnK77rkUzacggatzgsneEfVvmc6HnzPHn0VhzJRoMqjV2xQHDcO/A2jTslhczeO1viPfvjdOFpHg8xJu+fsZq3iOHaZDTufZ9iqZ1hyced2ntVy7RaZ4zHzc13mbwuFXjvUvNAsIx87jgMVDJZh9QWwGYHge205Jh6xNvtEUkpZzpkTJUp9Nt/wj34Vf/yHfnnsj1X1BWoFWxE1kSBKH2qapXcgVg2RBJ9b3XIa5Pya9yfrmaLIPY05j0NVZ+4UwZMcKI05yOn6y+JZgM71G4frUb1WA5K+xpu8m+zcy+e1AEo3+b7FlPflv/wVrzECKHxvch8e+WQH7WRnTv82/XVUWbkWANw8XBnmj0RzTM4cPTe/VtelDVDQeM3f1ffCl11SZC7PtTfcexYPP3OzSan8ndSZm5e9QddJqdF5XMvInNZWlXb9INMnAejcc7ZeSglXx+PY1mtfJ+HXH7+Cz/89F4yyNqBjXJA5XxcyWJqlPJtZA5kT5L9lU4jTxjV2cyDc0izl93IMgGI3t/KCs/2Vz31+e36bZnm7TTdF5pQX75O3M80yR2FeRs5cSizr35cIwxEjR4LaRI7W18WTAQstM51EXvAbVACS6ZDXD1c4szkrKkDSZAM7u2UTaNmJScmKuMh6WXLmHDLHQgX1Ju0XZb0v/v3higVQ9EUGMObi6Yus6J4aNkvaZLkYeUWzpPNK6Yku1MqPTCuL7viciK8qmYw6egMvPzeLNk3RtIbYpllyrcCVy23LeXBMiyNBBhedLtS+qI4bGzRtJ3GaTiYbuPShJQoxuHkgeVrrGI0zDTACARpXm+fic23kHPLKsENeG7gwG1urgHBMWmfutAIoh+tBnTkqTTDrbdkJFn0R5yhGphrBXM+iuXDzS471KF5jXRH6plelC/V5PSrWcnhOaschc7/68GX82qO5PpKgcYfrAUNKuPfcJt7+bV9mjP9WH9oBBjcOnRa9l3H0apa9eycFXc9qmPZYnn9KcZRroTlmLRqgvBeMzElbjzlo3sCtabfH1N6K9XMbYq5lKjUdeV/zbShBLZjx9TlzUjtOgwY1cu+DNct1rAyzuuyC9sXT+VauDxzo9FRwRSj1vB7drpkGoDGzgkF+7mjJoHydvSMtVSPjc1RRHBWFMO9h1x6z4wx1M05dXWJExknev5YAykmlCdjxs8/Czvv8d/7s+uG6FKkG9J3YIGSOnxE72yG08y11rtv5BKBQeaVfcr+ilvzyC9tYx4Qre+pkecf8hTSZV74GaTc6cycKoCABoX6+Nw7XZT30OXMStLh+uMYQk6FZTuW+PvLcHq7ur/D77j8/0sYt0ADo++/py31vaZbyvJs0S3L8vEDNoZv/IYw5c8cgc4HfQYfe8l4q6+eF7cVtNcvbbbpVpQmiLRou+RiyWDNFSHLbpNCz5BNxpJwNRyNc0dXOnKXr2WjNOipNc06GYxydPOUa670pDU4W1DaCxrXYGP0QChqfgxe02oiHGTPviMl5l2ulkMqLzBRUdnjKWAbJmdM+GJplzE70pnO62EmUfntHglUIOXcv91eoiqAxg0WmiBLgc9RazzI/iylkbmYMD6bosJEiffG0L0OVE0O0MhhrA8NHkX30VB20Bs0Sel4+VhZ0Lk3gDTtvgFnqJMyxPIcA61C2AgsydPoMrVMQo9aZ09IEJyNzEhyxpQk60wePMFZOQfD9hRmHNs2yNkQ9As4Oj0fmiuz3hIHLOS1TxiVgnbbjcuvOj9FnQEVbDlcRKSW86uIOLuwsTOQdsGp1sQQN1EDOY+bnpBoD3uHxDpqoE8vtcVBq6QIXZT2PFinjMbMBrOMFL7jFaJEjb7DZZyw5qu49buQZSpCIBabymNXP6fHn9x1iYg1fi8zpWsr1IicLSg+xMsy8Y83zk/7Ma3S0azRf7zg0Xn4fC8Jj++CFogSZM4XhnQOwcA7xOkYjKDLvO0LmLOOkcrpCIFEp0Pi2jVSPwMm7Gd0a3hIwYUfoJAEUX+ib+5vPrwfIta4frEydXDG0Nxs6AICOq5zXz51Az41rPfq+yOdiu2WapQayeE2SOXwrpQnEJpB7X5HdcBpkbvTlynwEMkK4HGLWPQjk8HMQICVcG2vYnTc5cxPO3LN7AIDX3XM2n2NcB56+fliOWZe56wJAIZg9TxHrBs2yC3XAy62zBZnrcoDNO3Pr8V0D7DopARVP1xbNAiDvIbdplrfbZBNDqCAxDaNgiCqdz+qTkm+m1B7ntJGhYByWMb/GO3Pe8ZOFbOYcE6YtCmJ3hlSA9Hwo9wBMb3pGRp0cNI4qyjpaK1fWkXKmxMg4MGy+jtaoFfRG+sZIFxsQ4hwozdIKoByuhrKR9O7emH5YjEF+xuIgSHSzs89CI/uaXK8GeP7DOzlMidGor0Z5WwsTFzyeUp00QQH3XHhjHKITNSEnxiNz6kDYe6oEUILW3qqESpqoggqEcB8qdDTUKKZH8Wb0jsq/k6hN0CLlVa5YebZZUY2RueNk3NMY1DlLyJwUOz1P1GepX8f3LLQ6dlb4eowwyvhOOX6eTsbzgSmZPhBQ8r86P2YtyuvkMBieJRsY8teXvPbO6icXiGbJz9gjc+zUxqlxaKC6Mtf9M/aUNka65ByeXshGhr7TMN/5uc7vpq6Z9rlxEwXFmmZp+xBkHGKDfkxro45DDmotnDMn7wyvOb/11HVTi82jGJqTmHPmOLDmUbwqZ46UDqcZHNZJkcYlC1gcA5jIHWzNnZQMyi+BUqbKybXWsV0KpELmyHnwtEOPTHAOsHdU/b0dFzxhlpD83iPWfvx8PhPfw1RjZM6sGy2nSpy5w1VJPwB07m4Q9bJlj8jnOneUNZNp+bHp8HdujsiQCY2/oLfknKja6At3ApSGPV6HAk5nNmdFXGSqJYw12yiN4zrVBJ71XZWvKY6YKGee35qX8ZkWybFBBwkkPH39iI7RPROwwRfe84rN2UB3s61n33efM+f3fk+zlL1NjinHyjtI9guQg17iEJ773UyzDCH84xDCMyGED9FnfyeE8GQI4f3jf3+MvvuOEMLDIYSPhhD+CH3+NeNnD4cQ/uan/1Y+c1sx3gmZMwZEZxUEd1zdNiPmEY6T2WejS0UReG56Q7czL5zK9HvE78bhqogy+IU//5v/XxcjlH8NxdE5Xc08DfpMPm/lzOXr6edGWdA4y5ojWPWBnF25Z6ZZcq07obeW4stBUVb5vfxbEpmpvyk51KvTYsRcYFyNbXbw6N6cQRnH87aog61ivjuUM8dRaLm2dKPyqgAAIABJREFURPTl/70RqNfWHD593nqsNzBico6f9KFRG6pGk6yRLecudeaGRM4gxmPr88o7UtOo7DOUYeOIps+vK3TTjnLF3PswxDFnbhYo+jq9WQjfX2iWh+sB1w9XWMeEO3c3jADKQNRSjqb7OVNJ0XdK15Nx9MEYRjNbypnS1849Y69mKf2r5ftvjWYpkN1Xvu6usb86lneQM9cKMLTrlbWdoyYqFq3TVSFzdN6hes/tec1zS148B+Ox9rkFejd1zczHtuhRkhdZIXPO4Snj0EJ1XR8kQGiYD2VtSnjHb34KD37n20sfPvb0TXNvMt9UZlwNtnWMLog0jm+lDpn/Ta21xL2bk85cqFE8fva1Amj9LFISgRuY87T6qznUti+l+Hpvjekh1qhXK1BagifOIWkXWm+/b3nv0P9X+qZ1btjo9s453wMA/Fdf/SD+0OvvMtfpJ56Fd8j57+sHlm4q9+UFUFrX6EK97pQ1Krbvge8xhDpnjgPnn3x+H9/4I+8uqSm3kjM3RBu4YEr8mc2ZyYUF8rP6Z7/2eHFgUkoICCaN4wbVBNaAvrU3AeD50Zm7sMM5c+29qaSzuDltkLlBAxFyD/IbmzOXP2dxqfJdF6r3R9F8u2aI7VQjcw0BlHGNs/vjeG+jvdd3WRPid7Oa5T8B8DWNz38gpfR5438/CwAhhDcC+EYAnzP+5h+FEPoQQg/gHwL4owDeCOCbxmM/K1pB5siZY+UkK0+tXOP8W1gFRdkYxmOlxghgja6ZOCZRE9P/1Vu/vEK/fFIsRzT0Bc+LqiBzrUV5kooX1FCQ49lJtFF8jNdzuTidRhUrg4ec0pI7WKgnei+syNeRQSoLu5wzLxC6yc5JUl6ekck9iuoss2JWC0GS5+kdpWJI0XlTsk5boLHh8Q0BlTEodE92orkxzXLpnDnZwFqGqHeyOTfIb8jsMJV+Ruv4eXEMXqRrwQGU8/pxFSR2RlHDfF65B5Tzelqop56poagoqo+mlzwiNrLFiXFGJOfMLWayYesz+e3n9oyzIgaBUJqX61hyaS/uKmWwyplrGuR+zOw4sBE4JQRTNkF6fxXxqyP3U/TNliM1xIS//L89hK/6vnfCNxY6sQIoo3qbOy8A3DHS04/W0TiUU6yBloiFd4BbqJinhR2t/fwNVQCrcjZofLxB3lfPAuOxEgyyVFE/DtKm8m5bDo/mfNpx8Ot5NhoxivrY9y1G4F9/5GnTh4PV0HQoCzLn1JTlVQiB1nhXqNrOOZj+tQQvpFkUuabglaBd5DGDG7N6XtcOcE1VFoEYPw4emWN0gx0sRsCqILALqvQdCZWU97ibrBdZIXDyXjTOW8bPOdaAKm8CwH/5hx7Aj/7ZLzTX8c60P1crp+6Ail8DhMxxaYJQ90s+l+O5xqYvDdXK35O/ZekRGj/vb9/z9o/gPY88X46/FWROnnNrfdjdmONgNRhU6wNPXMN3/PPfwLe/7YMARgGUAJPGYZTHu1BUYuXW5H73j1RY7yQ1yxZLYO2cObFN2HaQ3yxpz/OMiZl/ZpXgUZ4XJUBBaTnizPVdwHd+7RvwX/zB1xjGFjN7PFOBn+VyFCmb993vXpplSumXADx/0nFj+3oAP5FSOkop/TaAhwF80fjfwymlR1JKSwA/MR77WdFkQZDNL7rIkKpO5onGES6l9mktnKlJGZNFiaQ0wRCzGMCDd5+pNxx6YWxxX90ohxGZEwPT0hr85jaBApAzxigTq7hpPTNP0WuXJpD7qB0mp/goDrBxKGGO5b55ZM7QQqNFwAQhCUH7FEKoItosg2sQQjYoyQARJ4lV9lrjoFQb+xmQF+YWpc8KoLRELNRB7QPTLG0BV6uuZ42nVUyg0xZD3+SNOOOS5zM73vlaKPfE98g0QB8hr5T42CCvghDjeSmPMf9b15GS87Iyo6c4ynnTaPhm1T+LzD1+eR9f+X3vxP/48x8r4yQGgYgCDTHh+b3szN2xszBzsUXVzrlX+nzys3C0L3Y+vdHq5hc/Y7lXdjZoOpiAkKci1vL9+d1/x28+jUee28NPve8Jkx/CoJ2fwoHOKwg4gEJNrVBtZygFumdPJ1MHuEZ1fY1Cb5CbZzERwPIUoq6xVkyhQWatmHhu3PwcUWEV56CFjPjxniJjtHJIuOR/raLmOqshGHHHjhZrl3FsOZTFmev0HBxRt8ic7a9xbFwwonKWu/pY+bxGunQspxDKFvLu85orEbAwLVLl84COo1mW+3BOSC02NU1Va+WfcsCK+71yUv087hpEUltFmDtTreUM5s/tv/57Zip59c/6XPYatXNPiqVurvvfB9jn4evTvvexK+b+bkkAZaTK+8BmF1CC5zdJ3EjERf7lbzwFYAxuQdccAKW81NmteXlfW/manBfMoiat5qmTYlM9c+MIG7POIL9NZI7GRsZ7Gplr06rroJlQJDPQ8Be+7NXF8V86u6YXGzDWa4PYezng2mE5aM7dS6n9TnLmvjWE8MGRhnlh/OxlAD5Jxzwxfjb1+WdFK0muJckZhrPN6FUXgokgZiXIWkExmsVIDTyDzFFUxm8iK28o0Hm7YBXYYky4cUTIXCOR2UeT2Qg3zkFHCJozukpkdLAIkKFZuk2W5YQZofR0s8pB6fTYRAuV0GdMnTl2lp2RWPLzJjZTT20QREQ+k/Hx0Wt/rePGoTh+7hkPRFvixsjcerAImgiVqDPFhlJdd6igoO4+h6rOHNEs3fPS2jp6T96I9MivoQF6B63krhxDS3IbhTd+eBP3i3/b+YShWTIdR0R+BIUQY+9gROF++gP/royTbFhiwAwp4fLNnJdw586GiSZyzlwO8qDpxFQovBjvLSSmMb84GJOpRjq+FtUNlbM9hYrJ5irtrf/nB/Bfv+0DaDXeWPndyedVI2FnpGNlIQ2bs2Tuza0LMaFaS31QqhgD5PCUwsZjzTTjdEUXwHLOp44DR73b96bzDypy4x2/RtDGU+X0Gfuota7R9RrixqE4Jirg1dFcP0PojD6L2jkSytS80E27EviQ66hD2UbQ5Hf6d6iDieZY7Zddo2GO5XItPnewOKX0eUVrdoEAKdjN86w4qhUyhzKW3HcWF2E1y/wbn7uquV4t1NE3n3PH8yRMjLUPUgKwJQQabbI0gRs/vkcATWSOr8VGu/2bHQNNjZC9ldWT5Xde5EX2gezg28Au54sBaJYrOKlJoKMERCgQIO8SUy0lHw4Aru2vjFpuSnmtvH7AyFyHlawD7p2oi9sfV1g+/2uYXCkjc3ef3SxqtDI2fN6MTOv65NfPGU0MRlP9Hn3k6izK+r2/HLBd1f5slc0RJ97ei6RCbMy6UqbkpUi1vFVn7n8C8BoAnwfgKQDf/+nqUAjhL4UQHgohPPTss89+uk77/2uT92NOm94QFQUQB20YoXxekDIy5wpVOwdCJjfDy32nKFM2XjH+Pv/rKTyidjUFQ6+HpBtvkEWwUQfMb3rkqMrnxciMKt1tz2EdAYH05Xz+WG/wDwnVfUi0HshjYREO/b2PHHtkzkb06oi2nKfKSXTn4LHQelDQ8ya7AMu5ppFPMh7Kc2vTJrap1l+NgjpaHW10HIAoz2WonzdQGwKyiZoovUcKaFw9vUtO1SpqnZI1fjwKIr/NjrNFg6vIu3Pm2PGr0Wc+r8+9kv5mh3red+X9FwNDqIRPXj0o4yS5RKLgNgwJz+0pzZLfyTpnLjrDWfvA/Zf+8r1VNbKKgaWCQnIdRo6sEViPjeYR1YazNyyZssPf+DpzTC1nZy4lHYdE71ORgW8Y+uJ0yX14594EAsb3rEKWq2CEE6nqWM2yDvK08hSBtuCQlEfw9OuW8TFJs3RrdAnKkKHj36Gyf3R5DFYUcNMgWo1OyLrrnURRZhRDrjjGpGRX2AhOxGJmHDi9FhuC/h3ge5K/pwRQ2OmSc3hBK3Y0T0LxFBWoSxOwanIew1T+NflqDZol053DxH0qJdi+39w8zZKDH/Zc+psWMndSs85g3V9TFoCdOVazbCBzfu/lzytkrtOasy1Ez4upyGgNMWLeaRC4VQpE5vQLabJet9gk4syxA3ed2AvXD1d5PaT+DzEVZK7kzA12b5pScGZGTN3PcRyNSBlGZ27D1DGsnLkwRbOskTl+Zp6GqWsG2V8po5XbGzNzrloLQNOQWA1W+ivI3GnLB30mtlty5lJKT6eUhpRSBPC/INMoAeBJAK+gQ18+fjb1eevcP5JS+oKU0hdcunTpVrr3GdfEGCnI3BAxRFsjR/KJui7gnnOb5bcx2ULVPSEnVa5Z4npQnTqJ5PiJYmM78mxVCNU5qmmLgI2k6XlrbrVE9rtgET8vHMKGCS/2IcA4QNy39VAjMjF6+W6nyBc4bw/GyZPzS+4SI3PFMCkb2Tg2JIsvffRUJkZqasEVh6jQZ3aTbkTxxfCL7CTp+LYW552F1hBcradrhcm5TI5Ux8+8Zfyr0WCfoaJiwR1b0dRCLbzgI8tMd/JIdWWQ0zOQovYVeuWcR/EfWptgJVvfqQNcRxNHGtUsVBuFTxgHVLFru4HMXdhZmHnEOYyCmBiU1BvvZKh7Q39qzKpgTBcmN8y+q5Fjf14/ZlON0XIen5SEVgRz3i974CLeeO/ZgpIOjXXMq1mGQEiXd4CLQYLyuRf7USfRvpNl3aUxk/P6fJAWQlE/N78u1Me2ZOdbOb5AnRcpqrSsCFs5lGSAiVCJ0qTG66WEA1KW25r3BZHye5WX5Pd11/j+iiBCb+cU31P+u6VYWo+v/L0a/Fpi13BpeV631yNeEytEtQQTukIh9c/iqAig6HsN2P1AzsH94X9XsaaUlb/dmA0tp58CFHzsckjGgTPOmDO0T9OmctN8UMCflwVQZCmwyFx9rvx5WylZ9rdmqQVn0wjaJQIZfs/idivInASCfZ5332netChaxphwdV+dOdlPpc6cnE+QvLNjztzKBSL8eqjIXDdZZ84jcyKAcuNwjbObc7NOM71c/mWapQw7lyWRxjZkFQDyyqSj+Nke1SrmIIkPcBT6vUPmYkolB9jX+3sptVty5kII99L/fgMAUbr8aQDfGELYCCG8CsADAH4NwL8F8EAI4VUhhAWySMpP33q3X1pNinIWdKbAvWSIJTVI7z23hX/9178CQJ5oh6toCmDLpJy5l9PkY438bu+AyPGtOkprF9kvKMVoLPsijp1byLsQpik8qaba+Cg+34fdpEP1gjP1rzbMvaPqxqbzYil6vJxXnDkuTeAFEORZeGGQYKK+9t44d08cYO/ksINSIZTeCSdjjpGwki/ZMJi7Lpg8l1adOaZXMbrio2jeMFQjcBpd9VHQlgOheV7aL0ANXJEBLzldqUYKWgZYlfdYnII2Mhcb87aVEyPIhjf2ZINf9F0xrGRsLaqUzPGb4+YUY86ZO789r8pkDJFr64HqisH0r6IXdrXwhzfeZdyLgmKUIFGw76nbMFXKHmY8m3llLtDQMgs5sR+oBVDE+HnrVz9Y5nXJ03IG52ptjZri3B+D6tpgVztoUDkgnSrmyT109A6ZcTCGKMx5puTw2SAJ7rlxK8/Y9beVz1vybt27WQkJdCqAouMgQUqrLHd2a4ZhaKODRZnOORstBLcqIsxrkFtjapqljod1AltOLej7ifXcvy8xGoenaxijsu4MjWchaIMXQBmi7cO8gcz15j209ybN58y1nP5JmqVjyLDR3XoWJ7VJBK1hT/Bz5dIE0kzO3HEOe0MQJ42BsGbuf+OzDz15He997AreMAaLAJ2n3KZQreOa7Bn6bsq+V9Msv+r734nv/tmPlN+WtZb6GmPOmQshB21nfWiq7QINFsW4draaBqb0WKZQcx1DppfLsbzXFVYBGu9zR8icXxsagbBCsywFykexlEagsbLfaI1bDQmLmSJzL0URlBOduRDCPwPwbgCvCyE8EUL48wD+hxDCb4QQPgjgKwG8FQBSSr8J4CcBfBjAzwH4qyOCtwbwrQDeAeAjAH5yPPYl3dZDbKpD+SZGk80Vqw0bVqe7d0TnYsoFt1nNErA0F5m0skgBWhhbpcJ5kaopPH2vG04LTWrROv063pMRzkhgSjCGbhWt7/RYoKbzsZPoI7zrWMtIV44UjUMIowIovcglHyiwUEH+jIunDxFNg4eVseT+akcifyeRdbleT4sgOzk+P0fOtZxwahmhlM9Xg+bn+dbTfXqDnCmpgh6Wa7gFssptI4fanLebRle98lrXhRJB1nuSZ+DQtsb8msp7EkqqUQ41hov+Vp5RNE62PS/PcZkbnhYqSNu874j+msoYSbs8Uikl+rg564tDfvnmssjuswHHlJEiYMRIzNiHWsVR86mqvLKqzIalWXbBzge/YU4h0k0D189NOpd8JWuYOQyEDq7r+VDQINeHSUGR2H6fqv66d3Ky7EJQ6rSM4STy2TIoZW1rzDNZF7zzOWVc2gBD/rwVqde5Y/ui+V8Y+6KIv+a7jddLqQg0fPc3vKkgUi100BcNL7W6Vn59ayEIds6V8esaNEv3ffk7TEug5/t0+6ULIum6Ub8DPmglZRd4TlY0y34M3pggUjmtK6hd2wLHOTTcF3Y4Pvb0DRLPqsepyplrOJStwttTze+Rej25r/b3XDRcmlGznBBO6btaTEPeeQ6Ete6H7+lfvP9J9F3AW9/yoCJoDWN/Kt/suCbOEM8n6ac4sTeOMhr36OV989tMlR9p5/QOHq2z4JawKFo53kC9Jst+AwCPPrdnSi0MPvews7nqBplz8987czKuhbHmnHgN6Fgapq+zKM/yaDWU+aDvVW3XlICrWxviOGaMzLXW08/0dqIzl1L6ppTSvSmleUrp5SmlH00pfUtK6c0ppf8gpfR1KaWn6PjvTim9JqX0upTS2+nzn00pPTh+993/vm7oxWyv/Vtvx5//sX974nGyWFrnyHLtpzbpIcIKoJDDUi/UlrqYz1vTKJhiIhNepKEzQqTH5fM6R4qMFm4hsBFkz8EbTt9YuPg3FTIXwqQjwEWvq6i6N65ibcRn415/L+fiKJCqlNUIEpBzZnw+wKqxoMm98b3k6GFtVEjOnEHNyEmsUU5LtZl14djoEi/ovJGrOiTKvcrX7DjLsR7h5ecSQvvYSumw2nAbAj3OwGVjz4tYqPFu6U5d0GN9vSdxTNTpVqOqGLL+vOVzUEFSHTeAhR60XqHMe97gJF+sqLXN82a8jplGInkjPI/WQ1TJ7a5GHdlR5f52QXOvWu+pHXc9r1y/PLdYU1mm6GgrF4w5LgoMKM0103nqz/29cVBKhZVgvmvSLKNHjlDujX8r7+RA76TPB+S5GpONUCsi1Uay+fdTwS52Pn2AYUoA5fjacXpdWR+nxFIYhRuio7fTPnGwGvDg3bv45t9/f3nGiQJg5b2gIEfznun+poqG83Hy+XFiKX6sa/aE/b6c1+w/ds+rBKRCnUfcd5zCYM+/dE4tG8SGdtjX98H95W616Io+Z+5XHn4Of/gHfglve+iJus6c7IFDdA5lPe4yzjsNh8s3+9zqz/2zlLa7UQurnK7OXFux1Oe+c398GgEwprnMepzbnpsgqG+nCez7xs4QYJVbPc3St/WQKqaCBwXmnUrty/2UYOcEW+KZ64f4g9/3Tvy9n/st7ScFoOVfRptnZJtUx3bBjJf09dKZrHz7F7/81fpdV9Ofp3PmlPU1dyheTh+x86kCK8jGzorTqiT/UhRAOfkNvN2Obb/4USvScrga8KEnr+ELXnlH+UwQIYsG2ei35Lt5ykdMCYfrWCFzwuEGdCHiCPqsC8VB4zyPfDwZ4bSQeuWxCt52hrXf2BhlaiFoGi3FxHl1obQba60QxhQ9Pw5+MZDovsn7K86cGqo5Z84aFX0XKqOtKho8xGqjq+iHnV6PneMuUNSXNv8hpULP5XFoORJATbXpQziWw8/GkxcUsIXNlQ6xGhI253yNVrJ//m4dE+azKSPHLqYtpcOaYmaNEZ//1cxta1zP0yFtjp8a3YmcuQrFc1RiOS8rihYEYqXGWlHKFGSONgzJhZBo6Mass0EI9056eq8IlbQCQjWtbrq+WlXbrFM0E8jrikyz1Ijoq8OOcjxQy8tzJFcaryYiDiOouvk88Ptv700U2fh9OM5RkLk+7fC4MYsNJ7ElskBBG+MAH0OzVCdzPLYKlDRoluNvW6hAyaF049AKSsl8KCjgpEiCCAlE7MxtgEEoT1ukLKfPYrzH8bxKs3Q5cw1nzCNdUwgP0yxlWI3T4Ma6FkCxc1mPtdfgz1ZDzSJpIR414m3vt11nTs/LZSB8MMz3sSXsUaiw4/mfvJJFl37kXY/gkWdv4k0vO1f9xlPluWSSOon5s+2Gw+Ub99erb/rvXwgyZ412PcbSTa391GKZAOrIch9NkMO9x9xuxf6XYItff0MACaC0nTkRsQrUL9kvZhQkqeyhsr401s4hFXXlJ66oMFf0+25HqUEhFGZX69g+eGQuX293Y4ZHv/drzT1x4MTbTrUyaWblMENkMmeuYzVLa6tkmmX83U+zvN1eWPuen/0I/tQPvxsPP3OjfCZGT0m4HDdDpp54VUQ+9pBgZF5M1PFTiXubF6bFSn0Uqk5sp8iF60PZYJ2aZStnrkVty/1tKAhO5EUdS7Osjq2NtjqPa1Q8bOTXMaWwC8E4iXKc33h9VJwda7mfKcelJC3L9cixZopedAuP9MUjHMWoiC7voQ/HqmuZ58IbIDk8OiYo41rRLN0z1A2wVpNSpw3mN001S3efcqwXQGkVfvaFlPm8/j3jvLIQyMmPej2vMtg6r9DUvCPFQjqK7I3OHEFOV/YtzXJDaJbR5u0xus9UIRN5dMGNFgIuhr4PHpWcRDrW5FB2wT1XNnTbjjnA+WpyrEXcppqsS6WJ8ULvHl9HkESDBjnHT7osDtpASJd81wrGVDRLKWbbFH2y64oPjHlZ/9aYNVURncOu71DDmZtCX90zFiqtz+MGOGcO5Z7XMZpAAtOHD5YDthaujE7DWfY0y9kUzZLWtCn1O2mtnKvjaJZ17bj693zv/LfZf1x/pusD1oqlvjQBl0RpIWxTTiaPE++Rcj2PzEmw5OFnbk7SLD0zZNGo7Sbj33K4fJukx7o++u93WjlzhMyd5CTyMWx7WLTTvtP8mVFudbYAt1tG5miP5fVzY9Zh3gdTmoDbOiZV96V9YUXB5XbO3Ph7J9QjDtm/+s2nAWiqj1wLYEXZUSWTnNGiZpnqPcA4c8d4Ha13W+5F9lKe37JutXJv7XqAKjDKAf3l+rNUAOV2m24PP3sTAPDkVZXZlg2VjTk2pLwACsATDWNZgK4cC1hnTj63yFxnNmm/WFcR157rzMnGkY8vNEC3QfIml8/VStoXwyRWC6Kn8EicuSUb3Nog/TiIU6tIl7terA1dVp3rO1KdI1VQkciuKY75WC8M0oVQ07TI4OFcGmNUsDEYa5pldnKmaI11svqRW5D++Ofeh4e+8y3lexk/b5wzEsNRw1WcphR5I7DKJQlKq/POt0doOfeyus8GWpEmDFGPePLiX6M2UuMxH8ulCap5W+Yiyuc+D1a+U6NV51GpXUQG+JURmZPSBJsjzbKUG2gEIdZuvRDE3wdSWuqF4hSwYwPUOWgdOaryuQ9cSMs04DaC3kTmphI6gZI0xxFn+TgENUg8Va4EpQxS6/pAz2gowS6Y/lXy/TR3yrri5fT53jhI1HEubs2I0LG0c0cDN/p9Frlh53Nc8yaMy5Zz31J8LHTTyuitg3Mx5b5xhBzIY7m/WhfDXtbtmGjMvDNHxiHQLt0grYXMTTlgfh9t/a41H/R7NH/XWs8ZDQrhhBw/em59p4a2oF7yGH2qgS/Qftw4tBwTXTfyBZ7fU1XE6n7NmOnnYuhyH2Q93l6cjMxNOZ/HPWtA60dyYwGUMNX3wOPn9mwXgNUUEu5v/pf3VhmPds5c/uwf//Jv4wd/4ePV960m75xH/PJelKmWNw5Xzd+WtBhwELJmchxNrDk+f1dyXKUUAosZMVNHziEiTxKsOj5nTtd6nge+8VetouGMTBdWTrK543Jv/t2vwAp6h0XNUubJSzFn7jbN8tPQEm1WZzYyz5lfQIl8Tcr3d7UqYtksymSF+fxoXaNXOWfOGuGFauONcIq0A9YIqqLqx1CDuAkSyMeWBbHhqLZyTYCaMhgCKkdAF9o6+VsNR5i+LCmvTJ5FSoqUhcB9s1Egji61xB08zfLIG3gUBTJIYGiNQy0dL+fwRZl5HPzm7pG5O3cWuLi7Ya7lxy8E3RD4uHxsw2k9RrKbpwkXlPZO9lJQMZozHokNdJ+A3ZjE+Zyiwnhkgx00dhK7wPTm/K+liLkNt+Pzolmv7LCRG1SQOaZZOgEUQfLKhumcDcmZ26H6OoqC6PMBGInRc6RkaZKTxnsIZj7w8+fj5G/vBHmhB490TTX5pjhzKaEbgz0BDeeIHCyPBmnwyB7LSqhTDrARCZHzFuNH1mKXmN/VeaceHfS0ZDM2NCft5xRkcePepFkmW6JFggleXKjrQqlRqgE7mPHVtR+j6nGojK0hjjRLkQnvaU9x80yCFvOZRdvUMYY53vTBGP31+Mm98nmqv1sOGo/pCY6SEQZxfZR1t7xvjXw3+Z2nWXKuLjtjCzdO/ron0RY9Mvf8ni14fZp7lz7w93tH+TmeJmeuhYSZ/k443i87v1WdK0wcO4nUdva5sQo2X7vlIK9iY01t5qjmf7/rZz4MAHj1pR388c+9rzrO/makKdK+ma+Tv9+cdVXtRv6tBrfGd3AM6LPzf/OotmVkDPj/i8MzzpF9KjPiGTESwMzlTEJB7KeOZefoOGeu9fx4beA5pPn9rfzo6dJIfq0fUsJyVLNc3KZZfnY35jQLz/nKPjtzyRhSLWoQUFMrukCRlrLg5e88Mic1N7TOXEBOVm84BV2d/C2OwmlkuuUl8IF15kZXVMSGYEvLMAes4yefr12U2hjhJnm7JZc/3sf/dAIOAAAgAElEQVS6RjM96iBfr9apigJNRdVbNMvaeM3fidEvv5dFJv8/yvmjow7K974QtqJFdWkCH13iNVQWxVY9FnY4Ga2Mqd5wK3GNiRy+loNm0FVnTHinIkzODc17ZJqqnJfPIQZ5C7URwRY5lnPmqvk5+L5B65VV894a+rIJ5nthmmVeL0pR3L4vARqu/8WGs6GMdJQXWjn69VxUlVsZBzdm9J6JoS/naIkEAPbdq6nIzpEPari2ziWtrI0FKbUGSGWQhGk0qKJZhloJVcesZiN48ZzOnZeDEZbyrucpa39vx4evPTXPZP7ysfK3Nz425131e/lbqdHa36JmOTHXdb3pEGP+vNTuIqPoYDlgu9RElfpq9fpw5Oim6hhPI5c+QFB9f4xjPHUuPhatz6gv/DtvKJbfhUZ+3JSj1NU0SyOAQsduzq1aX76u9qF1P7PGPcj5PTLHb6IPFEjbcOwgAHjNXTsAgD/3pa/ESW3qWfgcdN93EQKZalOUWB4Tv35m+6lGLvkzORenF/hAITdZz+4+mwOm//zXnzi234CWoJgKFHa0X/j1UVM2WHG7DshPiQJ5W01yO9WZG/APf/FhfPvbPtDMW81rBgqyeBwyx8yBY3y5JrWYg2aWBstIpEWNs11j30vPlpJr5TqCAxa3aZafnY2jys/e0CiXRMr5s8QGYePl4oKpPsqkL5FfjGqHJ7/IinJIfTVfgNQa1nYRY4SHkROgrvvhG+c6eUomozrFYHK5YiU3K/pC1i0xABoHt5jXynkYj62RJclJkvtqCavId8tCN9XPgPZz8/lh0t9Hn9srkbu+m5AOJkOqcnIqmfnx2Ggd9q4LJfpdxoaslUk6pDiShCpMRm27OrdNo5nRCFp0DSNHTpXVQKeMLnvsENsRt2PrzNG7lzyKx/0Nej15vVsiIRXiF1SKvqaytJ0N/g4Aro45czJGs1EwJZ+3jlIKks+1H6vIoxsHK3hh6abBj5k39Bu0Wz5OxlfG7UR0v6uROZ4v4ufJmiPzMSUYFNej2opQ1tH2OiglTm09Zs08uGgdP1/ounfPwubiwo6Du57pV7DH+s9z32nNQzCBASAb/16wSX7XmpM+Z24KqZU9heceC/scrAiZ67Q2n38vmH4MaM7clNiWD6xJm0KqmnlwE2tMy/Hz+yWQ591Jfcg16fyYta8767pqHIpB7Ng0IoA2iUI11mj+vS+LcmV/idfdfQbf9EWvAGALXk+d16OHAHDXmU08+r1fi695E5cebjfrXOrnLcS1NXZTbQqlaz/j/P9rQtvyOcbj3N4C2LQPv65zk3fl+kEO7r/nkedPLCQ+jMhWTe+m9SwlwyD6a295YPxtApDMehijZQrNuq4OsHd6Xzw2snbKfewfDfj77/go3vbeJ0qqELNqmAkiKDyg4AU7ibzUH4fMHSfec+SpsWbdsr9Zrq0N2TdsJ91L8/Oc9+G2AMpnY2PPnR032UD5MzYWMmLSdnh87lWz+DQZJn7zEYid6T5emEHO63OPZpT/4Y3iiso08TL2XZ1rUhbPwRqOxukqm2Woji330ZCkB2q5fHaOWo6fj+SKRLtcn405n+tQR7hQxqdCEifyw/7z//29+OAT18brjY5qhfjJvdUqpK3FvnVvsy5U9AxzLtNfe0wk450dHBkH/t3UWLNTIdebolnW46f9aUUTfYQ9JVsrrJzXO7hBRSGqAMIwVZqA5rIY5J622HEpCXtsS1ZdNj2JVl7cXRQBlJKr2UsNQumvHQ+PpLA0dOVIuXEXtC2xk1iM99rpYkGcHPCwYyqtb3xeOXPUh+NplvZeCjIHK4DilSRnIxuhmTPnHAXOHawLdtfPuKCvMja+zhyt556a6gNjpzFgfTR8StwhBEvZBXKdQr+uSB89uie1QFs5xfU6ltG29RBVMY8cv30ngLIeC7h7x/pwNaALjYBmobcHd129BxbimELQynvo1s/Wsd5x5N+bPjQcPN8H+w7Ye6u/1/di1tl8Wn7nAUXmpp05VH+38utkTl3eW+Kusxv4nPuyiuUhUfKn9oaWM/dC2sZEOYHWM5a5/jWfc485xxvvPVudd9Khbziz1sFrIXP1vZugWWffeW4xZmT6YDXg9/6e8zhYDXj/41er47iJ017WSUczlvdV1o1v/yOvw5c9cGnsVyQ1Szlfjcx56vJkULJT9VkA2GOapaP9Sr8KTZT295phRpMImLQf/XfeDj1auXSWjmiWDeTVv+8+t7mwPmKtZvlSROZu58zdYmND5JkbKnZyOCaN/rNfexxf++Z78aUPXBypT/n7Fs1yyqi1DoTdOJfraDa1MBq13sDTnDm481pDgY1lNUhlgXHHjv8mt6hnA61Nk1k52gjfWyvq5Y2cuu6YGl1+Y2ipwAG1s8w5hd6IWa6tk2hEY9w4LIeEhcuLmBIG4YieoF6eY14iZzEZOegcXZpw/IZYCs/K9z66xEvoJFUpKPVMrsmbtzeCvJhCK9op38tpinNEc3nKSJFzyCc1fbM2yDUoMBHJa+VTucijKd5bzTn7/krR8NQ4tpVfJ89b5vTF3Q08P9Is5fh518EXZuVrDsOoWtZrHsvSPQsZpqGBiHr6NQcFzLiHvGFyribPo6ncoalaglzfr6ZZ1ps804f4uCnET4IGTI/1hiwb57JmVutK9M9YxYtqZG6w9xaCQTONAEqjjEG5V+e0eadr2ngPlfGxOe+qmnyt39l7rgNurdIjWWirVlhcDRHLdcT2WLJgNq6ZLcR6HVPTKSsGl4u083rF4hc+gOXPN537puPQory2nGwfEG2ft75G37f723ehOFHi8BuDmPqwOatFQKb608r/EkO8IHN7S7zqzu0iLnJAYhdTDpHsRbdSHNvfw0nO3Ksu7uBHvuXz8eUPXjLn+Km/+gcqVGyqaLjOZf3cpyKUczSeMa8FU/nY3IaY8PwYlPvCV96B9z1+FU9dO6yO4yZOu+75baaBqhyrKnIpTRCsY7KOyZT8qJ22fO1lY03mnDmeE1mQR/stOc+CurPqML/vgM2VBI6nWTaRuRI0G3B2S12WLqBC5mzRcH6+NnAivwfaapbLBvL6md5uI3O32DgS+sx1ReEOiNr2n/2TXwNgBVJaNEt20PxmMIXMLYe2Ws+aoiLFWakMYF408mesJuYX8zqq3h4TdqQ8NagyrLv6WPm2JWs/pZJZ1/lpG9ByrF+sNc+oPq83Dlr0OiAbc5OIqnPQknOMmPZUO6rHlTyw/fUS2SflzB1ngIjRKvdt5xmdo1P6BBuBJ1/DPpcWIu2PncyZ62rKYDHeh1SdK0ah6tlnKIplcuqMXCV7LJ3X3ketkumdOY3sdiSAkr+7dGZDaZZjICNvkJzzas9blSYIxwdH8v/bMWtJ3FfU5+AEcTqrZslrQVOEgdY2c97uBGTO0TWLmqWbb606c0OMtpagM8CkhprMdUYoK4lwOkdG8ev31Ct1lvElB3jyWDN++jc7kW0xD7+eO2RurobzZM6WM+68MiPnfwVyVNdDHA1GO757RzmSv22QOU9j1fObumVOGZTvn/8fOAaZazg0p6LguXegOraz/1a/n/zcvgOt71U8pxvXJw0i8bFbC02DaJ2rhTq2AmTy+yt7S1zYWRTlUUuz1PuxpQnyc71FYM7OSXZUhULqrPw//Dn3mN8AuWTLritVMLnnNAIB7Ff4YC3QLs7OqRweQeM2pIQro5DVKy5k0ZabR+2yAuU30daZq1NE8jpZ6o/OexWzGcbSBNA1Ofp9oavF2Dz7gNfO9aA0yz0ngOJpzKKePnOBiBiPR+b8/9vv9O8+2OcXU106QhgFrZxI/z4vJ+xCQT5ZzbL1fD/T221n7hbbinIU/t01La54SM7cay7tAsgUMF4MKgEUiiZ4GLni37MT4xbrKu+hRHusARxCKGhbZVg3REK8tC1/z63rpp3P7EjRsaFxrHFM+NhafXPKiek7RfFaClTeEUgJTWN5TdC9fL50Cy0/N2/g+v6WZFseryBFw/345u+9IqRJ4vUORvTOfVcjc/S92bTcWFd15hqGj9wn/676foKOJ38bRHriGh4x9TlzBUls5cE15lydp6kbG9NshTZp+zCe14u+BBbHsP1t5W/6ouGXdjdK0fBVjCZyWKjTpR6XbkBe/daXFVADrnby8pidxlGtBXFOquUk98mfFWo5PaPjAvzyVbnfQrNMpkhuMxdvvLcpVJzfdVWztPdTvZOBaZZj3zp7Xp4nMTplYTo2hHpd8ONX3hH6/rii1jUy1zePbV1jCl1tBef6rstI7aCiPPKdCIFxzpwGE+vr8xqkNfvaga0ZeRiGrTAxfqExfi1DkX93EtJ1EsJZ92Hse2PM5dgSDOvUcAemBVD4Obdozfx3a6wFeblxtMa5rXlRoWQUZuo+GRG9lWYRVf1cqJPP3DjyPzlVs89SP2+NQyuPkI9l9LDF8PAURW5DTHhenLk7tgFogOMTz97EV33/O/ELH37a/GY50vuOy6mNKRWF1I1ZZ/YoQeaYwcB2zrzvCO12+1iDZim6CwBw7UBFcvx8LMcmQeY6mrt2bPnv3I9q6Mx5/d/TiDmJn3V2f/TAiA2wSz90b1kNsRrbl1q77czdYuMI2VNUU+5gNeDVl3Zw6cxGiSBFZyzUyfX5u4raF0Iz50aO9RtHjpTUuQyVsk/XUNwyThcvfhqlaPHOubG4g08Ub9UdaxUzBhrCH4HzPBrHOmdkNRGJWq1rVU+PXHKuk891mDxvU1jFU1Pr8eqC9HfC+awQSjWuqvEd/L2hSr7mp3aSiIUibnbjPUkSeipa3Ebb8v+z4S3X9H8z9czndJQghnuGjFJLH70wTW/GWvsWU01z4zkXgn0GU5S4/4+9Nw+35arqRcesWmvvtbvTd0lOTk6ak4T0fUeAhIQ0iiDSCIpEQXkoKo2o8d6LYvvh9fl88ryfT959COpFvXptuF4+lRtR7FB8Kp10IZcuhLQkOd3ee61V9f6oGjXHHHOMqlrr7G7tPX7fd75dp1bVrFlVs+YczW+MUSUPwsWGeubKv7PTPr4pqCmZFLGigZeJPIfQAispFRBch347URZStsBTWhKlWaIV1j9/baGN5xVsr7q3FnQtvN/AM0fGZFwrqazNlws19Lh3P/GGgJgeG1Ofh1lIyaSWXToe0iRMGkM9vtjH6jkpz1JKtKEJ2c7FCRl6XVlwFj1HWn+SkF5b3VsmJ0A5UQqu01Ua/SKjJqU103mgS5Uysq7Rfkp9nG5QVJPg2wTxWLqEiUKj8NzVOVNTGIW+c0MpopMklcEAIKaqoZKh1euSnoMcM5d5KneawCzSLPtyAhTNIzoOwufvt684tBMAAD7+4FNjtdvkcaXenCZPbW8qpoJSyh43TlEMs7yKfT5YeuaOl0ryf//oV+GBR4/Dd//GP1UKHkCh7M1Ndao+cuYNzpOUZokKS1E0vFjXK8UkWhdi+n01HzKDM5YXwG+exlGKylzuvXDUMzfM4hAVinrPXP04jksThEbNtDIKsQQoTk78BFDcA9IsPYNK7eKGhSlzY4JaZqhn7mQ/g4M7Z+Hyg9urCTK0gAvZz4iwoVIcK6XATzCR2zsLPzrkGhft+r4XwpzimauJ2+OeOT7eqbLBaURRHbQkTlSiCuyJQC9UlU8p+UjxW59PMg7pavGCzp8DzQrlvQ7F30Lxqw/uxr90kkAvR1wImzwHJozEaeb9eAgXqtgzB6ytajcTRrAIN15Ht+zHwhFVWpsWTtUrIfSNT8y0LVS8+DvkhdRdzVj2ClpxbE68UU103cDbHhkQYuEU28V3OdNNSYZLX0sncZ4GzOeKiuJCDDc85TytqUT7libSd+oFBPq8PQ3Vzxep8N5pG/Ta9BvRDB7VOWSblyCgBcap8MILayN9m8YvxsqcH1NIs+T9lY7NsjAWj8bu8bm4MBJBdX2s8cafk0rBY4YJ7IN0rBRrqFHaZIXRn6f1xyvGifcKM5olrZEI4FOd0+dLj++SC3dJXAw9Bk+jAl0YR+z7ztcyfg91dEd6Ld4u7qe/S8ogAFfOy76n8rvi8yomzgGIqWqYPCT0zNV/h5IXisZEdVNXeebop6itDaeqzFHQe3vGaQun1JY2B0lMIknZptszXeqZK/5K9X+10gSPHyuUud1z0zA3lVaK219+5tHqOKq0HlsawNx0JzAqFtcmc3WWV99Fr5sGRiT8tirFhDM5EhdR1n0uhDjjIx0fFHQN8sdm1X40oknHRp65GmVOOi9N5bYSVxpdyfVoaQL+jfcH8trfH2aQ5cX4xuaHE6jNmTI3JlDo6XUT+OqTYQKUmW4C0920olwWpQmK31PnxBgLgDKDIuNsRzQtIiTySWqYF3XmOuxYKTskH9g0zXac+IMJg8rHWKegiR+XIDABxB4pSVDVPH70mcXCfezxQ6G9UkZIfzkVUcvcGAmqDVQPev00kT0nVX/ZO1azhWacqhhn3KKpKzTqj6cM+nHXRiDizyRul2wzCh7flq6nCUHOeUqkZD0NBSa5fhiAH0fUMkdTy9O/3OOXuJhWyxd9Gl9UKW7l3+lO6pOiEM9cJ/VZEWPqZJlRkFyvWrRxzBGlFp8VPsuYtgxVu/Q6fr83ImixQ5JSgJZSbqiiAfMIOq1EMXM5Pc4LL5zl0EniGEoaY0L7nLiYoRAlH2EJW7JcNkJIlmjfdnislqJd+l7qPOjSeQiJLib1pa5d0TuV4DqTQZfRLCMjZRqvHbS/XSH2LcoMKiho3VTuu8gOUOYYmVVQ7RKVzzYeaemZacdyz1nh7fCeOXosKhk0NlJP7hKPM2zr2NIAHn56sfw9qeIbKTRq6koqc3TcT3dSuOfGs+AXXnLZWG01UZW18SIlAaPKHJ37JAo/x2BYeOacA9g204XZ6Q4cXxrA0mAIH/3Kk/Dt1x8CAIB//bLPcHl8aQBz02lMsyRjLstZAhQyR0VZfyvPXBhDBiDEiWfcYO2zWVLPPkCRcIuP/8JohnKMNxAOM33M0+cqQfouw/fLY+bk9RHp7NWxiSB3l3/RA9lN/bOdRJqlZbMcEyjcHNo1C599+BgsDYYw3UmLOjvdFDppUg2SoFZVSqiTaThY4wQoXuGhdCoAgKVhrHTRDIT07zI/lghzXKjtD3OYp0JZQmP8IDiWIxRqIWi/P8yqYGt/b4xeSARpHisSWZfKn+sygEpUr9i6FKYb9x6V2PMZxf2Q59u00EsKcEG/qkkpL9FjuWcJJ/ZhGGdIaxgh+OQm9Rc9JjTDZuaoAOHbECfeBgGDbrcRtPB6Qd+DbZ+RMI695DFzgteW9QuPpzFzleJcKQVxofUqoyE3TGCpAbo4I82yVMawRk9eCcm+b1iagMc7YCydpCDwsRxRfl2s4KneK6IY47laXKQkSNF4tSgbWosFE/vhaZbhvUjf+sk+i18k3yntWxiTHF5PMgj5Mi/knpVxRNsIhXpOE9bGffyNaAqcZOmmwpisYMjn694e3xYfTyl7vpR+ydcOf70wPhypmWgAdeR6tM3iN2XeEJSnUEGTtyWDUZNS1mZbVN6VObgoTVBQeQHCUisAECUCia9L7qd6LyTBTLn9ht/512pfN3WyMqcop9PpCipzbMz+5AsvGb+tprW3QdkG8HLLzJQQM5f5MhG85AxFlhelCWZL79n8dAeOLQ2gXyo9h3fPwVm7Z4NyBceXh4VnDuezQTgf4ffmY+ZCzxzkYbbOrFxD0jLEhxqkubd/mWd8TPzav3NuChb7PoZxaTCMYhOp4hnUmSNrMf5OUeeZa3p/XAGvyiOwb41nvabZLHm7yKDrpj4evEUEwIaDeebGBC7UZ+4sAl0ffqoY+Itl0dSZbhrQLKnwqAlMUuxVnaeLD2yaXQggFHjCxTumUwWeuUiJwY8ArT3lj2zAS/2l8UuRQsniaOgn3mSxpokrQuFSV454BlBcPMMEKFAdy4UrreCv9N74tqQAp0kxefDCx1ohcFqUPRoPUQKUehqblJa5aA+qwtq0j7Rd+T79+Xwf7Sc9r40QKRbpZdetklgwRT/2bBLhvRKW6bWI8iB45qinmyufXCngniPaBiqUSIdBT9wgC6mTRfximKGWJzLg6eHpNj6mvqDAcm8wHXN0Px/jlFpIf+fbPntv8f9lQRjIWqyY9H4ByjpzLuxX0Z7/1qNyGexY+q3Xlfrgz4EXwAYIFRd/b05so07xpsfx6/p9IB7bTLOMz2vrLeLbVEHoKjRLWvNpmc3x9HgaM4dlVZb64fvE0zQDomRYaONBa/LoSR4NXZkm28I1mlgHeAz9JnhmUUmZ01kS2CY5VpD0OmkCc9OxPV/zbK6oZ65GoD+VtqRnHcbM+fOoorNMaIz8fLq+NyVAoQa2uemCZkkzAV9z1i74u88/BkuDIWApj/mpTjSf0fUpywAWB5jN0pcmGGLMnCNG3Uyui1fcDwT3wLNld1JXrUELvXBcLPbjer6UFYH0a4DSq6yMc/pcJUhKIO8jwrnYUFnHOMDlxh+L91Y+W0Kz5GW3JgGmzI0JHES756cAAODoUpH552S/8ND1ukmVISqg8SVxHBy1XsdConxsnseT2JDFDlGXcyTUKvzhyIsX9MGfL0HyMlVekmFYBDVI2MAESn6NOiFnkMVekn4m9zfOZonxYbFQK2X11FK/Zzm3HNP7jCcZ39fCYqQlz4i9jqBns4xi5uLr8eck9rc0CuBcVgjO8nmSssY9qr4/cRvahC8JPHUCEyaxkWKk1MyLbGHD3/G/PKMn7YOYmEah63nPOklqUsoB/ZKqRt8hpVr7OpEkPqlatOOYOX6ftF3af+f0hC1RkpDy74DQJCXlgbZBz8P7juv+uIAyBhDSgL0nvji/ymaZQ5jNchDOIZ3ERfuq2OFB6I0s5gpdeaX/L+bXPEiWQtuWhPg+FzQahHtJWWijCEh6Tq+r0SzD++TnNynnobAVUrmWSqGIjl++ftF2A2WuVBTQAMr7yeNuqrYEhTOcl/w2r9lZdx6nZ8ftynOtpOS1US5TV1CHNZolp70VfZT748ds7JmjSBMnZqjUDGsbVZnTn3/990b3o2Ii0yzjBChS0fDKwFYOmLmpDhxfGpISJQAvuOJ0eHpxAB/89CNVPF3gmVPmHZrNsqKNl2wOB5767vuA3w2hJVYKol8Xwufh8wIs9LrBvS0OhszQkwT1h5GiCQCRwStlY6/u1YueObKPOzA4W0pT3EVqfLmP0ixxX5vkXBsNpsyNCRSqZ7phlinqmVscDKtaVVSA1RKgLA8yJgwLKb2FRQYAKg/FIItTRvcl+mZNNksuZGuxYhxpQmlseA+kXUU5khZT7hWjfafHSgowz/hIBT8eEzLMy2QJzKpbZAUN29VSv2t9DO8tfmYYj6Y9B6lOWiWYMwGDe/FEZQ7ifvG+oQWL0iwlwSfaFsYG7YEkPKqUTUEwoX3nQiZ60URvkiK0ypY8f24ulSaovhEpMU045vz4RAOAbwsDxQfDYtHtkDFXFH/3329FI2FjDuk0kgU29hzH2Tfr4jSDe0bjhvCd0d+jPlTPzD8HzUgkAWNBqhpDlWcuL72Dvl1sD68b7SPKVcKfgxK/rJYbyGXPnDQvR9lvxTFXPz5140m8TfepdeZc+Jf2i/dNTJAieeaqMcJDAkhadHq9cpvOr1yZwy5LHkEKkQ6p3E9I84/706TganOJ9A3Q761p/nSuXAuSMAEKbbfZM0f2V4Jt/HwpKK0saJfOy66+jXFRl55+5LYaxrIWo0rvDccp1vMr2vXzcrwGCMpcKX/hsUizpCyXZ567G7b1OvBXn32sqkE3N52SODZuZEuCBCjTnTSgVHLPnGdnxTFz0vpIn10ncYHSSLHYHzLZKcwkXKxr3hDBM4FT1CnyouyUOPI8wjE/4HNOGp/P2+WKuadZJuTZql3csLCYuTGBQs9MuUAMhkXK3/4wL2PmiliepUEWlCZIEwfHl/QEKJFnTqlJBxAXUKySJTChQSowzq31lGbJ+6ApnxyJi+NE0mDiCIU5X8YAgnPibXINwTvILYm1dEi2CKOwLCuJ4fONFWvfVl1CEb6P/kaVuSguKosT10RCuKIENXnmtLgn53yqf2wnyeRrhMK5sI8pXXy7KXCdbgd9ZwtFVOONTMiSkEOP4f2l3tbIe0XfC1vYfHB1eC20XgaeuXKhwDIilOePCh4euzzIVG97litCf7XPX4dTHOk9078DnmiICC9cYRwCpwHH7VIhhZ9fl22VtxNlsyQKRDRf1cXMsjkGF20/B0HwHPy8jd7aZmMN9+7xd6eP+/gbCZ9vg3DiHAzK5xTEzLUY9/x3vGf+u0SDwm9/ia0T1DMnXUPyzCHliXvkNc+cpBxpXn4qoFJFy2d/Je0KbbSprygpfqq3gPUXvTAAegIUiqYEKE2KmOSt09oCCD2b42Kqk0ShHKeKJo91G88cjt2mbJa8tiRFNX+Xx8xNd+D4ckiz7KQJLPS6sNQfwvGlYXUcVxL9OywUe+xfr0s8c2UsniP3UvQhi/orPQ9p7kSl8Vnn7YHnnL8XpjsJ/Mz/+BQsMUcDTSiSJOiZ8+EDdZ45TX7kv/F5MhvmkczL51gpqQ32kV8Df0dGwZTRLLcm0CKAAcTLw6xaiGa6aZVNbLE/DBSDQikoByCzbALEE09U2LFmgPK0tIEQwy0aTCmo6nNE2Sz17JAcdR40OX6Je6TiiSfeHx/LA9c1IUqKgyuyFsbtxnXmYqWrTSxZnQKcOhfyvrnyGSXwIOcKz0zrD1rQaQ+SxKlKNGazdK5oMxDSR1BatbhHqe9q7S3xPsPrcu+KRvUUqUi0Mef7mZUeW60PXNjzHtNwbNR55vrDHLqJgzT1VMTlYcYUvzBTZ2X4UepPFv0Jn12UzVb4zqgXT2prwONuJUFfeEdayY40kWiWHriWUkWX7k/JvYXzZ0IEovBYTk2XxiSNi0xc6MVDymtwXjV/+n3++82D3/h74aPBBg8AACAASURBVH2QnmUbr7hk2OkJQiltV3tvTZ6u4JvFrKvlO16qxqQfv9y4R69BswyiorDY56UJ9PmT940bR4vrysfyjM+8j42UV5bMhx/b5LGmx9CxR4uGB4qoRLNUxjIeG64BsmcOAOCWC/bCDz3vfLmPZHslPHOYRKVOoB8VbT0w/He6f7lSlqSi4TLrgwON6Sh/zZXZLPn63k0LevfxZU+zxP5ExvCE15mTPHM+C/OQyYBhRu5wzHO5sJM4r9ROpfD6W8+DHbNFGNFiPzbm+XI8vp/4HDTjYfFco0dHjqX9iQ1S/H6i2nHKt19XwmSReuacf7aTBvPMjQlcqCnNErm3vam0WiwW+xksDYaVpTTwdAmCAB+McZ05ciyVQ50vassFkyyXk0rQdrEPNHMTtrE4DK2llXDEnolMN0HBRrCgL/PEH/JzEAXVoI/hefGEWPwWlUegAlp5DP5clwClWtxbKCOS8IRwTp/AAeKsntJzUKk27Lz+MI9m0U65n9eZQ9riOIJf08JK+6ln6ou3JYEUfx9wobnme+Jt8P7SbFa06DNvl5dK4B4takAA4DFzRbuDoeyZo4klvLcdynaK39CC2hGEI64sD7Kstu91z4kqeR0yUUnxc7KCAeR30m6DMIdzCxq8sGablAAlzOKqF4cFaJ5jNFo4JqOJ6i3VGOQimmX5Vy8aDtG2JrA7YbuTOFgu9/WEtP+0PbovfCYQHUuvS/uOWVcrz1y5/tGYObEP5TZVMHDMY5w5H5eqZ04Yy+Fzks+b6sTfDTcS8W2t3VBolMa9PLfx5Dk4xgAwI6A/VqRZBn3323sXpqPf5di4Yt+7v+u6YL825lZCmZvqJABLYbunilAJ9/tFBaBBmZNi5oIEKDivCzFzVQKrshPz02lBs2Sxs500gcEwq2Lm5lGZcwXbgV4bjavoPZruSjFzRNbLeNHw2JhWJaWLSvfELKyq/mMZPoTopNQpkQQxc3GB8XDc1Hllg/cT1JdLAIAn/ZNL0yC07LTcAIpy+1THM3NqIgA2LMwzNyaqgr/lAB8Qz1yvk1TK28n+EJYGWTWZpomLYgt0obdeMEkDywWUqc3lTEZN1J5UsIIAhEoMV/w4JKEJm4q9AzGlQFM4xUyIiqBQZHzkSRy8QMoXb57UIPBmsgkg8pI2KJxFP+Pf6TnSQqQ9B1HAEIQvgHAixHpQvAeSkIIJUGhSGG2xlD2xymSqCDTyuI6fpbYIOweRV6zJ+1J3LB5CY+YqxTl436Rd+p2yZyp51quacpkQM5dllZCLMZ3F4hjGwdZ75sI+xIHuvu8SnU/yUESZYAXBuckjrRkC6lBRW0kCFAAX9IuPHXw2lCrqhHcozTHVM8vySBjAIr2hQJ5U10BwyhR/Fm2o0TSbndTfJlodFbyaFB7VANNAaeffbhQzJ3gF6LVD+qGDqU5Sxa/wMax75vx2paC1GGdTghFE+0aaqKkSE0B7ZuHaELYbeOaYB5jWDZSuS6+3f6EHAABHS2UBQPbMUSUnaFe5z2mhD6MC35GmZI8D3RiO3x1VaPyxdPxJpQm8MSw2kGtFw6kiMzfdgcV+JlIB+8PcJ0ApQ3U0RXSQsTpz5W+YzRIcBPsoE0zKWp2Q75XLOPgcvKHBe935s/V1TctsluUaSOPX6XX9udGjI/dNtoV1nPdhoMyx/Dp1NEvMFDqVpkECtEmDKXNjAoU3pFn2h1m1EPW6aWXhObmMylzx/8ATIwxAjdonCkdMkB/mcswc/u63IdpfZ9GogtjZR8B5xaGFN+xDFDOTxJ4uncYW91dTgNNEyCJHJjreByw6ydvN87hGS5TWfhRlRZjBCk+Q/7/sLYrvXXsO2jjC++Drp+y5QOppToS5+j4AyIIfPc8p29I909+brO2Jc3GGT0F45edpxZMpzSLOZqnfe50BAcALtrQeDxZexvczGOaBBwwTqwxzyTMnZ7ml+xL67amKgH82zum/F3EYQPZLz8T/3qQ8NXrmcpYAhcbMuVCo4soRj1+k7aiKPhuHMX0zjlelx0tKq5Y4SovraKIB1xk0AMJnOiXEhxXnxW2pylrD99RlChGmd5es5NKznuqEY2A6TXzSIYdzlv9uJNC+TaeSYi2eNqJnTt/Hryc9M+19p2w8JAmpM5fzZy2sHwqNfd+2wjP3+DFfJyxNXLQGdZWHIxkVAGTv3qighqqVglofsOx7V7kfySgteeZoAiqeIAkxlSalIuW9R+hxO7o4KK9X9idNYJBlcKyKmUujvlOjQJZ5z9xUGhYNhzyMIS4USjkxFleI4hI7SbSN426xP1SN12nilc7qeQnyh++H/u5VeYZRuvFYbkRVaf3CuPCeOV9nDvdZzNwWwoBls1we5mHMXLl/cTCEpf7Qe+aI9U2y1HLhM6ZZyoM9Kdul2Sy1SbkpVXVM0dO9ABSyd0BZAB2lxwl9UBVRPBbU37VsdtI1PM0ShQflfgSrGe1D0yQiTWBJoljj1ecQ90ETRiQPrmO+ubS65/j5UM8kFfR1b2Tze5HOE9sVztPLHMRxVG0s6Bp1yi+KcZ25Wg96Jiv6PAMqfqcAPpslvp9BKQyg8NFJnLf2MmWjolmK9xz30bH7lJ6J6HEmSqkUw9BEJ9M8T5IwR3fhUlrFh1TxdWE21yibbRp//7Q/6vftsA/l/TJauHPU00baEJWu4q+WiVjzdIseaeX7l74zVWlompcbvmn6zCTqpB+TnFIc03KL9lxwHIIqoNyQpCVRoP3F8zUFmYIaV6tr1nzftC/8d2mM6+NMen74vCCkWTbch9bf/dsKz9zjx5aD43kCE01B1tbslUqAwq9xqqDPaf/2XrXNlRd+XSlDalNJD9zmnrluqcxQRQazp6IyV32naWGko6UJAGIlA7eHeQ5LwwymOgnQOp+FZy5n+yCMmauRL4rSPf4eOgGt0ZX3VTyjxf5QlaOS8von+0N454c+D8Ocz1vhu64z5DWxAHidOU6zbPrWAOI5+ySWJujQmDm1ixsWpsyNCaR29dAzN8i8K7ybeGVueQjLgwymy/9Li7dmxZNqsdXR0fI8tIo0WaHpfp6G3/8uxHQpE7G0uNBD+e9aOve683CzTljJmXW3jpI5zKHMUBe321TcV1Nsgr4Lil91jnPiM9OEEdEarwkY5Idug2dOEhI5n76tp0D3Ktb3s40XuVL8GoQuXTGM+1AocP53PDzL49IEdcq9ViBa4vQjZbCgWSaV8laUJvALMXrmcBv76xyhWVaW7vjeJOU/fibtvtllFpsmKdnieFG+kbZyYWCFBlJnjuzn8yd+/9I4Uz1h7FvIeSZUF3vmg3YFoUmjAHGPf3WeOG/I7yqkiIbn0GsV26RdUTEh9ykJf8q3GRUNr4mZk94Fp/4FyhwbPzxmTjIsTgveNny+3LMleeaaBEnNONJonGrYrlgCLqRZakbTuusCAOwrY+aQKYTgMW+St4/v15T+cbESCiEHylrXHd4F20h9tMpbE9yPP0/0zBGaJb3byPvPYuY6aQJZFTMXXnd5GHqsu2WSJkyAMk8SoER9LMfEoEyWBeDXLKR9O3IvyM6qo99XnrmMJ7vSv9HFQVw0nG7j9X7u/Z8uDRHk2bBxUzeMVOO1sJ4UMqQ+59BvIpzrw7+oVM9OpdU1J5FmaQlQxgR6lWbLiWSQZaRGR1otLFLMHKI59srVWh64Yjdkk4narhAPoHnxxEVN+RpHzerF761NIVbZut20WCoTliuLhudxUdDa84TnoF8jbpceJ98bPcZvj1L8V7oP3gORekYUC/4OhiC/Y7rdFOtY7Cf9lCZp5VkmrlCE9GvE/ZIUa369NCnSHidEuUbDCG0jvN+wX17xC4+VPOuooA1KL1y1uA5z6LOYuSXm2QMIs46J80KDIqA/HwAYykIiLRoe3qc2H+H5xRiPy0TEkpRYNLxcpIOi4a79ePHXq/+22nisfKkK8i6kZB/l7zHNMvy9Td+1YyWFvJM4+LXvuBr+6rOPql4dce5S3qE0h4dGPxbHWcPgkOYNTcGi/ZDeC15jiV3De338cXgej/eaFo+NzwOQ4+Ca1p9Q6aWUV4iOpXGXwzKpBY/NROyZn4rO59fbt9ADCVx51mr3Se9hpYBtLzPP1qngwPYe/OZrroNrD+8K9s+W9EV9TojvP6RZ0u/CH5MkcQKUbprA0mAYFA33FMVwHuikxfx9fGkAaeIC1hZAbGQaMo8fQDFmBqjMOd92Vip+YswcMRr4+wrXlWqbZcOkCmLcbkjhpewwvAeKOq9sE4OMU7czZrjD+Gj+/UhsCvwdlbleJ62umU2ga86UuTHhSxMUj3B5SAs7JpWFB7NZTgv0gmahqyF9P5vMeQCuWnNDUjbUiau+vxSjKF2y4kfOr3kmxfn0WLkPXghXPmqyeEoWa4lWRvumUsiE+5SeWTHxCO1qSof0HBqEVwA5+D1oQ7Dc91nSjKKfcRxR+Du2EQvvTYJQU50+AK+MjqLo6x6/+N6KRbHYl2W+NAE1NuBC0ezdKo7lSVQ6SUyzxPfjs1l6w0/1/TNrOU+A0pSMZpTModLzi+iMwvdStxBnUXwGRAg/ERYzV2WzzMGRBCi0L/R4fo1mYxjU/t6UtER7ZtK1WxmMiHDif/f3I8VLJc7BnRcfgDsvPgB/9smvyf0V3nGTd1X7bngJHJ4JT/2my//wGCzqtaGeaN4WvQbtj0ydLP72WGp/VCqoTqFmqHThNfl1RaMA2YkxUVob1bhwXnDnxwIA/M2P3goL097zFD4vfxz1LlHw561lCJ1SlM+VAD53KYHIqeBZR/ZG+y45fTsAADz45MlqH30v0v1TmqWW7KuTJJEy2k0dZFloBEWFaonHkqYJHF8aFIytkjpJr8HnM4zF67L3XZQmKOZDbNuH2pRtBe8Svyn5vqSxSa9ZZ/A/TpLtZFn47GLPnD6oNOOBVy7lcBYqq2GCGa1APJ+3sHg7fjdp4iaSZmnK3JioShNMlZMTp1mWC8vJ/hCW+j4BSl3MAUBMCagoQ9Kx7OMbZFlQ56SNxVrySIWCi79nTgvh411UNpQPSloANY+LtDg33U/YX7kt52Krl8qTDwSe+Fqae1/qA72X0ILecG+C0qW9N8mKxufJjnDPeNpAKOPAr6F7Zbyni7cb9Xnke87FsUP71kbokt4tj5njpQlwe1jW4PNtKf0pj02J0p4klGaZwXy34z1zWRYE0AfKHBtrdQlQ5EyTSh9rLJZ0W0rVD8CoSLXfpF7rrg54XFA03MXjrdoOYiqkb0D+XvwcBMrv8v46L9Qye3fSN7sw7ZdgySOqja1grgRhnlONLvG+ZoMRiMfyWm0+jlNgoQjPer4Xih8S9bFSpJgXjydfoedThhQ+J80zhzUfi2Pl+5SeQ6js1R+LBl/6O0A8dtJybhjmsjJ3cOds8H+Jloq4+5IDkacqplnKRr5pxTu1EkBFUUrtv9K4/MwdAADwwKPHq33aPIjQDJRcduHKaJEApJC/8LkiLdIbOaDa3x/mkYHMM2VIH5PYSF8cm/ii4Y7Mk1kRHlDHGNCMoZLMoFFuuYHoi4+fqP6/PMxgrkOV4vA51w0pjYW7faYb9VGLo09RmWtppDqx7BMXFvuNZrml4EsTFI9wkGXVYtbrpNCbYjRLoZCnn8x9u5pXQTyWLZbLQ4yZC932Rbsgb5ftaXEcsjVe/hrlGJRmoUGiQIXp02l/BYGzQcHQJnC0hNLMYapXRxBMtLIAoudTUObSxMmJVRTFUFJMwt/JttBfHkRfF5fTJ7XNaNvSPt4Gerr0d+TP89ZK5Vk3KbsN41O3PMZ9SByLmcuEY11RC6iNxy9xBTWVf8eBZy4hpQlYNksam8HHIs41KDiIGfVUIR3ItvR8Idq3PMxCT4DwrLW00kiJ074tCbxoONJeeLIbAFkh5duSkC19W3UCiz9WOE8YW7hGOOb1o31HIYW3K7M2yO/Ce9NifCUlRZs3pOeqGa0oPdA5kgBFjF/x2ygIz03Lypz0fDXPnKTM0WL0+Dv3Sk0Lx2rzBvec8PuRMiTS9zMrpLsH8AoqvZflQUbmnfpvpCt4XRC/+sqro+PbJkCZUrwxK4FrD++Cv7n/MdgzP72i7Uo4sm8+2id5ljWosksSe266ZSbW/jCHXhfXNPTMhTRLzGZJFT/6O5/LsdxAeKwvGk77t8SozhI7S2UDCHkT6DXV+piJgyeO+2Q7x5cGsEAMNaN45rSERTvL4uX6Gk77mQBApif6YnMHeubQe++ceea2FDDuZZYUDa9i5roJKU0wgOVhJtIsZQXCX0Osr6YMUOegdcycSPVQLbn6R8AhCfpN9Dr13hqEZU2wkRSwukl5eZiVdVHq25IEQ91L6vsmCUS0HTkOLjxG2pbehSp0NdQTCovPF395jFTzOCHt4n+U+2ga16HAHV9Dm9CbPFK6V8b3xVMp8yibZXEeAAzrFEZ+zzE1taozh0XDKc0yy2CKeNZ58iGA4t3WUtpIvJp07+piXjNvLPUz0YvUJi5Sy9JWB140vEqAAhCk4gaIFUd/XYj2NzIURjDmABDKjjD+cY3gyjU9fxtV5hrmxOaak0p/xTlG+RaE87T5iBsAo9qHQpF5AG8Jn+fKXBqvkUh94qNFep/TAoUP+869Up5mmUfH8v7K5Ry0Y+N3wYVwxNxUyNbZPtOFzz58lHjmoBZB+Yn6Q4t+sFIQWmmCbuDdDn97xyuuhNO3yzF5bfADzz0P7rrkAFxwYGHsNtqikybwhtuOwDl756p9kmFDg7S2aOdRjyNXpKrEQBjHVmaz5HHp0reJ68Uwy8Jsk6VCWHjmPM3SrwvFtcISJXhfylwmzGvdFkbW1Dl4+4svhRf8yt/CieUhHF0cwBk75WP5NTm0pDy756aiPqqGREkOEOfW4v/LgwwSR+cf88xtKWBtK1xslinNspNWHxGmpZX4/LLSJS+ATZTBNPE106RjmwS7VvQCNtnw8d7kFdMs9z5Zgv+9kWapCCCSUqEpZagA06Lh9PzGbJaKVavpOdDjRlGYZOqPIuwJY4ovzLL13wuiTRnzmrxeTYIzPU+ig9C26HlNXtAw6Qb9HaJjaR9ov6XSBFoftGQTkleBZqwbZHlQ26af5dAfZKSmTpwkCLcX+0xwVo0fxXPQvu+me8Ptxf6Q0brCv3V9kARcybhBd/GMtGEClDDgXhrr0X1IglLjHATisfT59aQ4LRSslARPtI89hdKGzbXyrjYkNpDevRbzKd1nQCsV0pfjMXUGBjr3L5bK3NxUM83yGy89Df7kYw/B39z/WHBs5b0mx1aeuWEsiFHqYHCs5pkj21JtNE0glhRSCnpslZK+PPbgzhn4i08/UhVfbvbM1X9PHNwzx6mrCG1+AAB4weWnN16nDkni1kSRQ7zpeecH/9e+kb/+kVvhkaOLwbE0IZP27hH4XSwNhn7+rrJZYqmU4thumkAfMxeLyhwE+4ZZXjJlHNsPAJAHxi1eskbysurzdBJtdxWDDM9+ft6+BfjZF10Cb/rdj8LRxb46/wDU0yxvvWAfvOWO8+G6s3cH+3eiMqfEiYaeOWGuF54zlRNmumkwN1oClC0EXDC6aQKdxAU0y+lOUlm90IVbeeYaBHIpNgugeYAmruDbB8GvwWAHciwE5xXtJ+KxTconRRM1RaNGSdnCJBqRdmyTQFlnYdeKhvPztMkAoXO59WeWOP4u4vM14bLJqyvGzPE6cy6+D6pYNHk5m7yu0r3F2/E4afLQNlE28ViqpPPz6t5nMcHHpQkAiHKufE9NVnpeNLyTJNX7GWZZWa4gHjP83jDtuFS3KaIEDXmcobKd6Pd2kilzdYYA3oaPrfL72tK3Ipol1plTnnlHEXA7wr1J80qQ5EabN8iJs0wgp7/zbJZV/Jdy641xjwrFXprnNOXdpzcnbSnXaHrHHWZ4XM54nTm5Dzh2ecycxF65/aL9AABw1yUHgmOlYvJoMKUKGgrSMc2yOJZ65pzyHNCDGD4zeY6WjLYU9DkssJT0B3fOwtIgg4dLpaLpG5lSPH7q8TxmrsVJbb/TSYG2Bpy5axbO3BXGJGpri/RMcMwvD6lnrqRZluMdv+9OUnjmaH4Der1IactzGA7zwJNaGAWzKLvvMqM6izROYc7HfvFtqjypBuvy2JluMZ6PLw+DdnlCnjrDw47ZKfj+5x6J9u8qlbnwe5X7LjOcIPodt7NhHvQRjbmTBlPmxoSvK1V8OAHNspNAkhRaP2b5qWLmGgR9NZmCdCxbZIcZMM+cbKWWPV3+upoAzIWGnKVAEa3xLYR/Kdap0cqvfZyCQKla40sFmGZf0rIwSdeo84YAFJOspNTSNkehTkqW+aZjnQv7Q9EhtALe7oAlvGjycsq1rJrfveRV0BQ73Appocp5hUamenslwYwq05lQmoBeQ+2voCxwaoinWRaLOS68/WFeliuoV9A6iYOTpXdjSvBQxN7aXBVi5NIOcVuL/aEcM6fF+wptaGOn6gstTVAVBy/20cUVvytR6dLmkIb5iM8LA24IUMb63JTkmSv+6tksZXqbPCa1dxXvbzI+AYAYux3SVNt7XzUvXV3yBQCvbEU0SyFmrpsm8OmfvitSxrAf1AvnvW2eZolsmR73zBFqM0IbO3NM6QLQ6XrTVdwNiJDaRQH8zF0zAABVMokmRUpj22jgCU86qTwOKTaZLqeOZQlNDAYKSrOsPNOVt455rNOkSICS5WLGbH5dniUTjx1mJe3c+e+LU51DA1zZZvBdAvk9Hv9d5RuWDP40ayv99mNlDkbGjtmCjv70yb7YH8lwp72/eL3IgwRJzk0mzbL5azaI6JcUKeeK1OJIs0wTR9zsCRxfQm/d6DRLSdjQrMWJK+J8gpi5hsWftqfVBZE+dpVCEggj4V/ed0kACwXvuK2w3fb3pj0zVyrAGa0z55rfhaSAiFlKFWGRXr+Oy83Pa6JvSpQJSRlCzHSFY5wXkpLgfQjCsKIU+EWJnq/004ULH78nqQ1dSfTHNiqfooHAP9Oi1lOcVY4qfHIfhHbZN4+UwUFWKG6VVXeQQZbTTH3x/WB7dZ65pu9FT8GuzxtZLmfP04va688X74GDvmueAKXKZknWWMkIpMWuNjIbJGNE8E3Tfvr9mKlQms/6xOBH92sy9DgGIwD/XWv9pe8T1yF9zvTnyc9BedZNfRfuOUqAIhgmAApFjCsruMZSxQ3PpwoeekV63DPXjTuE1n+A8PnMNXjmwucbG20p6K2hZxK/ZcxW+cXHj0fXaEIbATkqTdCQAGTUPkwCAi91YwIUv62xlRAVpVKKmcMEKKggVZkvs8jQJ113WMp1XUZtLDxzWKoFqusXv8c0SYlirX3PlTKnlSYQ1oggays5drbb3jOnAYvBP73olblgfqfPJpWeY/3aH3jmErc5lTnn3Lucc4845z5B9u1yzn3AOfe58u/Ocr9zzr3DOXe/c+5jzrmryDn3lMd/zjl3z+rcztphSKwqU2kC/WEGi/1hMGF2ExfRLJusxdqkIQlE/OMbZoU7vhLk21jNJYVS+di5AsFpe5KA0MYS7hM2gPh7E9VO82xKwimnnmLRcJE6mda/l6C/TVmplNU2ELwF4V/6PTg2eOb+WOw7vSqfQ3HyFevMCUXDi/tQ+i4Imq0E5wYhW/xelOcjeS5HoeNSZRHTQWt9a2qLbnOhF2NiBsMcUuKZWyyFOk+zlA0sncTBiWU9FldUClo8B6kOEf19ilgv5efgt6VraPNcHSrPHE2A4sLfmoxEdFs3EsXfkWooCZS5WDnCPvSHRWA995zpc4HUhxZzQbmpJQeg9yzR/ZuemXbvHUHQS5xvu6MYwxDz03IcWxtq3w8973zoJA7O2u2TW6CCRhU89AJqiiPFvgWfYZH2F/uJcaoA8hwGICvLFGG7oTJ3xo7CM/elJ9p55rR2NXCapVZnbtR2Jwl0/KKCoKFNfDgClZ7CoJ+wfZxmmUB/kJXFveN2I/ZQVQg8XAOGOfHMMcUR1xCpALxmdJO+Zx4bR6/P99OsrfQe+LcnGfKagIminj7p69lpBtUmZou0PvJi8ZtSmQOAdwPAXWzfvQBwX57nRwDgvvL/AAB3A8CR8t9rAeBXAQrlDwB+AgCuB4DrAOAnUAGcVBSZ6IqB0E2Lmh9LgyxQ5rA4JIBssWuTvAAhUzLDySbL84KzLcbc+LYkIaWNQMRTKUc0ywblU7PMi0KDMuHIglZ8P/Q8NXV54ksT0Hgpqa0mq7lUZ64paDq6hiB4a14USWGkbdHU26h0c+UbJ1+5zpxcF6xJ0Q/6pjxL0TOnvG/Js6i226T4NSzOdBwWCVD0vjcJwLw9BPXM9YcZdBNfmmCxXIi71Xfm75OXJEHB0gvA5PcGJaYpkYNGJ5OKOjfdu9Zuk5AYeeaq0gR+PKfCN9BEuWxDW2yau+g1ZqcxTssL+j5mThHWFJplk3ew6VlrAlRAAxTT/4N4rKSwqwlQiKAqtSW9b600QRvK4O0X7Yf7f+4bAqrmtOCZu+OiA/CdNx2Gtz7/IvFaFJqXGfuJ1GYA/T7x+fK1ESEpc1VCmOkObOt14KtPljFzIyhSypAKENEsW5zUpt1JAn3+O2anao4MDaFN9YGpMsflOgy/8d4uB33MPC4oS+E49KyNDpt/q5g5cl8RzbKpnAzZ7hFvta8zFyuD/N7xEJrQiP6+EjTLbaUXO/TMyXNRVa6njexZbtN7x/V/0tD4qeZ5/iEAeILtfiEAvKfcfg8AfDPZ/xt5gQ8DwA7n3GkAcCcAfCDP8yfyPP86AHwAYgVxojAgdT+KmLkiAQrl3nZT4pnDgoTCQNLoeqJAVLOgZ3kRgIuUkjYCchN9U/bMyV+jJCAkmkAqCBCjUMGaPk7abi3NMs8hy3KfnEBR0KTrNSkzkmeLI4z5Eq7VpFAqv+9diOv48NeGypzkqe2zouGVEq08E9q2tGhIvwfHKs9SOk9TNuoyVPLr+wbXSAAAIABJREFUSsdSz1RGaJZSG6qSLYxxXkMPvQXFYp5U10ehrkog0fDNAlCPvyxcVoaFFu+tSYkJrbzx701jtY2nGoHCMN5Xlc1SuKemuA8AmoRFE0jqv19tzKEAsxR4beRvqKqVxG49EZ6ldG+ql656vn4fxpjw86p1SJs/pW9eeceSYqcpe9L7jksTxPPRKPCeuTCO7m0vuDigUALERcQ5aHe5Bw1Arw+IfeizjJqS13FOaHeh163igkbxYLRRgLk3solmCNBsdJk0BMYpQaEPj1XWC2HtxWdJk5rgviWWCKkoTZCVITGxsiUpSkvDkJKJSVTy8qbwHJ5BN6DGY3iI8l1S75REs9TlrOIYnkQEMTtCAhQNZ+wsvNZXHfI+IM0I5Y1C8u/SGhBmFYZq7Z8kjJsAZX+e5w+V218DgP3l9hkA8GVy3FfKfdr+CM6510Lh1YNDhw6N2b3VB80aOZUmsDzMYJi7gIvfSRKBZgnk93gRbrIsO3p+UOeniA/IczmORlNoqqLhiuIhHVt55th4F+mQitAgCUfhx0m2JeG/YaKl23VCcV6moJepYA3CqfZ8xfuRJzDpPnUFmDyzSvHzv9PJS1LmOLDgvSQYDrK8iqkDiFPF8/5I71aPK4PoPG38SV7KcZVdTbHj7yspxwUqEKKBQH338TZXKrJAmXPVgok0y66goGmeAPzWu0ofZEOJ/Bwk4V3yOtD92oIp7ddKQ9QBz6GpovFMvFyThxzAv8Pw3kE8VlSANQGoFFQWB14gp1ZyeiwmuOCYn+7A04sDkRKtGYSk90bHyI4Zr7zQOUT2zNXPefR3WuxZ9jqSfQ2p87XSBFz4awtUBlsdqwjyO2a78OSJftBf7Cf1zIXKHP1GsO6sV+4BCkEZ61khUEmkyudCrwNPniwKMLelIvM+aOD33EYB3GzK3Cj3o8tBZJ0uhwT1msXZLEPqYycpCowvD4bNWRjL/yz1h9GYQ4OjAz+f+tIESPWUZCf5vqhCQzPSJq5gqWgJjXCbJkChY3e2y2iWY4ypfQs9+Mu33AKn7/BzqDYn4z1rMqLEgIholuHnOxE4ZSd6XqiwK6bG5nn+zjzPr8nz/Jq9e/euVLMrjj7xzFU0yz6nWbpamqXowWlQIOo8c2jhE+NoGoRa1XIhKUeKYiIXg/a/N3kaNEtYE81SFXhQ4dGeaYIFOX1NNTrPqJ454d6klOicKirNYU20vCaPqmZVp/EfKCXyxbueZpkFbaPAkQa0EN+W5E3T35HfPq0sQKslUxlF2ZDaUOsVCgYU+l5paYJQaY3PbxKGo0yJpDRBN/GeOfx+UTHTaJaBMlf+QNO8j5LEQh5T/nw6n9FtNBZQ6i6nqvDracYRCZXxALMOkjpzvI02BiMpAYpaT1MYO9o8JnnmUJhY7A+DY88sE1w8emwpuNdnHdkb9U38NpX3Vn0XgpLPj8V3SGlEVEgUDXJkPJy5axb+33uugdffei7sJYqdZBTUDDTS/dA+0yLPo6DJ00LBk4Eg7ihLIdCpEj1oGKfKryUZPHituxmBmcM9kwBFHNeTJ0rP3JiKhwZOs6yD9L1sBoxCG9WZHPG+MDlJ6IVFBQvb8/ODnDFaui5NrAJQevdItmUcV7w0gTROqSxCx01P8MwVbcVeZWkt7XVS0QjFaZbjDqnDe+bEIugA4VwjZcUNj6XPJO5j4kjCrQnCuMrcwyV9Esq/j5T7HwSAM8lxB8t92v6JxYDEpuk0S5/NshpgwmKn0SwD63ZT7anEwYnSciiln9as8VLMUlM9LjyfD/e6DIF1fZes/JpiKHln9GLZ8fPlQtAwy4v05tJEqgT/NtEVpFThAHLAeRN9s5Fapfy+d6EXXYtf3QuMcbv9oaeeAgA8VgqgZxDLGKcmIGS6XnwNAIALywKydO7Uleiw/ei60oJLZjhVsWPKOVo90RvU/Nzl/kgLGy70g2GRubKT+pi5k1UClHiuCOhtwsJFBUPp29Cev2jYIb/Td0wX0mcc2AYAoYC7a0722kjKkaTMSZ6CuM5cfUKRRgOM5vEX5w352CCb5XTsmUPv1SNHl4J2sY4VxkQhfvFll8P7vv+ZVWFcAO9ZU41wgsKuseYkz9GAeI72b5MTf0h13wAAbnvGfvjhOy8UlWGN7tukvBfXKf4e3j2eMqcpaKMc+7MvuhT+4PtuqjJLApDYtr5srpeoyANm2u8J2TN5nT3c91RJs2zzzKQ+aBjl+aBss9li5kaKQ9TmB8FARmvA8Rpvy0JpAoDC2BPSGIVrVQphFnnxUHZxZN9STcycE+Yz+jwkmiVtQ6Jr02OTxFVtSOyR6j5XyECgvR8pZq7JWdEjcvumzWap4H0AgBkp7wGAPyb7X1VmtbwBAJ4q6Zh/BgB3OOd2lolP7ij3TSxoUeVuSbNcGmRREKkvWppW+xD0I0A0DUCJ6lSc5zMZSZkzdZplOMHQa9H+UEVKW2RCwTC+H21bUtBk2mL95Kpdoy5lN2azlBQ0zcosvTdJkeCTlmQZlSlxpI8aBVewDtK2thFBAffyORStUZQShPc0yDJx0qXZ4xZINjBpDKvKObmnC0pl7n89drzat3OuKx7b6LmUKKuadVV47tSbl+W+1kwzxTbuIz1PioHwyU4SsrizBCja9yApcz1ZmZOsyIHyLrRLnym1WFLB4KLTC2Xusw8frfbtnvfKSGAJFe5HLE0Q7fHnhAlQyjaE+UgzhMh9APFY0WigXAM9c1TQR2XuxPIwpFmWCsKDT54M7rHXTeGygzuCfdvLmDfJEBDdh6BIUQSeo3J9Wiaeo1klcQGuI63oeNVaSOdMKkg2t/HV8rmctXu24UgZo3jmtGO7aRLE5QB4+tjyUFbmwmdWHjvgyhxmxPRKv+SZW+h1fF2yEYTeNoeO8nykjICbAW3GoT/Wb0tzQUDt68TzQ5WlmL1PWrJANpbH110aZCysplTmwLOKUucixVHz0EsJwqQEKPQ+6PVpqY8wu28n2scxipGiDrSZTsO8FdAo6bxfnsfj/SZQl2uOmXPO/TYA3AIAe5xzX4EiK+XbAeC/OudeAwBfBICXlYe/HwC+AQDuB4ATAPBdAAB5nj/hnPtpAPhIedxP5XnOk6pMFGhx36mSZrk8zIJBQ4V3nqjAUeVIEDoAZMFQDcoVBDxdyQn/VueVSqmk/GiWDQoal9KsoLWbxMLf43ugfeR9a6SQJmUCFFJnLhB0FeFT8mCIv7MHPNVJKu+peB/CM2uiXGoCqbRo8T2zlYBBlLnyoBPLQ7EW02EiaG1TFAhJ+ZayagF4Ze7+R45V+3bPyZ4CkfM/goEg9GxQobN89+S4XClNIHpPG7xBoSEA4yiG1W9VNkukWUqFwMXFyrc9r2QvlBTgJs8v/Z3GLwWeudMKZe7TX/PK3ILiHWz6DiXgYlqVJqCLK+tn6LWk48yfguOvTRynOHcpyjs+nyUipM9MpbAw3YGjS4Og3dN2FN5yLuhLQOojVSAoHVKinmssOmnsDBTFJBSKMCFJY3eVmqny9p0X74fPP+qNN4iHny68/2etiWeufXydpHRRSN7MQRZKg9X7JO9e6u8CnVNHEHrbKF0j0SyFuKPNAHykC4JXlCNQ5oS5IKRZxopQpbSVcwNOTTiPnlweKjFt8XezNBhGcdPD0qFQrVsJKU0g1JmT7oEOMarQhDFosWIfsHLIsfPTKTx2rH7srtSQ0tZ+319/LGU9SPMSZxlNomeucUTnef4K5afbhGNzAHi90s67AOBdI/VuA+PBJ0/C/jLep5O6smj4EHaQBZd+qCgYo/WDjuc26afpAOx1k4hvLVFp1OxDNcrG8lCzFhHhQRMaRM+J/13yUBTHx/cgeXVaKVpC3zWlqyoanuVi39WAX+GZ0IQjkjAIINc2CvsT35tW0LdJ8ZMQxcxV9KE4ecPRxUEgPCJoOufQGwRkO35fc4qgf86eeQAA+J5nnVPt26PUe0KoNEtp3LZQunhtqMQVAdByaYL4uo0eZWHcL1axDSRmbtkreLwtOhbR+jmVJtU7nVEW1yalX3p+wQKv0CxP296Dc/fOwetvPa/a15jqXxHuEXmwXfyvolmSbJb8Gtq8Ihm42rw3OcOvPy8sGo40y1A52rttGo4+OoiEjLc+/yK46lDohZOAdZUw5hoAYNcsLWod30cbxoSWoKM6lipz3fbeGclwocXM/dp3XCO28dbnXwQHPvQA3Hju7sbrSVgJz5wEXkKBI0gwU2WzDJ/vL7/8Snj3330BLiceWMngNj9N4hdbPHcU6tu8o1Hu2cd9tT5loiCtbRxNMoZE7QPwihQyLniGSfx9cTBslLVwW6JZFgl1fPwb9cxJMXNNhruZIAEKubdqPQJ/7JR8LM5bdWN3pQwEWk1gKQEgzWZL96NSz+O9h9kmVOYMMbIsh/sfOQbffn2x6HTLenJL/SzMZhl45orBj7QcOlZ0mhA5hgzA7TNdWOwv1Qin5WSitKUqG50EYEn2bmmKDYWkPDV54+h+zRImTTw6fROibb3OXCEkDrVslkpaXilRxFm7vMdKui4AiJ4uSdnVxoCk7GrHAhTjYGmQVcfy11YJooFnLhxndWiiWdK2ZlmAMWKqk8AX3v6NQbtzUxolAu+jeRxIionmafHxrL5/QWmCBqpnUymFOs9cl8TMHS/jz6Q05kFsQ/l8wmBw+Vi53IXvY+jhie9tRvHMOefgvh+6BTRIccDUwyN5CaR00DiuqsWVxIhI95Yq1L4poZhz0xyiji2yjYI+97btnZ+GBx49HilYr7n57OgeJeC3d3TRK3PbZmTvayVAsQmnm7oy9tUf21NS51ftCtb3NsKXNHdpgqSG8/bNw8+/5LLG4zSMpqy0Fyi17Jrz0x04thQq7F5ZDp/vge09uPfuC8V2riTK/UJPfscaOpUy13ioaEzUgIboUWiJkwDMYdBUMByAf/N+P8114FzBIhBpiZVnjtEsy99PLofetrrs5sucZpkUrKIO+LqbSeLDerh3kLcr1QnVEqBsn52Crz61GMwvQYyZIDPUjd0RPr1aaPJx5Zkj97tL8cy9+KqD8IkH/62ieBfnbVKapSHGV75+Ehb7GZy/v/AqdNME+lXRcNligQoWz+zDj9Nq81BheVuvCw8/vaQeKyVbaUrvT/soKRiiVZ0NeElp0GKL5AQnTYK5PDHJXjOZxsqfSZHoAgLrFkJTYKX6gJLlhxfplhZTifogPf+gXXJvwbNms+Q//vvbYTDM4PXv/eeyPyEqithATrm9jWy/6MozIuVuTqkrIylSQYKOhtm8SVkLx5Hfps9KfJ9BvTd/HufYY8xcVZqgaawq36FoICg3UYHuJIV3LU0cPF0K7qggaN8/PneNVtbktVE9c4ISQ8fsKMIg9d7juAmTgchp+hFR0fDKM5dH37VU7qXYH8+/uuGHjp2aOY8dqwn66Kkf1wrtlTlfJFf7Lk4v6Zv8s9rW68Ljx5eDaVrzzC30OnB0MaSFSuuBBho/jtg5KwtQq4VRxuco0BSahZ6kzI3Wh0/+5J3BM9s2Is2ykzhYqukjRXeEvm1WmiXOOz/w3PMajmRzozAXIMUxB5ZzgClSSH3kcaU8Di5h81p83ZhmmecQ0M55zFwTW0KaI4u+++29C9PwqYe4QTG+XwAvM9SN3ZUaU01hRvTeKWOOnvft158Fn37oKLzqprNI/zYpzdIQA4P+j+wv4n26ZTbLxf4w+CCkmDlaiwPR68pCMQ5GngWxEo4Uj4CPddA+ZPzLlA3BIyAJg9q3KNKTFIFJTEBB2hLTzNPfFWG6rWKI7Q6zImauUqQUmpaUjYy3hUDF7uGnw6x16B248Zzd8AO3nafeh9ZuIjwH7R0DCJ419vtMN6ZZ0iQW1Hr5S996BXBoqduxYDHtj0azbEIwxsu/TSUu6Dbt4zbF6s3pZIW1VS5N4I0btI9Kf5klFsB7e6t6QMSKi4J7lRFMGbeUZilBTkbT/D3I33q8SLYBvQaOKbrvwLY42ypdQHEL+1Jlsww8c8J8o8w93PsaHSt891o2S3rs7JS8hKIyN64Sg9/uMUKzpKCfEGaYfeL4cnDMK647BL/ywfuD9QXXIa7MfcuVZ8B7/v6L4prVRrCRhEOtcPlqYbW9SLx250KvAw89FRb9ltgXdeAUzqDESIv7KZSIYStvx/QonjmkWW4yZW7H7FTEBNHQVBYkUCCCxD/h78vlXO8ToMiKkKSAaeUPqqLh3XBd5HXmKCSmhxbSQU/fU87fYb1K2YuHMkPduFmp7zSMe4+NjvSb6Cgsq6lOErEBEre1slluaTzwWJGs4dy91DOXlZ45SrMsBs1Uh8a2yIu/nIGy+MutHF5I9wOOfjxSW7LCE/YBPwLRCkUnlSSBM3bMwNtffGlwvqRItRMihd9J31C4pckAtGN9jJB871xAzrIwAQptN4hTEuImtEkJaTNLjHqFAuV5++bhpnP3lNeD4C/vQ7g/vpZ2bNBPiJVAAJlmuZt4GNvEFUj9QGWWPh9qxBhFvpWei7boiAJ54CGSefM+Zq74f6+bwtdPLIulCSSLtU4ljvf5ZCdh5spO4uDpk6FnTqNRSzRLCpGiTJ+jEj+Hgqi21o7idaDPrBoP5Hfp26HZ3JFy6bNZ0nMh+E37ZiUPYxvqZPXMFKVWU7Ip9pfK6rixFxLNkoJe9/TthTL3yNNh/bo3P+98+Njb7gi+42mFZvnj33Qx/PWP3BokCuhV824LZU4wPIa0rcYm1gW0CHod/vItt8Cfv/HZwb5XP/NsAAhreo6SWEXCwjSlrjcfP0o9uKsP74Tbn7GvVT86Fc2y1eGbEiFrQZCJyAFdwTPnnINu6ssFYKkjqpTRkgae+kiuW8P6yPK8oJ07f/5JVp6Kgr5LiWZJEXjmhG9Ec0AgFbzOMyeVaBoH4f34bSlmbrR2HSghxRsa5pkbA8cWB+Cct/R7muUwqL+BHwQVgiTPHEBRW+czDx+VLeWKx4Uu9FLgelDDhDQh0YgA5I9ASyTxt/c+N7oHvE8qwGgT0yiUQXxmVDlKlGOlVOzYdp7zY0s6XdY8kc529UX6WUf2BP+/oPTYckwLynJTbblgESkXASofagK/BP4zTsjUskwTnIyrzKFCSJ8lDa4eLT00FaiLv23iMKX4L3o/dYL+zeftgff+45fg1gv3Re1izGu7mM1YwMX3yS2oBc2y8Myhkq3Rr2cbYpkaaaHCMwXwcRCagtLGM/eG247AL9/3uWDuweykx5dlxQQhWUOxL3VFw/WYYzInNtAsRQ+4prCz5/PG24/A9WeHSTu+5aoz4OhiHy46bXt0T22AtCBuEOJ9BAA4vfTMPXw0ZAIkiYtig6rU+UxaSRNX1cHzx5aKX4vsm9I8RrERY6/+8i23tJ7jDu+JM2y+/LpD8K3Xnhnc26g0S44FJUOwhlHokOfunYf/fM+1cPje/9F4rFbmYiuhyTOXoOyS54Hnh1MuF3nMHPXMsXIDeI50Xa4wDrA0AXgvKnryZwR5RTZYyfdO+4AGjyeOe8q3mgClnG+0BEv/8SWXwY3njJfgiCM04lFGXPtvQkKayPHbGx2mzI2BxdIDhx/7VKewviz2s8BigYOKWuu0GIuzds/CZx4+GipogjAI4HnJWFyUHgtQ72ErfpcHex3Nso01pcqOSGKwmihQbbx4EpWpOa4q7O/cVBHfQD0UzhVCYp7nohclEOIVJfzDP3ZbUBcNIJzMKaRi7lJ8nealm1MoXQht7sL9PIYPqaOUZinx39uAnofFo6lS31QwmuNFV54Bf/gvDwb7pDqHOnW3/J0cSwU3iWaJe55/2Wnw7r/7Atz3qUeia+A4aJOMR+ovbnPPXDdNqpg5XCi17xd/5+nPqz4Inrc2GXN7U/VK4lTa7HV44+1H4E3POz/YhzTLJ0/0g/1XHdoB//ylJ6v/h9ksS7jie/BFw0nAv2Ds0oL4p4T4LzUmTmAjaLUEAQDeeHt4vwAA+xZ68MN3ygkv2qDp26NdQJolf74SmkoThMfWZ76k8Mkdmr3Fq4m3f8ulcKSMZW+CpKCNCq6kjkJFljA/cgKU9nGNowDnJW2O2QrQPXP416+oEs0SoPC8LQIvGi4fK9Es9XwKCWRYNLzcPd1NKkOvlJtBUn60MUavtWehmL8fPeY9/7TOnBTPfYwxCp51ZA/89eceg5ddc6Z4vXEQGvT9Nq5T4ypkk0qzNGVuDCz2hyyNawLHlsJ4FwC/sFFrnUazvPLQTvjzf3s4yIomCWIAvtYX9QLSmK5pwSoTUt6KPtDaSABECRQmkzZWDvyQ6XfQRE/iizxmYEyFCUKDJIjxOWpmKi2UOeYlWR5k8HTerzIzqjRLRZE6sD2O/QEA+NAP3xp4vABkJRs3c0qZDTxM/vxR6h1J0DxzmvV/NM+c39aEd+lYDf/7Sy+Hn33RJeJ5gUCuGAO8B80fSwUtiZaM519yRuFN+dITJ4o2BM8c7YOWDh89aJJSJnnm+Hma0oXfr0bha/oeNCriTHVdsdlWgqrkgUGaJR8P73n1dfBbH/4S/PyffhoAwsUXNx0U90M9c9xDS9/PglIuA789ukg30ZnbeIBXC3iNCw+EXv5tvQ48vTgIDDNIbTq0q7nYNipobWR0Lc2+BJ7Bj2OtEmm8/LpDa3IdDafqmds5K1PBNUhJllYC+B7bKP2bFYEskMbff/i7HAcnJTiRyhjQ37WY8JDhAaVnzs+HVEaRPHN0nkRlT/OY02ORWfHYUa/Mafc734tLqgAA/Pp3Xqtm0B0XmmcO16lxDRGYAG3SYMrcGDi5PGQeuKSytFPPG9bnoPxlzcPz2mefA3vmp+CFV5xe7dMSlXzHjYdhvteFbybHBnERZEHB4rV0gkDr33FWwFrKgomCGM2qpkGKKwtpi2R/JYiFx89Pd2BpsBxS9BRlDjOwSfQBrgDPTaXwKNuPz7U/zOHi07cJ/W1+bxoO7Y4FK8nzieOITnSa13G+odBpk5DJf8UxQ8eR9Hsb0D4jzfLrJ5bFY9sKKVyBlhe78BwECkUqFVEI5sbTpzsJTKVJ1X96jSrTZAvaIioWojKH2SyZEDzVSaqFUvPMzVaeuWYKnqSY9JQyEb2GumLjeh28MheOh4VeN6gpJi2gzrnAUhrUmRMMQlrRZSlWTPOAN3sz436uBj74lluCLLkAALc9Yz/84b88GAh2zjl43/c/UzUsUYySoENLsy9BY0RUv28R1t6pxszRBFStPHPIslnh54tz0EoL4JMEzWhIxzrurvOg+e3Y4BEqiWX7inEwVKCSMpulZypQ2bNXExZCj23jMT9vX+HpvubwTvH3QLYs18ejTJnrpAmc4qcRQUvAVnmV2dj97e+5AT7yhSca202cZbPcMlgchHRKukCGnrlSmSOjWFNM0sTBS5kLWlsg08TBS64+GOyjsRFUmds204WjS2HKac3D4xOn+H37thVWmaeVQHwKyYPWlHCAC46z0yk8flz2RHBs63XhaBm/yNvlkxQqBmFNML998enbo/484zRvFdc8c6OgUuYU4ROhCfF1721pkKlCZkWzZL/3uin820/dGdSMoRhFmaPAuDtNmTtVI7LmjaPPFamvbeLKfNY+3+a2mQ48dmw5OnZ2ut5rRt8XenulBR09c2ilxYWaLshSMiQAP8fwxer07T346lOLYt8kryUAs9Y2xOKNm/odLbtfFzy1dMyGtJjQU50JlN2qlAJphM6DEvWcehq0kiW8feyD1O5q4myBBvj2F18K/9tzzgkSlQAAXHawuRA5wGieI1zXeHydBElQBfC12NbCm7kRQJNbjIMgS2CLcYbPe6VlT0+z3LqeObrmi0nhBBo3398NPHPFX9WrVSa/oV53jfWRJgUzI/TM+cRYTd/bjMAa0XD6jhn4+x97rpgIBSBUPlGe4TTL1QDtuhS3yj1zN567OzAe6u1OJs1yi9jLVhaL/dAzR5WYIDBUoFk2xT1RVMUPW6wPNMaCTixVmniFikQhJUDZL6QQ1yApPJpi0lEWPXw+kieCY76mHhenD6BnLQkmT98uFv2mvx/c6b1rWh9GQZVoQxE+EeEk5bc1ZQ7fZ1OSAR4zB1C8My3zlDZOKGjBW8SuyjMne3PHjZ+REvcE3hUym+1o8MxJCzGlk21TkqXg+KSTvSboV545QSnwdeZCIZjOD5r3D78zvlj93vfeBL/wksvCYs2C92pWqQ9YKXPKqjCuZw4Va0475tent0Nplonz2cXkBCh+37RCpcWyIAMtjlOIiWmjsK81pjspXHhg2ymd3/7Y9jRL9ITzmDk0CHHDw2bFSiZ6aZfNMk461gYYZ6nhbd90MTzzvN1w7eFdI7W7mTCvlNOhNEtcUzXqpE9Q48dGmM3Sb5+7rzDefI2UNKLX7QZevKSgWZLXjvO6RLHkmK1olo2HAgDAadtnWsXDouG9KdnVSkBb2/FdjGuISJ2DSbRhmGduDBTKHKXgyVzlKUGZa/OhIVARO7EcC0Ec1ItCLehSTbr5adnj4q3XfobYt9AudTOATEUMY4tigZLHa+EE2ibxh0RjW1A425Jn7uXXHYK///zjsH9bL1AQzt4zBy+95qB4/qkAJ8OO0F8KTYjUaJZz04UXSZvcKiWu5cT9iy+9HH73I18OFigNv/ma66N6eii8f/34+DRLCXiWXlTVb+8qhcuTfXlRkTxzgTIXeHj8eXNVkh8S26ooc5jtVvJI+zpz6Jkr9s8onjkqIOAxXIA7Y8eM4N2P72FWoVlOC5RfinHjgaY7KVx+5g6458azot/oUJAC1p3jtJe8Gge8eDjdx9uu4ihUmiXdjo0G6+GZWw2MopBXFPAW2SzPKAuX81f4ulvOhbf+0Sdg++x4Xv6tjFFolqPEB332Z+5uFOKP7F+A//LdN7RuczNC9bbROGf0timGaikmOqwz57fPK0td/a/HjjceO5U6GGRZ6Zkr2kYZpY3hWZKHxgW9t7NWzYUIAAAgAElEQVR3Fwrpy69d/dhV7fM49Zg5o1luGSz2hwE1jSpz9EPCD5wmJKmrv8GB1k6t1hAFBsEDhALN9irFtVcINQ8PfgSUVrNvYRTPXHuapZbEYVaosTWrxKvNCx6pXYoXoOKIM6/Yr3/XdVG7H3zLLdG+UWPmJPBiogDhe0OotD3lvXkPpXzdUefrF199EF7MaLwa5qc7ML83zB63Z24ann/ZafCdNx1ekf4gXnL1QfiTj30Vrj/HW4upMkwXJjSEYO02jjCbZazEbFMyX6IX/iQxsGhKAfaNGiwwFTXPZokLdTB/UIoOebc4FttYHqsERoEhRbY4V/UcmfCeJg6GWX5Kmfr++PXPFPernjnh+gBhAhRUuLU1W/K+0me2j7AOZJqlLKBt1JppbTCKV9EbOZoFGyyP8PjxsNbdd9xwFnzHDbESb2jGKDTLURKVnGrGza0I+s3vmo9L74SeORdta9RLOq9gbFpQ2olcNzwvKYwsuTduzYzgmZMyJo+LQN6c7bYuyr6S16XoCpT6UVAwQUyZ2xJY7GewZ94/Ouo56jXQLEcBD36vg0TXA/DKHC1j0ESzpMKcFuMnQaRZKoqJ5m2T4u50z1wpzJEPD9Pia31rE/8hQYsrGwVSFizJMydluwTQPXPtaZZrgyRx8CvfdpXejzG1uVfffDa8+uazg307icWfCuRoCKHjPugj9UgJRgzq6ZYywQblHNSYuZhygpkqsV9oecVFnypzNLNdmO2ypHq2GMqSZ56yCuirwG89UuacgyHkY8fM1YH2i2Z09TRLF2SzpH1+5Q2H4H9+6mH45FefamxbYh2cRbI/ht7Tcp8wnvixmxlaTToJqMzxwuVbEa+5+Wy47OB49QUBfMbSNoZfnD+2cgmBtQD95pGt9PTJQbWmSkXDAbwMSNcITfHbLcSkaWtLN02gz2LmUHZqSn4C4NcZPsZ+/PkXwQc/80jj+RsBTYm6xqV2JwnA8gTSwk2ZGwMn+8NAyaFemyCbZZUAZTwhaMcI1BQtWYWkzKmeuVQW5tpCUro0CppGBaiEZeL50CxNKCzTzEm7ZmUFGN/RiaXxuNyjeFQ1SDE+2wQFTbP4ac8BabPNpQk2nxBK6VtSApSnWRbW33rN9fDH//pg8CyqmLkBpVnKwe84jqjnV/LqAHhF/cSSPxY9fk+UFFRfZy6kygAA7CUUZ3pvOPf0W2hz22emovO1mn89JeFFmjiAIUB3FSz6dKxLt1PQLB2pM+fxnPP3wuuecy48tyzuHrUtxNLRewtolAKDgPZt1yZS5k7b3oOXtvC8j7JuVcocK1y+FfHW5190SufvmpuCpxcHrepkfdMVp8M/fuGJqOC7YWVB50lkKz1+fKlSpLoCtbLYjpkRUpkDxBtuOxK8S4neidfrD7OCqVDumynXjjYZa3EN4WKNZDDdqEDR6DSWxbcy3I1p4JjUBCimzI0BTrOkytEsqSOHH/W46YpH8cxpytGtF+6DX/vQA3DFmT5RhZYdUqJZjgLNi3fO3jl44NHjTCiupwxSb4aaoAPT4BKBnRfwRqCQfEJIwrBWwImdPl7JM0eF7SsP7RT3U7RJVAKwdp45DX/+pmfDhx94fEXb3DFDhGwyTnYonrmbj+yBm4/sCfZJxZHDBCj+WBxHIc1SXpyRQkvHMraLdDS03B7eMwf//KUng2+Ifv8hTTKu56hhZ5UASf69Dc3y/3z5FfCO+z43UrxvW9D7yoI6c2GCGU+z9Km4nXNw7916YW4pLpJba8/cNQNffuKkWGhd8rLy/ZOIv/+x21od18bCjzi9jJnbyqnsVwrn7ZuHLzx+Qq3/SfHK6w/BS68+ONK7MrRHJ3EwyPLA2IMZvh87tlwZwJrqzAW0/g41+IcT85ued37wfy0mvJsmkOeFwuJj5uqzEVPMKvWOJwl43+eyMI/OKWZiLZS5U+vbemDy3+g6YLGfBXFwVDHpTcWZjEap7UOxQ/EySdAE/RvO2Q2f/um7gsle48yjwMMXkY/8+9tbWQm1di88sAAPPHo8ULo05RP38wQmEqQ0uLsVmiVSEKiXZK2RVsqcf75N8QttFPrK69iQQWq9HQrn71+A8/cvNB84AoLMjeT+dsxgzFxzfcQqNojM4FqK+3mJZhkoAn4bFXWq+KHHDz1zqOBfdNo2+AN4MDhWo+OgUrW3RXIi9O5rlBM6JnqKMnfnxQfgzosPNF5rHGiCB/bWlccMSZ25tuM4Fbyv3Fr73153E3zwM48ERhVU7IJ438D72u76k45R1i00clx02viZNg0FfvFlV8D7PvrVVs/SOWeK3CpiZiqFo4zyKiWFo17sMFtlfTmiJsOQlgEct/vDrDLSeiNfs6w2MxWH1EwacG0/Z29YwgWf2dg0S9fuGW40mDI3Bhb7w8BKHXjmCE1Ko1m++plntxIIJAreOGg72WO64p2M3tlGaKzDDefshvd//GvBxKF55nD/sRZK19VnFYkwLjnDxydo3kGMNxvX67gSqILVW5h9LjptG7xAKebNgTTLY4oCjIr+ZhdC6cKH9GJej1HCtECz3K6UJsAFk9IsZ4iVUypNQD1zc1MdSBylWRbXxjqHXyCZzCgCunLi4P96xZWBt13DjoYkSmEsHWaYXTuDR0CzlBZQF9aZo7Si5rYlZS78/vdt68G3ssxr+G60pEeT7plri1HDA/76R261rJUrgO0zXUsas0EwN9WBo4sDkWYJ4DNFHyBUP8mblgSeOdlIJ4HOYZ0gm6VXxvAQlD35LPqTL7gYPvTZR4N96B3ERFyTiM8/WqyV3DPXXQGapSVA2SJoW5pAo1n++De149SvdYzTS64+CL2pFL7x0tNWtN1XXn8W7Jidgm+4xFv3Nc/cN156GvzCn30GXnB5syJz47m74R/+3W2tauG94rpDcP8jx+D7bjm3fcdXGJixMGsxUbz/Dc9q3W7bQp1SnbnNhGDhSxP4zM/cFcQZaPDZLCnNkipo/lg03FBl7lKS7EBS5mhpkSRxsG2mC4+XCkNKPHMAuqeWZ7b7phbfBwDJ6rmoJYJpplmuJprqzAGUyhz9ZBrmxV43gcV+Bo48ylGC4l9y9UH45y8+Cd95kxw7shLpvCcBoya8sbgtw2aDRF2kBmPcHWawjNlZWsyyVrsNoXrmyv3FXB3SLLl4cc9Nh+Eellka5ddTMdz94HPPg//+sYfGPv9UccdF++G3//FLUcx0OoLRXELC15sJgSlzI6I/zGCQ5WHRcGLBlVLQjpsAZVRcceaOsXnCAMUgbqNErUS7WhKWw3vmRkpt27aoea+bws++6NLW7a4GxqkJ1AbzVezgxqZZrja4x6RtrCoKrVSZO7LP00GDTJICzTIsLuvbRaomrxO5rdeFJ8uC6jhHbJ/twi+//Aq4isRIUozrDcIsjJoyJyVWWUvvNR2TQcxcaV924MA5CGiWTbjjogPwvo9+NaRZCu9Yw2ufXW/w2YyJhCRslfs0GDRgWSRp+t0+063S3zvn4PKD2+GjX3kqMPShl0ibv5s8c2F9OiJbktqo+JlWSmYLiiCujW3iMjW8+Y4L4M13XDD2+aeKWy/cJ8qKaMAdvzSB0Sy3BFCIk4qDc1SlCcaMmRsVf6TUctqIWIki3GuN937P9Y3erzqgde9UXPj/8SWXRcW4n1F6dc7fPy+dssn9cR7jekzw+6TJGy44sAA/882XwF9+5pFAqMXSAjefFyZRuWD/Anzm4aMs0D2Baw/vhNew7GDU60ctsy+84gy1j+NmUNxexQ4qFFwyNfVWYIEfFcF90c8CSxO44r1mQQKUevzCSy+DN95+JGBMTHXaK3NN2Co0S4NhqwMThfA58R/+3W0w3UngmW//CwAoFIBf+bar4Ofe/ym4lIR9eJpl2C4mdaLeNglSmQMAr7AsD3zMnFbCSQI6GBbXMSHcagGf6bhylmWz3CJAjnFYq6ne6jJuNkuAIg5hNcbVH3zfTevKCx61cOn/fPOzWwuZf3vvc4P4p5XCTefuaT6oBlrM3P/zqmvgyRPL0ikRXnbNmdG+687eBX/+pmfDkX2yMofY7Jb2cctHSGnrAQBeecNZ8EoWu5IkDj70w7dWGc0Qv/+9N8LnHz0ePGPnHPze626KrkeTqzRZZn/vdTfCH/3Lg0FQ/SjYOVfvmQti5tYhKD6kWcrzEc1mCdDsYZ7upHAOi6PAOfhUpryZbgon+0PRSr9Z8YsvvRwuOLCySYsMhkkBertOMnaFxAg6c9cs/Oorrw724bzdYdpcr5PA8eVhs2cuiJmjnrmSZkk8c7iOtZnifHz05MbMacDnNK7hzmLmtgjQkjHdIqlI9xSLhgOsXhyCRuc6VfzR65+5ItZvjvP2tRcoMJHLRoNmMXreRftPue26LJE42W9WGfQHbzsC77jvc2OfL5UmqMOh3fE3udDrtkpIAhAqczw1Nce1h3fBtYd3tWpXQlNWTypLVDFza0izDBOg+G2ezRIVvVESoFCMajyS8K3Xngnv/rsvNL6zzYQXt0ggZDBsVlx+5g74q88+GpSqoUDjnSb6oxLHdbZeN4Xjy8Mqjl6DRrPEdpeHGSnVUvzWxqtUZS6f4AQoGtCDOa5C5ly7sj8bDabMjQiJZqlhJZS5SUNbgXYrwgfmrs8Eulkdc29+3vnwZlafZxRIRcNXE0izdG71KXsYFK9lx5OyWa7lQkY9mTkRifLc/x575kZ/ZisxB7/1+RfBD952RM3EazAYNhfecNsRuPm8PXD1WbLx+20vuBje9r5PVvR7DlTA+DyPc20TzVIrNo6yZZ77dR3Lq5yzp56hA+BDC9Yyc/Fa4dCuWfiWq86Iwhvaoki4NXnanK1KI8LTLJuVOV9nzurAGGidufW5/mbPZjkuqtIEa1TwGOPY2mTaPFU45+D+n707EibmpztRKQsUEC4j2TlXG4FnTvgunCsEGnw1eSsSUYxRMzNKSBPXqu6jwWDYHEgTB9edrTMjXnL1wdryNzin8pjnqth4gzGPJteiVE1Ku8emz94zB+/6zmvg+rN317YJ4BNjHdy5+TLQpomD/+NlV4x9vhUN3yJYLC0ZPZbU5Fe+7cpIWF7rbJaGjY1LylpiN5wzPm1uHOCo3KyeuVMFCvpvfN6RNbkeKnNrZf2T0l//wffdBO//+EORUer9P/gsOGPn2tGUqZKZC9ksAQBSB2PVmaMYN57SYDAYxgWyMFTPXINBb/tMF87aPQtffPwEy5ROcjaQGfG5F7YL2Thn7zz8+ndde0oU/s0K59ZubV5JmDI3IhIHcNbu2SDuBQDg+ZfFKf19nTlT5gwF//7/+w+3w+75UyvCPi5MnJWRJG6kchinir0Lxftf6RIVo+D8/QtinOVFp29b0364IAGK31/RLCFMgEJpRePg9mfsaz7IYDAYVgA7Sg8Yj9/CMJ02c9kffO9N8Jsf/iJcSBIRBXG7Y86Ht15gc6GEhGRPniSYMjcirj5rF/zVD9/a6lik5OxZJ+F9o+O933P9psymVIf1UuQAzDO3UbBvoV1txK0AarCWKJTOxamix6ULf+xtd7SKdTYYDIaVALIweA3YUbJJ7p6fhjfeHsaEBzTLU+2kIUBqNEsDxyVnbIe/+KHnRGmyDQVONdW/oR2898Om/Y0A9MwZeGkCvz9nx2DSoHFj5gAgYlMYDAbDamLHbJlNmJWGwTCdceu8BTRLs9KuKJJkMmmWxv9bZZgiZ9gosDl/Y2CfKXMVqDIXxMxV2y6iWZpNwmAwTAJ2zBTsLJ5sChOPjJuYqWOeuVWDM8+cwWDYiDC/3MbCelJtNxqogUEyhsbZLG0cGwyGyQB65vjcdu/dF8IzTluAWy7YO1a7U4FnbuzuGQQklgDFYDBsZBgdY2NgtWvLTRJCmiXNZulBs1lCDuCMT2IwGCYA25Vi471uCt967aGx2+0E2SwNK4nUTWadOVsWDQaDwbAuCOrM0fVTyWZZ7DPxxWAwbHygZ26lEdaZs/lwJeEmNJvlKSlzzrkvOOc+7pz7V+fcP5X7djnnPuCc+1z5d2e53znn3uGcu9859zHn3FUrcQMGg6EeNtdvPNg7KaB75opt51yQzfJUEqAYDAbDWoIW/V5JdM0zt2pInBMp/xsdKzHSbs3z/DHy/3sB4L48z9/unLu3/P+PAsDdAHCk/Hc9APxq+ddgMBi2FP71x++YSOvfSiMJaxOIWMk6cwaDwbBWWC2v2UrUmTPISBzAcAK1udWgWb4QAN5Tbr8HAL6Z7P+NvMCHAWCHc+60Vbi+wWAQkE/gBLVZsX2mCzvLOpRbHajPBZ45QrMsEqCgZ86UOYPBsLURZrO0CXElkSaTGTN3qp65HAD+3DmXA8Cv5Xn+TgDYn+f5Q+XvXwOA/eX2GQDwZXLuV8p9D4HBYFhF2GRv2LhAGmVQZw6VOVcEpOP/8zw34cVgMEwMfv91N8LCCte4tGyWq4etWprg5jzPH3TO7QOADzjnPk1/zPM8LxW91nDOvRYAXgsAcOjQ+Nl+DAZDiAmcnwxbAEXcXK5aQ6MEKCa8GAyGCcE1h3eteJudhHrmDCuJxE0mi+mUaJZ5nj9Y/n0EAP4QAK4DgIeRPln+faQ8/EEAOJOcfrDcx9t8Z57n1+R5fs3evePV4DAYDB4m/Bo2MnB8CskswYED58DHzK1pzwwGg2HjIU1cNW/a+r6ySCbUMze2Muecm3POLeA2ANwBAJ8AgPcBwD3lYfcAwB+X2+8DgFeVWS1vAICnCB3TYDCsMibQ2GTYAsCMlnkQM4fZLMO6PzaGDQbDVodzDrpJIb4b7XxlkRDj4SThVGiW+wHgD8tsPR0AeG+e53/qnPsIAPxX59xrAOCLAPCy8vj3A8A3AMD9AHACAL7rFK5tMBhawk/1kzdBGTY/fAIU+fcgmyVYXSWDwWDopg6Wh+aZW2lghuU8zydqrRlbmcvz/AEAuFzY/zgA3CbszwHg9eNez2AwjIcJmo8MWxCiZ47+TrOL5bnZoQ0Gw5ZHJ00AYGjr+woD16MsB0gn6NmuRmkCg8FgMBhawQmeOZ7N0hKgGAwGg4evNWcT4kpCKpUzCTBlzmDYIpiwucmwRUALh/MsYg5cRLM0GAyGrY5u6TYy49bKAqmVkxY3Z8qcwbDJYQHSho2MlEgjfv30CynNLpbnZoc2GAwG9MzZfLiySKuYuXXuyIgwZc5g2CKYsLnJsEVAg8xzlrXSOYA0oaUJJiso3WAwGFYDHfPMrQqMZmkwGAwGw4ggLMsoo6VzLAEKmCXaYDAYplIrTbAa8AlQTJkzGAwbCGa5M2xknL9/Abb1isTKVT058ntideYMBoMhQEWztPV9RYHMjyxb546MCFPmDIYtAhOEDRsRv/Xd18P33nJesK+iWYILslnmuQkvBoPBMN2xmLnVQGo0S4PBsBFhwq9ho4PHKeSlb87TLIt4unLvuvTRYDAYNgqmUJmzBX5FgdmVTZkzGAwGg2EESLXmECkp4prnuRknDAbDlgcqc4aVhSPrzSTBRoPBsEWQWz5LwwYFDzr3NMsimyWAz2hpupzBYNjqmLKYuVWBZbM0GAwbEpbtyrDRgdZQXD/zav/k0l4MBoNhtVDRLG19X1FYNkuDwbChMWFzk2ELAa2heTRIXUWzHGa5JUAxGAwGoDFz69yRTYbUaJYGg8FgMIwOlEdwAaVKXVpqesM8L4qGmyXaYDBscVg2y9VBFb89YdqcKXMGw2aHzfaGDQ6kUnLPnHOe9pJnVprAYDAYACxmbrWQMMr/pMCUOYNhi2DC5ibDFoKWQaxIgOI9c8Wxa9kzg8Fg2Hiw0gSrgwQTbk2YNmfKnMGwyWFTvWGjA8dozrJZAvh4umFm+VgNBoMBAGC6kwKAre8rDUuAYjAYDAbDGEiYZ84XDXdBNss8t5g5g8FgQM/cpCkdGx03nrMbfuPV18Fp23vr3ZWR0FnvDhgMhrVBnCnQYNgYqLJZMt+bAwizWeJOg8Fg2MJAZa4/tHV9JbFvWw/2bZssRQ7APHMGw6aHceoNGx2RZ47SLBOvzAGYLmcwGAyYAGV5mK1zTwwbAabMGQwGg2F9wdJBozLnHK37k1sWH4PBYADimRuYMmcwZc5gMBgM6wwtHbQD57NZljRL8zQbDIatDk+zNGXOYMqcwbDpYaKvYaODx8xRnS5OgGIwGAxbG1g03GiWBgBT5gyGTQ+c9BPzaBg2KHBo+pg5X1MuJfF0OVidOYPBYKhi5gbGPTdYNkuDYdPjP3zjRbBrfgruvuTAenfFYBDBa/tQ8STFIq6WAMVgMBgAAGC6azRLg4cpcwbDJsf22S782N3PWO9uGAwqnBIzR38bZrn4u8FgMGw1TKVF0XBT5gwARrM0GAwGwzqjipnLq6rhABBns8whtwQoBoNhy8MSoBgoTJkzGAwGw7rCleTJ/jCH+z71MDx6bKnY71g2y9xolgaDwdBNi5lw2YqGG8BolgaDwWBYZ6Bn7kf/28fg4w8+Vf3fAc9mCabNGQyGLY9uanXmDB7mmTMYDAbDumLbTBcAAD7+4FMA4LNa9rppRbNENpEzbc5gMGxxGM3SQGHKnMFgMBjWFVce2lFtn7N3DgAADu+ehV1zU5CUq9Rbfu+j8OCTJ9ejewaDwbChUHnmTJkzgClzBoPBYFhnzE55xv/3POscAAC48+KilMbBHbMAAPClJ04AAMCnHnp6jXtnMBgMGwtVzJzRLA1gMXMGg8Fg2AD4qRdeDO/9hy/By689Ew5s78HN5+0BAIBDu2fh/37l1fBf/uGL8NefewwWB8N17qnBYDCsL6qi4ZYAxQCmzBkMBoNhA+BVNx6GV914GAAAbr1gX/DbXZccgLsuOQB/9/nH4PTtM+vQO4PBYNg4wJi53IpvGmAdlDnn3F0A8MsAkALAf87z/O1r3QeDwWAwTB5uOnfPenfBYDAY1h3bZ7rwfbecCy+44vT17ophA2BNlTnnXAoA/wkAngcAXwGAjzjn3pfn+b+tZT8MBoPBYDAYDIZJhHMOfuSuC9e7G4YNgrVOgHIdANyf5/kDeZ4vA8DvAMAL17gPBoPBYDAYDAaDwTDxWGtl7gwA+DL5/1fKfRWcc691zv2Tc+6fHn300TXtnMFgMBgMBoPBYDBMCjZcaYI8z9+Z5/k1eZ5fs3fv3vXujsFgMBgMBoPBYDBsSKy1MvcgAJxJ/n+w3GcwGAwGg8FgMBgMhhGw1srcRwDgiHPubOfcFAC8HADet8Z9MBgMBoPBYDAYDIaJx5pms8zzfOCc+34A+DMoShO8K8/zT65lHwwGg8FgMBgMBoNhM2DN68zlef5+AHj/Wl/XYDAYDAaDwWAwGDYTNlwCFIPBYDAYDAaDwWAwNMOUOYPBYDAYDAaDwWCYQJgyZzAYDAaDwWAwGAwTCFPmDAaDwWAwGAwGg2ECYcqcwWAwGAwGg8FgMEwgTJkzGAwGg8FgMBgMhgmEy/N8vfugwjn3KAB8cb37IWAPADy23p0wbDnYuDOsNWzMGdYaNuYMaw0bc4b1wKjj7qw8z/dKP2xoZW6jwjn3T3meX7Pe/TBsLdi4M6w1bMwZ1ho25gxrDRtzhvXASo47o1kaDAaDwWAwGAwGwwTClDmDwWAwGAwGg8FgmECYMjce3rneHTBsSdi4M6w1bMwZ1ho25gxrDRtzhvXAio07i5kzGAwGg8FgMBgMhgmEeeYMBoPBYDAYDAaDYQJhytyIcM7d5Zz7jHPufufcvevdH8PmgHPuTOfcB51z/+ac+6Rz7g3l/l3OuQ845z5X/t1Z7nfOuXeU4/Bjzrmr1vcODJMK51zqnPsX59yflP8/2zn3D+XY+l3n3FS5f7r8//3l74fXs9+GyYRzbodz7vedc592zn3KOXejzXOG1YZz7k3l2voJ59xvO+d6NtcZVhLOuXc55x5xzn2C7Bt5bnPO3VMe/znn3D1trm3K3AhwzqUA8J8A4G4AuAgAXuGcu2h9e2XYJBgAwA/leX4RANwAAK8vx9a9AHBfnudHAOC+8v8AxRg8Uv57LQD86tp32bBJ8AYA+BT5/88DwC/leX4eAHwdAF5T7n8NAHy93P9L5XEGw6j4ZQD40zzPLwSAy6EYezbPGVYNzrkzAOAHAeCaPM8vAYAUAF4ONtcZVhbvBoC72L6R5jbn3C4A+AkAuB4ArgOAn0AFsA6mzI2G6wDg/jzPH8jzfBkAfgcAXrjOfTJsAuR5/lCe5/9cbh+FQsA5A4rx9Z7ysPcAwDeX2y+E/7+9+3mxugrjOP5+aJLSQPsBUk1RQbTNVkIRomFQUhupoCiM/oAWEdSuhbsIF0IbTSgiiEnKXQgFtUlKXAS1s9IRf9HkBAX9oE+L77l6GyHn6r0zfaf3azP3nPOFexYPz73PPT8G3k7nC2BdVd28xNNWz1XVNPAosKe1C9gMzLRHFsbcIBZngC3teWlRqmot8CCwFyDJ70nOYZ7T5E0B11bVFLAaOIm5TmOU5DNgbkH3qLntYeBgkrkkPwEHubhAvIjF3GhuBY4PtWdbnzQ2bUvHBuAQsD7JyTZ0CljfXhuLGoddwMvAX619I3AuyZ+tPRxX52Oujc+356XFuhM4C+xrW3v3VNUazHOaoCQngNeBY3RF3DxwGHOdJm/U3HZZOc9iTvoPqarrgA+AF5P8PDyW7upZr5/VWFTVNuBMksPLPRf9b0wB9wFvJtkA/MKFbUeAeU7j17apPU73Y8ItwBoWsdohjdMkc5vF3GhOALcNtadbn3TFqupqukLu3ST7W/fpwbai9vdM6zcWdaXuBx6rqu/ptoxvpjvPtK5tRYJ/xtX5mGvja4Efl3LC6r1ZYDbJodaeoSvuzHOapIeA75KcTfIHsJ8u/5nrNGmj5rbLynkWc6P5Eri73YC0iu4A7YFlnpNWgLYffy/wbZI3hoYOAIPbjJ4DPhrqf7bdiLQRmB9aypcuKckrSaaT3EGXyz5J8jTwKbC9PflFC8YAAAEuSURBVLYw5gaxuL097wqKFi3JKeB4Vd3TurYA32Ce02QdAzZW1er2WTuIO3OdJm3U3PYxsLWqrm8ryltb37/yn4aPqKoeoTtnchXwVpKdyzwlrQBV9QDwOfA1F84vvUp3bu594HbgB+CJJHPtA2k33VaRX4EdSb5a8olrRaiqTcBLSbZV1V10K3U3AEeAZ5L8VlXXAO/QneecA55KcnS55qx+qqp76S7cWQUcBXbQ/bBsntPEVNVrwJN0N0cfAV6gO4tkrtNYVNV7wCbgJuA03a2UHzJibquq5+m+/wHsTLLvku9tMSdJkiRJ/eM2S0mSJEnqIYs5SZIkSeohizlJkiRJ6iGLOUmSJEnqIYs5SZIkSeohizlJkiRJ6iGLOUmSJEnqIYs5SZIkSeqhvwHPEgc5VG5pzwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### Number of units coming in per day\n",
"### Typically ranges from 800-2000 daily, so definitely >5000 monthly\n",
"f1, ax1 = plt.subplots(1, figsize=[15, 6])\n",
"ax1.plot(range(units_per_day.shape[0]), units_per_day['n_units'], label='# units')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Relevance scoring/binning"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"### Full dataset takes up too much memory, so dropping all but the most recent now\n",
"### This keeps 276122 of the original 1.5e6, or a little less than 1/5th of the total\n",
"df2 = df.loc[df['read_date'] >= '2019-03-01']\n",
"del df"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"276122.0 0.18449151947830678\n"
]
}
],
"source": [
"new_units = df2['n_units'].sum()\n",
"downscale = new_units/units\n",
"print(new_units, downscale)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"### Assign a made-up CPD relevance score\n",
"\n",
"### Words associated with CPD\n",
"cop_words = [\n",
" \"cpd\",\n",
" \"police\",\n",
" \"officer\",\n",
" \"cop\",\n",
" \"officers\",\n",
" \"pigs\",\n",
" \"policeofficer\",\n",
" ]\n",
"### Count number of times relevant words appear in title or text\n",
"df2['cop_word_counts'] = 0\n",
"for w in cop_words:\n",
" df2['cop_word_counts'] += df2['bodytext'].str.lower().str.count(w)\n",
" df2['cop_word_counts'] += df2['title'].str.lower().str.count(w)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 72783.000000\n",
"mean 1.801300\n",
"std 4.715337\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 1.000000\n",
"max 240.000000\n",
"Name: cop_word_counts, dtype: float64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2['cop_word_counts'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'CPD_model')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### Does the word count measure the same thing as the CPD_model column? \n",
"### No, doesn't look very correlated actually...\n",
"f1, ax1 = plt.subplots(1, figsize=[14,6])\n",
"ax1.scatter(df2['cop_word_counts'], df2['CPD_model'], alpha=0.3, s=5)\n",
"ax1.set_xlabel('cop word count')\n",
"ax1.set_ylabel('CPD_model')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Two Chicago police officers were accused of child abuse. A decade later, they were fired\n",
"Two Chicago police officers have been fired a decade after they were first\n",
"accused of hitting a child in their care and failing to seek medical attention\n",
"for the 8-year-old boy, who suffered fractures to the face and arm that the\n",
"child said was inflicted by one of the officers.\n",
"\n",
"In voting 9-0 to dismiss Officers Yasmina Vaval and Teresa Foster, the Chicago\n",
"Police Board said it was “deeply troubled” that this case, and others, take so\n",
"long to resolve.\n",
"\n",
"Advertisement\n",
"\n",
"“The Board continues to be deeply troubled by cases such as this, in which the\n",
"charges were filed more than nine years after an incident occurs,” the board\n",
"wrote in a 28-page decision handed down last week. \"In this case, the impact was\n",
"particularly serious on the victim, who was eight years old when abused and is\n",
"now an adult.”\n",
"\n",
"The alleged abuse occurred in 2008 and 2009. The Illinois Department of Children\n",
"and Family Services removed the boy and two other children from the officers’\n",
"home at the end of 2009 after finding evidence of abuse. It took about six years\n",
"for the now-defunct Independent Police Review Authority to complete its\n",
"investigation and recommend the dismissal of Vaval and Foster. Nearly four more\n",
"years passed before city officials filed disciplinary charges and referred the\n",
"case to the Police Board.\n",
"\n",
"Bill McCaffrey, a spokesman for the city’s Law Department, blamed “parallel\n",
"investigations by other law enforcement and child welfare agencies” for why it\n",
"took so long for IPRA to investigate the case and for city officials to\n",
"ultimately bring several disciplinary charges against the officers, including\n",
"for mistreatment, making false statements and bringing discredit to the Police\n",
"Department. IPRA has since been replaced by the Civilian Office of Police\n",
"Accountability under a series of reforms.\n",
"\n",
"** [ [Most read] Two years ago, Kraft Heinz raised eyebrows on Wall Street when it named a 29-year-old as its CFO. Now he’s out. » ][1] **\n",
"\n",
"\"While this matter may have spanned numerous years, the end result is that (city\n",
"officials were) successful in separating these officers from the police\n",
"department,” McCaffrey said in a statement.\n",
"\n",
"The officers have not been criminally charged, even though the board cited\n",
"instances of the officers inflicting harm and lying to police and DCFS workers\n",
"about it. The Cook County state’s attorney’s office reviewed the case in March\n",
"2010 and concluded there wasn’t sufficient evidence to file criminal charges,\n",
"the office’s spokeswoman, Tandra Simonton, said in a statement.\n",
"\n",
"The officers and their attorney were unavailable for comment.\n",
"\n",
"In its decision, the Police Board detailed several incidents between spring 2008\n",
"and November 2009 where one or both officers “physically maltreated” the boy or\n",
"did not seek prompt medical attention.\n",
"\n",
"The officers, who are married, became foster parents to three boys — 6, 8 and 9\n",
"— at the end of 2007. The boy at the center of the allegations suffered from\n",
"fetal alcohol syndrome and “had cognitive disabilities,” according to the\n",
"board’s report. Foster also has two biological children.\n",
"\n",
"** [ [Most read] Column: Cubs look closer to being dismantled in the offseason than they are to playing in the World Series » ][2] **\n",
"\n",
"The first allegation dates to the spring of 2008 when the boy was found to have\n",
"a fracture to a bone that supported one of his eyes. There was a delay in\n",
"treating the fracture and doctors were not able to correct the damage, according\n",
"to the board’s report.\n",
"\n",
"While the boy accused one of the officers of punching him, the board said there\n",
"was “conflicting evidence.” As for the delay in seeking treatment, the board\n",
"noted that it could not clearly blame the officers since DCFS workers were also\n",
"involved in the boy’s care.\n",
"\n",
"In February 2009, Vaval adopted the boy and the two other foster children.\n",
"\n",
"On April 29 of that year, the boy got into trouble at school and brought a note\n",
"home for either officer to sign. The next day, he came to school with bruising\n",
"on his arms and legs, the board reported. The boy told school staff that the\n",
"officers had “whipped” him. The school’s social worker contacted DCFS to report\n",
"possible child abuse.\n",
"\n",
"A DCFS investigator interviewed the boy, who told her Vaval whipped him on the\n",
"hands and hurt his arm as he tried to protect himself. The boy also told the\n",
"investigator that Foster beat him on his buttocks with his pants down, and that\n",
"he used his hands to try and protect himself.\n",
"\n",
"** [ [Most read] Financial adviser accused of swindling one of the ‘Dixmoor 5’ out of settlement money from infamous wrongful conviction case » ][3] **\n",
"\n",
"The two other adopted children corroborated his account. During a June hearing,\n",
"the investigator testified that Vaval initially denied any physical contact with\n",
"the boy. She claimed the boy sucked on his arms to create bruising and injured\n",
"his legs by rubbing them on the rail of his bunk bed.\n",
"\n",
"Advertisement\n",
"\n",
"The investigator, however, saw no way the rails of the boy’s bed could injure\n",
"him in the way Vaval described, according to the board. Vaval later admitted\n",
"whipping the boy with a belt on his hands, while Foster denied hitting him at\n",
"all.\n",
"\n",
"The investigator urged Vaval to take the boy to a doctor. The boy was taken May\n",
"1 to an emergency room, where records show bruising on the back of each thigh\n",
"and both forearms. The DCFS investigator reached out to the doctor, who examined\n",
"the boy and had questions about his injuries. But the doctor never called the\n",
"investigator, who could not reach the boy’s therapist. She closed out the case\n",
"without indicating child abuse, the board found.\n",
"\n",
"While a Chicago police detective reported the boy told medical staff his\n",
"injuries were self-inflicted, the board found the boy’s “recantation” at the\n",
"hospital “meaningless, given the presence of Officer Vaval.”\n",
"\n",
"The board cited the DCFS investigator’s testimony that she believed Vaval and\n",
"Foster caused the boy’s injuries, a finding corroborated by the boy’s account\n",
"outside the presence of the two officers and the accounts of the two other\n",
"adopted children. The board said it also considered the timing of the injuries\n",
"after getting a note from school.\n",
"\n",
"** [ [Most read] ‘Ooooh, the skulduggery!’: Inside the world of Steve McMichael, still one of the most colorful and beloved characters from the 1985 Bears » ][4] **\n",
"\n",
"“When the evidence is viewed in its totality, it is clear that Officer Vaval\n",
"whipped (the boy) on his hands with a belt and Officer Foster beat him on the\n",
"other parts of his body,” the board wrote. “The Board finds that not only did\n",
"Officer Vaval physically maltreat (the boy), but she also failed to protect him\n",
"from the beating he received from Officer Foster.”\n",
"\n",
"The board determined that Vaval would never have taken the boy to a doctor\n",
"unless told to do so by DCFS.\n",
"\n",
"In November 2009, Vaval was accused of failing to seek medical treatment after\n",
"the boy apparently suffered a seizure and, in another incident, lost\n",
"consciousness after hitting his head in the bathroom. At least one of the\n",
"officers claimed the boy did not have a seizure and, in the other incident, had\n",
"faked passing out.\n",
"\n",
"“It is apparent to the Board that ... the officers attempted to minimize (the\n",
"boy’s) seizure and loss of consciousness,” the report stated.\n",
"\n",
"The same month, a staffer at the boy’s school again saw bruising on his hands\n",
"and reported that he was complaining of pain, according to the board. The staff\n",
"reported suspected abuse to DCFS and another investigator took the case.\n",
"\n",
"** [ [Most read] 3 things we learned at Bears practice, including how wide receiver Anthony Miller is feeling as the Bears gear up for Week 1 » ][5] **\n",
"\n",
"A Chicago police detective saw bruising on the boy’s right hand, right forearm,\n",
"left shoulder, right shoulder blade, back and left thigh, according to the\n",
"board. A doctor from La Rabida Children’s Hospital on the South Side examined\n",
"the boy and documented the bruising, along with “linear marks” on the back of\n",
"his right hip and a fracture of the left arm caused by blunt trauma within the\n",
"past week, the board said.\n",
"\n",
"Latest Breaking News\n",
"\n",
" * [ Two Chicago police officers were accused of child abuse. A decade later, they were fired ][6]\n",
"\n",
"50m\n",
"\n",
"[ ][6]\n",
"\n",
" * [ At the McHenry VFW Queen of Hearts raffle Tuesday night, they won’t stop drawing tickets until there’s a $2.7 million grand prize winner ][7]\n",
"\n",
"1h\n",
"\n",
"[\n",
"\n",
"![At the McHenry VFW Queen of Hearts raffle Tuesday night, they won’t stop\n",
"drawing tickets until there’s a $2.7 million grand prize winner][8]\n",
"\n",
"][7]\n",
"\n",
" * [ Alleged gunman in killing of 9-year-old Tyshawn Lee denied bid to represent himself at trial ][9]\n",
"\n",
"1h\n",
"\n",
"[ ][9]\n",
"\n",
" * [ Tinley Park Post Office evacuated after suspicious backpack found ][10]\n",
"\n",
"2h\n",
"\n",
"[\n",
"\n",
"![Tinley Park Post Office evacuated after suspicious backpack found][8]\n",
"\n",
"][10]\n",
"\n",
" * [ Man, 13-year old girl identified in fatal Round Lake Beach crash ][11]\n",
"\n",
"2h\n",
"\n",
"[\n",
"\n",
"![Man, 13-year old girl identified in fatal Round Lake Beach crash][8]\n",
"\n",
"][11]\n",
"\n",
"Advertisement\n",
"\n",
"DCFS removed the three children from the home shortly after the November\n",
"incident.\n",
"\n",
"In firing Vaval, the board said she showed disregard for the boy’s safety.\n",
"“Officer Vaval’s intentional and material false statement about criminal\n",
"activity also render her unfit to be a Chicago police officer,” the board wrote.\n",
"\n",
"As for Foster, the board said she tried to cover up her abuse of the boy by\n",
"“repeatedly falsely stating to Chicago police detectives that she did not\n",
"inflict any injuries on the child.”\n",
"\n",
"The officers can appeal their firings to the Cook County Circuit Court.\n",
"\n",
" [1]: https://www.chicagotribune.com/business/ct-biz-kraft-heinz-cfo-knopf-\n",
"basilio-20190826-tmqpd4gle5dqjfqwwqgsvzqfdm-story.html#nt=interstitial-auto\n",
"\n",
" [2]: https://www.chicagotribune.com/sports/cubs/ct-cubs-struggling-\n",
"future-20190826-qgw3cfj4dnfdzhw6rti4pwldc4-story.html#nt=interstitial-auto\n",
"\n",
" [3]: https://www.chicagotribune.com/news/criminal-justice/ct-financial-\n",
"adviser-fraud-dixmoor-five-20190826-zu47xwb3bndtji3zjmeecsypwa-\n",
"story.html#nt=interstitial-auto\n",
"\n",
" [4]: https://www.chicagotribune.com/sports/bears/ct-bears-100-steve-\n",
"mcmichael-1985-super-\n",
"bowl-20190826-cv36vnxv2fgerdzvggtf52xpn4-story.html#nt=interstitial-auto\n",
"\n",
" [5]: https://www.chicagotribune.com/sports/bears/ct-cb-bears-anthony-miller-\n",
"injury-20190826-4f7qte6w3feovkq72ex7p5grku-story.html#nt=interstitial-auto\n",
"\n",
" [6]: https://www.chicagotribune.com/news/breaking/ct-chicago-police-officers-\n",
"fired-20190827-wyucxtjk7vbohewcfkv73kaifa-story.html#nt=related-content\n",
"\n",
" [7]: https://www.chicagotribune.com/news/breaking/ct-mchenry-queen-of-hearts-\n",
"draw-down-20190827-s7wslncf2rdphabt34sl2xw3ne-story.html#nt=related-content\n",
"\n",
" [8]: /pb/resources/images/tinygif.gif\n",
"\n",
" [9]: https://www.chicagotribune.com/news/criminal-justice/ct-tyshawn-lee-\n",
"gunman-trial-lawyer-20190827-pxll5kp7x5gsjnep7mklh5yoqq-story.html#nt=related-\n",
"content\n",
"\n",
" [10]: https://www.chicagotribune.com/suburbs/daily-southtown/ct-sta-tinley-\n",
"park-post-office-evacuated-st-0827-20190826-tli64tbo3jh25ijfwdy6necnym-\n",
"story.html#nt=related-content\n",
"\n",
" [11]: https://www.chicagotribune.com/suburbs/lake-county-news-sun/sports/ct-\n",
"lns-fatal-crash-victims-st-0828-20190826-7seym47jyresbadab7wt5ubxju-\n",
"story.html#nt=related-content\n"
]
}
],
"source": [
"### See examples that use the relevant words but didn't score highly in CPD_model\n",
"### Some definitely look relevant (e.g. article 650870)\n",
"relevant_but_zero = df2.loc[(df2['CPD_model']==0) & ((df2['CPD']==0))].sort_values('cop_word_counts', ascending=False)\n",
"print(relevant_but_zero.loc[650870, 'title'])\n",
"print(relevant_but_zero.loc[650870, 'bodytext'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"### Basic relevance score:\n",
"### - 50% human tagged \"CPD\"\n",
"### - 25% \"CPD_model\"\n",
"### - 25% usage of above words\n",
"df2['CPD_relevance'] = ( 0.5*df2['CPD'] # upweight because it means more\n",
" + 0.25*df2['CPD_model']\n",
" + 0.25*(df2['cop_word_counts']/(2*len(cop_words))).clip(upper=1.)\n",
" )\n",
"### 55% have relevance = 0\n",
"### \n",
"df['relevance_tier'] = 0\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"137183.0 0.4499402332962368\n",
"138939.0 0.5500597667037632\n"
]
}
],
"source": [
"### What number/fraction have score > 0?\n",
"print(df2.loc[df2['CPD_relevance']>0, 'n_units'].sum(), (df2['CPD_relevance']>0).mean())\n",
"### What number/fraction have score = 0?\n",
"print(df2.loc[df2['CPD_relevance']==0, 'n_units'].sum(), (df2['CPD_relevance']==0).mean())"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([1.3792e+04, 3.8480e+03, 2.5010e+03, 2.5090e+03, 3.3170e+03,\n",
" 2.6980e+03, 1.5210e+03, 7.6300e+02, 5.6900e+02, 9.2700e+02,\n",
" 4.0000e+00, 4.0000e+00, 6.0000e+00, 2.2000e+01, 4.9000e+01,\n",
" 7.6000e+01, 4.4000e+01, 2.3000e+01, 2.5000e+01, 5.0000e+01]),\n",
" array([0.0125025 , 0.06187737, 0.11125225, 0.16062712, 0.210002 ,\n",
" 0.25937687, 0.30875175, 0.35812662, 0.4075015 , 0.45687637,\n",
" 0.50625125, 0.55562612, 0.605001 , 0.65437587, 0.70375075,\n",
" 0.75312562, 0.8025005 , 0.85187537, 0.90125025, 0.95062512,\n",
" 1. ]),\n",
" <a list of 20 Patch objects>)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0IAAAFlCAYAAADGaFjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAZjElEQVR4nO3df6ye5Xkf8O9VXNLfgQQvyzCb0eJ2I1mnUItQRdqqMoEJVYy0NCJqh5tZ9R+lXX9prdNOY0oaiaxbWdESOlpYoOpCGOuGNUiZRVJFmwrFaVoSSFPOCAn2SHFjQrdFTUt67Y/zkL04xz6H8x6fc+z785GOzvNcz/287/XKt4799fM896nuDgAAwEi+bqMbAAAAWG+CEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAw9my0Q2s1nnnndfbt2/f6DYAAIBN6mMf+9ifdPfWpY6dtkFo+/btOXTo0Ea3AQAAbFJV9dkTHXNrHAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAw1k2CFXVbVX1TFV9coljP11VXVXnTftVVTdV1UJVPVJVF8+M3VNVj09fe2bq31VVn5jOuamqaq0+HAAAwFJWckXo/Ul2HV+sqguSXJ7kczPlK5PsmL72Jbl5GvuKJNcneUOSS5JcX1XnTufcnOSHZ877mvcCAABYS8sGoe7+aJJjSxy6McnPJOmZ2u4kd/SiB5OcU1WvTnJFkoPdfay7n01yMMmu6di3dfeD3d1J7khy9XwfCQAA4ORW9YxQVe1OcqS7/+C4Q+cneWpm//BUO1n98BL1E73vvqo6VFWHjh49uprWAQAAXnoQqqpvSvJzSf752rdzct19S3fv7O6dW7duXe+3BwAAzhCruSL0N5NcmOQPqurJJNuS/F5V/dUkR5JcMDN221Q7WX3bEnUAAIBT5iUHoe7+RHf/le7e3t3bs3g728Xd/fkkB5JcO60ed2mS57r76ST3J7m8qs6dFkm4PMn907E/rapLp9Xirk1yzxp9NgAAgCWtZPnsDyT5nSTfUVWHq2rvSYbfl+SJJAtJfjXJjyRJdx9L8q4kD09f75xqmcb82nTO/0zyodV9FAAAgJWpxcXaTj87d+7sQ4cObXQbSZLt++/d6BZe5MkbrtroFgAAYMNV1ce6e+dSx1a1ahwAAMDpTBACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEsG4Sq6raqeqaqPjlT+8Wq+sOqeqSq/nNVnTNz7B1VtVBVn66qK2bqu6baQlXtn6lfWFUPTfUPVtXZa/kBAQAAjreSK0LvT7LruNrBJK/r7u9M8kdJ3pEkVXVRkmuSvHY6531VdVZVnZXkvUmuTHJRkrdNY5PkPUlu7O7XJHk2yd65PhEAAMAylg1C3f3RJMeOq/237n5+2n0wybZpe3eSO7v7y939mSQLSS6Zvha6+4nu/vMkdybZXVWV5HuT3D2df3uSq+f8TAAAACe1Fs8I/eMkH5q2z0/y1Myxw1PtRPVXJvniTKh6oQ4AAHDKzBWEqurnkzyf5DfWpp1l329fVR2qqkNHjx5dj7cEAADOQKsOQlX1Q0m+L8kPdHdP5SNJLpgZtm2qnaj+hSTnVNWW4+pL6u5buntnd+/cunXralsHAAAGt6ogVFW7kvxMkjd395dmDh1Ick1VvayqLkyyI8nvJnk4yY5phbizs7igwoEpQH0kyVum8/ckuWd1HwUAAGBlVrJ89geS/E6S76iqw1W1N8m/TfKtSQ5W1e9X1a8kSXc/muSuJI8l+a0k13X3V6ZngH40yf1JPpXkrmlskvxskp+qqoUsPjN065p+QgAAgONsWW5Ad79tifIJw0p3vzvJu5eo35fkviXqT2RxVTkAAIB1sRarxgEAAJxWBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGM6yQaiqbquqZ6rqkzO1V1TVwap6fPp+7lSvqrqpqhaq6pGqunjmnD3T+Meras9M/buq6hPTOTdVVa31hwQAAJi1kitC70+y67ja/iQPdPeOJA9M+0lyZZId09e+JDcni8EpyfVJ3pDkkiTXvxCepjE/PHPe8e8FAACwppYNQt390STHjivvTnL7tH17kqtn6nf0ogeTnFNVr05yRZKD3X2su59NcjDJrunYt3X3g93dSe6YeS0AAIBTYrXPCL2qu5+etj+f5FXT9vlJnpoZd3iqnax+eIk6AADAKTP3YgnTlZxeg16WVVX7qupQVR06evToerwlAABwBlptEPrj6ba2TN+fmepHklwwM27bVDtZfdsS9SV19y3dvbO7d27dunWVrQMAAKNbbRA6kOSFld/2JLlnpn7ttHrcpUmem26huz/J5VV17rRIwuVJ7p+O/WlVXTqtFnftzGsBAACcEluWG1BVH0jyPUnOq6rDWVz97YYkd1XV3iSfTfLWafh9Sd6UZCHJl5K8PUm6+1hVvSvJw9O4d3b3Cwsw/EgWV6b7xiQfmr4AAABOmWWDUHe/7QSHLltibCe57gSvc1uS25aoH0ryuuX6AAAAWCtzL5YAAABwuhGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4cwWhqvrJqnq0qj5ZVR+oqm+oqgur6qGqWqiqD1bV2dPYl037C9Px7TOv846p/umqumK+jwQAAHByqw5CVXV+kn+SZGd3vy7JWUmuSfKeJDd292uSPJtk73TK3iTPTvUbp3Gpqoum816bZFeS91XVWavtCwAAYDnz3hq3Jck3VtWWJN+U5Okk35vk7un47UmunrZ3T/uZjl9WVTXV7+zuL3f3Z5IsJLlkzr4AAABOaNVBqLuPJPlXST6XxQD0XJKPJflidz8/DTuc5Pxp+/wkT03nPj+Nf+VsfYlzAAAA1tw8t8adm8WrORcm+WtJvjmLt7adMlW1r6oOVdWho0ePnsq3AgAAzmDz3Br3D5J8pruPdvdfJPnNJG9Mcs50q1ySbEtyZNo+kuSCJJmOvzzJF2brS5zzIt19S3fv7O6dW7dunaN1AABgZPMEoc8lubSqvml61ueyJI8l+UiSt0xj9iS5Z9o+MO1nOv7h7u6pfs20qtyFSXYk+d05+gIAADipLcsPWVp3P1RVdyf5vSTPJ/l4kluS3Jvkzqr6hal263TKrUl+vaoWkhzL4kpx6e5Hq+quLIao55Nc191fWW1fAAAAy1l1EEqS7r4+yfXHlZ/IEqu+dfefJfn+E7zOu5O8e55eAAAAVmre5bMBAABOO4IQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxnriBUVedU1d1V9YdV9amq+u6qekVVHayqx6fv505jq6puqqqFqnqkqi6eeZ090/jHq2rPvB8KAADgZOa9IvTLSX6ru/9Wkr+b5FNJ9id5oLt3JHlg2k+SK5PsmL72Jbk5SarqFUmuT/KGJJckuf6F8AQAAHAqrDoIVdXLk/y9JLcmSXf/eXd/McnuJLdPw25PcvW0vTvJHb3owSTnVNWrk1yR5GB3H+vuZ5McTLJrtX0BAAAsZ54rQhcmOZrk31fVx6vq16rqm5O8qrufnsZ8Psmrpu3zkzw1c/7hqXaiOgAAwCkxTxDakuTiJDd39+uT/N/8/9vgkiTd3Ul6jvd4karaV1WHqurQ0aNH1+plAQCAwcwThA4nOdzdD037d2cxGP3xdMtbpu/PTMePJLlg5vxtU+1E9a/R3bd0987u3rl169Y5WgcAAEa26iDU3Z9P8lRVfcdUuizJY0kOJHlh5bc9Se6Ztg8kuXZaPe7SJM9Nt9Ddn+Tyqjp3WiTh8qkGAABwSmyZ8/wfS/IbVXV2kieSvD2L4equqtqb5LNJ3jqNvS/Jm5IsJPnSNDbdfayq3pXk4WncO7v72Jx9AQAAnNBcQai7fz/JziUOXbbE2E5y3Qle57Ykt83TCwAAwErN+3uEAAAATjuCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMZ+4gVFVnVdXHq+q/TvsXVtVDVbVQVR+sqrOn+sum/YXp+PaZ13jHVP90VV0xb08AAAAnsxZXhH48yadm9t+T5Mbufk2SZ5Psnep7kzw71W+cxqWqLkpyTZLXJtmV5H1VddYa9AUAALCkuYJQVW1LclWSX5v2K8n3Jrl7GnJ7kqun7d3Tfqbjl03jdye5s7u/3N2fSbKQ5JJ5+gIAADiZea8I/ZskP5PkL6f9Vyb5Ync/P+0fTnL+tH1+kqeSZDr+3DT+q/UlzgEAAFhzqw5CVfV9SZ7p7o+tYT/Lvee+qjpUVYeOHj26Xm8LAACcYea5IvTGJG+uqieT3JnFW+J+Ock5VbVlGrMtyZFp+0iSC5JkOv7yJF+YrS9xzot09y3dvbO7d27dunWO1gEAgJGtOgh19zu6e1t3b8/iYgcf7u4fSPKRJG+Zhu1Jcs+0fWDaz3T8w93dU/2aaVW5C5PsSPK7q+0LAABgOVuWH/KS/WySO6vqF5J8PMmtU/3WJL9eVQtJjmUxPKW7H62qu5I8luT5JNd191dOQV/D2L7/3o1u4auevOGqjW4BAAC+xpoEoe7+7SS/PW0/kSVWfevuP0vy/Sc4/91J3r0WvQAAACxnLX6PEAAAwGlFEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhrNloxuAUW3ff+9Gt/AiT95w1Ua3AACwblwRAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4Wza6AWBz2L7/3o1u4auevOGqjW4BADjDuSIEAAAMRxACAACGIwgBAADD8YwQp9Rmeu4EAABe4IoQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwVh2EquqCqvpIVT1WVY9W1Y9P9VdU1cGqenz6fu5Ur6q6qaoWquqRqrp45rX2TOMfr6o9838sAACAE5vnitDzSX66uy9KcmmS66rqoiT7kzzQ3TuSPDDtJ8mVSXZMX/uS3JwsBqck1yd5Q5JLklz/QngCAAA4FVYdhLr76e7+vWn7fyf5VJLzk+xOcvs07PYkV0/bu5Pc0YseTHJOVb06yRVJDnb3se5+NsnBJLtW2xcAAMBy1uQZoaranuT1SR5K8qrufno69Pkkr5q2z0/y1Mxph6faieoAAACnxNxBqKq+Jcl/SvIT3f2ns8e6u5P0vO8x8177qupQVR06evToWr0sAAAwmLmCUFV9fRZD0G90929O5T+ebnnL9P2ZqX4kyQUzp2+baieqf43uvqW7d3b3zq1bt87TOgAAMLB5Vo2rJLcm+VR3/9LMoQNJXlj5bU+Se2bq106rx12a5LnpFrr7k1xeVedOiyRcPtUAAABOiS1znPvGJP8oySeq6ven2s8luSHJXVW1N8lnk7x1OnZfkjclWUjypSRvT5LuPlZV70ry8DTund19bI6+AAAATmrVQai7/3uSOsHhy5YY30muO8Fr3ZbkttX2AgAA8FKsyapxAAAApxNBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4czze4QATont++/d6BZe5MkbrtroFgCANeaKEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGs2WjGwDg9LV9/70b3cJXPXnDVRvdAgCnEVeEAACA4QhCAADAcAQhAABgOJ4RAliG52AA4MzjihAAADAcQQgAABiOIAQAAAzHM0IAp5HN9LwSAJzOXBECAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxn0wShqtpVVZ+uqoWq2r/R/QAAAGeuTRGEquqsJO9NcmWSi5K8raou2tiuAACAM9WWjW5gckmShe5+Ikmq6s4ku5M8tqFdAQCso+37793oFr7qyRuu2ugW4JTaLEHo/CRPzewfTvKGDeoFADiFNtM/9jkxf06sxukUoDdLEFqRqtqXZN+0+3+q6tPr8LbnJfmTdXgfzkzmD/Mwf16Ces9Gd7DpmD/Mw/xhVeo9m27u/I0THdgsQehIkgtm9rdNtRfp7luS3LJeTSVJVR3q7p3r+Z6cOcwf5mH+MA/zh3mYP6zW6TR3NsViCUkeTrKjqi6sqrOTXJPkwAb3BAAAnKE2xRWh7n6+qn40yf1JzkpyW3c/usFtAQAAZ6hNEYSSpLvvS3LfRvexhHW9FY8zjvnDPMwf5mH+MA/zh9U6beZOdfdG9wAAALCuNsszQgAAAOtGEJpU1a6q+nRVLVTV/iWOv6yqPjgdf6iqtq9/l2xWK5g/P1VVj1XVI1X1QFWdcClHxrPc/JkZ9w+rqqvqtFiNh1NvJXOnqt46/fx5tKr+w3r3yOa1gr+7/npVfaSqPj79/fWmjeiTzamqbquqZ6rqkyc4XlV10zS/Hqmqi9e7x+UIQkmq6qwk701yZZKLkrytqi46btjeJM9292uS3JjEb6wgyYrnz8eT7Ozu70xyd5J/ub5dslmtcP6kqr41yY8neWh9O2SzWsncqaodSd6R5I3d/dokP7HujbIprfBnzz9Lcld3vz6LK/q+b327ZJN7f5JdJzl+ZZId09e+JDevQ08viSC06JIkC939RHf/eZI7k+w+bszuJLdP23cnuayqah17ZPNadv5090e6+0vT7oNZ/F1ZkKzs50+SvCuL/wHzZ+vZHJvaSubODyd5b3c/myTd/cw698jmtZL500m+bdp+eZL/tY79scl190eTHDvJkN1J7uhFDyY5p6pevT7drYwgtOj8JE/N7B+eakuO6e7nkzyX5JXr0h2b3Urmz6y9ST50SjvidLLs/JluJ7igu+9dz8bY9Fbys+fbk3x7Vf2Pqnqwqk72v7eMZSXz518k+cGqOpzFlX1/bH1a4wzxUv99tO42zfLZMIKq+sEkO5P8/Y3uhdNDVX1dkl9K8kMb3Aqnpy1ZvC3le7J4JfqjVfV3uvuLG9oVp4u3JXl/d//rqvruJL9eVa/r7r/c6MZgLbgitOhIkgtm9rdNtSXHVNWWLF4i/sK6dMdmt5L5k6r6B0l+Psmbu/vL69Qbm99y8+dbk7wuyW9X1ZNJLk1ywIIJZGU/ew4nOdDdf9Hdn0nyR1kMRrCS+bM3yV1J0t2/k+Qbkpy3Lt1xJljRv482kiC06OEkO6rqwqo6O4sPBB44bsyBJHum7bck+XD7JUwsWnb+VNXrk/y7LIYg9+gz66Tzp7uf6+7zunt7d2/P4jNmb+7uQxvTLpvISv7u+i9ZvBqUqjovi7fKPbGeTbJprWT+fC7JZUlSVX87i0Ho6Lp2yensQJJrp9XjLk3yXHc/vdFNzXJrXBaf+amqH01yf5KzktzW3Y9W1TuTHOruA0luzeIl4YUsPhh2zcZ1zGaywvnzi0m+Jcl/nNbY+Fx3v3nDmmbTWOH8ga+xwrlzf5LLq+qxJF9J8k+7290MrHT+/HSSX62qn8ziwgk/5D+BeUFVfSCL/9Fy3vQc2fVJvj5JuvtXsvhc2ZuSLCT5UpK3b0ynJ1bmMwAAMBq3xgEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhvP/AJWqHk9Jh7WrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### About half of scores are 0\n",
"### What is the distribution of the nonzero ones?\n",
"nonzero_scores = df2.loc[df2['CPD_relevance']>0].sort_values('CPD_relevance', ascending=False)\n",
"\n",
"f1, ax1 = plt.subplots(1, figsize=[14, 6])\n",
"ax1.hist(nonzero_scores['CPD_relevance'], bins=20)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"922.4575973915339"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5000*downscale"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"article_id\n",
"615662 0.485132\n",
"652807 0.407189\n",
"630258 0.353261\n",
"624546 0.320860\n",
"608502 0.299458\n",
"646388 0.281343\n",
"642065 0.266771\n",
"595837 0.252939\n",
"651855 0.247067\n",
"639736 0.231753\n",
"649482 0.214238\n",
"649597 0.196261\n",
"654330 0.178571\n",
"598984 0.160714\n",
"597207 0.142857\n",
"648693 0.125000\n",
"617132 0.107143\n",
"592013 0.095360\n",
"619221 0.086230\n",
"648261 0.071429\n",
"618711 0.062814\n",
"637667 0.053571\n",
"589184 0.049998\n",
"635322 0.037251\n",
"649248 0.035714\n",
"643199 0.035714\n",
"611136 0.032706\n",
"583819 0.025020\n",
"603843 0.018614\n",
"601597 0.017857\n",
"649000 0.017857\n",
"582941 0.017857\n",
"635029 0.017857\n",
"607456 0.017857\n",
"616191 0.017857\n",
"610175 0.013737\n",
"Name: CPD_relevance, dtype: float64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### Divide this sample into groups of 900 rows each, in order to get\n",
"### sizes needed for bins that would be ~5000 each.\n",
"### This ould actually be a bit too big, but you get the general idea\n",
"### Bins would have to get progressively smaller as we go down to stay equal in number\n",
"nonzero_scores['CPD_relevance'].iloc[[i*900 for i in range(1, int(np.ceil(nonzero_scores.shape[0]/900)))]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "projectname",
"language": "python",
"name": "projectname"
},
"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
}
| mit |
satishgoda/learning | python/jupyter/usecases/jupyter_notebook_usecases.ipynb | 1 | 102615 | {
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from IPython.core.display import Image, HTML"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Web Development"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"I have been experimenting with Jupyter Notebook to learn web development."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4+3nVn+P6lQ+bc/d5+vncd8hcv0xW2warzY80KDzp+eYKclUAANhyCFYRERER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W0SvwZCvVzumbuWnDkWVHtRqxOq4LZRDwAAQbhjFQAAAKCEYLWprIVgdSQJVgFgFIJBPgAAtGWs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\n/PZv/7b5g7e+zVz1iTu9fVeOG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OpLH33hravFqn8cff6K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\nkGVeZs9bJP8/mUGiWJjheqQAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image('./teachhtml/htmljquery_notebook.png')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6 Test2",
"language": "python",
"name": "python36test2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
wathen/PhD | MHD/FEniCS/ShiftCurlCurl/CppGradient/Efficient/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | 1 | 3648 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from dolfin import *\n",
"mesh = UnitSquareMesh(int(2),int(2))\n",
"order = 1\n",
"Magnetic = FunctionSpace(mesh, \"N1curl\", order)\n",
"Lagrange = FunctionSpace(mesh, \"CG\", order)\n",
"W = Magnetic*Lagrange\n",
"def boundary(x, on_boundary):\n",
" return on_boundary\n",
"bcW = DirichletBC(W.sub(0),Expression((\"1.0\",\"1.0\")), boundary)\n",
"bcuW = DirichletBC(W.sub(1), Expression((\"1.0\")), boundary)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print bcW.get_boundary_values()\n",
"print bcuW.markers()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{2L: 1.0, 4L: 0.5, 5L: 0.5, 7L: 1.0, 11L: 1.0, 12L: 0.5, 13L: 0.5, 15L: 1.0}\n",
"[ 2 4 5 7 11 12 13 15]\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = bcuW.get_boundary_values()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"aa = a.keys()\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"aa\n",
"L = [1,2,3] \n",
"\" \".join(str(x).strip('L') for x in L)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"'1 2 3'"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"[1, 2, 3]"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"AAA."
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
DoWhatILove/turtle | programming/python/notebooks/matplotlib/barchart_do.ipynb | 2 | 2942 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"n_groups = 5\n",
"\n",
"means_men = (20, 35, 30, 35, 27)\n",
"std_men = (2, 3, 4, 1, 2)\n",
"\n",
"means_women = (25, 32, 34, 20, 25)\n",
"std_women = (3, 5, 2, 3, 3)\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"index = np.arange(n_groups)\n",
"bar_width = 0.35\n",
"\n",
"opacity = 0.4\n",
"error_config = {'ecolor': '0.3'}\n",
"\n",
"rects1 = plt.bar(index, means_men, bar_width,\n",
" alpha=opacity,\n",
" color='b',\n",
" yerr=std_men,\n",
" error_kw=error_config,\n",
" label='Men')\n",
"\n",
"rects2 = plt.bar(index + bar_width, means_women, bar_width,\n",
" alpha=opacity,\n",
" color='r',\n",
" yerr=std_women,\n",
" error_kw=error_config,\n",
" label='Women')\n",
"\n",
"plt.xlabel('Group')\n",
"plt.ylabel('Scores')\n",
"plt.title('Scores by group and gender')\n",
"plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))\n",
"plt.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N = 5\n",
"menMeans = (20, 35, 30, 35, 27)\n",
"womenMeans = (25, 32, 34, 20, 25)\n",
"menStd = (2, 3, 4, 1, 2)\n",
"womenStd = (3, 5, 2, 3, 3)\n",
"ind = np.arange(N) # the x locations for the groups\n",
"width = 0.35 # the width of the bars: can also be len(x) sequence\n",
"\n",
"p1 = plt.bar(ind, menMeans, width, color='r', yerr=menStd)\n",
"p2 = plt.bar(ind, womenMeans, width, color='y',\n",
" bottom=menMeans, yerr=womenStd)\n",
"\n",
"plt.ylabel('Scores')\n",
"plt.title('Scores by group and gender')\n",
"plt.xticks(ind + width/2., ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
"plt.yticks(np.arange(0, 81, 10))\n",
"plt.legend((p1[0], p2[0]), ('Men', 'Women'))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "work"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
folivetti/BIGDATA | Spark/Lab2_Spark_PySpark.ipynb | 1 | 37351 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# **Spark + Python = PySpark**\n",
"\n",
"#### Esse notebook introduz os conceitos básicos do Spark através de sua interface com a linguagem Python. Como aplicação inicial faremos o clássico examplo de contador de palavras . Com esse exemplo é possível entender a lógica de programação funcional para as diversas tarefas de exploração de dados distribuídos.\n",
"\n",
"#### Para isso utilizaremos o livro texto [Trabalhos completos de William Shakespeare](http://www.gutenberg.org/ebooks/100) obtidos do [Projeto Gutenberg](http://www.gutenberg.org/wiki/Main_Page). Veremos que esse mesmo algoritmo pode ser empregado em textos de qualquer tamanho.\n",
"\n",
"#### ** Esse notebook contém: **\n",
"#### *Parte 1:* Criando uma base RDD e RDDs de tuplas\n",
"#### *Parte 2:* Manipulando RDDs de tuplas\n",
"#### *Parte 3:* Encontrando palavras únicas e calculando médias\n",
"#### *Parte 4:* Aplicar contagem de palavras em um arquivo\n",
"#### *Parte 5:* Similaridade entre Objetos\n",
"#### Para os exercícios é aconselhável consultar a documentação da [API do PySpark](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 1: Criando e Manipulando RDDs **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Nessa parte do notebook vamos criar uma base RDD a partir de uma lista com o comando `parallelize`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1a) Criando uma base RDD **\n",
"#### Podemos criar uma base RDD de diversos tipos e fonte do Python com o comando `sc.parallelize(fonte, particoes)`, sendo fonte uma variável contendo os dados (ex.: uma lista) e particoes o número de partições para trabalhar em paralelo."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ListaPalavras = ['gato', 'elefante', 'rato', 'rato', 'gato']\n",
"palavrasRDD = sc.parallelize(ListaPalavras, 4)\n",
"print type(palavrasRDD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1b) Plural **\n",
"\n",
"#### Vamos criar uma função que transforma uma palavra no plural adicionando uma letra 's' ao final da string. Em seguida vamos utilizar a função `map()` para aplicar a transformação em cada palavra do RDD.\n",
"\n",
"#### Em Python (e muitas outras linguagens) a concatenação de strings é custosa. Uma alternativa melhor é criar uma nova string utilizando [`str.format()`](https://docs.python.org/2/library/string.html#format-string-syntax).\n",
"\n",
"#### Nota: a string entre os conjuntos de três aspas representa a documentação da função. Essa documentação é exibida com o comando `help()`. Vamos utilizar a padronização de documentação sugerida para o Python, manteremos essa documentação em inglês."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"def Plural(palavra):\n",
" \"\"\"Adds an 's' to `palavra`.\n",
"\n",
" Args:\n",
" palavra (str): A string.\n",
"\n",
" Returns:\n",
" str: A string with 's' added to it.\n",
" \"\"\"\n",
" return <COMPLETAR>\n",
"\n",
"print Plural('gato')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"help(Plural)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert Plural('rato')=='ratos', 'resultado incorreto!'\n",
"print 'OK'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1c) Aplicando a função ao RDD **\n",
"#### Transforme cada palavra do nosso RDD em plural usando [map()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.map) \n",
"\n",
"#### Em seguida, utilizaremos o comando [collect()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.collect) que retorna a RDD como uma lista do Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"pluralRDD = palavrasRDD.<COMPLETAR>\n",
"print pluralRDD.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert pluralRDD.collect()==['gatos','elefantes','ratos','ratos','gatos'], 'valores incorretos!'\n",
"print 'OK'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** Nota: ** utilize o comando `collect()` apenas quando tiver certeza de que a lista caberá na memória. Para gravar os resultados de volta em arquivo texto ou base de dados utilizaremos outro comando."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1d) Utilizando uma função `lambda` **\n",
"#### Repita a criação de um RDD de plurais, porém utilizando uma função lambda."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"pluralLambdaRDD = palavrasRDD.<COMPLETAR>\n",
"print pluralLambdaRDD.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert pluralLambdaRDD.collect()==['gatos','elefantes','ratos','ratos','gatos'], 'valores incorretos!'\n",
"print 'OK'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1e) Tamanho de cada palavra **\n",
"#### Agora use `map()` e uma função `lambda` para retornar o número de caracteres em cada palavra. Utilize `collect()` para armazenar o resultado em forma de listas na variável destino."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"pluralTamanho = (pluralRDD\n",
" <COMPLETAR>\n",
" )\n",
"print pluralTamanho"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert pluralTamanho==[5,9,5,5,5], 'valores incorretos'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (1f) RDDs de pares e tuplas **\n",
"\n",
"#### Para contar a frequência de cada palavra de maneira distribuída, primeiro devemos atribuir um valor para cada palavra do RDD. Isso irá gerar um base de dados (chave, valor). Desse modo podemos agrupar a base através da chave, calculando a soma dos valores atribuídos. No nosso caso, vamos atribuir o valor `1` para cada palavra.\n",
"\n",
"#### Um RDD contendo a estrutura de tupla chave-valor `(k,v) ` é chamada de RDD de tuplas ou *pair RDD*.\n",
"\n",
"#### Vamos criar nosso RDD de pares usando a transformação `map()` com uma função `lambda()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"palavraPar = palavrasRDD.<COMPLETAR>\n",
"print palavraPar.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert palavraPar.collect() == [('gato',1),('elefante',1),('rato',1),('rato',1),('gato',1)], 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Parte 2: Manipulando RDD de tuplas **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Vamos manipular nossa RDD para contar as palavras do texto."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (2a) Função `groupByKey()` **\n",
"\n",
"#### A função [groupByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.groupByKey) agrupa todos os valores de um RDD através da chave (primeiro elemento da tupla) agregando os valores em uma lista.\n",
"\n",
"#### Essa abordagem tem um ponto fraco pois:\n",
" + #### A operação requer que os dados distribuídos sejam movidos em massa para que permaneçam na partição correta.\n",
" + #### As listas podem se tornar muito grandes. Imagine contar todas as palavras do Wikipedia: termos comuns como \"a\", \"e\" formarão uma lista enorme de valores que pode não caber na memória do processo escravo.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"palavrasGrupo = palavraPar.groupByKey()\n",
"for chave, valor in palavrasGrupo.collect():\n",
" print '{0}: {1}'.format(chave, list(valor))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert sorted(palavrasGrupo.mapValues(lambda x: list(x)).collect()) == [('elefante', [1]), ('gato',[1, 1]), ('rato',[1, 2])],\n",
" 'Valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (2b) Calculando as contagens **\n",
"#### Após o `groupByKey()` nossa RDD contém elementos compostos da palavra, como chave, e um iterador contendo todos os valores correspondentes aquela chave.\n",
"#### Utilizando a transformação `map()` e a função `sum()`, contrua um novo RDD que consiste de tuplas (chave, soma)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"contagemGroup = palavrasGrupo.<COMPLETAR>\n",
"print contagemGroup.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert sorted(contagemGroup.collect())==[('elefante',1), ('gato',2), ('rato',2)], 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (2c) `reduceByKey` **\n",
"#### Um comando mais interessante para a contagem é o [reduceByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.reduceByKey) que cria uma nova RDD de tuplas.\n",
"\n",
"#### Essa transformação aplica a transformação `reduce()` vista na aula anterior para os valores de cada chave. Dessa forma, a função de transformação pode ser aplicada em cada partição local para depois ser enviada para redistribuição de partições, reduzindo o total de dados sendo movidos e não mantendo listas grandes na memória."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"contagem = palavraPar.<COMPLETAR>\n",
"print contagem.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert sorted(contagem.collect())==[('elefante',1), ('gato',2), ('rato',2)], 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (2d) Agrupando os comandos **\n",
"\n",
"#### A forma mais usual de realizar essa tarefa, partindo do nosso RDD palavrasRDD, é encadear os comandos map e reduceByKey em uma linha de comando."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"contagemFinal = (palavrasRDD\n",
" <COMPLETAR>\n",
" <COMPLETAR>\n",
" )\n",
"print contagemFinal.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert sorted(contagemFinal)==[('elefante',1), ('gato',2), ('rato',2)], 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Parte 3: Encontrando as palavras únicas e calculando a média de contagem **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (3a) Palavras Únicas **\n",
"\n",
"#### Calcule a quantidade de palavras únicas do RDD. Utilize comandos de RDD da API do PySpark e alguma das últimas RDDs geradas nos exercícios anteriores."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"palavrasUnicas = <COMPLETAR>\n",
"print palavrasUnicas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert palavrasUnicas==3, 'valor incorreto!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (3b) Calculando a Média de contagem de palavras **\n",
"\n",
"#### Encontre a média de frequência das palavras utilizando o RDD `contagem`.\n",
"\n",
"#### Note que a função do comando `reduce()` é aplicada em cada tupla do RDD. Para realizar a soma das contagens, primeiro é necessário mapear o RDD para um RDD contendo apenas os valores das frequências (sem as chaves)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"# add é equivalente a lambda x,y: x+y\n",
"from operator import add\n",
"total = (contagemFinal\n",
" <COMPLETAR>\n",
" <COMPLETAR>\n",
" )\n",
"media = total / float(palavrasUnicas)\n",
"print total\n",
"print round(media, 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert round(media, 2)==1.67, 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Parte 4: Aplicar nosso algoritmo em um arquivo **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4a) Função `contaPalavras` **\n",
"\n",
"#### Para podermos aplicar nosso algoritmo genéricamente em diversos RDDs, vamos primeiro criar uma função para aplicá-lo em qualquer fonte de dados. Essa função recebe de entrada um RDD contendo uma lista de chaves (palavras) e retorna um RDD de tuplas com as chaves e a contagem delas nessa RDD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"def contaPalavras(chavesRDD):\n",
" \"\"\"Creates a pair RDD with word counts from an RDD of words.\n",
"\n",
" Args:\n",
" chavesRDD (RDD of str): An RDD consisting of words.\n",
"\n",
" Returns:\n",
" RDD of (str, int): An RDD consisting of (word, count) tuples.\n",
" \"\"\"\n",
" return (chavesRDD\n",
" <COMPLETAR>\n",
" <COMPLETAR>\n",
" )\n",
"\n",
"print contaPalavras(palavrasRDD).collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert sorted(contaPalavras(palavrasRDD).collect())==[('elefante',1), ('gato',2), ('rato',2)], 'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4b) Normalizando o texto **\n",
"\n",
"#### Quando trabalhamos com dados reais, geralmente precisamos padronizar os atributos de tal forma que diferenças sutis por conta de erro de medição ou diferença de normatização, sejam desconsideradas. Para o próximo passo vamos padronizar o texto para:\n",
" + #### Padronizar a capitalização das palavras (tudo maiúsculo ou tudo minúsculo).\n",
" + #### Remover pontuação.\n",
" + #### Remover espaços no início e no final da palavra.\n",
" \n",
"#### Crie uma função `removerPontuacao` que converte todo o texto para minúscula, remove qualquer pontuação e espaços em branco no início ou final da palavra. Para isso, utilize a biblioteca [re](https://docs.python.org/2/library/re.html) para remover todo texto que não seja letra, número ou espaço, encadeando com as funções de string para remover espaços em branco e converter para minúscula (veja [Strings](https://docs.python.org/2/library/stdtypes.html?highlight=str.lower#string-methods))."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ola quem esta ai\n",
"sem espaco esublinhado\n"
]
}
],
"source": [
"# EXERCICIO\n",
"import re\n",
"def removerPontuacao(texto):\n",
" \"\"\"Removes punctuation, changes to lower case, and strips leading and trailing spaces.\n",
"\n",
" Note:\n",
" Only spaces, letters, and numbers should be retained. Other characters should should be\n",
" eliminated (e.g. it's becomes its). Leading and trailing spaces should be removed after\n",
" punctuation is removed.\n",
"\n",
" Args:\n",
" texto (str): A string.\n",
"\n",
" Returns:\n",
" str: The cleaned up string.\n",
" \"\"\"\n",
" return re.sub(r'[^A-Za-z0-9 ]', '', texto).strip().lower()\n",
"print removerPontuacao('Ola, quem esta ai??!')\n",
"print removerPontuacao(' Sem espaco e_sublinhado!')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OK\n"
]
}
],
"source": [
"assert removerPontuacao(' O uso de virgulas, embora permitido, nao deve contar. ')=='o uso de virgulas embora permitido nao deve contar', 'string incorreta!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4c) Carregando arquivo texto **\n",
"\n",
"#### Para a próxima parte vamos utilizar o livro [Trabalhos completos de William Shakespeare](http://www.gutenberg.org/ebooks/100) do [Projeto Gutenberg](http://www.gutenberg.org/wiki/Main_Page). \n",
"\n",
"#### Para converter um texto em uma RDD, utilizamos a função `textFile()` que recebe como entrada o nome do arquivo texto que queremos utilizar e o número de partições.\n",
"\n",
"#### O nome do arquivo texto pode se referir a um arquivo local ou uma URI de arquivo distribuído (ex.: hdfs://).\n",
"\n",
"#### Vamos também aplicar a função `removerPontuacao()` para normalizar o texto e verificar as 15 primeiras linhas com o comando `take()`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Arquivo já existe!\n",
"0: the project gutenberg ebook of the complete works of william shakespeare by\n",
"1: william shakespeare\n",
"2: \n",
"3: this ebook is for the use of anyone anywhere at no cost and with\n",
"4: almost no restrictions whatsoever you may copy it give it away or\n",
"5: reuse it under the terms of the project gutenberg license included\n",
"6: with this ebook or online at wwwgutenbergorg\n",
"7: \n",
"8: this is a copyrighted project gutenberg ebook details below\n",
"9: please follow the copyright guidelines in this file\n",
"10: \n",
"11: title the complete works of william shakespeare\n",
"12: \n",
"13: author william shakespeare\n",
"14: \n"
]
}
],
"source": [
"# Apenas execute a célula\n",
"import os.path\n",
"import urllib\n",
"\n",
"url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt' # url do livro\n",
"\n",
"arquivo = os.path.join('Data','Aula02','shakespeare.txt') # local de destino: 'Data/Aula02/shakespeare.txt'\n",
"\n",
"if os.path.isfile(arquivo): # verifica se já fizemos download do arquivo\n",
" print 'Arquivo já existe!'\n",
"else:\n",
" try:\n",
" urllib.urlretrieve(url, arquivo) # salva conteúdo da url em arquivo\n",
" except IOError:\n",
" print 'Impossível fazer o download: {0}'.format(url)\n",
"\n",
"# lê o arquivo com textFile e aplica a função removerPontuacao \n",
"shakesRDD = (sc\n",
" .textFile(arquivo, 8)\n",
" .map(removerPontuacao)\n",
" )\n",
"\n",
"# zipWithIndex gera tuplas (conteudo, indice) onde indice é a posição do conteudo na lista sequencial\n",
"# Ex.: sc.parallelize(['gato','cachorro','boi']).zipWithIndex() ==> [('gato',0), ('cachorro',1), ('boi',2)]\n",
"# sep.join() junta as strings de uma lista através do separador sep. Ex.: ','.join(['a','b','c']) ==> 'a,b,c'\n",
"print '\\n'.join(shakesRDD\n",
" .zipWithIndex()\n",
" .map(lambda (linha, num): '{0}: {1}'.format(num,linha))\n",
" .take(15)\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4d) Extraindo as palavras **\n",
"#### Antes de poder usar nossa função Before we can use the `contaPalavras()`, temos ainda que trabalhar em cima da nossa RDD:\n",
" + #### Precisamos gerar listas de palavras ao invés de listas de sentenças.\n",
" + #### Eliminar linhas vazias.\n",
" \n",
"#### As strings em Python tem o método [split()](https://docs.python.org/2/library/string.html#string.split) que faz a separação de uma string por separador. No nosso caso, queremos separar as strings por espaço. \n",
"\n",
"#### Utilize a função `map()` para gerar um novo RDD como uma lista de palavras."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"shakesPalavrasRDD = shakesRDD.<COMPLETAR>\n",
"total = shakesPalavrasRDD.count()\n",
"print shakesPalavrasRDD.take(5)\n",
"print total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Conforme deve ter percebido, o uso da função `map()` gera uma lista para cada linha, criando um RDD contendo uma lista de listas.\n",
"\n",
"#### Para resolver esse problema, o Spark possui uma função análoga chamada `flatMap()` que aplica a transformação do `map()`, porém *achatando* o retorno em forma de lista para uma lista unidimensional."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'zwaggerd', u'zounds', u'zounds', u'zounds', u'zounds']\n",
"903705\n"
]
}
],
"source": [
"# EXERCICIO\n",
"shakesPalavrasRDD = shakesRDD.flatMap(lambda x: x.split())\n",
"total = shakesPalavrasRDD.count()\n",
"print shakesPalavrasRDD.top(5)\n",
"print total"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert total==927631 or total == 928908, \"valor incorreto de palavras!\"\n",
"print \"OK\"\n",
"assert shakesPalavrasRDD.top(5)==[u'zwaggerd', u'zounds', u'zounds', u'zounds', u'zounds'],'lista incorreta de palavras'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4e) Remover linhas vazias **\n",
"\n",
"#### Para o próximo passo vamos filtrar as linhas vazias com o comando `filter()`. Uma linha vazia é uma string sem nenhum conteúdo."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"shakesLimpoRDD = shakesPalavrasRDD.<COMPLETAR>\n",
"total = shakesLimpoRDD.count()\n",
"print total"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert total==882996, 'valor incorreto!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4f) Contagem de palavras **\n",
"#### Agora que nossa RDD contém uma lista de palavras, podemos aplicar nossa função `contaPalavras()`.\n",
"\n",
"#### Aplique a função em nossa RDD e utilize a função `takeOrdered` para imprimir as 15 palavras mais frequentes.\n",
"\n",
"#### `takeOrdered()` pode receber um segundo parâmetro que instrui o Spark em como ordenar os elementos. Ex.:\n",
"\n",
"#### `takeOrdered(15, key=lambda x: -x)`: ordem decrescente dos valores de x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"top15 = <COMPLETAR>\n",
"print '\\n'.join(map(lambda (w, c): '{0}: {1}'.format(w, c), top15))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert top15 == [(u'the', 27361), (u'and', 26028), (u'i', 20681), (u'to', 19150), (u'of', 17463),\n",
" (u'a', 14593), (u'you', 13615), (u'my', 12481), (u'in', 10956), (u'that', 10890),\n",
" (u'is', 9134), (u'not', 8497), (u'with', 7771), (u'me', 7769), (u'it', 7678)],'valores incorretos!'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Parte 5: Similaridade entre Objetos **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nessa parte do laboratório vamos aprender a calcular a distância entre atributos numéricos, categóricos e textuais."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (5a) Vetores no espaço Euclidiano **\n",
"\n",
"#### Quando nossos objetos são representados no espaço Euclidiano, medimos a similaridade entre eles através da *p-Norma* definida por:\n",
"\n",
"#### $$d(x,y,p) = (\\sum_{i=1}^{n}{|x_i - y_i|^p})^{1/p}$$\n",
"\n",
"#### As normas mais utilizadas são $p=1,2,\\infty$ que se reduzem em distância absoluta, Euclidiana e máxima distância:\n",
"\n",
"#### $$d(x,y,1) = \\sum_{i=1}^{n}{|x_i - y_i|}$$\n",
"\n",
"#### $$d(x,y,2) = (\\sum_{i=1}^{n}{|x_i - y_i|^2})^{1/2}$$\n",
"\n",
"#### $$d(x,y,\\infty) = \\max(|x_1 - y_1|,|x_2 - y_2|, ..., |x_n - y_n|)$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Vamos criar uma função pNorm que recebe como parâmetro p e retorna uma função que calcula a pNorma\n",
"def pNorm(p):\n",
" \"\"\"Generates a function to calculate the p-Norm between two points.\n",
"\n",
" Args:\n",
" p (int): The integer p.\n",
"\n",
" Returns:\n",
" Dist: A function that calculates the p-Norm.\n",
" \"\"\"\n",
"\n",
" def Dist(x,y):\n",
" return np.power(np.power(np.abs(x-y),p).sum(),1/float(p))\n",
" return Dist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Vamos criar uma RDD com valores numéricos\n",
"numPointsRDD = sc.parallelize(enumerate(np.random.random(size=(10,100))))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"# Procure dentre os comandos do PySpark, um que consiga fazer o produto cartesiano da base com ela mesma\n",
"cartPointsRDD = numPointsRDD.<COMPLETAR>\n",
"\n",
"# Aplique um mapa para transformar nossa RDD em uma RDD de tuplas ((id1,id2), (vetor1,vetor2))\n",
"# DICA: primeiro utilize o comando take(1) e imprima o resultado para verificar o formato atual da RDD\n",
"cartPointsParesRDD = cartPointsRDD.<COMPLETAR>\n",
"\n",
"\n",
"# Aplique um mapa para calcular a Distância Euclidiana entre os pares\n",
"Euclid = pNorm(2)\n",
"distRDD = cartPointsParesRDD.<COMPLETAR>\n",
"\n",
"# Encontre a distância máxima, mínima e média, aplicando um mapa que transforma (chave,valor) --> valor\n",
"# e utilizando os comandos internos do pyspark para o cálculo da min, max, mean\n",
"statRDD = distRDD.<COMPLETAR>\n",
"\n",
"minv, maxv, meanv = statRDD.<COMPLETAR>, statRDD.<COMPLETAR>, statRDD.<COMPLETAR>\n",
"print minv, maxv, meanv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert (minv.round(2), maxv.round(2), meanv.round(2))==(0.0, 4.70, 3.65), 'Valores incorretos'\n",
"print \"OK\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (5b) Valores Categóricos **\n",
"\n",
"#### Quando nossos objetos são representados por atributos categóricos, eles não possuem uma similaridade espacial. Para calcularmos a similaridade entre eles podemos primeiro transformar nosso vetor de atrbutos em um vetor binário indicando, para cada possível valor de cada atributo, se ele possui esse atributo ou não.\n",
"\n",
"#### Com o vetor binário podemos utilizar a distância de Hamming definida por:\n",
"\n",
"#### $$ H(x,y) = \\sum_{i=1}^{n}{x_i != y_i} $$\n",
"\n",
"#### Também é possível definir a distância de Jaccard como:\n",
"\n",
"#### $$ J(x,y) = \\frac{\\sum_{i=1}^{n}{x_i == y_i} }{\\sum_{i=1}^{n}{\\max(x_i, y_i}) } $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Vamos criar uma função para calcular a distância de Hamming\n",
"def Hamming(x,y):\n",
" \"\"\"Calculates the Hamming distance between two binary vectors.\n",
"\n",
" Args:\n",
" x, y (np.array): Array of binary integers x and y.\n",
"\n",
" Returns:\n",
" H (int): The Hamming distance between x and y.\n",
" \"\"\"\n",
" return (x!=y).sum()\n",
"\n",
"# Vamos criar uma função para calcular a distância de Jaccard\n",
"def Jaccard(x,y):\n",
" \"\"\"Calculates the Jaccard distance between two binary vectors.\n",
"\n",
" Args:\n",
" x, y (np.array): Array of binary integers x and y.\n",
"\n",
" Returns:\n",
" J (int): The Jaccard distance between x and y.\n",
" \"\"\"\n",
" return (x==y).sum()/float( np.maximum(x,y).sum() )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Vamos criar uma RDD com valores categóricos\n",
"catPointsRDD = sc.parallelize(enumerate([['alto', 'caro', 'azul'],\n",
" ['medio', 'caro', 'verde'],\n",
" ['alto', 'barato', 'azul'],\n",
" ['medio', 'caro', 'vermelho'],\n",
" ['baixo', 'barato', 'verde'],\n",
" ]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"# Crie um RDD de chaves únicas utilizando flatMap\n",
"chavesRDD = (catPointsRDD\n",
" .<COMPLETAR>\n",
" .<COMPLETAR>\n",
" .<COMPLETAR>\n",
" )\n",
"\n",
"chaves = dict((v,k) for k,v in enumerate(chavesRDD.collect()))\n",
"nchaves = len(chaves)\n",
"print chaves, nchaves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert chaves=={'alto': 0, 'medio': 1, 'baixo': 2, 'barato': 3, 'azul': 4, 'verde': 5, 'caro': 6, 'vermelho': 7}, 'valores incorretos!'\n",
"print \"OK\"\n",
"\n",
"assert nchaves==8, 'número de chaves incorreta'\n",
"print \"OK\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def CreateNP(atributos,chaves): \n",
" \"\"\"Binarize the categorical vector using a dictionary of keys.\n",
"\n",
" Args:\n",
" atributos (list): List of attributes of a given object.\n",
" chaves (dict): dictionary with the relation attribute -> index\n",
"\n",
" Returns:\n",
" array (np.array): Binary array of attributes.\n",
" \"\"\"\n",
" \n",
" array = np.zeros(len(chaves))\n",
" for atr in atributos:\n",
" array[ chaves[atr] ] = 1\n",
" return array\n",
"\n",
"# Converte o RDD para o formato binário, utilizando o dict chaves\n",
"binRDD = catPointsRDD.map(lambda rec: (rec[0],CreateNP(rec[1], chaves)))\n",
"binRDD.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# EXERCICIO\n",
"# Procure dentre os comandos do PySpark, um que consiga fazer o produto cartesiano da base com ela mesma\n",
"cartBinRDD = binRDD.<COMPLETAR>\n",
"\n",
"# Aplique um mapa para transformar nossa RDD em uma RDD de tuplas ((id1,id2), (vetor1,vetor2))\n",
"# DICA: primeiro utilize o comando take(1) e imprima o resultado para verificar o formato atual da RDD\n",
"cartBinParesRDD = cartBinRDD.<COMPLETAR>\n",
"\n",
"\n",
"# Aplique um mapa para calcular a Distância de Hamming e Jaccard entre os pares\n",
"hamRDD = cartBinParesRDD.<COMPLETAR>\n",
"jacRDD = cartBinParesRDD.<COMPLETAR>\n",
"\n",
"# Encontre a distância máxima, mínima e média, aplicando um mapa que transforma (chave,valor) --> valor\n",
"# e utilizando os comandos internos do pyspark para o cálculo da min, max, mean\n",
"statHRDD = hamRDD.<COMPLETAR>\n",
"statJRDD = jacRDD.<COMPLETAR>\n",
"\n",
"Hmin, Hmax, Hmean = statHRDD.<COMPLETAR>, statHRDD.<COMPLETAR>, statHRDD.<COMPLETAR>\n",
"Jmin, Jmax, Jmean = statJRDD.<COMPLETAR>, statJRDD.<COMPLETAR>, statJRDD.<COMPLETAR>\n",
"\n",
"print \"\\t\\tMin\\tMax\\tMean\"\n",
"print \"Hamming:\\t{:.2f}\\t{:.2f}\\t{:.2f}\".format(Hmin, Hmax, Hmean )\n",
"print \"Jaccard:\\t{:.2f}\\t{:.2f}\\t{:.2f}\".format( Jmin, Jmax, Jmean )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assert (Hmin.round(2), Hmax.round(2), Hmean.round(2)) == (0.00,6.00,3.52), 'valores incorretos'\n",
"print \"OK\"\n",
"assert (Jmin.round(2), Jmax.round(2), Jmean.round(2)) == (0.33,2.67,1.14), 'valores incorretos'\n",
"print \"OK\""
]
}
],
"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 |
gregorjerse/rt2 | 2015_2016/lab13/Extending values on vertices-template.ipynb | 1 | 6569 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extending values on vertices to a discrete gradient vector field\n",
"During extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires quite a lot of code: first we find a star, then link and finally lower link for the given simplex."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from itertools import combinations, chain\n",
"\n",
"def simplex_closure(a): \n",
" \"\"\"Returns the generator that iterating over all subsimplices (of all dimensions) in the closure\n",
" of the simplex a. The simplex a is also included.\n",
" \"\"\"\n",
" return chain.from_iterable([combinations(a, l) for l in range(1, len(a) + 1)])\n",
" \n",
"def closure(K):\n",
" \"\"\"Add all missing subsimplices to K in order to make it a simplicial complex.\"\"\"\n",
" return list({s for a in K for s in simplex_closure(a)})\n",
"\n",
"def contained(a, b):\n",
" \"\"\"Returns True is a is a subsimplex of b, False otherwise.\"\"\"\n",
" return all((v in b for v in a))\n",
"\n",
"def star(s, cx):\n",
" \"\"\"Return the set of all simplices in the cx that contais simplex s.\n",
" \"\"\"\n",
" return {p for p in cx if contained(s, p)}\n",
"\n",
"def intersection(s1, s2):\n",
" \"\"\"Return the intersection of s1 and s2.\"\"\"\n",
" return list(set(s1).intersection(s2))\n",
"\n",
"def link(s, cx):\n",
" \"\"\"Returns link of the simplex s in the complex cx.\n",
" \"\"\"\n",
" # Link consists of all simplices from the closed star that have \n",
" # empty intersection with s.\n",
" return [c for c in closure(star(s, cx)) if not intersection(s, c)]\n",
"\n",
"def simplex_value(s, f, aggregate):\n",
" \"\"\"Return the value of f on vertices of s\n",
" aggregated by the aggregate function.\n",
" \"\"\"\n",
" return aggregate([f[v] for v in s])\n",
"\n",
"def lower_link(s, cx, f):\n",
" \"\"\"Return the lower link of the simplex s in the complex cx.\n",
" The dictionary f is the mapping from vertices (integers)\n",
" to the values on vertices.\n",
" \"\"\"\n",
" sval = simplex_value(s, f, min)\n",
" return [s for s in link(s, cx) \n",
" if simplex_value(s, f, max) < sval]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us test the above function on the simple example: full triangle with values `0`, `1` and `2` on the vertices labeled with `1`, `2` and `3`."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1,): []\n",
"(2,): [(1,)]\n",
"(3,): [(1, 2), (1,), (2,)]\n"
]
}
],
"source": [
"K = closure([(1, 2, 3)])\n",
"f = {1: 0, 2: 1, 3: 2}\n",
"for v in (1, 2, 3):\n",
" print\"{0}: {1}\".format((v,), lower_link((v,), K, f))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us implement an extension algorithm. We are leaving out the cancelling step for clarity."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def join(a, b):\n",
" \"\"\"Return the join of 2 simplices a and b.\"\"\"\n",
" return tuple(sorted(set(a).union(b)))\n",
"\n",
"def extend(K, f):\n",
" \"\"\"Extend the field to the complex K.\n",
" Function on vertices is given in f.\n",
" Returns the pair V, C, where V is the dictionary containing discrete gradient vector field\n",
" and C is the list of all critical cells.\n",
" \"\"\"\n",
" V = dict()\n",
" C = []\n",
" for v in (s for s in K if len(s)==1):\n",
" # Add your own code\n",
" pass\n",
" return V, C"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us test the algorithm on the example from the previous step (full triangle)."
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"({(2,): (1, 2), (2, 3): (1, 2, 3), (3,): (1, 3)}, [(1,)])"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"K = closure([(1, 2, 3)])\n",
"f = {1: 0, 2: 1, 3: 2}\n",
"extend(K, f)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"({(2,): (1, 2), (2, 3): (1, 2, 3), (3,): (1, 3)},\n",
" [(1,), (2, 4), (2, 3, 4), (3, 4), (4,)])"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"K = closure([(1, 2, 3), (2, 3, 4)])\n",
"f = {1: 0, 2: 1, 3: 2, 4: 0}\n",
"extend(K, f)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"({(2,): (1, 2),\n",
" (2, 3): (1, 2, 3),\n",
" (3,): (1, 3),\n",
" (3, 4): (2, 3, 4),\n",
" (4,): (2, 4)},\n",
" [(1,)])"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"K = closure([(1, 2, 3), (2, 3, 4)])\n",
"f = {1: 0, 2: 1, 3: 2, 4: 3}\n",
"extend(K, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
annajur/cbp | GALI.ipynb | 1 | 1231011 | null | mit |
vicolab/ml-pyxis | examples/torch-dataset.ipynb | 1 | 8070 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyTorch dataset interface"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example we will look at how a `pyxis` LMDB can be used with PyTorch's `torch.utils.data.Dataset` and `torch.utils.data.DataLoader`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"\n",
"import numpy as np\n",
"\n",
"import pyxis as px"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual, we will begin by creating a small dataset to test with. It will consist of `10` samples, where each input observation has four features and targets are scalar values."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input: [1 2 3 4] -> Target: 0\n",
"Input: [2 4 6 8] -> Target: 1\n",
"Input: [ 3 6 9 12] -> Target: 2\n",
"Input: [ 4 8 12 16] -> Target: 3\n",
"Input: [ 5 10 15 20] -> Target: 4\n",
"Input: [ 6 12 18 24] -> Target: 5\n",
"Input: [ 7 14 21 28] -> Target: 6\n",
"Input: [ 8 16 24 32] -> Target: 7\n",
"Input: [ 9 18 27 36] -> Target: 8\n",
"Input: [10 20 30 40] -> Target: 9\n"
]
}
],
"source": [
"nb_samples = 10\n",
"\n",
"X = np.outer(np.arange(1, nb_samples + 1, dtype=np.uint8), np.arange(1, 4 + 1, dtype=np.uint8))\n",
"y = np.arange(nb_samples, dtype=np.uint8)\n",
"\n",
"for i in range(nb_samples):\n",
" print('Input: {} -> Target: {}'.format(X[i], y[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data is written using a `with` statement."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"with px.Writer(dirpath='data', map_size_limit=10, ram_gb_limit=1) as db:\n",
" db.put_samples('input', X, 'target', y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be sure the data was stored correctly, we will read the data back - again using a `with` statement."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pyxis.Reader\n",
"Location:\t\t'data'\n",
"Number of samples:\t10\n",
"Data keys (0th sample):\n",
"\t'input' <- dtype: uint8, shape: (4,)\n",
"\t'target' <- dtype: uint8, shape: ()\n"
]
}
],
"source": [
"with px.Reader('data') as db:\n",
" print(db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with PyTorch"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" import torch\n",
" import torch.utils.data\n",
"except ImportError:\n",
" raise ImportError('Could not import the PyTorch library `torch` or '\n",
" '`torch.utils.data`. Please refer to '\n",
" 'https://pytorch.org/ for installation instructions.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In `pyxis.torch` we have implemented a wrapper around `torch.utils.data.Dataset` called `pyxis.torch.TorchDataset`. This object is not imported into the `pyxis` name space because it relies on PyTorch being installed. As such, we first need to import `pyxis.torch`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pyxis.torch as pxt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pyxis.torch.TorchDataset` has a single constructor argument: `dirpath`, i.e. the location of the `pyxis` LMDB."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"dataset = pxt.TorchDataset('data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `pyxis.torch.TorchDataset` object has only three methods: `__len__`, `__getitem__`, and `__repr__`, each of which you can see an example of below:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': tensor([ 1, 2, 3, 4], dtype=torch.uint8),\n",
" 'target': tensor(0, dtype=torch.uint8)}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyxis.Reader\n",
"Location:\t\t'data'\n",
"Number of samples:\t10\n",
"Data keys (0th sample):\n",
"\t'input' <- dtype: uint8, shape: (4,)\n",
"\t'target' <- dtype: uint8, shape: ()"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pyxis.torch.TorchDataset` can be directly combined with `torch.utils.data.DataLoader` to create an iterator type object:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch: 0\n",
"\t tensor([[ 1, 2, 3, 4],\n",
" [ 2, 4, 6, 8]], dtype=torch.uint8)\n",
"\t tensor([ 0, 1], dtype=torch.uint8)\n",
"Batch: 1\n",
"\t tensor([[ 3, 6, 9, 12],\n",
" [ 4, 8, 12, 16]], dtype=torch.uint8)\n",
"\t tensor([ 2, 3], dtype=torch.uint8)\n",
"Batch: 2\n",
"\t tensor([[ 5, 10, 15, 20],\n",
" [ 6, 12, 18, 24]], dtype=torch.uint8)\n",
"\t tensor([ 4, 5], dtype=torch.uint8)\n",
"Batch: 3\n",
"\t tensor([[ 7, 14, 21, 28],\n",
" [ 8, 16, 24, 32]], dtype=torch.uint8)\n",
"\t tensor([ 6, 7], dtype=torch.uint8)\n",
"Batch: 4\n",
"\t tensor([[ 9, 18, 27, 36],\n",
" [ 10, 20, 30, 40]], dtype=torch.uint8)\n",
"\t tensor([ 8, 9], dtype=torch.uint8)\n"
]
}
],
"source": [
"use_cuda = True and torch.cuda.is_available()\n",
"kwargs = {\"num_workers\": 4, \"pin_memory\": True} if use_cuda else {}\n",
"\n",
"loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=False, **kwargs)\n",
"\n",
"for i, d in enumerate(loader):\n",
" print('Batch:', i)\n",
" print('\\t', d['input'])\n",
" print('\\t', d['target'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As with the built-in iterators in `pyxis.iterators`, we recommend you inherit from `pyxis.torch.TorchDataset` and alter `__getitem__` to include your own data transformations."
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
cliburn/sta-663-2017 | notebook/09_Machine_Learning.ipynb | 1 | 1360513 | null | mit |
erinspace/share_tutorials | SHARE_Curation_Associates_Overview.ipynb | 1 | 21508 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Jupyter Notebooks and the SHARE API\n",
"----\n",
"\n",
"Learn About\n",
"- Jupyter Notebooks and Python\n",
"- Making API Calls\n",
"- Using the SHARE Search API and related tools"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## How YOU Can Use Jupyter Notebooks\n",
"\n",
"- Learn Python and experiment with new code\n",
"- Send your code to others for them to use\n",
"- Nicely document your code using a combination of text and code blocks"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Many great resources on the web\n",
"\n",
"### Jupyter/iPython Documentation\n",
"http://jupyter.readthedocs.io/en/latest\n",
"\n",
"### Collections of Interesting Notebooks\n",
"https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation\n",
"\n",
"Get started by installing python on your system!\n",
"\n",
"## https://osf.io/zk9xa/wiki"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Using Jupyter for Making API Calls\n",
"\n",
"- You can use Jupyter to run any code in python (or 40+ other supported languages!)\n",
"- This workshop will focus on making calls to APIs on the web, and soon, making calls to the SHARE Search API"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## API\n",
"\n",
"- Application Programming Interface\n",
"- Can refer to any way to for a computer to interact with a source of data\n",
"\n",
"- APIs can oftentimes be accessed over the web"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import json\n",
"import requests\n",
"\n",
"iss_url = 'http://api.open-notify.org/iss-now.json'\n",
"\n",
"data = requests.get(iss_url).json()\n",
"print(json.dumps(data, indent=4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Lattitude and Longitude of C'Ville\n",
"LAT = 38.0293\n",
"LON = 78.4767\n",
"\n",
"iss_url = 'http://api.open-notify.org/iss-pass.json?lat={}&lon={}'.format(LAT, LON)\n",
"\n",
"print(iss_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"data = requests.get(iss_url).json()\n",
"print(json.dumps(data, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Parsing the Data\n",
"\n",
"We got some datetimes back from the API -- but what do these mean?! \n",
"\n",
"- We can use python to find out!\n",
"- Lets use a new library, arrow, to parse that.\n",
" + http://crsmithdev.com/arrow/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import arrow"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## open your terminal\n",
"## ```pip install arrow```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"from arrow.arrow import Arrow\n",
"\n",
"\n",
"for item in data['response']:\n",
" datetime = Arrow.fromtimestamp(item['risetime'])\n",
" print(\n",
" 'The ISS will be visable over Charlottesville on {} at {} for {} seconds.'.format(\n",
" datetime.date(),\n",
" datetime.time(),\n",
" item['duration']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"pokeapi = 'http://pokeapi.co/api/v2/generation/1/'\n",
"\n",
"pokedata = requests.get(pokeapi).json()\n",
"\n",
"# Take that data, print out a nicely formatted version of the first 5 results\n",
"print(json.dumps(pokedata['pokemon_species'][:5], indent=4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Let's get more info about the first pokemon on the list\n",
"# By following the chain of linked data\n",
"\n",
"# Narrow down the url we'd like to get\n",
"bulbasaur_url = pokedata['pokemon_species'][0]['url']\n",
"\n",
"# request data from that URL\n",
"bulbasaur_data = requests.get(bulbasaur_url).json()\n",
"\n",
"# Let's remove the 'flavor text' because that's really long\n",
"del bulbasaur_data['flavor_text_entries']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"bulbasaur_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Some Great APIs YOU can use!\n",
"\n",
"- [Twitter](https://dev.twitter.com/overview/documentation)\n",
"- [Google Maps](https://developers.google.com/maps/web/)\n",
"- [Twillio](https://www.twilio.com/api)\n",
"- [Yelp](https://www.yelp.com/developers/documentation/v2/overview)\n",
"- [Spotify](https://developer.spotify.com/web-api/console/)\n",
"- [Genius](https://docs.genius.com/#/getting-started-h1)\n",
"\n",
"...and so many more!\n",
"\n",
"Many require some kind of authentication, so aren't as simple as the ISS, or PokeAPI."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Access an OAI-PMH Feed!\n",
"\n",
"Many institutions have an OAI-PMH based API.\n",
"\n",
"This is great because they all have a unified way of interacting with the data in the repositories, just with different host urls.\n",
"\n",
"You can create common code that will interact with most OAI-PMH feeds with only changing the base access URL."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"from furl import furl\n",
"\n",
"\n",
"vt_url = furl('http://vtechworks.lib.vt.edu/oai/request')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"vt_url.args['verb'] = 'ListRecords'\n",
"vt_url.args['metadataPrefix'] = 'oai_dc'\n",
"\n",
"vt_url.url"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"data = requests.get(vt_url.url)\n",
"\n",
"data.content"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Let's parse this!\n",
"\n",
"### ```conda install lxml```\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"from lxml import etree\n",
"\n",
"etree_element = etree.XML(data.content)\n",
"\n",
"etree_element"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"etree_element.getchildren()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# A little namespace parsing and cleanup\n",
"namespaces = etree_element.nsmap\n",
"namespaces['ns0'] = etree_element.nsmap[None]\n",
"del namespaces[None]\n",
"\n",
"records = etree_element.xpath('//ns0:record', namespaces=namespaces)\n",
"\n",
"records[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# What's inside one of these records?\n",
"one_record = records[0]\n",
"one_record.getchildren()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# We want to check out the \"metadata\" element, which is the second in the list\n",
"# Let's make sure to get those namespaces too\n",
"# Here's a cool trick to join 2 dictionaries in python 3!\n",
"namespaces = {**namespaces, **one_record[1][0].nsmap}\n",
"del namespaces[None]\n",
"\n",
"# Now we have namespaces we can use!\n",
"namespaces"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Use those namespaces to get titles\n",
"titles = records[0].xpath('//dc:title/node()', namespaces=namespaces)\n",
"titles[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# SHARE Search API\n",
"\n",
"Also a fantastic resource!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# One Way to Access Data\n",
"\n",
"Instead of writing custom code to parse both data coming from JSON and XML APIs"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The SHARE Search Schema\n",
"\n",
"The SHARE search API is built on a tool called elasticsearch. It lets you search a subset of SHARE's normalized metadata in a simple format.\n",
"\n",
"Here are the fields available in SHARE's elasticsearch endpoint:\n",
"\n",
" - 'title'\n",
" - 'language'\n",
" - 'subject'\n",
" - 'description'\n",
" - 'date'\n",
" - 'date_created'\n",
" - 'date_modified\n",
" - 'date_updated'\n",
" - 'date_published'\n",
" - 'tags'\n",
" - 'links'\n",
" - 'awards'\n",
" - 'venues'\n",
" - 'sources'\n",
" - 'contributors'\n",
"\n",
"You can see a formatted version of the base results from the API by visiting the [SHARE Search API URL](https://staging-share.osf.io/api/search/abstractcreativework/_search)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"SHARE_SEARCH_API = 'https://staging-share.osf.io/api/search/abstractcreativework/_search'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from furl import furl\n",
"\n",
"search_url = furl(SHARE_SEARCH_API)\n",
"search_url.args['size'] = 3\n",
"recent_results = requests.get(search_url.url).json()\n",
"\n",
"recent_results = recent_results['hits']['hits']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"recent_results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"print('The request URL is {}'.format(search_url.url))\n",
"print('----------')\n",
"for result in recent_results:\n",
" print(\n",
" '{} -- from {}'.format(\n",
" result['_source']['title'],\n",
" result['_source']['sources']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Sending a Query to the SHARE Search API\n",
"\n",
"First, we'll define a function to do the hard work for us.\n",
"\n",
"It will take 2 parameters, a URL, and a query to send to the search API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import json\n",
"\n",
"def query_share(url, query):\n",
" # A helper function that will use the requests library,\n",
" # pass along the correct headers, and make the query we want\n",
"\n",
" headers = {'Content-Type': 'application/json'}\n",
" data = json.dumps(query)\n",
" return requests.post(url, headers=headers, data=data).json()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"search_url.args = None # reset the args so that we remove our old query arguments.\n",
"search_url.url # Show the URL that we'll be requesting to make sure the args were cleared"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"tags_query = {\n",
" \"query\": {\n",
" \"exists\": {\n",
" \"field\": \"tags\"\n",
" }\n",
" }\n",
"}\n",
"\n",
"\n",
"missing_tags_query = {\n",
" \"query\": {\n",
" \"bool\": {\n",
" \"must_not\": {\n",
" \"exists\": {\n",
" \"field\": \"tags\"\n",
" }\n",
" }\n",
" } \n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"with_tags = query_share(search_url.url, tags_query)\n",
"missing_tags = query_share(search_url.url, missing_tags_query)\n",
"\n",
"total_results = requests.get(search_url.url).json()['hits']['total']\n",
"\n",
"with_tags_percent = (float(with_tags['hits']['total'])/total_results)*100\n",
"missing_tags_percent = (float(missing_tags['hits']['total'])/total_results)*100\n",
"\n",
"\n",
"print(\n",
" '{} results out of {}, or {}%, have tags.'.format(\n",
" with_tags['hits']['total'],\n",
" total_results,\n",
" format(with_tags_percent, '.2f')\n",
" )\n",
")\n",
"\n",
"print(\n",
" '{} results out of {}, or {}%, do NOT have tags.'.format(\n",
" missing_tags['hits']['total'],\n",
" total_results,\n",
" format(missing_tags_percent, '.2f')\n",
" )\n",
")\n",
"\n",
"print('------------')\n",
"print('As a little sanity check....')\n",
"print('{} + {} = {}%'.format(with_tags_percent, missing_tags_percent, format(with_tags_percent + missing_tags_percent, '.2f')))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Other SHARE APIs\n",
"\n",
"SHARE has a host of other APIs that provide direct access to the data stored in SHARE.\n",
"\n",
"You can read more about the SHARE Data Models here: http://share-research.readthedocs.io/en/latest/share_models.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"SHARE_API = 'https://staging-share.osf.io/api/'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"share_endpoints = requests.get(SHARE_API).json()\n",
"\n",
"share_endpoints"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Visit the API In Your Browser \n",
"\n",
"You can visit https://staging-share.osf.io/api/ and see the data formatted in \"pretty printed\" JSON"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## SHARE Providers API\n",
"\n",
"Access the information about the providers that SHARE harvests from"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"SHARE_PROVIDERS = 'https://staging-share.osf.io/api/providers/'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"data = requests.get(SHARE_PROVIDERS).json()\n",
" \n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## We can print that out a little nicer\n",
"\n",
"Using a loop and using the lookups that'd we'd like!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"print('Here are the first 10 Providers:')\n",
"for source in data['results']:\n",
" print(\n",
" '{}\\n{}\\n{}\\n'.format(\n",
" source['long_title'],\n",
" source['home_page'],\n",
" source['provider_name']\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
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"display_name": "Python 3",
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
xiph/rav1e | doc/regress_log-bitrate_wrt_log-quantizer.ipynb | 1 | 35672 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rate-control Empirical Analysis"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"<script>\n",
" function code_toggle() {\n",
" if (code_shown){\n",
" $('div.input').hide('500');\n",
" $('#toggleButton').val('Show Code')\n",
" } else {\n",
" $('div.input').show('500');\n",
" $('#toggleButton').val('Hide Code')\n",
" }\n",
" code_shown = !code_shown\n",
" }\n",
"\n",
" $( document ).ready(function(){\n",
" code_shown=false;\n",
" $('div.input').hide()\n",
" });\n",
"</script>\n",
"<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Show Code\"></form>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple linear regression\n",
"\n",
"We performed a simple linear regression of the bitrate with respect to the quantizer,\n",
"operating on the logarithm of both.\n",
"The data set used was all of the video clips on https://media.xiph.org/video/derf/\n",
"as well as subset3 (for extra I-frame data).\n",
"To enable processing an arbitrarily large data set, an online regression algorithm was implemented.\n",
"In practice, [440MB of text formatted data](https://ba.rr-dav.id.au/data/rav1e/rc-data.tar.xz) were sufficient.\n",
"\n",
"The raw final state of the online regression for each segment follows."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" 419495824559273192),\n",
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" 5.3433937458969078745e+39,\n",
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" 0,\n",
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" 122946,\n",
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" 2008965,\n",
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" 4.968108793442648871e+17,\n",
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" 118980,\n",
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" 9: (9.975517053850623995e+17,\n",
" 1.1090356952206949738e+18,\n",
" 6.3766381711546202313e+38,\n",
" 4.9175748637866167454e+38,\n",
" 344157,\n",
" 934416127407980544,\n",
" 1062614886965399552),\n",
" 10: (8.988889746543214081e+17,\n",
" 1.1136802629381279622e+18,\n",
" 8.4472033903792019157e+39,\n",
" 7.9521884857715649864e+39,\n",
" 900915,\n",
" 740431215345503000,\n",
" 1063491898722519832),\n",
" 11: (8.6822745952314770606e+17,\n",
" 1.1882069866621482475e+18,\n",
" 2.587423673737934511e+40,\n",
" 2.6973534766185294057e+40,\n",
" 2199288,\n",
" 687266562658809392,\n",
" 1063599953263306288)}\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('svg')\n",
"from matplotlib import pyplot as plt\n",
"plt.rcParams['svg.fonttype'] = 'none'\n",
"\n",
"from glob import glob\n",
"import numpy as np\n",
"from pprint import pprint\n",
"import tarfile\n",
"from tqdm import tqdm_notebook\n",
"\n",
"# Klotz, Jerome H. \"UPDATING SIMPLE LINEAR REGRESSION.\"\n",
"# Statistica Sinica 5, no. 1 (1995): 399-403.\n",
"# http://www.jstor.org/stable/24305577\n",
"def online_simple_regression(accumulator, x, y):\n",
" Ax_, Ay_, Sxy, Sxx, n_, minx, maxx = accumulator or (0, 0, 0, 0, 0, None, None)\n",
"\n",
" first = n_ == 0\n",
" n = n_ + x.size\n",
" rt_n, rt_n_ = np.sqrt((n, n_), dtype=np.float128)\n",
"\n",
" Ax = (Ax_*n_ + x.sum(dtype=np.float128))/n\n",
" Ay = (Ay_*n_ + y.sum(dtype=np.float128))/n\n",
" \n",
" minx = x.min() if first else min(minx, x.min())\n",
" maxx = x.max() if first else max(maxx, x.max())\n",
" \n",
" X = Ax if first else (Ax_*rt_n_ + Ax*rt_n)/(rt_n_ + rt_n)\n",
" Y = Ay if first else (Ay_*rt_n_ + Ay*rt_n)/(rt_n_ + rt_n)\n",
"\n",
" Sxx += np.sum((x - X)**2)\n",
" Sxy += np.sum((x - X)*(y - Y))\n",
"\n",
" return Ax, Ay, Sxy, Sxx, n, minx, maxx\n",
"\n",
"def conv_px(s):\n",
" w, h = s.split(b'x')\n",
" return int(w)*int(h)\n",
"\n",
"conv_fti = [b'I', b'P', b'B0', b'B1'].index\n",
"\n",
"def collect(filename, queues):\n",
" px, log_target_q, byte_size, frame_type = np.loadtxt(\n",
" filename, dtype=np.int64, delimiter=',',\n",
" converters={1: conv_px, 4: conv_fti},\n",
" skiprows=1, usecols=range(1, 5), unpack=True)\n",
"\n",
" blog64q57_ibpp = np.round((\n",
" np.log2(px, dtype=np.float128) - np.log2(byte_size*8, dtype=np.float128)\n",
" )*2**57).astype(np.int64)\n",
" \n",
" # These are the fixed point found by repeating this whole process\n",
" boundaries = [\n",
" [0, 381625*2**40, 655352*2**40, 967797*2**40],\n",
" [0, 356802*2**40, 848173*2**40, 967797*2**40],\n",
" [0, 288436*2**40, 671307*2**40, 967797*2**40],\n",
" [0, 264708*2**40, 622760*2**40, 967797*2**40]\n",
" ]\n",
"\n",
" for fti in np.unique(frame_type):\n",
" buckets = list(zip(boundaries[fti][:-1], boundaries[fti][1:]))\n",
" for bi, bucket in enumerate(buckets):\n",
" low, high = bucket\n",
" idx = (frame_type==fti) & (log_target_q >= low) & (log_target_q < high)\n",
" if np.sum(idx, dtype=int) == 0: continue\n",
" b = (bi << 2) | fti\n",
" x, y = log_target_q[idx], blog64q57_ibpp[idx]\n",
" queue = queues.get(b, ([], []))\n",
" queue[0].append(x)\n",
" queue[1].append(y)\n",
" queues[b] = queue\n",
"\n",
"def aggregate(queues, partials):\n",
" for b, queue in queues.items():\n",
" x, y = np.concatenate(queue[0]), np.concatenate(queue[1])\n",
" partials[b] = online_simple_regression(partials.get(b, None), x, y)\n",
" queues.clear()\n",
"\n",
"partials = dict()\n",
"# https://ba.rr-dav.id.au/data/rav1e/rc-data.tar.xz\n",
"with tarfile.open('rc-data.tar.xz', 'r:xz') as tf:\n",
" queues, last_name = dict(), None\n",
" for ti in tqdm_notebook(tf, total=1077*255, leave=False):\n",
" name = ti.name.split('/')[0]\n",
" if last_name and name != last_name:\n",
" aggregate(queues, partials)\n",
" last_name = name\n",
" collect(tf.extractfile(ti), queues)\n",
" aggregate(queues, partials)\n",
"pprint(partials)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fixed-point approximation\n",
"\n",
"The regression results are converted to a fixed-point representation,\n",
"with the exponent in Q6 and the scale in Q3."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" I: exp=48 scale=36 bucket=0\n",
" I: exp=61 scale=55 bucket=1\n",
" I: exp=77 scale=129 bucket=2\n",
" P: exp=69 scale=32 bucket=0\n",
"B0: exp=84 scale=30 bucket=0\n",
"B1: exp=87 scale=27 bucket=0\n",
"B1: exp=139 scale=84 bucket=1\n",
"B0: exp=120 scale=68 bucket=1\n",
" P: exp=104 scale=84 bucket=1\n",
"B1: exp=61 scale=1 bucket=2\n",
"B0: exp=68 scale=4 bucket=2\n",
" P: exp=83 scale=19 bucket=2\n"
]
},
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"<Figure size 504x432 with 1 Axes>"
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"plt.figure(figsize=(7, 6))\n",
"plt.axis('equal')\n",
"plt.xticks([0, 10])\n",
"plt.yticks([0, 10])\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major')\n",
"plt.grid(b=True, which='minor', alpha=0.2)\n",
"\n",
"segments = dict()\n",
"for b, accumulator in partials.items():\n",
" Ax, Ay, Sxy, Sxx, n, minx, maxx = accumulator\n",
"\n",
"\n",
" fti = b & 3\n",
" beta = Sxy/Sxx\n",
" alpha = Ay - beta*Ax\n",
" exp = int(np.round(beta*2**6))\n",
" beta_ = exp/2**6\n",
" alpha_ = Ay - beta_*Ax\n",
" scale = int(np.round(np.exp2(3 - alpha_/2**57)))\n",
" label = ['I', 'P', 'B0', 'B1'][fti]\n",
" print('%2s: exp=%d scale=%d bucket=%d' % (label, exp, scale, b>>2))\n",
"\n",
" xs, ys = segments.get(label, ([], []))\n",
" xs = [minx/2**57, maxx/2**57]\n",
" ys = [xs[0]*beta_ + alpha_/2**57, xs[1]*beta_ + alpha_/2**57]\n",
" xs_, ys_ = segments.get(label, ([], []))\n",
" xs_.extend(xs)\n",
" ys_.extend(ys)\n",
" segments[label] = (xs_, ys_)\n",
"\n",
"best = dict()\n",
"for label, xy in segments.items():\n",
" plt.plot(xy[0], xy[1], label=label)\n",
" \n",
"plt.legend();"
]
},
{
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"The endpoints of each linear regression, rounding only the exponent, are detailed in the following output.\n",
"We use a cubic interpolation of these points to adjust the segment boundaries."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'B0': ([0.39294372842822706,\n",
" 2.2002986504858395,\n",
" 2.250924723555803,\n",
" 5.114042917135398,\n",
" 5.137773646423531,\n",
" 7.379457452900416],\n",
" [-1.4148623981702452337,\n",
" 0.95729093703037120824,\n",
" 1.1300545385380504622,\n",
" 6.4984011514997921106,\n",
" 6.5594675085524934565,\n",
" 8.941256552934184157]),\n",
" 'B1': ([0.7858874568564541,\n",
" 1.955812458298777,\n",
" 2.0338149703000394,\n",
" 4.745877754380938,\n",
" 4.768869761992487,\n",
" 7.380207231894769],\n",
" [-0.7078397903627942796,\n",
" 0.88252700847286351155,\n",
" 1.032874654152443289,\n",
" 6.9231360133281455494,\n",
" 7.0480332114869880963,\n",
" 9.536964237487599543]),\n",
" 'I': ([0.06648789020906937,\n",
" 2.910837019748877,\n",
" 2.928984366459142,\n",
" 4.995073556916916,\n",
" 5.018565704152715,\n",
" 7.21693359533783],\n",
" [-2.1348326785650876126,\n",
" -0.0015708314102318150611,\n",
" 0.016045673317236682116,\n",
" 1.9852869329723028162,\n",
" 2.0293453729387404098,\n",
" 4.6742567420208319777]),\n",
" 'P': ([0.0,\n",
" 2.700439718141098,\n",
" 2.72792045456319,\n",
" 6.453269166068452,\n",
" 6.483814370183836,\n",
" 7.3733719613652795],\n",
" [-1.9815443653140106649,\n",
" 0.92986720580686056843,\n",
" 1.04808682998986787,\n",
" 7.1017784861859188554,\n",
" 7.1273316591101902764,\n",
" 8.280976660173624982])}\n"
]
}
],
"source": [
"pprint(segments)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Piecewise-linear fit\n",
"\n",
"We applied a 3-segment piecewise-linear fit. The boundaries were aligned to integer values of pixels-per-bit,\n",
"while optimizing for similarity to a cubic interpolation of the control points\n",
"(log-quantizer as a function of log-bitrate)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I [1. 4.] [381625. 655352.]\n",
"P [ 2. 139.] [356802. 848173.]\n",
"B0 [ 2. 92.] [288436. 671307.]\n",
"B1 [ 2. 126.] [264708. 622760.]\n"
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"source": [
"plt.figure(figsize=(7, 6))\n",
"plt.axis('equal')\n",
"plt.xticks([0, 10])\n",
"plt.yticks([0, 10])\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major')\n",
"plt.grid(b=True, which='minor', alpha=0.2)\n",
"\n",
"from scipy import optimize\n",
"\n",
"for ft, xy in segments.items():\n",
" f = np.poly1d(np.polyfit(np.array(xy[1]).astype(float), np.array(xy[0]).astype(float), 3))\n",
" ys = np.linspace(min(xy[1]), max(xy[1]), 20)\n",
" def cost(X):\n",
" y0 = np.array([ys[0], X[0], X[1], ys[-1]]).astype(float)\n",
" x0 = f(y0)\n",
" f0 = np.where(ys<X[0],\n",
" np.poly1d(np.polyfit(y0[:2], x0[:2], 1))(ys),\n",
" np.where(ys<X[1],\n",
" np.poly1d(np.polyfit(y0[1:3], x0[1:3], 1))(ys),\n",
" np.poly1d(np.polyfit(y0[2:], x0[2:], 1))(ys)))\n",
" return ((f0-f(ys))**2).sum()\n",
" X = optimize.fmin(cost, [2, 5], disp=0)\n",
" X = np.log2(np.ceil(np.exp2(X)))\n",
" print(ft, np.exp2(X), np.round(f(X)*2**17))\n",
" y0 = [ys.min(), X[0], X[1], ys.max()]\n",
" x0 = f(y0)\n",
" plt.plot(x0, y0, '.--', lw=1, c='grey')\n",
" plt.plot(f(ys), ys, label=ft)\n",
"\n",
"plt.legend();"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
jGaboardi/AAG_16 | .ipynb_checkpoints/AAG_16 copy 2-checkpoint.ipynb | 1 | 1660794 | null | lgpl-3.0 |
emiliom/stuff | MMW_API_landproperties_demo.ipynb | 1 | 15760 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model My Watershed (MMW) API Demo\n",
"[Emilio Mayorga](https://github.com/emiliom/), University of Washington, Seattle. 2018-5-10,17. Demo put together using as a starting point instructions from Azavea from October 2017."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"The [Model My Watershed API](https://app.wikiwatershed.org/api/docs/) allows you to delineate watersheds and analyze geo-data for watersheds and arbitrary areas. You can read more about the work at [WikiWatershed](http://www.wikiwatershed.org/) or use the [web app](https://www.app.wikiwatershed.org/).\n",
"\n",
"MMW users can discover their API keys through the user interface, and test the MMW geoprocessing API on either the live or staging apps. An Account page with the API key is available from either app (live or staging). To see it, go to the app, log in, and click on \"Account\" in the dropdown that appears when you click on your username in the top right. **Your key is different between staging and production.** For testing with the live (production) API and key, go to https://app.wikiwatershed.org/api/docs/\n",
"\n",
"The API can be tested from the command line using `curl`. This example uses the production API to test the `watershed` endpoint:\n",
"```bash\n",
"curl -H \"Content-Type: application/json\" -H \"Authorization: Token YOUR_API_KEY\" -X POST \n",
" -d '{ \"location\": [39.67185,-75.76743] }' https://app.wikiwatershed.org/api/watershed/\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MMW API: Obtain land properties based on \"analyze\" geoprocessing on AOI (small box around a point)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Set up"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import requests\n",
"from requests.adapters import HTTPAdapter\n",
"from requests.packages.urllib3.util.retry import Retry"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def requests_retry_session(\n",
" retries=3,\n",
" backoff_factor=0.3,\n",
" status_forcelist=(500, 502, 504),\n",
" session=None,\n",
"):\n",
" session = session or requests.Session()\n",
" retry = Retry(\n",
" total=retries,\n",
" read=retries,\n",
" connect=retries,\n",
" backoff_factor=backoff_factor,\n",
" status_forcelist=status_forcelist,\n",
" )\n",
" adapter = HTTPAdapter(max_retries=retry)\n",
" session.mount('http://', adapter)\n",
" session.mount('https://', adapter)\n",
" return session"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MMW **production** API endpoint base url."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"api_url = \"https://app.wikiwatershed.org/api/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The job is not completed instantly and the results are not returned directly by the API request that initiated the job. The user must first issue an API request to confirm that the job is complete, then fetch the results. The demo presented here performs automated retries (checks) until the server confirms the job is completed, then requests the JSON results and converts (deserializes) them into a Python dictionary."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def get_job_result(api_url, s, jobrequest):\n",
" url_tmplt = api_url + \"jobs/{job}/\"\n",
" get_url = url_tmplt.format\n",
" \n",
" result = ''\n",
" while not result:\n",
" get_req = requests_retry_session(session=s).get(get_url(job=jobrequest['job']))\n",
" result = json.loads(get_req.content)['result']\n",
" \n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"s = requests.Session()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"APIToken = 'Token 0501d9a98b8170a41d57df8ce82c000c477c621a' # HIDE THE API TOKEN\n",
"\n",
"s.headers.update({\n",
" 'Authorization': APIToken,\n",
" 'Content-Type': 'application/json'\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Construct AOI GeoJSON for job request"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters passed to the \"analyze\" API requests."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from shapely.geometry import box, MultiPolygon"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"width = 0.0004 # Looks like using a width smaller than 0.0002 causes a problem with the API?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# GOOS: (-88.5552, 40.4374) elev 240.93. Agriculture Site—Goose Creek (Corn field) Site (GOOS) at IML CZO\n",
"# SJER: (-119.7314, 37.1088) elev 403.86. San Joaquin Experimental Reserve Site (SJER) at South Sierra CZO\n",
"lon, lat = -119.7314, 37.1088"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"bbox = box(lon-0.5*width, lat-0.5*width, lon+0.5*width, lat+0.5*width)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"payload = MultiPolygon([bbox]).__geo_interface__\n",
"\n",
"json_payload = json.dumps(payload)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'coordinates': [(((-119.73119999999999, 37.1086),\n",
" (-119.73119999999999, 37.109),\n",
" (-119.7316, 37.109),\n",
" (-119.7316, 37.1086),\n",
" (-119.73119999999999, 37.1086)),)],\n",
" 'type': 'MultiPolygon'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"payload"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Issue job requests, fetch job results when done, then examine results. Repeat for each request type"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# convenience function, to simplify the request calls, below\n",
"def analyze_api_request(api_name, s, api_url, json_payload):\n",
" post_url = \"{}analyze/{}/\".format(api_url, api_name)\n",
" post_req = requests_retry_session(session=s).post(post_url, data=json_payload)\n",
" jobrequest_json = json.loads(post_req.content)\n",
" # Fetch and examine job result\n",
" result = get_job_result(api_url, s, jobrequest_json)\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Issue job request: **analyze/land/**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"result = analyze_api_request('land', s, api_url, json_payload)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Everything below is just exploration of the results. Examine the content of the results (as JSON, and Python dictionaries)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(dict, [u'survey'])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(result), result.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`result` is a dictionary with one item, `survey`. This item in turn is a dictionary with 3 items: `displayName`, `name`, `categories`. The first two are just labels. The data are in the `categories` item."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[u'displayName', u'name', u'categories']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['survey'].keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"categories = result['survey']['categories']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(16,\n",
" {u'area': 0.0,\n",
" u'code': u'grassland',\n",
" u'coverage': 0.0,\n",
" u'nlcd': 71,\n",
" u'type': u'Grassland/Herbaceous'})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(categories), categories[1]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"land_categories_nonzero = [d for d in categories if d['coverage'] > 0]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{u'area': 897.6442935164769,\n",
" u'code': u'shrub',\n",
" u'coverage': 1.0,\n",
" u'nlcd': 52,\n",
" u'type': u'Shrub/Scrub'}]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"land_categories_nonzero"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Issue job request: **analyze/terrain/**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result = analyze_api_request('terrain', s, api_url, json_payload)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`result` is a dictionary with one item, `survey`. This item in turn is a dictionary with 3 items: `displayName`, `name`, `categories`. The first two are just labels. The data are in the `categories` item."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"categories = result['survey']['categories']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3,\n",
" [{u'elevation': 404.43, u'slope': 5.240777969360352, u'type': u'average'},\n",
" {u'elevation': 404.43, u'slope': 5.240777969360352, u'type': u'minimum'},\n",
" {u'elevation': 404.43, u'slope': 5.240777969360352, u'type': u'maximum'}])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(categories), categories"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{u'elevation': 404.43, u'slope': 5.240777969360352, u'type': u'average'}]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[d for d in categories if d['type'] == 'average']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Issue job request: **analyze/climate/**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result = analyze_api_request('climate', s, api_url, json_payload)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`result` is a dictionary with one item, `survey`. This item in turn is a dictionary with 3 items: `displayName`, `name`, `categories`. The first two are just labels. The data are in the `categories` item."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"categories = result['survey']['categories']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(12,\n",
" [{u'month': u'January',\n",
" u'monthidx': 1,\n",
" u'ppt': 9.198625946044922,\n",
" u'tmean': 7.592398643493652},\n",
" {u'month': u'February',\n",
" u'monthidx': 2,\n",
" u'ppt': 7.788037872314454,\n",
" u'tmean': 9.737625122070312}])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(categories), categories[:2]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"ppt = [d['ppt'] for d in categories]\n",
"tmean = [d['tmean'] for d in categories]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"44.89247835278511"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ppt is in cm, right?\n",
"sum(ppt)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"import calendar\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calendar.mdays"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.830176646088901"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Annual tmean needs to be weighted by the number of days per month\n",
"sum(np.asarray(tmean) * np.asarray(calendar.mdays[1:]))/365"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:odm2client]",
"language": "python",
"name": "conda-env-odm2client-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
CRPropa/CRPropa3 | doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb | 1 | 11591 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D MHD models\n",
"This notebook explains how to use cubic results of 3D MHD models on a uniform grid in CRPropa.\n",
"\n",
"## Supplied data\n",
"\n",
"The fields need to be supplied in a raw binary file that contains only single floats, arranged as follows: Starting with the cell values (Bx,By,Bz for magnetic field or rho for density) at the origin of the box, the code continues to read along z, then y and finally x.\n",
"\n",
"On https://crpropa.github.io/CRPropa3/ under \"Additional resources\" you can find a number of MHD models used with CRPropa in the literature. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note: \n",
"The parameters used for the following example refer to the MHD model by Hackstein et al. (2018), as provided under \"Additional resources\". However, CRPropa does in general not take any warranty on the accuracy of any of those external data files.\n",
"\n",
"Note that in some previous version of this notebook the used MHD model has not been representing the results from Hackstein et al. (2018). This has been due to two issues: (1.) the size of the grid has not taken the dimensionless Hubble parameter into account and (2.) the X- and Z-coordinates of the available data files have been transposed. But since 20.05.2022 both of these issues have been fixed and the following example can be used to include the MHD model data from Hackstein et al. (2018)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"from crpropa import *\n",
"\n",
"## settings for MHD model (must be set according to model)\n",
"filename_bfield = \"clues_primordial.dat\" ## filename of the magnetic field\n",
"gridOrigin = Vector3d(0,0,0) ## origin of the 3D data, preferably at boxOrigin\n",
"gridSize = 1024 ## size of uniform grid in data points\n",
"h = 0.677 ## dimensionless Hubble parameter\n",
"size = 249.827/h *Mpc ## physical edgelength of volume in Mpc\n",
"b_factor = 1. ## global renormalization factor for the field\n",
"\n",
"## settings of simulation\n",
"boxOrigin = Vector3d( 0, 0, 0,) ## origin of the full box of the simulation\n",
"boxSize = Vector3d( size, size, size ) ## end of the full box of the simulation\n",
"\n",
"## settings for computation\n",
"minStep = 10.*kpc ## minimum length of single step of calculation\n",
"maxStep = 4.*Mpc ## maximum length of single step of calculation\n",
"tolerance = 1e-2 ## tolerance for error in iterative calculation of propagation step\n",
"\n",
"spacing = size/(gridSize) ## resolution, physical size of single cell\n",
"\n",
"m = ModuleList()\n",
"\n",
"\n",
"## instead of computing propagation without Lorentz deflection via\n",
"# m.add(SimplePropagation(minStep,maxStep))\n",
"\n",
"## initiate grid to hold field values\n",
"vgrid = Grid3f( gridOrigin, gridSize, spacing )\n",
"## load values to the grid\n",
"loadGrid( vgrid, filename_bfield, b_factor )\n",
"## use grid as magnetic field\n",
"bField = MagneticFieldGrid( vgrid )\n",
"## add propagation module to the simulation to activate deflection in supplied field\n",
"m.add(PropagationCK( bField, tolerance, minStep, maxStep))\n",
"#m.add(DeflectionCK( bField, tolerance, minStep, maxStep)) ## this was used in older versions of CRPropa\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"to make use of periodicity of the provided data grid, use"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"m.add( PeriodicBox( boxOrigin, boxSize ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"to not follow particles forever, use"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"m.add( MaximumTrajectoryLength( 400*Mpc ) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Uniform injection\n",
"\n",
"The most simple scenario of UHECR sources is a uniform distribution of their sources. This can be realized via use of"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"source = Source()\n",
"source.add( SourceUniformBox( boxOrigin, boxSize )) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Injection following density field\n",
"\n",
"The distribution of gas density can be used as a probability density function for the injection of particles from random positions."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"filename_density = \"mass-density_clues.dat\" ## filename of the density field\n",
"\n",
"source = Source()\n",
"## initialize grid to hold field values\n",
"mgrid = ScalarGrid( gridOrigin, gridSize, spacing )\n",
"## load values to grid\n",
"loadGrid( mgrid, filename_density )\n",
"## add source module to simulation\n",
"source.add( SourceDensityGrid( mgrid ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mass Halo injection\n",
"\n",
"Alternatively, for the CLUES models, we also provide a list of mass halo positions. These positions can be used as sources with the same properties by use of the following"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"filename_halos = 'clues_halos.dat'\n",
"\n",
"# read data from file\n",
"data = np.loadtxt(filename_halos, unpack=True, skiprows=39)\n",
"sX = data[0] \n",
"sY = data[1] \n",
"sZ = data[2] \n",
"mass_halo = data[5] \n",
"\n",
"## find only those mass halos inside the provided volume (see Hackstein et al. 2018 for more details)\n",
"Xdown= sX >= 0.25 \n",
"Xup= sX <= 0.75 \n",
"Ydown= sY >= 0.25 \n",
"Yup= sY <= 0.75 \n",
"Zdown= sZ >= 0.25 \n",
"Zup= sZ <= 0.75 \n",
"insider= Xdown*Xup*Ydown*Yup*Zdown*Zup \n",
"\n",
"## transform relative positions to physical positions within given grid\n",
"sX = (sX[insider]-0.25)*2*size\n",
"sY = (sY[insider]-0.25)*2*size\n",
"sZ = (sZ[insider]-0.25)*2*size\n",
"\n",
"## collect all sources in the multiple sources container\n",
"smp = SourceMultiplePositions()\n",
"for i in range(0,len(sX)):\n",
" pos = Vector3d( sX[i], sY[i], sZ[i] )\n",
" smp.add( pos, 1. )\n",
" \n",
"## add collected sources\n",
"source = Source()\n",
"source.add( smp )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"additional source properties"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"## use isotropic emission from all sources\n",
"source.add( SourceIsotropicEmission() )\n",
"\n",
"## set particle type to be injected\n",
"A, Z = 1, 1 # proton\n",
"source.add( SourceParticleType( nucleusId(A,Z) ) )\n",
"\n",
"## set injected energy spectrum\n",
"Emin, Emax = 1*EeV, 1000*EeV\n",
"specIndex = -1\n",
"source.add( SourcePowerLawSpectrum( Emin, Emax, specIndex ) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Observer\n",
"\n",
"To register particles, an observer has to be defined. In the provided constrained simulations the position of the Milky Way is, by definition, in the center of the volume."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"filename_output = 'data/output_MW.txt'\n",
"\n",
"obsPosition = Vector3d(0.5*size,0.5*size,0.5*size) # position of observer, MW is in center of constrained simulations\n",
"obsSize = 800*kpc ## physical size of observer sphere\n",
"\n",
"\n",
"## initialize observer that registers particles that enter into sphere of given size around its position\n",
"obs = Observer()\n",
"obs.add( ObserverSmallSphere( obsPosition, obsSize ) )\n",
"## write registered particles to output file\n",
"obs.onDetection( TextOutput( filename_output ) )\n",
"## choose to not further follow particles paths once detected\n",
"obs.setDeactivateOnDetection(True)\n",
"## add observer to module list\n",
"m.add(obs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"finally run the simulation by"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"N = 1000\n",
"\n",
"m.showModules() ## optional, see summary of loaded modules\n",
"m.setShowProgress(True) ## optional, see progress during runtime\n",
"m.run(source, N, True) ## perform simulation with N particles injected from 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": "3.9.2"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
phockett/ePSproc | notebooks/ePSproc_test_BLM_numerics_260721.ipynb | 1 | 2688513 | null | gpl-3.0 |
KIPAC/StatisticalMethods | tutorials/old/GPRegression.ipynb | 1 | 25060 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Week 8 Tutorial\n",
"\n",
"## Gaussian Process Regression\n",
"\n",
"In this example, we return to the \"straight line\" problem, generate some mock data, and investigate a \"model-free model\", a Gaussian Process, for them. The idea is to find a flexible model that can _interpolate_ between the data we have, in order to predict future data lying in the gaps, or beyond the observed domain.\n",
"\n",
"### Requirements\n",
"\n",
"You will need to `pip install scikit-learn` and check that you have v0.18 or higher as a result."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0)\n",
"plt.rcParams['savefig.dpi'] = 200\n",
"\n",
"class SolutionMissingError(Exception):\n",
" def __init__(self):\n",
" Exception.__init__(self,\"You need to complete the solution for this code to work!\")\n",
"def REPLACE_WITH_YOUR_SOLUTION():\n",
" raise SolutionMissingError"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The Data\n",
"\n",
"Let's generate a simple Cepheids-like dataset: observations of $y$ with reported uncertainties $\\sigma_y$, at given $x$ values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pylab as plt\n",
"\n",
"xlimits = [0,350]\n",
"ylimits = [0,250]\n",
"\n",
"def generate_data(seed=None):\n",
" \"\"\"\n",
" Generate a 30-point data set, with x and sigma_y as standard, but with\n",
" y values given by\n",
"\n",
" y = a_0 + a_1 * x + a_2 * x**2 + a_3 * x**3 + noise\n",
" \"\"\"\n",
" Ndata = 30\n",
"\n",
" xbar = 0.5*(xlimits[0] + xlimits[1])\n",
" xstd = 0.25*(xlimits[1] - xlimits[0])\n",
"\n",
" if seed is not None:\n",
" np.random.seed(seed=seed)\n",
"\n",
" x = xbar + xstd * np.random.randn(Ndata)\n",
"\n",
" meanerr = 0.025*(xlimits[1] - xlimits[0])\n",
"\n",
" sigmay = meanerr + 0.3 * meanerr * np.abs(np.random.randn(Ndata))\n",
"\n",
" a = np.array([37.2,0.93,-0.002,0.0])\n",
" y = a[0] + a[1] * x + a[2] * x**2 + a[3] * x**3 + sigmay*np.random.randn(len(x))\n",
"\n",
" return x,y,sigmay\n",
"\n",
"def plot_yerr(x, y, sigmay):\n",
" \"\"\"\n",
" Plot an (x,y,sigma) dataset as a set of points with error bars \n",
" \"\"\"\n",
" plt.errorbar(x, y, yerr=sigmay, fmt='.', ms=7, lw=1, color='k')\n",
" plt.xlabel('$x$', fontsize=16)\n",
" plt.ylabel('$y$', fontsize=16)\n",
" plt.xlim(*xlimits)\n",
" plt.ylim(*ylimits)\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(x, y, sigmay) = generate_data(seed=13)\n",
"\n",
"plot_yerr(x, y, sigmay)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Fitting a Gaussian Process\n",
"\n",
"Let's follow [Jake VanderPlas' example](http://www.astroml.org/book_figures/chapter8/fig_gp_example.html#book-fig-chapter8-fig-gp-example), to see how to work with the [`scikit-learn` v0.18](http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#gaussian-processes-regression-basic-introductory-example) Gaussian Process regression model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import RBF as SquaredExponential"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Defining a GP\n",
"\n",
"First we define a kernel function, for populating the covariance matrix of our GP. To avoid confusion, a Gaussian kernel is referred to as a \"squared exponential\" (or a \"radial basis function\", RBF). The squared exponential kernel has one hyper-parameter, the length scale that is the Gaussian width."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h = 10.0\n",
"\n",
"kernel = SquaredExponential(length_scale=h, length_scale_bounds=(0.01, 1000.0))\n",
"gp0 = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now, let's draw some samples from the unconstrained process, o equivalently, the prior. Each sample is a function $y(x)$, which we evaluate on a grid. We'll need to assert a value for the kernel hyperparameter $h$, which dictates the correlation length between the datapoints. That will allow us to compute a mean function (which for simplicity we'll set to the mean observed $y$ value), and a covariance matrix that captures the correlations between datapoints. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"xgrid = np.atleast_2d(np.linspace(0, 399, 100)).T\n",
"print(\"y(x) will be predicted on a grid of length\", len(xgrid))\n",
"\n",
"# Draw three sample y(x) functions:\n",
"draws = gp0.sample_y(xgrid, n_samples=3)\n",
"\n",
"print(\"Drew 3 samples, stored in an array with shape \", draws.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's plot these, to see what our prior looks like."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Start a 4-panel figure:\n",
"fig = plt.figure(figsize=(10,10))\n",
"\n",
"# Plot our three prior draws:\n",
"ax = fig.add_subplot(221)\n",
"ax.plot(xgrid, draws[:,0], '-r')\n",
"ax.plot(xgrid, draws[:,1], '-g')\n",
"ax.plot(xgrid, draws[:,2], '-b', label='Rescaled prior sample $y(x)$')\n",
"ax.set_xlim(0, 399)\n",
"ax.set_ylim(-5, 5)\n",
"ax.set_xlabel('$x$')\n",
"ax.set_ylabel('$y(x)$')\n",
"ax.legend(fontsize=8);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Each predicted $y(x)$ is drawn from a Gaussian of unit variance, and with off-diagonal elements determined by the covariance function. \n",
"\n",
"Try changing `h` to see what happens to the smoothness of the predictions. \n",
"\n",
"> Go back up to the cell where `h` is assigned, and re-run that cell and the subsequent ones."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"For our data to be well interpolated by this Gaussian Process, it will need to be rescaled such that it has zero mean and unit variance. There are [standard methods for doing this](http://scikit-learn.org/stable/modules/preprocessing.html), but we'll do this rescaling here for transparency - and so we know what to add back in later!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Rescale():\n",
" def __init__(self, y, err):\n",
" self.original_data = y\n",
" self.original_err = err\n",
" self.mean = np.mean(y)\n",
" self.std = np.std(y)\n",
" self.transform()\n",
" return\n",
" def transform(self):\n",
" self.y = (self.original_data - self.mean) / self.std\n",
" self.err = self.original_err / self.std\n",
" return()\n",
" def invert(self, scaled_y, scaled_err):\n",
" return (scaled_y * self.std + self.mean, scaled_err * self.std) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"rescaled = Rescale(y, sigmay)\n",
"print('Mean, variance of original data: ',np.round(np.mean(y)), np.round(np.var(y)))\n",
"print('Mean, variance of rescaled data: ',np.round(np.mean(rescaled.y)), np.round(np.var(rescaled.y)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that we can undo the scaling, for any `y` and `sigmay`: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y2, sigmay2 = rescaled.invert(rescaled.y, rescaled.err)\n",
"print('Mean, variance of inverted, rescaled data: ',np.round(np.mean(y2)), np.round(np.var(y2)))\n",
"print('Maximum differences in y, sigmay, after round trip: ',np.max(np.abs(y2 - y)), np.max(np.abs(sigmay2 - sigmay)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Constraining the GP\n",
"\n",
"Now, using the same covariance function, lets \"fit\" the GP by constraining each draw from the GP to go through our data points, and _optimizing_ the length scale hyperparameter `h`. \n",
"\n",
"Let's first look at how this would work for two data points with no uncertainty. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Choose two of our (rescaled) datapoints:\n",
"x1 = np.array([x[10], x[12]])\n",
"rescaled_y1 = np.array([rescaled.y[10], rescaled.y[12]])\n",
"rescaled_sigmay1 = np.array([rescaled.err[10], rescaled.err[12]])\n",
"\n",
"# Instantiate a GP model, with initial length_scale h=10:\n",
"kernel = SquaredExponential(length_scale=10.0, length_scale_bounds=(0.01, 1000.0))\n",
"gp1 = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)\n",
"\n",
"# Fit it to our two noiseless datapoints:\n",
"gp1.fit(x1[:, None], rescaled_y1)\n",
"\n",
"# We have fit for the length scale parameter: print the result here:\n",
"params = gp1.kernel_.get_params()\n",
"print('Best-fit kernel length scale =', params['length_scale'],'cf. input',10.0)\n",
"\n",
"# Now predict y(x) everywhere on our xgrid: \n",
"rescaled_ygrid1, rescaled_ygrid1_err = gp1.predict(xgrid, return_std=True)\n",
"\n",
"# And undo scaling, of both y(x) on our grid, and our two constraining data points:\n",
"ygrid1, ygrid1_err = rescaled.invert(rescaled_ygrid1, rescaled_ygrid1_err)\n",
"y1, sigmay1 = rescaled.invert(rescaled_y1, rescaled_sigmay1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"ax = fig.add_subplot(222)\n",
"ax.plot(xgrid, ygrid1, '-', color='gray', label='Posterior mean $y(x)$')\n",
"ax.fill(np.concatenate([xgrid, xgrid[::-1]]),\n",
" np.concatenate([(ygrid1 - ygrid1_err), (ygrid1 + ygrid1_err)[::-1]]),\n",
" alpha=0.3, fc='gray', ec='None', label='68% confidence interval')\n",
"ax.plot(x1, y1, '.k', ms=6, label='Noiseless constraints')\n",
"ax.set_xlim(0, 399)\n",
"ax.set_ylim(0, 399)\n",
"ax.set_xlabel('$x$')\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"In the absence of information, the GP tends to produce $y(x)$ that fluctuate around the prior mean function, which we chose to be a constant. Let's draw some samples from the posterior PDF, and overlay them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"draws = gp1.sample_y(xgrid, n_samples=3)\n",
"for k in range(3):\n",
" draws[:,k], dummy = rescaled.invert(draws[:,k], np.zeros(len(xgrid)))\n",
"\n",
"ax.plot(xgrid, draws[:,0], '-r')\n",
"ax.plot(xgrid, draws[:,1], '-g')\n",
"ax.plot(xgrid, draws[:,2], '-b', label='Posterior sample $y(x)$')\n",
"ax.legend(fontsize=8)\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"See how the posterior sample $y(x)$ functions all pass through the constrained points."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Including Observational Uncertainties\n",
"\n",
"The mechanism for including uncertainties is a little esoteric: `scikit-learn` wants to be given a \"nugget,\" called `alpha`, to multiply the diagonal elements of the covariance matrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Choose two of our datapoints:\n",
"x2 = np.array([x[10], x[12]])\n",
"rescaled_y2 = np.array([rescaled.y[10], rescaled.y[12]])\n",
"rescaled_sigmay2 = np.array([rescaled.err[10], rescaled.err[12]])\n",
"\n",
"# Instantiate a GP model, including observational errors:\n",
"kernel = SquaredExponential(length_scale=10.0, length_scale_bounds=(0.01, 1000.0))\n",
"gp2 = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9, \n",
" alpha=(rescaled_sigmay2 / rescaled_y2) ** 2,\n",
" random_state=0)\n",
"\n",
"# Fit it to our two noisy datapoints:\n",
"gp2.fit(x2[:, None], rescaled_y2)\n",
"\n",
"# We have fit for the length scale parameter: print the result here:\n",
"params = gp2.kernel_.get_params()\n",
"print('Best-fit kernel length scale =', params['length_scale'],'cf. input',10.0)\n",
"\n",
"# Now predict y(x) everywhere on our xgrid: \n",
"rescaled_ygrid2, rescaled_ygrid2_err = gp2.predict(xgrid, return_std=True)\n",
"\n",
"# And undo scaling:\n",
"ygrid2, ygrid2_err = rescaled.invert(rescaled_ygrid2, rescaled_ygrid2_err)\n",
"y2, sigmay2 = rescaled.invert(rescaled_y2, rescaled_sigmay2)\n",
"\n",
"# Draw three posterior sample y(x):\n",
"draws = gp2.sample_y(xgrid, n_samples=3)\n",
"for k in range(3):\n",
" draws[:,k], dummy = rescaled.invert(draws[:,k], np.zeros(len(xgrid)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"ax = fig.add_subplot(223)\n",
"\n",
"def gp_plot(ax, xx, yy, ee, datax, datay, datae, samples, legend=True):\n",
" ax.cla()\n",
" ax.plot(xx, yy, '-', color='gray', label='Posterior mean $y(x)$')\n",
" ax.fill(np.concatenate([xx, xx[::-1]]),\n",
" np.concatenate([(yy - ee), (yy + ee)[::-1]]),\n",
" alpha=0.3, fc='gray', ec='None', label='68% confidence interval')\n",
" ax.errorbar(datax, datay, datae, fmt='.k', ms=6, label='Noisy constraints')\n",
" ax.set_xlim(0, 399)\n",
" ax.set_ylim(0, 399)\n",
" ax.set_xlabel('$x$')\n",
" ax.set_ylabel('$y(x)$')\n",
" ax.plot(xgrid, samples[:,0], '-r')\n",
" ax.plot(xgrid, samples[:,1], '-g')\n",
" ax.plot(xgrid, samples[:,2], '-b', label='Posterior sample $y(x)$')\n",
" if legend: ax.legend(fontsize=8)\n",
" return\n",
"\n",
"gp_plot(ax, xgrid, ygrid2, ygrid2_err, x2, y2, sigmay2, draws, legend=True)\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Now, the posterior sample $y(x)$ functions pass through the constraints _within the errors_."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Using all the Data\n",
"\n",
"Now let's extend the above example to use all of our datapoints. This additional information should pull the predictions further away from the initial mean function. We'll also compute the marginal log likelihood of the best fit hyperparameter, in case we want to compare this choice of kernel with another one (in the exercises, for example)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Use all of our datapoints:\n",
"x3 = x\n",
"rescaled_y3 = rescaled.y\n",
"rescaled_sigmay3 = rescaled.err\n",
"\n",
"# Instantiate a GP model, including observational errors:\n",
"kernel = SquaredExponential(length_scale=10.0, length_scale_bounds=(0.01, 1000.0))\n",
"# Could comment this out, and then import and use an \n",
"# alternative kernel here. \n",
"\n",
"gp3 = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9, \n",
" alpha=(rescaled_sigmay3 / rescaled_y3) ** 2,\n",
" random_state=0)\n",
"\n",
"# Fit it to our noisy datapoints:\n",
"gp3.fit(x3[:, None], rescaled_y3)\n",
"\n",
"# Now predict y(x) everywhere on our xgrid: \n",
"rescaled_ygrid3, rescaled_ygrid3_err = gp3.predict(xgrid, return_std=True)\n",
"\n",
"# And undo scaling:\n",
"ygrid3, ygrid3_err = rescaled.invert(rescaled_ygrid3, rescaled_ygrid3_err)\n",
"y3, sigmay3 = rescaled.invert(rescaled_y3, rescaled_sigmay3)\n",
"\n",
"# We have fitted the length scale parameter - print the result here:\n",
"params = gp3.kernel_.get_params()\n",
"print('Kernel: {}'.format(gp3.kernel_))\n",
"print('Best-fit kernel length scale =', params['length_scale'],'cf. input',10.0)\n",
"print('Marginal log-Likelihood: {:.3f}'.format(gp3.log_marginal_likelihood(gp3.kernel_.theta)))\n",
"\n",
"# Draw three posterior sample y(x):\n",
"draws = gp3.sample_y(xgrid, n_samples=3)\n",
"for k in range(3):\n",
" draws[:,k], dummy = rescaled.invert(draws[:,k], np.zeros(len(xgrid)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"ax = fig.add_subplot(224)\n",
"\n",
"gp_plot(ax, xgrid, ygrid3, ygrid3_err, x3, y3, sigmay3, draws, legend=True)\n",
"fig\n",
"\n",
"# fig.savefig('../../lessons/graphics/mfm_gp_example_pjm.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We now see the Gaussian Process model providing a smooth interpolation between the points. The posterior samples show fluctuations, but all are plausible under our assumptions."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Exercises\n",
"\n",
"1. Try a different kernel function, from the list given in the [scikit-learn docs here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.gaussian_process). \"Matern\" could be a good choice. Do you get a higher value of the marginal log likelihood when you fit this model? Under what circumstances would this marginal log likelihood approximate the Bayesian Evidence well?\n",
"\n",
"----\n",
"\n",
"2. Extend the analysis above to do a _posterior predictive model check_ of your GP inference. You'll need to generate new replica datasets from posterior draws from the fitted GP. Use the discrepancy measure $T(\\theta,d) = -2 \\log L(\\theta;d)$. Does your GP provide an adequate fit to the data? Could it be over-fitting? What could you do to prevent this? There's some starter code for you below.\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Alternative kernel\n",
"\n",
"Go back to the gp3 cell, and try something new..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Kernel: RBF\n",
"Best-fit kernel length scale = \n",
"Marginal log-Likelihood: \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Kernel: ???\n",
"Best-fit kernel length scale = \n",
"Marginal log-Likelihood: \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Posterior Predictive Model Check\n",
"\n",
"For this we need to draw models from our GP, and then generate a dataset from each one. We'll do this in the function below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def generate_replica_data(xgrid, ygrid, seed=None):\n",
" \"\"\"\n",
" Generate a 30-point data set, with x and sigma_y as standard, but with\n",
" y values given by the \"lookup tables\" (gridded function) provided.\n",
" \"\"\"\n",
" Ndata = 30\n",
"\n",
" xbar = 0.5*(xlimits[0] + xlimits[1])\n",
" xstd = 0.25*(xlimits[1] - xlimits[0])\n",
"\n",
" if seed is not None:\n",
" np.random.seed(seed=seed)\n",
"\n",
" x = xbar + xstd * np.random.randn(Ndata)\n",
"\n",
" meanerr = 0.025*(xlimits[1] - xlimits[0])\n",
"\n",
" sigmay = meanerr + 0.3 * meanerr * np.abs(np.random.randn(Ndata))\n",
"\n",
" # Look up values of y given input lookup grid\n",
" y = np.zeros(Ndata)\n",
" for k in range(Ndata):\n",
" y[k] = np.interp(x[k], np.ravel(xgrid), ygrid)\n",
" # Add noise:\n",
" y += sigmay*np.random.randn(len(x))\n",
"\n",
" return x,y,sigmay\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def discrepancy(y_model, y_obs, s_obs):\n",
" \"\"\"\n",
" Compute discrepancy measure comparing model y and \n",
" observed/replica y (with its uncertainty). \n",
" \n",
" T = -2 log L\n",
" \"\"\"\n",
" T = REPLACE_WITH_YOUR_SOLUTION()\n",
" return T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Draw 1000 sample models:\n",
"Nsamples = 1000\n",
"draws = gp3.sample_y(xgrid, n_samples=Nsamples)\n",
"x_rep, y_rep, sigmay_rep = np.zeros([30,Nsamples]), np.zeros([30,Nsamples]), np.zeros([30,Nsamples])\n",
"# Difference in discrepancy measure, for plotting\n",
"dT = np.zeros(Nsamples)\n",
"\n",
"# For each sample model, draw a replica dataset and accumulate test statistics:\n",
"y_model = np.zeros(30)\n",
"for k in range(Nsamples):\n",
" draws[:,k], dummy = rescaled.invert(draws[:,k], np.zeros(len(xgrid)))\n",
" ygrid = draws[:,k]\n",
" x_rep[:,k], y_rep[:,k], sigmay_rep[:,k] = generate_replica_data(xgrid, ygrid, seed=None)\n",
" dT[k] = REPLACE_WITH_YOUR_SOLUTION()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot P(T[y_rep]-T[y_obs]|y_obs) as a histogram:\n",
"\n",
"plt.hist(dT, density=True)\n",
"plt.xlabel(\"$T(y_{rep})-T(y_{obs})$\")\n",
"plt.ylabel(\"Posterior predictive probability density\");"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"livereveal": {
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| gpl-2.0 |
SimonBiggs/electronfactors | paper/Method -- Equivalent ellipse example.ipynb | 1 | 78704 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import yaml\n",
"import numpy as np\n",
"\n",
"import descartes as des\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from electronfactors import (\n",
" equivalent_ellipse, shapely_cutout,\n",
" display_shapely, display_equivalent_ellipse,\n",
" make_ellipse\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from electronfactors.visuals.utilities import create_green_cm\n",
"green_cm = create_green_cm()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def reg_m(y, x):\n",
" ones = np.ones(len(x[0]))\n",
" X = sm.add_constant(np.column_stack((x[0], ones)))\n",
" for ele in x[1:]:\n",
" X = sm.add_constant(np.column_stack((ele, X)))\n",
" results = sm.OLS(y, X).fit()\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import rc\n",
"rc('font',**{'family':'serif',\n",
" 'size':'16'})\n",
"# rc('text', usetex=True)\n",
"rc('legend', fontsize=16, scatterpoints=1, fancybox=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"model_cache/12MeV_10app_100ssd.yml\", 'r') as file:\n",
" cutout_data = yaml.load(file)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"XCoords = np.array(cutout_data['P22']['XCoords'])\n",
"YCoords = np.array(cutout_data['P22']['YCoords'])\n",
"width = np.array(cutout_data['P22']['width'])\n",
"length = np.array(cutout_data['P22']['length'])\n",
"poi = np.array(cutout_data['P22']['poi'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.05, -0.53])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poi"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(10.25)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"length"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-3.4000000000000004, 3.5]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[poi[0]-width/2, poi[0]+width/2]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fc8446f6748>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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k7LDXStpcEOW05qIo3qBVp1YEhAVSlFNEWWSZ0eF4pfqSyXIp5ZqG7EgIsVyD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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc84475d278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(6, 6))\n",
"ax = fig.add_subplot(111)\n",
"\n",
"cutout = shapely_cutout(XCoords, YCoords)\n",
"ellipse = make_ellipse(ax=ax, poi=poi, width=width, length=length)\n",
"\n",
"\n",
"cutout_patch = des.PolygonPatch(\n",
" cutout, fc=green_cm(0.75), \n",
" lw=1)\n",
"ax.add_patch(cutout_patch)\n",
"\n",
"ellipse_patch = des.PolygonPatch(\n",
" ellipse, fc=[ 0, 0, 0, 0 ],\n",
" lw=2)\n",
"ax.add_patch(ellipse_patch)\n",
"\n",
"\n",
"plt.plot(\n",
" [poi[0]-width/2, poi[0]+width/2],\n",
" [poi[1]]*2, '--',\n",
" lw=2, color='black', marker='|', mew=3, ms=60)\n",
"\n",
"plt.plot(\n",
" [poi[0]]*2,\n",
" [poi[1]-length/2, poi[1]+length/2], '--',\n",
" lw=2, color='black', marker='_', mew=3, ms=60)\n",
"\n",
"plt.text(0.5, -0.35, \"width (cm)\")\n",
"plt.text(-0.6, -1.5, \"length (cm)\", rotation='vertical')\n",
"\n",
"plt.grid(True)\n",
"\n",
"plt.xlim([-5, 6])\n",
"plt.ylim([-6, 5])\n",
"\n",
"plt.xlabel(r'x (cm at 100 SSD)')\n",
"plt.ylabel(r'y (cm at 100 SSD)')\n",
"\n",
"# plt.savefig('figures/example_ellipse.png', bbox_inches='tight', dpi=600)\n",
"# fig.savefig('figures/example_ellipse.eps', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"custom_data = dict()\n",
"\n",
"key = 'Concave'\n",
"custom_data[key] = dict()\n",
"custom_data[key]['XCoords'] = np.array([-3.5, -3.5, 0, 3.5, 3.5, 0.5, 0.5, 3.5, 3.5, 0, -3.5]) / 0.95\n",
"custom_data[key]['YCoords'] = np.array([0, 4, 5, 4.5, 2, 1.5, -1.5, -2, -4.5, -5, -4]) / 0.95\n",
"\n",
"labels = [key for key in custom_data]\n",
"ellipse_raw = [equivalent_ellipse(**custom_data[key]) for key in labels]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fc8446966a0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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x4/raSinqkvXUJetZ07H+jPst36zO7KyO9x/lvPPPrZlZ9fT01HTmq9Z6VvGr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\n9djwIxGJLm8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc844751be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"concave_cutout = shapely_cutout(custom_data[key]['XCoords'], custom_data[key]['YCoords'])\n",
"concave_ellipse = make_ellipse(**ellipse_raw[0])\n",
"\n",
"xlims = np.array([-4, 4])\n",
"ylims = np.array([-9.5, 9.5])\n",
"scale = 0.5\n",
"\n",
"fig = plt.figure(figsize=(scale * xlims.ptp(), scale * ylims.ptp()))\n",
"ax = fig.add_subplot(111)\n",
"\n",
"cutout_patch = des.PolygonPatch(concave_cutout, fc=np.random.uniform(size=3), alpha=0.5, lw=2)\n",
"ax.add_patch(cutout_patch)\n",
"\n",
"ellipse_patch = des.PolygonPatch(concave_ellipse, fc=np.random.uniform(size=3), alpha=0.5, lw=2)\n",
"ax.add_patch(ellipse_patch)\n",
"\n",
"plt.grid(True)\n",
"\n",
"plt.xlim(xlims)\n",
"plt.ylim(ylims)\n",
"\n",
"plt.title(r'Convex test shape')\n",
"plt.xlabel(r'x (cm)')\n",
"plt.ylabel(r'y (cm)')"
]
}
],
"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
}
| agpl-3.0 |
NlGG/Projects | OLS_ML_GMM.ipynb | 1 | 7468 | {
"cells": [
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# 統計用ツール\n",
"import statsmodels.api as sm\n",
"import statsmodels.tsa.api as tsa\n",
"from patsy import dmatrices\n",
"\n",
"# 自作の空間統計用ツール\n",
"from spatialstat import *\n",
"\n",
"#描画\n",
"import matplotlib.pyplot as plt\n",
"from pandas.tools.plotting import autocorrelation_plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 空間計量モデルとOLS・最尤推定・GMM"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 1 CSVをpandasで取り込む。"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv('bukken_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['apart_dummy', 'building_year', 'dk', 'fX', 'fY', 'floor', 'k', 'lk',\n",
" 'mansyon_dumy', 'new_dummy', 'pay', 'published_date', 'r', 'rc_dummy',\n",
" 'room_nums', 'sdk', 'sk', 'sldk', 'slk', 'south_direction_dummy',\n",
" 'square', 'teiki_syakuya_dummy', 'walk_minute_dummy'],\n",
" dtype='object')"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2 空間隣接行列を作成する。"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"S = np.matrix(S_matrix(df, 10, 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3 OLS推定"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: pay R-squared: 0.800\n",
"Model: OLS Adj. R-squared: 0.798\n",
"Method: Least Squares F-statistic: 454.3\n",
"Date: Sun, 20 Nov 2016 Prob (F-statistic): 0.00\n",
"Time: 03:19:31 Log-Likelihood: 397.34\n",
"No. Observations: 1489 AIC: -766.7\n",
"Df Residuals: 1475 BIC: -692.4\n",
"Df Model: 13 \n",
"Covariance Type: nonrobust \n",
"=========================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"-----------------------------------------------------------------------------------------\n",
"Intercept 5.7449 0.018 316.295 0.000 5.709 5.780\n",
"square 0.0199 0.000 43.492 0.000 0.019 0.021\n",
"k -0.1522 0.016 -9.276 0.000 -0.184 -0.120\n",
"lk -2.984e-15 4.25e-17 -70.152 0.000 -3.07e-15 -2.9e-15\n",
"dk -0.0410 0.019 -2.190 0.029 -0.078 -0.004\n",
"sdk 0.0329 0.132 0.249 0.803 -0.227 0.293\n",
"sldk -0.2925 0.085 -3.436 0.001 -0.459 -0.126\n",
"south_direction_dummy -0.0055 0.014 -0.407 0.684 -0.032 0.021\n",
"building_year -0.0097 0.000 -21.794 0.000 -0.011 -0.009\n",
"new_dummy -0.0140 0.010 -1.329 0.184 -0.035 0.007\n",
"mansyon_dumy 5.7449 0.018 316.295 0.000 5.709 5.780\n",
"teiki_syakuya_dummy 0.0334 0.031 1.076 0.282 -0.027 0.094\n",
"walk_minute_dummy -0.0012 0.004 -0.293 0.769 -0.009 0.007\n",
"r -0.1390 0.017 -8.350 0.000 -0.172 -0.106\n",
"rc_dummy 0.0153 0.022 0.689 0.491 -0.028 0.059\n",
"room_nums -0.0304 0.015 -2.035 0.042 -0.060 -0.001\n",
"==============================================================================\n",
"Omnibus: 873.427 Durbin-Watson: 1.487\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 984703.367\n",
"Skew: 1.211 Prob(JB): 0.00\n",
"Kurtosis: 128.959 Cond. No. 3.33e+18\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The smallest eigenvalue is 2.5e-31. This might indicate that there are\n",
"strong multicollinearity problems or that the design matrix is singular.\n"
]
}
],
"source": [
"vars = ['pay', 'square', 'k', 'lk', 'dk', 'sdk', 'sldk', 'south_direction_dummy', 'building_year', \n",
" 'new_dummy', 'mansyon_dumy', 'teiki_syakuya_dummy', 'walk_minute_dummy', 'r', 'rc_dummy', 'room_nums']\n",
"eq = fml_build(vars)\n",
"\n",
"y, X = dmatrices(eq, data=df, return_type='dataframe')\n",
"\n",
"logy = np.log(y)\n",
"\n",
"model = sm.OLS(logy, X, intercept=True)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"なお空間統計でSEMを考えても、OLSやGLSは空間相関の有無に関わらず不偏であり、したがって汎化にはあまり関係ない。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/win_usb_device_plugged.ipynb | 1 | 2750 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# USB Device Plugged\n",
"Detects plugged USB devices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: USB Device Plugged\n",
" id: 1a4bd6e3-4c6e-405d-a9a3-53a116e341d4\n",
" description: Detects plugged USB devices\n",
" references:\n",
" - https://df-stream.com/2014/01/the-windows-7-event-log-and-usb-device/\n",
" - https://www.techrepublic.com/article/how-to-track-down-usb-flash-drive-usage-in-windows-10s-event-viewer/\n",
" status: experimental\n",
" author: Florian Roth\n",
" tags:\n",
" - attack.initial_access\n",
" - attack.t1200\n",
" logsource:\n",
" product: windows\n",
" service: driver-framework\n",
" category: null\n",
" detection:\n",
" selection:\n",
" EventID:\n",
" - 2003\n",
" - 2100\n",
" - 2102\n",
" condition: selection\n",
" falsepositives:\n",
" - Legitimate administrative activity\n",
" level: low\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='event_id:(\"2003\" OR \"2100\" OR \"2102\")')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
nitin-cherian/LifeLongLearning | Python/Python Morsels/multimax/my_try/multimax.ipynb | 1 | 8189 | {
"cells": [
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def multimax(iterable, key=None):\n",
" \"\"\"\n",
" Function that takes an iterable and returns all\n",
" maximum values found in the iterable\n",
" \"\"\"\n",
" input = list(iterable)\n",
" if key:\n",
" return [it for it in input if key(it) == key(max(input, key=key))]\n",
" return [it for it in input if it == max(input)]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[4]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimax([1, 2, 4, 3])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[4, 4]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimax([1, 4, 2, 4, 3])"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 1, 1]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimax([1, 1, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus1: Make sure the function returns an empty list if the iterable is empty"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimax([])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus2: Make sure the function works well with iterator such as files, generators etc"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[5]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers = [1, 3, 8, 5, 4, 10, 6]\n",
"odds = (n for n in numbers if n % 2 == 1)\n",
"multimax(odds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus3: The multimax function accept a keyword argument called \"key\" that is a function which will be used to determine the key by which to compare values as maximums. For example the key function could be used to find the longest words in a list of words "
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"words = [\"cheese\", \"shop\", \"ministry\", \"of\", \"silly\", \"walks\", \"argument\", \"clinic\"]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['ministry', 'argument']"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimax(words, key=len)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ministry'"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words = [\"cheese\", \"shop\", \"ministry\", \"of\", \"silly\", \"walks\", \"argument\", \"clinic\"]\n",
"max(words, key=len)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'argument'"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words = [\"cheese\", \"shop\", \"argument\", \"of\", \"silly\", \"walks\", \"ministry\", \"clinic\"]\n",
"max(words, key=len)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unitests"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"........\n",
"----------------------------------------------------------------------\n",
"Ran 8 tests in 0.004s\n",
"\n",
"OK\n"
]
}
],
"source": [
"import unittest\n",
"\n",
"\n",
"class MultiMaxTests(unittest.TestCase):\n",
"\n",
" \"\"\"Tests for multimax.\"\"\"\n",
"\n",
" def test_single_max(self):\n",
" self.assertEqual(multimax([1, 2, 4, 3]), [4])\n",
"\n",
" def test_two_max(self):\n",
" self.assertEqual(multimax([1, 4, 2, 4, 3]), [4, 4])\n",
"\n",
" def test_all_max(self):\n",
" self.assertEqual(multimax([1, 1, 1, 1, 1]), [1, 1, 1, 1, 1])\n",
"\n",
" def test_lists(self):\n",
" inputs = [[0], [1], [], [0, 1], [1]]\n",
" expected = [[1], [1]]\n",
" self.assertEqual(multimax(inputs), expected)\n",
"\n",
" def test_order_maintained(self):\n",
" inputs = [\n",
" (3, 2),\n",
" (2, 1),\n",
" (3, 2),\n",
" (2, 0),\n",
" (3, 2),\n",
" ]\n",
" expected = [\n",
" inputs[0],\n",
" inputs[2],\n",
" inputs[4],\n",
" ]\n",
" outputs = multimax(inputs)\n",
" self.assertEqual(outputs, expected)\n",
" self.assertIs(outputs[0], expected[0])\n",
" self.assertIs(outputs[1], expected[1])\n",
" self.assertIs(outputs[2], expected[2])\n",
"\n",
" # To test the Bonus part of this exercise, comment out the following line\n",
" # @unittest.expectedFailure\n",
" def test_empty(self):\n",
" self.assertEqual(multimax([]), [])\n",
"\n",
" # To test the Bonus part of this exercise, comment out the following line\n",
" # @unittest.expectedFailure\n",
" def test_iterator(self):\n",
" numbers = [1, 4, 2, 4, 3]\n",
" squares = (n**2 for n in numbers)\n",
" self.assertEqual(multimax(squares), [16, 16])\n",
"\n",
" # To test the Bonus part of this exercise, comment out the following line\n",
" # @unittest.expectedFailure\n",
" def test_key_function(self):\n",
" words = [\"alligator\", \"animal\", \"apple\", \"artichoke\", \"avalanche\"]\n",
" outputs = [\"alligator\", \"artichoke\", \"avalanche\"]\n",
" self.assertEqual(multimax(words, key=len), outputs)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" unittest.main(argv=['first-arg-is-ignored'], exit=False)"
]
},
{
"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
}
| mit |
braysia/covertrace | doc/jupyter_examples/ktr_modeling.ipynb | 1 | 1314572 | null | mit |
akimbekov/Stock_prediction_using_ML_and_Deep_learning | .ipynb_checkpoints/Project-checkpoint.ipynb | 1 | 785102 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outline is based on this sentence:\n",
"\n",
"For example, a project that performs various binary classification methods on a social science dataset you may want to focus on data munging, method selection, method evaluation, feature extraction, and presentation of analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center> <h1> Prediction of stock raise/fall using state-of-the-art Machine Learning and Deep Learning methods. </h1>\n",
"\n",
"<h2> STA208 Final project </h2>\n",
"\n",
"<center><img src=\"https://stockmarketvideo.com/wp-content/uploads/2016/04/stock-market-prediction.jpg\" alt=\"Drawing\" style=\"width: 200px;\"></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sample outline:\n",
"\n",
"1. Introduction\n",
" - stock price prediction challanges, relation to news, why ML and Deep learning approach might work\n",
"2. data munging and feature extraction\n",
" - scraping of news data from motleyfool.com\n",
" - getting the sentiment scores using the dictionary of positive and negative words\n",
" - designing a \"sentiment\" feature, word cloud for one POSITIVE and one NEGATIVE articles\n",
" - quandl.api to get the data, etc...\n",
"3. method selection, evaluation, and comparison\n",
" - baseline classification methods, logistic regression, SVM, random forest\n",
" - feed forward neural nets, recurrent neural nets (description also)\n",
" - tuning the neural net parameters, etc.\n",
"4. results and conclusion\n",
" - comparison of accuracy results, confusion matrix, ROC, PR, curves, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whether one trades in Stocks, Index, Currencies, Commodities, a person would like to know questions like:\n",
"\n",
"- What is the market trend? Will it continue?\n",
"- Whether market will close higher or lower compared to its opening levels? \n",
"- What could be expected high, low, close levels?\n",
"\n",
"There could be many more such questions. The first challenge is to know the trend and direction of the market. If there was a crystal ball that could provide meaningful prediction in advance on the trend and direction of the markets that could help take correct trading position to make profitable trades.\n",
"Predictive analytics based on historical price data using Data Mining, Machine Learning and Artificial Intelligence can provide prediction in advance on whether the next day market will close higher or lower compared to its opening levels. \n",
"\n",
"We chose to investigate whether there is a connection between the sentiment in the news for a given day and the resulting market value changes for Apple, Inc on the same day. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Data Munging and Feature Extraction\n",
"\n",
"To get the news data related to Apple, Inc., we webscraped related news with term **Apple** from the [Motley Fool](https://www.motleyfool.com) of the last three years.\n",
"\n",
"**Note**: we commented out lines of codes where the scraping part is done. The scrapped data is available in github repo with name *\"mfool.csv\"*."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#data munging and feature extraction packages\n",
"import requests\n",
"import requests_ftp\n",
"import requests_cache\n",
"import lxml\n",
"import itertools\n",
"import pandas as pd\n",
"import re\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import string\n",
"from bs4 import BeautifulSoup\n",
"from collections import Counter\n",
"from matplotlib import pyplot as plt\n",
"from wordcloud import WordCloud\n",
"plt.style.use('ggplot')\n",
"plt.rcParams['figure.figsize'] = [10, 8]\n",
"\n",
"#machine learning from scikit-learn\n",
"from sklearn.metrics import classification_report,confusion_matrix, precision_recall_curve, roc_curve, auc\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"#Deep learning from Tensor Flow\n",
"#feed forward neural network\n",
"import tensorflow as tf\n",
"from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier\n",
"from tensorflow.contrib.layers import real_valued_column\n",
"\n",
"#recurrent neural nets\n",
"from tensorflow.contrib.layers.python.layers.initializers import xavier_initializer\n",
"from tensorflow.contrib import rnn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def motley_page_links(page):\n",
" \"\"\"\n",
" Given a page number, it returns all article links.\n",
" \n",
" Input: a page number (default = 1)\n",
" Output: a list with links on the given page\n",
" \"\"\"\n",
" \n",
" response = requests.get(\n",
" 'https://www.fool.com/search/solr.aspx?page={}&q=apple&sort=date&source=isesitbut0000001'.format(page))\n",
" response.raise_for_status()\n",
" html = response.text\n",
" parsed_html = BeautifulSoup(html, 'lxml')\n",
"\n",
" div_with_links = parsed_html.find_all(name = 'dl',\n",
" attrs = {'class' : 'results'})\n",
" links = []\n",
" for link in div_with_links[0].find_all('a', href = True):\n",
" links.append(link['href'])\n",
" \n",
" return links\n",
"\n",
"def motley_all_links(no_pages = 1):\n",
" \"\"\"\n",
" Given number of pages, it returns all the links \n",
" from \"no_pages\"\n",
" \n",
" Input: number of pages (default = 1)\n",
" Output: a list with links from the pages\n",
" \"\"\"\n",
" all_links = []\n",
" for page in range(1, (no_pages + 1)):\n",
" all_links.extend(motley_page_links(page))\n",
" \n",
" return all_links\n",
"\n",
"def motley_article_info(url):\n",
" \"\"\"\n",
" Given an article url, it returns title, date, content\n",
" and url of that article.\n",
" \n",
" Input: article url\n",
" Ouput: a dictionary with 'title', 'date',\n",
" 'article', and 'url' as keys.\n",
" \"\"\"\n",
" \n",
" response = requests.get(url)\n",
" response.raise_for_status()\n",
" html = response.text\n",
" parsed_html = BeautifulSoup(html, 'lxml')\n",
" content = parsed_html.find_all(name = 'div',\n",
" attrs = {'class' : 'full_article'})\n",
"\n",
" date = parsed_html.find_all(name = 'div', attrs = {'class' : 'publication-date'})[0].text.strip()\n",
" title = parsed_html.find_all('h1')[0].text\n",
" article = ' '.join([t.text for t in content[0].find_all('p')])\n",
" \n",
" return {'title' : title,\n",
" 'date' : date,\n",
" 'article' : article,\n",
" 'url' : url}\n",
"\n",
"def motley_df(no_pages):\n",
" \"\"\"\n",
" Creates DataFrame for the articles in url\n",
" with author, text, title, and url as column\n",
" names.\n",
" \n",
" Input: A url, number of pages\n",
" Output: DataFrame with 4 columns: author,\n",
" text, title, and url.\n",
" \"\"\"\n",
" \n",
" #get all links in the specified number of pages\n",
" #from url\n",
" links = motley_all_links(no_pages)\n",
" \n",
" #create dataframe for each link and\n",
" #combine them into one dataframe\n",
" article_df = pd.DataFrame(index = [999999], columns=['article', 'date', 'title', 'url'])\n",
" for i, link in enumerate(links):\n",
" try:\n",
" append_to = pd.DataFrame(motley_article_info(link), index = [i])\n",
" article_df = article_df.append(append_to)\n",
" except:\n",
" pass\n",
" \n",
" article_df = article_df.drop(999999)\n",
" return article_df\n",
"\n",
"#df = motley_df(1000)\n",
"#convert_to_csv(df, \"mfool.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.\n",
"sentiment scoring"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ew8ND/uVf/kWWL18uZWVl1dreOmF3/fp1GT9+vHI5npeXl4wdO1YuXryotP/222+la9eu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\nECEiItUYIkREpBpDhIiIVPtfZGJugYsTzq8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d559160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"motley = pd.read_csv('mfool.csv')\n",
"\n",
"negative = pd.read_csv('negative-words.txt', sep = ' ', header = None)\n",
"positive = pd.read_csv('positive-words.txt', sep=' ', header=None)\n",
"\n",
"def score_word(word):\n",
" \"\"\"\n",
" returns -1 if negative meaning, +1 if positive meaning,\n",
" else 0\n",
" \n",
" input: a word\n",
" ouput: -1, 0, or + 1\n",
" \"\"\"\n",
" if word.lower() in negative.values:\n",
" return -1\n",
" elif word.lower() in positive.values:\n",
" return +1\n",
" return 0\n",
"\n",
"def get_scores(article):\n",
" \"\"\"\n",
" returns sentiment scores for a given article\n",
" \n",
" input: an article\n",
" output: sentiment score\n",
" \"\"\"\n",
" wordsArticle = article.split(' ')\n",
" scores = [score_word(word) for word in wordsArticle]\n",
" return sum(scores)\n",
"\n",
"motley['sentiment'] = motley['article'].apply(get_scores)\n",
"\n",
"plt.hist(motley.sentiment, bins=50)\n",
"plt.xlabel('sentiment scores')\n",
"plt.ylabel('frequency')\n",
"plt.title('Distribution of sentiment scores of articles');\n",
"\n",
"# motley.to_csv('motley_with_s_scores.csv', encoding='utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lPHseJX87HYD6Y/+ViHeVrW+JfqW0J963eL8Awu+sz+hXvii592RACKH+v3wB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\n1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DYZVtXk6Feeh+HoIwEA4QUL097WdMHZ0mSE+yMKWsuLaZZ741QrXrnHiYo6eqEriops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\n3AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121340eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"most_positive_article = motley['article'][motley['sentiment'] == np.max(motley['sentiment'])].values[0]\n",
"wc = WordCloud().generate(most_positive_article)\n",
"plt.imshow(wc)\n",
"plt.axis('off');"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ILPDBnxZR8Y9HxXfZvHc1vxXHSSNxXTFWv55bQuhXERrveeoD/F/Ma3F/aaOt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\n7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rtcgKwCHp469fCGKK4eT8KwrKpZ3x9YH4HApIWkOjeHBWtpYZw/1o2gqCAh038YTGWr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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12196a4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"most_negative_article = motley['article'][motley['sentiment'] == np.min(motley['sentiment'])].values[0]\n",
"wc = WordCloud().generate(most_negative_article)\n",
"plt.imshow(wc)\n",
"plt.axis('off');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.\n",
"merging data sets\n",
"\n",
"APPLE stock data was obtained using **[Quandl](https://www.quandl.com)** API at \"https://www.quandl.com/api/v3/datasets/WIKI/AAPL.csv\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path = \"../datasets/\"\n",
"aapl = pd.read_csv(path+'WIKI_PRICES_AAPL.csv')\n",
"fool = pd.read_csv(path+'motley_with_s_scores.csv')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def format_df(stock_df, news_df, word):\n",
" \"\"\"\n",
" merges stock_df and news_df on \"date\"\n",
" column\n",
" \n",
" input: stock df, news df, word\n",
" output: merged df\n",
" \"\"\"\n",
" \n",
" stock_df['diff'] = stock_df['close']-stock_df['open']\n",
" news_df['Count'] = news_df['article'].apply(lambda x: x.count(word))\n",
" news_df.loc[news_df['Count'] <= 5, 'sentiment'] = 0\n",
" news_df['date'] = pd.to_datetime(news_df['date'])\n",
" news_df['date'] = news_df['date'].dt.strftime('%Y-%m-%d')\n",
" news_df = news_df.groupby(['date'], as_index = False).sum()\n",
" news_df = news_df[['date', 'sentiment', 'Count']]\n",
" merged_df = pd.merge(news_df, stock_df)\n",
" merged_df['bin_sentiment'] = pd.cut(merged_df['sentiment'], [-np.inf, -0.001, 0.001, np.inf], labels = [-1, 0, 1])\n",
" merged_df['bin_diff'] = pd.cut(merged_df['diff'], [-np.inf, -0.001, 0.001, np.inf], labels = [-1, 0, 1])\n",
" return merged_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"merged_df = format_df(aapl, fool, 'Apple')\n",
"merged_df.head()\n",
"#merged_df.to_csv('merged_df.csv', encoding='utf-8')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Methods selection, evaluation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_ROC(y_test, scores, label, color):\n",
" \"\"\"\n",
" plots ROC curve\n",
" input: y_test, scores, and title\n",
" output: ROC curve\n",
" \"\"\"\n",
" false_pr, true_pr, _ = roc_curve(y_test, scores[:, 1])\n",
" roc_auc = auc(false_pr, true_pr)\n",
" plt.plot(false_pr, true_pr, lw = 3,\n",
" label='{}: area={:10.4f})'.format(label, roc_auc), color = color)\n",
" plt.plot([0, 1], [0, 1], color='black', lw=1, linestyle='--')\n",
" plt.xlabel('False positive rate')\n",
" plt.ylabel('True positive rate')\n",
" plt.legend(loc=\"best\")\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlim([0.0, 1.0])\n",
" plt.title('ROC')\n",
"\n",
"def plot_PR(y_test, scores, label, color):\n",
" \"\"\"\n",
" plots PR curve\n",
" input: y_test, scores, title\n",
" output: Precision-Recall curve\n",
" \"\"\"\n",
" precision, recall, _ = precision_recall_curve(y_test, scores[:, 1])\n",
" plt.plot(recall, precision,lw = 2,\n",
" label='{}'.format(label), color = color)\n",
" plt.xlabel('Recall')\n",
" plt.ylabel('Precision')\n",
" \n",
" plt.legend(loc=\"best\")\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlim([0.0, 1.0])\n",
" plt.title('PR')\n",
" \n",
"def plot_confusionmatrix(ytrue, ypred):\n",
" \"\"\"\n",
" \n",
" plots confusion matrix heatmap and prints out\n",
" classification report\n",
" \n",
" input: ytrue (actual value), ypred(predicted value)\n",
" output: confusion matrix heatmap and classification report\n",
" \n",
" \"\"\"\n",
" \n",
" print (classification_report(ytrue, ypred))\n",
" \n",
" print ('##################################################################')\n",
" \n",
" cnf_matrix = confusion_matrix(ytrue, ypred)\n",
" sns.heatmap(cnf_matrix, cmap='coolwarm_r', annot = True, linewidths=.5, fmt = '.4g')\n",
" plt.title('Confusion matrix')\n",
" plt.xlabel('Prediction')\n",
" plt.ylabel('Actual');"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(779, 20)\n"
]
}
],
"source": [
"apple = pd.read_csv(path + 'merged_df.csv')\n",
"apple.head()\n",
"print (apple.shape)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AHqIM9x3WBreaZrT5uoCe5/s5Z7fjQw89hOuvvx4vfOELceKJJwIA/u7v/i4y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\n919T5SMGwgNkRyBv4SuVSuGCCy7A4sWLceWVV+Lwww/HoEGDsHLlSrz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Lr4Zx/+PhB+qQ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\n+gWMwgBFK3361QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d93df98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"apple.plot('date', 'diff');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is exterme fluctuation betweeen opening and closing prices of **Apple, Inc.** (as expected).\n",
"\n",
"Let's choose the features and label (bin_diff) and make the dataframe ready for machine learning and deep learning."
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>date</th>\n",
" <th>sentiment</th>\n",
" <th>bin_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-05-05</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-05-06</td>\n",
" <td>15</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-05-07</td>\n",
" <td>40</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-05-08</td>\n",
" <td>31</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-05-09</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date sentiment bin_diff\n",
"0 2014-05-05 53 1\n",
"1 2014-05-06 15 -1\n",
"2 2014-05-07 40 -1\n",
"3 2014-05-08 31 -1\n",
"4 2014-05-09 3 1"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aapl = apple.copy()[['date', 'sentiment', 'bin_diff']]\n",
"aapl.head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GN+b+++/fbfur74m1JzNmzMiMGTP2b2UAAMOYS0wAAAWILACAAkQWAEABIgsAoACRBQBQ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\n/D+gXyrvJR4B8AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x126dcd588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(aapl['bin_diff']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's drop the observation with \"0\" and make it binary classification."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aapl = aapl[aapl['bin_diff'] != 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, to make the models work properly, from now on, we **re-code** *loss* category from -1 to 0."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"label = aapl['bin_diff'] == 1\n",
"label = label.astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"let's look at the features and standardize them."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"InputDF = aapl.copy().drop('bin_diff', axis = 1)\n",
"InputDF = InputDF.set_index('date')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"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>sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-05-05</th>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-06</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-07</th>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-08</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-09</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentiment\n",
"date \n",
"2014-05-05 53\n",
"2014-05-06 15\n",
"2014-05-07 40\n",
"2014-05-08 31\n",
"2014-05-09 3"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"InputDF.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"InputDF = InputDF.apply(lambda x:(x -x.mean())/x.std())"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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>sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-05-05</th>\n",
" <td>0.692653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-06</th>\n",
" <td>-0.497135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-07</th>\n",
" <td>0.285620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-08</th>\n",
" <td>0.003828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-09</th>\n",
" <td>-0.872857</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentiment\n",
"date \n",
"2014-05-05 0.692653\n",
"2014-05-06 -0.497135\n",
"2014-05-07 0.285620\n",
"2014-05-08 0.003828\n",
"2014-05-09 -0.872857"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"InputDF.head()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_size = 600\n",
"xtrain, xtest = InputDF.iloc[:test_size, :], InputDF.iloc[test_size:, :]\n",
"ytrain, ytest = label[:test_size], label[test_size:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic regression"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.27 0.11 0.15 75\n",
" 1 0.54 0.78 0.64 102\n",
"\n",
"avg / total 0.43 0.50 0.43 177\n",
"\n",
"##################################################################\n"
]
},
{
"data": {
"image/png": 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eV8hVVyrt/yUFeklAlRYZ7tCV1Rx6+f1C1aru0KC7QvT0/z+tyDCpUbSTtAVlchhcufik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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121375d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"logreg = LogisticRegression()\n",
"logreg_model = logreg.fit(xtrain, ytrain)\n",
"logpred = logreg_model.predict(xtest)\n",
"logscores = logreg_model.predict_proba(xtest)\n",
"\n",
"plot_confusionmatrix(ytest, logpred)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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78fOKiaHAiBEErl3r2JZZogQxH3xAZpUq4Kbf/43+QOS0qcwKFSqwd+9eAA4d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\n91Iwk5sYhoHNZiP811+ZEBrK7JMnCfxjp56+FBERcRvdYyZ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aPC8oKChfmcXrRsy0YoDv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125dd79b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ROC(ytest, logscores, 'Logistic regression', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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6nR07rBo7Nll5eXYNGuTVkiX5XFwBAMARiFqYWa1WTZ48udrH2rdvH/75aaed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\ngjADAAAwCcIMAADAJAgzAAAAkyDMAAA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"text/plain": [
"<matplotlib.figure.Figure at 0x126d78a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_PR(ytest, logscores, 'Logistic regression', 'b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Support Vector Machines"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.54 0.33 0.41 75\n",
" 1 0.62 0.79 0.70 102\n",
"\n",
"avg / total 0.59 0.60 0.58 177\n",
"\n",
"##################################################################\n"
]
},
{
"data": {
"image/png": 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JCwAAljE5caFwAQDAMi6D77JIqwgAABiDxAVXx+1Wi7HjFRHXRK7wcB1asUxlR4/o5umz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\nAJxKrhnGRlScAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ddf8e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"svm = SVC(probability=True)\n",
"svm_model = svm.fit(xtrain, ytrain)\n",
"svmpred = svm_model.predict(xtest)\n",
"svmscores = svm_model.predict_proba(xtest)\n",
"\n",
"plot_confusionmatrix(ytest, svmpred)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Rw5Yya9asyeHfpkeTkpKoUqWK/b2goCB8fX3x8fHB19eXoKAgMjMzHRVFRETE\n7R0+fJg2bdpQq1Ytli5d6pB9mE+dolyXLn+UMrOZlDlzyO7RwyH7kyvnsBmzxo0bc/ToUZ555hkM\nwyA2NpatW7dSsWJFGjVqxOeff87EiRMxmUzUqlWLunXrOiqKiIiI27JareTm5hIREcFLL71Ucs+6\n/H/MJ04QFROD92+zYoaPDxdff52c9u0dsj+5OibDMAxXhyguTcd7Li2neC4dO8+m4+fefvnlF0aM\nGEGTJk0YM2bMJe+X1PHzTkoiqkcPzD//DIDh64tl4UJy77vvmrctf83tljJFRETk8vbs2UPbtm25\n/fbbGTlypMP2433sGFFdu9pLmc3fH0t8vEqZm9IjmURERJzIMAxMJhM//vgjs2fPpkWLFg7bl8+R\nI0Q9/DBeKSkA2IKCsMTHk3fHHQ7bp1wbzZiJiIg4ydmzZ+natSv/+9//ePTRRx1aynwPHiQqJuaP\nUhYSQvLq1Splbk7FTERExAnee+892rVrx913302jRo0cui/f/fuJfPhhvNLTAbCFh5O8fj35Dt6v\nXDstZYqIiDiYzWZjzZo1LFy40OGPIPTbtYvIAQMw5eQAYC1XjuS1aymoXduh+5WSoRkzERERBzl1\n6hRDhgwhKyuLJUuWOL6U7dxJZL9+f5SyihVJTkxUKfMgKmYiIiIO8M4779C+fXsaNGhAUFCQw/fn\n//bbRA4ciCkvD4CC6GguJCZSUMRTdcS9aClTRESkhJ06dYpp06YRHx9P/fr1Hb6/gMREwkePxmSz\nAVBQrRrJ69ZhrVzZ4fuWkqViJiIiUkJOnDjBrl276N+/Px9++CHe3o7/azZw9WrCxo/H9Nv94vOr\nVyd57Vps//iHw/ctJU9LmSIiIiVg8+bNdOzYEbPZDOCcUrZsGeHjxv1RymrXJnnjRpUyD6YZMxER\nkWv05ptv8uqrr7JmzRrq1KnjlH0GzZ9P2JQp9td5t95K8urVGJGRTtm/OIaKmYiIyFVKSkoiLy+P\ntm3b0rp1a4KDgx2/U8MgOC6O0Jkz7UN5DRqQnJCAERbm+P2LQ2kpU0RE5AoZhsG6devo0qUL33//\nPf7+/k4rZSHTpxcqZblNm5K8Zo1KWSmhGTMREZErNHXqVD744AM2btxIzZo1nbNTwyB08mSCFy+2\nD+W0bMnFpUsxAgKck0EcTjNmIiIixfT70mWPHj3Ytm2b80qZzUbYU08VLmWtW2NZtkylrJRRMRMR\nESmCYRg8u6IcAAAgAElEQVTEx8fTpUsXvvrqK6pXr06AswqR1Ur4E08QtHKlfSi7XTssixaBv79z\nMojTaClTRETkbxQUFBAbG8sPP/zAm2++yY033ui8nefnEz5yJIFvvmkfyurcmZS4OHDC7TjE+XRU\nRURELsNisRAZGckDDzxAmzZt8HfmDFVeHsTEFCplmT16kPryy/DbvdKk9NFSpoiIyP9jGAYLFiyg\nTZs2ZGVl8eCDDzq3lOXkEDlgAGzebB/K7N2b1BkzVMpKOc2YiYiI/MnFixcZPXo0ycnJbNq0icDA\nQKfu35SVRWS/fvjt3Wsfyxg0iLTnngOTyalZxPlUzERERH5jtVoBqFevHsOGDcPX19ep+zelpxPZ\npw9+Bw7Yx9JHjSJ93DiVsjJCS5kiIlLm2Ww25syZw8CBA4mIiODxxx93filLTSWqZ89CpYwpU0gf\nP16lrAzRjJmIiJRp58+fZ9SoUWRnZ/P666+7JIOXxUJkz574fvGFfSz1uecImzgRzp51SSZxDc2Y\niYhImXbo0CFuu+02NmzYQKVKlZy+f6/z54nq1q1QKUuZOpXMwYOdnkVcTzNmIiJS5litVmbNmkXF\nihXp1asX999/v0tyeJ07R1RMDD7ffQeAYTKRMnMm2T16uCSPuJ5mzEREpEw5d+4cMTExHDx4kDZt\n2rgsh/nUKcp16fJHKTObSXntNZWyMk4zZiIiUqbMmDGDFi1aMHz4cMwuuieY+cQJomJi8P7t/DHD\n25uL8+aR0769S/KI+1AxExGRUi8/P5+4uDh69uzJzJkz8fJy3YKRd1ISUT16YP75ZwAMX18sCxaQ\n68LZO3EfWsoUEZFS7fTp0zz00EMcPXqUwMBA15ayY8eI6trVXsps/v5Y4uNVysROxUxEREqt/Px8\nevToQfv27YmPjycyMtJlWXyOHKFc9+6Yk5MBsAUFYUlIILdlS5dlEvejpUwRESl1cnNz2bx5MzEx\nMWzfvp2QkBCX5vE5eJCoRx/FKz0dAFtICMkJCeQ3auTSXOJ+NGMmIiKlyvfff0/Hjh35z3/+Q05O\njstLme/+/UQ9/PAfpSw8nOT161XK5C+pmImISKnx7bff0rFjR2JiYli8eDEBAQEuzeO3e/evM2VZ\nWQBYo6K4sGED+XXrujSXuC8tZYqIiMfLzs7m22+/5ZZbbmHTpk1Ur17d1ZHw27mTyMGDMeXlAWCt\nUIHkdesoqFHDxcnEnWnGTEREPNo333xDhw4dWLVqFV5eXm5RyvzffpvIgQPtpawgOpoLiYkqZVIk\nFTMREfFYO3fu5KGHHqJ///5MmzbN1XEACEhMJCI2FlNBAQAFVauSvGkT1n/+08XJxBNoKVNERDxO\nZmYmVquVm266ifXr11O7dm1XRwIgcPVqwsaPx2QYAOTfeCPJ69Zh+8c/XJxMPIVmzERExKN8+eWX\n3H///bz55ptUq1bNfUrZ8uWEjxv3RymrXZvkxESVMrkiKmYiIuIxVq9eTUxMDKNGjaJ3796ujmMX\n9MYbhE+caH+dd+utXFi/Hlv58i5MJZ5IS5kiIuL28vLy8PX1xdfXl82bN7vFCf6/C46LI3TGDPvr\nvAYNSE5IwAgLc2Eq8VSaMRMREbd25MgR7rnnHr788ku6du3qPqXMMAiZPr1QKctt2pTkNWtUyuSq\nqZiJiIhbMgyDJUuW8OijjzJhwgRuvvlmV0f6g2EQ+u9/EzJnjn0ot0ULLAkJGMHBLgwmnk5LmSIi\n4pby8/P58ssvefvtt6lataqr4/zBZiPs6acJWrnSPpRz771YFi4Ef38XBpPSQDNmIiLiVg4dOkS3\nbt0wDINXXnnFvUqZ1Ur4E08UKmXZ7dphWbxYpUxKhIqZiIi4BZvNxuuvv86AAQMYOHAgfn5+ro5U\nWH4+4SNHErh+vX0oq3NnLs6fD76+LgwmpYmWMkVExC0kJSXx4Ycf8s477xAdHe3qOIXl5RERG0vA\n9u32ocwePUh9+WUwm10YTEobFTMREXGp/fv3c+jQIUaOHMmGDRswmUyujlRYTg6Rgwbh//779qHM\n3r1JnToVvLTwJCVLv6JERMQlrFYrr776KsOGDaNu3boAblfKTFlZRPXtW6iUZQwaROqLL6qUiUNo\nxkxERFxi+fLlfPTRR2zfvp2KFSu6Os4lTBkZRPbujd+BA/ax9JEjSR8/HtysQErpoWImIiJOtXv3\nbiIiInj00Ufp27cvZjc8R8uUmkrUI4/ge/iwfSxt3DgyRo92YSopCzQPKyIiTlFQUMC0adMYM2YM\nOTk5+Pr6umUp87JYiOrevVApS332WZUycQrNmImIiFOMHDmStLQ0duzYQbly5Vwd5y95nT9PVI8e\n+Hz1lX0sZepUsvr2dV0oKVNUzERExKH27t1L48aNmThxIv/4xz/wctOT5r3OnSMqJgaf774DwDCZ\nSJ0xg6yePV2cTMoS9/zdISIiHi8vL49JkybxxBNPcObMGaKjo922lJlPn6Zcly5/lDKzmZTXXlMp\nE6fTjJmIiJS47OxsunTpQoUKFXj33XeJiIhwdaTLMn//PVHdu+N99iwAhrc3F+fNI6d9excnk7JI\nxUxERErUjz/+SNWqVXn66adp1qyZ292b7M+8v/mGqJgYzD//DIDh64tlwQJy27RxcTIpq9xzTllE\nRDxOTk4OTz31FH369CE/P5/mzZu7dyk7doyoLl3spczm749l+XKVMnEpFTMREblmJ0+e5IEHHuDi\nxYts2bIFHx8fV0f6Wz5HjlCue3fMyckA2AIDsSQkkHvXXS5OJmWdljJFROSaZGVlERISwsCBA+ne\nvbtbz5IB+Bw8SNSjj+KVng6ALSSE5JUryb/9dhcnE1ExExGRq5SVlcXEiRMpKChgzpw5xMTEuDpS\nkXz37yeyTx+8srIAsIWHk7x6Nfm33ebiZCK/0lKmiIhcsePHj9OuXTtsNhvTp093dZxi8du9+9eZ\nst9KmTUqigsbNqiUiVvRjJmIiBSbYRgA/PDDD8TGxtK9e3cXJyoev507iRw8GFNeHgDWChVIXreO\ngho1XJxMpDAVMxERKZb09HQmTJjA3XffTbdu3Vwdp9j8t24lYtgwTAUFABRER5O8bh3Wf/7TxclE\nLqWlTBERKdLnn39O27ZtCQ4O5oEHHnB1nGIL2LSJiKFD/yhlVauSvGmTSpm4rWLNmCUnJ/Pjjz9S\nr149LBaL2z58VkREHGPZsmWMHz+ejh07ujpKsQWuWUPYuHGYflt+zb/xRpLXrcP2j3+4OJnI5RU5\nY/bpp5/yzDPPsGTJElJTU3n88cc5ePCgM7KJiIgLpaSkMHr0aM6cOcOrr77qWaVs+XLCx479o5TV\nqkVyYqJKmbi9IovZxo0befHFFwkKCiIiIoIXXniB9evXOyObiIi4yCeffELbtm0JDQ31uFWSoDfe\nIHziRPvrvDp1SN6wAVv58i5MJVI8RS5l2my2Qg+frVatmiPziIiIi2VlZTFq1CgmT55M27ZtXR3n\nigTHxRE6Y4b9dV79+iSvWoURFubCVCLFV+SMmZ+fHxcuXLDfyfn48eP4+vo6PJiIiDiXxWLh9ddf\nJyAggA8//NCzSplhEDJ9eqFSltu0Kclr16qUiUcpspg9/PDDTJkyhZ9++omJEycyc+ZMHnnkEWdk\nExERJ/n4449p06YNKSkp2Gw2t3/WZSGGQei//03InDn2odwWLbAkJGAEB7swmMiVK3Ips2bNmkyd\nOpWkpCRsNhs1atQgNDTUGdlERMQJPv30U4YMGcKrr75Kq1atXB3nythshE2cSNCKFfahnHvvxbJw\nIfj7uzCYyNUpcsbs9xP/69evT8OGDQkNDWXin06qFBERz/TLL79w4MAB6tevz3vvved5pcxqJXzs\n2EKlLLtdOyyLF6uUice67IzZK6+8wrlz5/j5558ZO3asfdxqteLtrQcGiIh4sj179jB69GgGDhxI\nkyZNPO7KS/LzCR89msA337QPZXXqRMrs2aC/o8SDXfZX76OPPsr58+dZsGAB/fv3t497eXlRuXJl\np4QTEZGSl5CQwKxZs3jttddo3ry5q+Ncubw8ImJjCdi+3T6UFRNDyowZYDa7MJjItbtsMbvuuuu4\n7rrriIuLw8ur8IpnTk6Ow4OJiEjJOnv2LEFBQdx1113861//orwn3tcrJ4fIQYPwf/99+1Bm796k\nTp0KXnrKoHi+Iud7P/nkE9avX09OTg6GYWCz2cjIyGDFn9b0/4rNZmPx4sX8+OOP+Pj4MGTIECpW\nrGh///Dhw2zcuBGAf/7znwwYMMB+Sw4RESlZ7733HmPHjmXatGncf//9ro5zVUxZWUT274/ff/9r\nH8sYOJC0SZNAf39IKVFkMVu5ciU9evTgP//5Dx07duR///sfAQEBRW744MGD5Ofn26/oXLFiBePH\njwcgOzubhIQEJk2aRGhoKG+99Rbp6em62lNExAGmTZvG5s2bWbRoEbfffrur41wVU0YGkb1743fg\ngH0sfcQI0p98UqVMSpVi3WD2zjvvpEaNGvj4+PDYY4/x6aefFrnhr776inr16gFw00038d1339nf\n+/rrr7n++utZsWIFzz33HGFhYSplIiIlLDU1FYD69evz7rvvem4pS00lqmfPQqUsbdw40idMUCmT\nUqfIGTNfX1/y8/OpWLEiP/zwA7fcckuxNpydnU1gYKD9tZeXF1arFbPZTHp6OseOHWPGjBn4+/vz\n3HPPcdNNN1GpUqW/3WZR74t70/HzXDp2nicxMZHY2FgOHDhQ6AIuj5OcDL16wZ8nBGbOJPSJJygr\n/5zX77+ypchi1rBhQ6ZPn86wYcOYOHEix48fL9bsVkBAANnZ2fbXhmFg/u1qmZCQEG688UbCw8MB\nqF27Nj/88EORv/jOnj1b5H7FPVWqVEnHz0Pp2HmW3Nxcnn/+eT788EOWLVtGtWrVPPb4eZ0/T1SP\nHvh89ZV9LGXqVLJ69gQP/ZmulH7/ea6rLdRFFrNWrVrRsmVLIiMjGT9+PMePH6dZs2ZFbrhmzZp8\n8skn3HnnnSQlJVGlShX7ezfccAOnTp0iLS2NoKAgvvnmG+69996r+gFERORXNpsNgODgYLZv306Y\nBz8j0uvcOaJiYvD57TQYw2QidcaMX0uZSClmMgzD+LsPjB49mri4uCve8O9XZZ48eRLDMIiNjeXw\n4cNUrFiRRo0asW/fPrZs2QLAHXfcQadOnYrcpv7V4Ln0rz7PpWPnGTZv3sySJUvYsmVLoVsceeLx\nM58+TVT37nj/+CMAhpcXKXFxZHfp4uJkzueJx09+5bAZs/Lly/P1119To0aNS+5n9ne8vLwYNGhQ\nobHo6Gj7fzdr1qxYM28iInJ52dnZPPvssxw4cID58+df0Z/T7sj8/fe/lrLfyojh7c3F118n54EH\nXJxMxDmKLGanT5/mueeew2w24+Pjg2EYmEwm4uPjnZFPRET+xtdff01BQQHbt28nODjY1XGuifc3\n3xAVE4P5558BMHx9sSxYQG6bNi5OJuI8RRaz559/3hk5RESkmAzDYN26dZw5c4Ynnnjiqk43cTfe\nX35JVI8emJOTAbD5+3Nx6VJy77rLxclEnKtYS5kiIuIeMjIyeOqppzh27Bjz5893dZwS4XP0KFE9\ne+KVkgKALTAQy4oV5N1xh4uTiThfkcVMRETcx6JFi/D392fbtm3FegqLu/M5dIioXr3wSk8HwBYS\nQvLKleR76M1wRa6VipmIiJszDIP4+Hjq16/PqFGjPP4E/9/5fvQRkb1745WVBYAtPJzk1avJv+02\nFycTcZ1i/e7Oy8uz3/YiNzfX0ZlEROQ3qampDBo0iDVr1hASElJqSpnf7t1E9uplL2XWqCgubNig\nUiZlXpG/w5OSkhgxYgTTpk3DYrEwdOhQvv76a2dkExEp8wYMGECFChV46623uOGGG1wdp0T47dxJ\nZN++eOXkAGCtUIHkxEQKbr7ZxclEXK/IYpaQkMCzzz5LSEgIUVFRDB8+nOXLlzshmohI2WQYBhs3\nbiQvL48lS5YwZcoU/P39XR2rRPhv3UrkwIGY8vIAKKhUiQuJiRTUqOHiZCLuochilpubS+XKle2v\nGzRogNVqdWgoEZGyymKx0LdvX5YvX05aWppHP1bp/wvYtImIoUMxFRQAUFC1KsmbNmH95z9dnEzE\nfRRZzLy9vcnIyMBkMgF6LJKIiKOkpKTQtm1bqlevzqZNmyhXrpyrI5WYwDVrCB85EtNvz/PMv/FG\nLiQmYr3+ehcnE3EvRV6V2blzZyZPnkxKSgpxcXEcPXr0kkctiYjI1bPZbHz++efcdtttrFixglq1\nark6UokKXL6c8IkT7a/za9Uiee1abLpPpsgliixmjRo1onLlyhw9ehSbzUbXrl0LLW2KiMjVO3/+\nPCNHjsQwDFavXl3qSlnQG28Q9sIL9td5depgWbMGW2SkC1OJuK8ilzLj4uK4cOECbdq0oW3btipl\nIiIl5PPPP6dt27bUr1+fhISEUnMrjN8Fx8UVLmX165O8fr1KmcjfKHLG7Oabb2bNmjWkpaXRqlUr\n7rnnHsLDw52RTUSkVCooKCAtLY3KlSsze/Zsmjdv7upIJcswCHnpJULmzLEP5TZpgmXFCgwPf9C6\niKOZDMMwivPB06dPs2vXLj7++GOqVq3KuHHjHJ3tErrwwHNVqlRJx89D6diVrHPnzjF8+HDq1q3L\npEmTHL4/hxy/vDzM587BZf76CFq+nOBFi+yvc1u0wLJ0KUZgYMnmKAP0+89zVapU6aq+V+xHMuXl\n5ZGfn49hGKVuul1ExBl27drF6NGj6devH8OHD3d1nCtjGPgePEjAxo0EbN2KV2pqsb6W06oVlkWL\noJTch03E0YosZlu3bmXXrl3k5+fTqlUrpk6dqqVMEZErUFBQgNlsJi8vjwULFtCkSRNXRyo284kT\nBCYmErBpE94nT17Rd7Pvv5+L8+aBr6+D0omUPkUWsxMnTtCvXz9uueUWZ+QRESlVTp8+zdChQxk2\nbBht27Z1dZxi8bJY8N+yhcCNG/E9fPgvP2MtVw4jKOgv3zO8vclp3570MWPAx8eRUUVKncsWszNn\nzhAdHc0DDzwA/FrQ/qy0PLNNRMRR3n33XZ588kmGDh1KmzZtXB3n7+Xm4v/eewQkJuL//vv2u/P/\nmS0sjOwOHcju2pW8Ro3gtxuPi0jJuWwxW7lyJRMmTOCVV1655D2TycTcuXMdGkxExJMZhsHu3btZ\nunQpDRs2dHWcv1aM88YMHx9y7r2X7C5dyLn3XvDzc0FQkbKjyKsyk5OTiYqKKjR26tQprnfBYzR0\nZYrn0pVFnkvH7sp8//33TJgwgTlz5nDddde5Os5fHj/ziRMEbtpEQGLiZc8by2vQgKyuXcnu0AFD\n9x1zGf3+81xXe1XmZS+vzMjIICMjg+nTp9v/OyMjg5SUlL+cRRMRKeveeustOnbsyP333095N3vc\nkMliIXD5csp16ECFFi0ImTXrklJWUKUK6Y8/zs///S8X3n6brD59VMpEnOyyS5mzZ8/m6NGjAAwY\nMMA+7uXlRdOmTR2fTETEg1gsFhYsWMDq1aupU6eOq+P8KjcXEhOJWLQI/w8+wJSff8lHdN6YiHu5\nbDGb+NsDZ+fNm0dsbKzTAomIeJJvvvmG9evX8/TTT7Nt2zZM7lBscnJ+vcnr3Llw8SIB/+9tw8eH\nnFatfj1vrHVrnTcm4kaKvCqzbdu2l1yRCboqU0TKNsMwWL9+PVOmTOHpp58GcH0ps9kI2LyZkJde\nwvvMmUvezmvQgKwuXch+8EEtUYq4KV2VKSJyFT744APmz5/Phg0bqFWrlqvj4LdnD6FTpuBz7Fjh\nN6pUIf2hh8jq0gWr/kEt4vaK/axMd6ArUzyXrizyXDp2hX355ZdcuHCB5s2bk5ubS0DA/18odC7v\nL74g9MUX8d+9u9C4NTKS9DFjCB83jrMXLrgonVwr/f7zXCV+Vebvzpw5w/vvv49hGMTFxTFixAi+\n+OKLq9qZiIinMgyDlStXEhMTw8WLF/Hy8nJpKTOfPk34yJGUb9u2UCmz+fuTPnIkv+zfT1a/fnoc\nkoiHKbKYLVy4EF9fXz799FOSk5MZMmQIa9ascUY2ERG38corr7BixQo2b95Mx44dXZbDlJJC6JQp\nXNeyJYGJiZh+W/QwvLzIfPhhftm7l/Qnn8QICXFZRhG5ekU+KzM/P58WLVqwdOlS7rjjDm655Ras\nVqszsomIuNzRo0epWrUqvXr1Yvjw4fj7+7smyG9XWobMmYNXSkrht1q3Ju3ppymoWdM12USkxBQ5\nY5afn09KSgqffvopdevWJSUlhby8PGdkExFxGcMwWLRoEb169SIpKYmKFSu6ppTZbARs2sR1d91F\n2AsvFCplefXqcWHjRizx8SplIqVEkTNm9913H8OGDeOOO+6gcuXKDB06lC5dujgjm4iISxiGweDB\ngzlz5gxvv/02VatWdUkOr3PniHzsMXw/+6zQeEHVqqRNmEBOhw66IaxIKVOsqzJtNhteXr9OrqWn\npxPionMXdGWK59KVRZ6rrB2706dPU7lyZfbt28ftt9+Or4tOnjddvEi5Ll3w+fpr+5g1MpKMxx8n\ns1evYp/UX9aOX2mj4+e5rvaqzCJnzHJyckhISODw4cNYrVbq1q1L3759CQwMvKodioi4I5vNxvz5\n81m0aBEffPABzZo1c1kWU1YWUX362EuZ4e1NxtChZMTGYoSGuiyXiDhekcUsPj4em83GuHHjsNls\n7Nixg6VLlzJ8+HBn5BMRcbiUlBSGDRtGZmYm27ZtI9KVd8XPzydi8GB8P/nkj3xxcWR37uy6TCLi\nNEWe/P/tt98ydOhQqlWrxg033MDgwYP57rvvnJFNRMThsrOzCQgIoFWrVmzcuJHo6GjXhbHZCB8z\nBv8PPrAPpT7/vEqZSBlSZDGzWq3YbDb7a8Mw7OebiYh4KqvVyquvvkrPnj3x9fVlwIABeHsXuYjg\nOIZB6OTJBG7aZB9KHzWKzAEDXJdJRJyuyD+F6tSpQ1xcHPfddx8mk4mdO3dyyy23OCObiIhD/PTT\nTwwfPhyTycSCBQtc//BxIPi11whessT+OrNXL9LHjXNhIhFxhSKLWZ8+fUhMTGTNmjXYbDbq1avH\nQw895IxsIiIlzjAMzpw5Q7NmzRg5ciRms9nVkQhMSCD05Zftr7Pbtyf1xRd1KwyRMqjIYmY2m+na\ntSuNGjXCbDZTpUoVt/jXpYjIlcjPz2fGjBn4+/szZswYGjZs6JwdGwbBcXH479oFf3V3IsPA50/3\nKctt3pyLc+aAGxRGEXG+IovZV199xaxZszCbzdhsNry9vRk/fjxVqlRxRj4RkWt25swZYmNjCQkJ\nYfbs2U7dt+/Bg4TOnFmsz+bVrYtlyRLw83NwKhFxV0UWs6VLlzJ06FDq1asHwKFDh1i4cCFTpkxx\neDgRkZKwcuVK/vWvfzFkyBCnX7zkde5csT6Xf9NNWBISMIKDHZxIRNxZsS5B+r2UATRq1Ih169Y5\nLJCISEnIy8tj2rRpdOrUiQkTJrg6DgC5zZqR9lcn9Ht7k1+rFgQEOD+UiLiVIotZ9erV2b9/P3fe\neScAR44c0TKmiLi1H374gdjYWP7xj3+41Z9XtshI8m+/3dUxRMSNFVnMjhw5wvvvv8+SJUvw8vIi\nLS0NHx8fDh48iMlkIj4+3hk5RUSKxWazMXToULp27Ur//v11sZKIeJQii9nkyZOdEENE5NpkZ2ez\ndOlSBg4cyFtvveWyh4+LiFyLIotZ+fLlnZFDROSqffvttwwZMoTq1auTn59PUFCQqyOJiFwVFz5/\nRETk2p09e5bOnTvz5JNP8sgjj2jpUkQ8moqZiHikrKwsDh06RMuWLfnPf/5DxYoVXR2psPx8Alet\nImTWLFcnEREPUqwb+uTl5XHy5EkMwyA3N9fRmURE/tbx48e5//772bp1K4B7lTLDwP+dd7iuVSvC\nJ07EfOGC/a28xo1dGExEPEGRxSwpKYkRI0Ywbdo0LBYLQ4cO5euvv3ZGNhGRS+zatYvu3bszbNgw\nXv7T8yXdge/Bg5Tr2JHIgQPxPnHCPl5QqRIX4+LI7NfPhelExBMUuZSZkJDAs88+y2uvvUZUVBTD\nhw9n+fLlTJs2zRn5REQASE9PJzMzk9tuu43NmzdTvXp1p+7fd/9+/N95B1NBwV++bz5zBv8PPig0\nZgsNJWPECDL69dPNY0WkWIosZrm5uVSuXNn+ukGDBqxdu9ahoURE/uzo0aMMHTqU3r17M3jwYCIi\nIpy6fy+LhahHHsGUl1eszxu+vmT26UP6yJEYkZEOTicipUmRxczb25uMjAz7lU5nz551eCgRkd+t\nWrWK6dOnM2XKFDp27OiSDObvvy92Kcvq3Jn08eOxutETB0TEcxRZzB566CEmT55MSkoKcXFxHD16\nlEGDBjkjm4iUYenp6QQHB1O5cmXefvttqlWr5upIABRcfz0ZQ4de+oaXF3m3305BrVrODyUipUaR\nxaxhw4ZER0dz9OhRbDYbXbt2LbS0KSJS0j755BNiY2OZPXs2d911l6vjFGIrV46sPn1cHUNESqki\ni1lGRgbBwcH2h5j/eUxEpCTZbDYWLFjAG2+8wUsvvUTTpk1dHUlExKmKLGYDBgy4ZCwiIoI33njD\nIYFEpGyy2WyYTCZSUlLYtm2bZuZFpEwqspitW7fO/t8FBQXs3btXFwCISIn6+OOPeeaZZ9i8eTNP\nPfWUq+OIiLhMse78/ztvb2/uvvtujh496qg8IlKGWK1W4uLiGDp0KBMnTiQkJMTVkUREXKpY55j9\nzjAMvvvuOzIzMx0aSkTKhp9++okjR46wfft293qs0p8ZBv4ffujqFCJSRlzxOWahoaH002NFROQa\n7Nmzh+3btzNt2jSWLVvm6jiXl5ND+LhxBG7aZB8qqFrVhYFEpLQrsphNmzaNG264wRlZRKSUKygo\nYDil90EAACAASURBVObMmWzYsIHZs2e7Os7f8vrpJyIfewzfw4ftY7m3307a88+7MJWIlHZFnmM2\nZ84cZ+QQkTLgrbfe4ujRo+zYsYPmzZu7Os5l+Rw+TPl27QqVssyePUletw5bVJQLk4lIaVfkjFmV\nKlXYu3cvtf6vvfuOrqJcvD7+PS0NCFWMQAygBhIVBLmIcAU1igh6AdFQjFyjiEhRlGoABSWADYVX\ninchckMRuVjooKiIiBQFjUqUooK0AIaWfsq8f4D5EUMKkJM5J9mftVwmM3MmO0w0m+eZ80zjxgQF\nBeVt1zpmIlJSn3zyCRaLha5du9K1a1es1gt631GZCl68mGrDh2PJyQHAsNk4NXYsGfHxcPbRdCIi\n3lJsMfvmm2/YtGlTge3nLqMhInI+ubm5TJw4keXLlzN9+nSfLmS43YROnEjlGTPyNnmqVSNt5kxy\nb7nFxGAiUpEUWsycTicOh4P58+eXZR4RKUdGjRrFkSNHWLNmDTVq1DA7TqEsJ09SfeBAgj77LG+b\nMzKStHfewe0jz+gUkYqh0L++jh49uixziEg58vHHH3Py5EkSEhKYM2eOT5cy25491Lr33nylLPvO\nOzm2dKlKmYiUuUKLmWEYZZlDRMqB7OxsEhISeP755zly5AjVq1fH4sP3ZQV+/jmX3XMPjj178rad\nfvJJ0mbPxtBityJigiKnMn/77bdCC5qW0BCRc7lcLrp160bdunVZs2YNoaGhZkcqnGFQ6a23CE1M\nxOLxAOAJCuLE5Mlkd+5scjgRqcgKLWapqam89tpr5y1mFouFN99806vBRMR//PDDD1x//fW8/PLL\nREdH+/QoGdnZVBsxgpDFi/M2ua+4grR33sF5/fUmBhMRKaKY1atXj5dffrkss4iIn8nKymLMmDFs\n2bKF1atXc+2115odqUjnWzQ2t0UL0mbNwnPZZSYmExE5w4ffuy4ivuzAgQN07NiRnJwcVq1aRUhI\niNmRiubxUDM2Nv+isT16cGzRIpUyEfEZhY6YRUVFlWUOEfEThmFw/PhxatWqxdChQ+nYsaNvT12e\nZT12LO8mf8Ni4dS4cWQ88ogWjRURn1JoMdODykXk79LT0xk5ciTp6enMmTOHTp06mR2p5M65X9Zz\n2WVkPPqoiWFERM7Pa1OZHo+H//znP4waNYqxY8dy+PDh8x4zYcIEPv74Y2/FEJFS8tNPP9GhQweC\ng4OZcc7q+CIiUnq8Vsy2bt2K0+kkMTGRXr16kZSUVOCYhQsXkp6e7q0IIlIKDMPA5XKRlZXFkCFD\neOWVVwgODjY7lohIuVTsszIv1s8//8wNN9wAQGRkJHvOWcARYNOmTVit1rxjRMT3nDx5kieffJLo\n6Gj69etHixYtzI4kIlKuea2YZWVl5XuXltVqxe12Y7PZ2LdvHxs2bOCZZ55h8TlrCRWnTp063ogq\nZUTXz79s2bKFHj16cM899zBq1CgCAwPNjnRpzrnJ32a1Vqifx4r0vZZHun4Vi9eKWXBwMFlZWXmf\nG4aBzWYDYP369aSlpfHCCy9w9OhR7HY7tWvXLnb07ODBg96KK15Wp04dXT8/s2DBAhISEujTp0+5\nuHbW1FTCzn7s9nhILQffU0novz3/puvnvy62UHutmDVq1Ihvv/2W1q1bs3PnTq688sq8fXFxcXkf\nL1q0iGrVqmlKU8QHpKWlMWLECAYPHszw4cPNjlNqbAcOUGn2bLNjiIgUy2vFrGXLliQnJzN69GgM\nw6B///4sX76csLAw3aci4oO2bNnCgAED6Ny5M5GRkWbHuXRuN4Hr1lFp7lwCP/0075mYAJ6qVU0M\nJiJSOK8VM6vVSt++ffNtq1u3boHjYmNjvRVBRErI6XTywgsvMGnSJGJiYsyOc0msx44RsnAhIfPm\nYf/jjwL73ZdfzqnnnjMhmYhI8bxWzETE9x09epRp06aRkJDAsmXL/GIF//MyDAI2byZk7lyCV6zA\n4nQWOCTnllvI6N2b7DvvBIfDhJAiIsVTMROpoL788ksGDx5M9+7dsVqtflnKLKdOEfz++1SaOxfH\nL78U2O+pVo3M2Fgy4uJwX3WVCQlFRC6MiplIBbRr1y4GDx7M66+/Ttu2bc2Oc8EcP/xASFISwR9+\niPWcd3//Jbd5czJ69ybrnntAi+GKiB9RMROpQA4dOsT27dvp2LEjX3zxBZUrVzY7UsllZRG8dCmV\n5s4lYPv2Ars9ISFkde1KRu/euK67zoSAIiKXTsVMpIL49NNPGTJkCI899hiA35Qy2+7dVJo3j5D/\n/Q/riRMF9jsbNybjoYfI6tYNo0oVExKKiJQeFTORCmDx4sVMmjSJt956i5tuusnsOMVzOglas4ZK\nSUkEfvVVgd1GQABZ99xD5kMPkfuPf+Rb1V9ExJ+pmImUY/v378disRATE8Ptt99OjRo1zI5UJOuB\nA1RasICQd9/FlppaYL8rIoLMuDgyu3fHU7OmCQlFRLxLxUyknFq1ahUjR47khRdeoHPnzmbHKZzH\nQ+AXXxCSlETQ2rX5FoIFMKxWsu+8k8zevclp2xasVpOCioh4n4qZSDk0ceJElixZwjvvvEPz5s3N\njnNe1j//JOS9984sBLt3b4H97ssvJ7NnTzJ69cJznsWpRUTKIxUzkXLk0KFDhIWFERMTQ//+/anq\no48eCl68mGrDhmHJzS2wL+ef/zyzEGz79loIVkQqHBUzkXLio48+YsyYMSxatIiWLVuaHadIlWfO\nzFfKPNWqkfnAA2Q89JAWghWRCk3FTMTP5ebmMnr0aL766iveffddoqKizI5ULMs5i8KeHDuWjLg4\nLQQrIgLoLloRP5aTk4PD4eCqq65izZo1XOeHC6tm33GHSpmIyFkqZiJ+yDAM3nvvPWJiYsjNzeXx\nxx/3mwVjRUSkcJrKFPEzGRkZPPvss/zwww/MmjWLwMBAsyOJiEgpUTET8TPHjh2jSpUqrFixgpCQ\nELPjiIhIKdJUpogfMAyDpKQkhg0bRkREBImJiSplIiLlkEbMRHzcqVOnGDZsGL/++iszZ840O46I\niHiRRsxEfNySJUuoWbMmy5Yt4yqt8SUiUq5pxEzEBxmGwaxZs4iIiCAuLg6LxWJ2JBERKQMaMRPx\nMcePH+eRRx7ho48+olGjRiplIiIViEbMRHzMsGHDqF+/Pm+99RYBAQFmxxERkTKkYibiAzweD3Pm\nzKFbt268+eabBAUFmR1JRERMoKlMEZMdO3aMuLg4li5dSlZWlkqZiEgFpmImYqKsrCzuuecemjRp\nwuLFiwkLCzM7koiImEhTmSImcLvdfPnll9x66628//771K1b1+xIIiLiA1TMRMrY4cOHGThwIDab\njTZt2qiUiYhIHk1lipShn3/+mbvvvps2bdqwYMECHA6H2ZFERMSHaMRMpAw4nU4OHz5MgwYNmDVr\nFjfeeKPZkURExAdpxEzEyw4cOJC3DEZgYKBKmYiIFErFTMSL1q1bR8eOHbn77ruZOHGi2XFERMTH\naSpTxAtycnKwWCxUrVqVt99+mxYtWpgdSURE/IBGzERK2e+//06XLl344IMPaNasmUqZiIiUmIqZ\nSClaunQp9957Lw888ADdu3c3O46IiPgZTWWKlALDMLBYLOzdu5f58+fTpEkTsyOJiIgf0oiZyCXa\nvXs3nTp14rfffmPQoEEqZSIictFUzEQuwf/+9z+6du3Kgw8+SP369c2OIyIifk5TmSIXKTMzk48+\n+ohFixYRFRVldhwRESkHNGImcoF27NjBoEGDCAgIYP78+SplIiJSalTMRErIMAzmzZtH9+7dadu2\nLXa7BpxFRKR06TeLSAlt27aNOXPm8OGHH3L11VebHUdERMohFTORYiQnJ7Nz507uv/9+Vq9erZGy\nS5WbC06n2SlERHySpjJFCmEYBrNnz+bBBx8kMDAQQKXsYhgG9t27qfT229To3Zuwa6/FfuCA2alE\nRHySfsuIFGLGjBksW7aMZcuWaSmMC2RJSyNwwwYC168n8IsvsB88eN7jPFWq4L7iijJOJyLiu1TM\nRP7m22+/pXbt2sTFxfHoo4/mjZZJEXJzCfj2WwK/+ILA9etxJCdjMYxCD3dFRJDTti0ZjzwCQUFl\nGFRExLepmImc5fF4mDlzJm+99RYzZswgPDzc7Eg+zZKeTvDixQR9/jkBGzdizcws9FhPlSrk/POf\n5LRtS07btrg1Aikicl4qZiJnDRw4kP3797Ny5Urq1q1rdhzf5XQSMn8+VSZPxvbnn+c9xLBacTZr\nRk67dmS3bYuzWTPQ/XkiIsXS/ymlwtuxYwdRUVEMGDCAyMhIHA6H2ZF8k2EQtGIFoZMmYf/ttwK7\nXeHh5LRrd+afNm0wqlY1IaSIiH9TMZMKy+12M3XqVJKSkli2bBnXXnut2ZF8VsDmzYSOH0/Atm35\ntrvq1CGjXz+yb7/9zPSkxWJOQBGRckLFTCqk9PR0HnnkETweD6tWrSIsLMzsSOYwDBzffot9z55C\n97N+PbWWLMm32VO1KqcHDSIjPl4374uIlCIVM6lwjh8/TrVq1ejRowedO3fGZrOZHck0we+/T/Wn\nnirx8UZAABnx8ZweNAijenUvJhMRqZi0wKxUGC6Xi0mTJnH//ffj8Xi47777KnQpAwjcuLHEx2be\ndx9H1q/n1HPPqZSJiHiJRsykQjh48CD9+/cnJCSE9957r8IXsvPJbdECV8OGBbaHXHEFR+++G+f1\n15uQSkSkYlExk3LP7XaTm5vLXXfdxeOPP47VqoHi88no2ZOsHj0KbA+pUwdnISv3i4hI6VIxk3Ir\nNzeXCRMm5P37iSeeMDuSiIhIkTR0IOXS3r176dq1K3v37mXYsGFmxxERESkRjZhJufT555/TpUsX\n+vTpg0Vra4mIiJ9QMZNyIzs7m3HjxhETE8PDDz9sdhwREZELpqlMKRd2797NvffeS1paGi1btjQ7\njoiIyEXRiJmUC2PHjqV3797ExcVp6lJERPyWipn4rczMTF5//XUGDhxIUlKSlsEQERG/p99k4pd+\n+eUXOnXqxOHDh7Hb7SplIiJSLmjETPzOiRMn6NmzJyNGjCA2NlZTlyIiUm6omInfSE9P55NPPqFr\n166sW7eO0NBQsyOJiIiUKs3/iF/48ccf6dChA19//TUej0elTEREyiWNmInP27RpE4899hgvvvgi\nXbp0MTuOiIiI16iYic86efIkqampNGvWjGXLllG/fn2zI5Ur1tRU7Dt3mh1DRETOoalM8Unbtm3j\nrrvuYvXq1QQGBqqUlSJLejpVXn2V2m3aELB9e952o3JlE1OJiAhoxEx80LvvvsvEiRN56aWXuPvu\nu82OU344nYTMn0+V11/HduxYvl3ZMTHkxMSYFExERP6iYiY+Iy0tjcqVK3P99dezYsUKwsPDzY5U\nPhgGQStXEjpxIvbffsu3yxkVxanRo8lp1w607IiIiOlUzMQnbN68mQEDBjBhwgTat29vdhy/ZcnK\nwv7LLzh27MCekoJjxw4cKSlYT57Md5yrTh1ODxtGVrduYLOZlFZERP5OxUxMZRgGU6dO5Z133uG1\n114jRtNpJWMY2A4exL5jR175su/Ygf2337B4PIW+zBMaSvqgQaTHx0NwcBkGFhGRklAxE9Pk5uYS\nEBBAaGgoK1eupE6dOmZH8k1ZWTh++SWvfDlSUs6Mgp04UeJTeKpVIzM2ltODBmHUqOHFsCIicilU\nzMQUX375JcOGDWPp0qXEx8ebHad0uVwX9zrDwHrkyJnidXYkzJ6Sgv3XX4scBct3CosFV8OGuKKi\ncEZH44yKwnXttbjr1NE9ZCIifkDFTMqUy+Vi8uTJvPfee7zxxhvUrl3b7Eilx+Ohet++BK1ejcUw\nvP/lQkP/r3xFR+OMjsbVqBGGpihFRPyW14qZx+Nh1qxZ7N27F4fDQb9+/QgLC8vbv3z5cjZu3AhA\ns2bNeOCBB7wVRXxIeno6Bw8eZPXq1Vx22WVmxylVju+/J3jVqlI/r0bBREQqDq8Vs61bt+J0OklM\nTGTnzp0kJSUxfPhwAFJTU9mwYQMTJkwA4Pnnn6dly5ZERER4K46YbMWKFUydOpXZs2fzxhtvmB3H\nKywZGfk+Ny7i3Y5GlSo4GzfWKJiISAXltWL2888/c8MNNwAQGRnJnj178vbVrFmThIQErNYzDx5w\nuVw4HA5vRRET5ebm8tJLL7FixQqmTJmCpYKM8OS0bs2f//uf2TFERMTPeK2YZWVlERISkve51WrF\n7XZjs9mw2+2EhoZiGAZz586lQYMGJXpHnt61538+/vhj9u/fz7Zt26hVq5bZcS7d1q0QHw9/W6gV\nALc778PAwMBy9fNanr6XikjXz7/p+lUsXitmwcHBZGVl5X1uGAa2c6Z2cnNzmTFjBsHBwfTp06dE\n5zx48GCp5xTvWLVqFWlpaTz44IPMnDmTWrVqlYvrV23SJEJ++qnY47JtNtLKwfcLZ34plIdrV1Hp\n+vk3XT//dbGF2msPMW/UqBHbzz4geefOnVx55ZV5+wzD4JVXXiEiIoK+ffvmTWmK/8vOzmb06NGM\nGzeOqKgogHI1fWk9darYY9w1a5JR3pYAERGRMuG1EbOWLVuSnJzM6NGjMQyD/v37s3z5csLCwvB4\nPOzYsQOn08l3330HQK9evYiMjPRWHCkjr7zyCqmpqaxZs4aqVauaHcer0mbOPO+Dv42AALBrJRoR\nEblwXvvtYbVa6du3b75tdevWzft4/vz53vrSYoIlS5bQvHlzhg4dSlBQkO+OkhkGQcuWUfk//8F2\n6NAFv9x6/Pj/nSowEOOc+yhFREQulf5aL5ckKyuL5557jq+//ppZs2YRHh5udqRCBXz9NaGJiQSc\nnWK/ZEFBpXMeERGRs1TM5KIZhkGvXr2oW7cuq1evpnLlymZHOi/7L78QOmECQWvXlto5c5s0IadV\nq1I7n4iICKiYyUUwDIMvv/ySW265halTp1KvXj2fnLq0Hj5MlddeI2ThwnzPmjQCAsh45BEyevc+\ncz/YBZ/Yiqd2ba26LyIipU7FTC5IRkYGI0eO5Mcff+T999/3yalLy+nTVJ4+nUr/+Q/W7Oy87YbF\nQtZ993F6+HDc9eqZmFBEROT8VMykxI4ePcp9993HTTfdxMqVKwn2tccE5eYSMn8+VSZPxpaWlm9X\ndtu2nBo1Ctd115kUTkREpHgqZlIswzD4448/CA8PZ8KECdxyyy1mR8rPMAhavpzQSZOw//57vl3O\n6GhOjR5NTrt25mQTERG5ACpmUqRTp04xbNgw0tLSWLRokc+VsoBNmwgdP77AOy1ddetyesQIsrp2\nBS1gLCIifkK/saRQP/30Ex06dKBmzZrMnTvXp27wt+/aRfX4eGp165avlHmqVuXkmDEcWb+erG7d\nVMpERMSvaMRMCjAMg+zsbCpXrsyoUaPo1KmT2ZHyWA8fpsrkyYS8++5532l5euBAjOrVTUwoIiJy\n8VTMJJ+0tDSGDBlCZGQkzz77LBEREWZHAs6+03LGjDPvtMzKyrcv8693WvrgO0RFREQuhOZ5JM/W\nrVu56667aNCgAUOGDDE7zhlOJyFz5lC7TRuqTJmSr5Tl3HILR9as4cT/+38qZSIiUi5oxEwwDAOL\nxUJKSgqJiYm0b9/e7EgAOLZto/qTT2L/7bd82/VOSxERKa9UzCq4o0eP8tRTTzFgwAB69+5tdpx8\nqg0fnq+UuerU4fTw4WTddx/YbCYmExER8Q5NZVZgGzZsoEOHDjRt2pSbbrrJ7DgFWM9ZJPbU0KEc\n+fJLsh54QKVMRETKLY2YVVCGYfD222/z+uuv07ZtW7PjFCuzZ08ICjI7hoiIiFepmFUwhw8fJjEx\nkcTERN55550Svca2ezeVkpKwHT168V84OJjqf3s3ZXGsJ05c/NcTERHxQypmFcjnn3/OM888w7//\n/W8qVapU7PHWI0fOrBm2YAEWt/uSv/4lPVnThxa3FRER8RYVswri0KFDJCQkMH36dG6++eYij7Vk\nZFDprbeoPGMG1szMMkpYOGdUFJ7atc2OISIi4nUqZuXc/v37Wbt2LQ8//DDr16/H4XAUfrDTSci7\n71Jl8uQC05Y5bdqQ2b07hv3ifmRqVK9O2vHjF/7CwMAzy2JoxExERCoAFTN/YhgEf/ghAZs3l+jw\n5X/8waDNm3kqKoqqKSnFHh/w9dc49uzJt80ZFcWpUaPIufXWSytHdeqQffDgxb9eRESkAlAx8yMB\nX39N9UGDSnTsB8AIYAlw8/btcM6DvkvCHRbGqeHDybr/fi1PISIiUkZUzPyIfefOYo/ZA5wGOgG3\nARf6OG9PlSqkDxxI+qOPQvAl3a4vIiIiF0jFzE/ltGpFVufO+bZ9kJzM8KVLGd+xI/WbN8cCXMiC\nE0ZICNm3345Ro0apZhUREZGSUTHzU65Gjcg85xFKr776Kh9+/TXzFi+mSZMmmP9eShEREblQeiST\nn/v111/Jzc3lX//6F6tXr6ZJkyZmRxIREZGLpGLmxxYtWkTnzp1JTk4mMjKSKlWqmB1JRERELoGm\nMv2QB3h8wwa2bNzIokWLiIqKMjuSiIiIlAKNmPmZk5y5aDF16rBy5UqVMhERkXJExcxPGIbBnC1b\nuBY4BcQ2bEhISIjZsURERKQUaSrTD5w+fZrhw4ezZ8sW1gKhQIbZoURERKTUacTMx3k8HlwuF1de\neSWf9OtHY7MDiYiIiNeomPkowzB4++23iY+Pp3r16jz77LMEF/UAchEREfF7msr0QcePH2fIkCEc\nOnSIGTNmmB1HREREyohGzHzQ1q1bCQ8P56OPPqJ+/fpmxxEREZEyohEzH+HxeJg5cyaVK1emd+/e\ntG/f3uxIIiIiUsZUzHzAsWPHeOqpp0hPT2f6a68R+PHHWDMKvu8y4LvvTEgnIiIiZUXFzAe8/PLL\nXBcdzXPh4dSIjcWWmmp2JBERETGBiplJ3G4306dPp0vnzky+/XaqT5qEY9euEr/e1bChF9OJiIiI\nGVTMTJCamsrAgQOxnD7N42vWUHv79nz73WFh5Nx0E1gs5329q1EjMnv1KouoIiIiUoZUzMqYy+Wi\ne9euxAYGMm7nTmzn7PNUrkz6gAFkPPYYRnCwaRlFRETEHCpmZcTlcrFk7lwe3r2bTfv3U8Ptzttn\n2O1k9O5N+uDBeGrWNDGliIiImEnFrAwc3LOHQT17Enr4MI+43dQ4Z1/WvfdyasQI3A0amJZPRERE\nfIMWmPUml4vUqVPpdOutdDlwgFVuN5XO7spp1Yqjy5ZxfOZMlTIREREBNGLmHYaBZfVqDrz4Ijfu\n3cunwHVndzmvuYZTCQnk3HlnoTf3i4iISMWkYuYFR195hT5TpnAtMIczpcx9+eWcHjqUzNhYsOuP\nXURERApSQyhl69ev58np0xkFPMnZd1o+8QQZfftihISYHU9ERER8mIpZKcnOzsbpdBIREcFHUVG0\nTk4GIG36dHJiYkxOJyIiIv5AN/+Xgt27d3PvvfeycOFCIiIiuLFKlbx9RkCAiclERETEn6iYXaIP\nPviArl270rt3b/r06WN2HBEREfFjmsq8SE6nE4fDgcvlYuHChVx77bVmRxIRERE/pxGzi/Dzzz/T\nvn17kpOTiY2NVSkTERGRUqERs6Lk5GD988+8Tw3DYMGSJUyYNo3nn3qKG2rVgoMHC7zMkpNTlilF\nRESknFAxK4Tjm2+o8fDD2I4fz9vmBL4HNgBR48bBuHFmxRMREZFySFOZhQh5//28UrYNaA+4gCQg\n6gLO46levfTDiYiISLmkEbNCWHJyMIBpwDiLhSmhoQQEB+Mu4esNm43sjh1x6f4zERERKSEVsyL8\nAswDVo8cyRUDB5JqdiAREREp1zSVeR7btm3j1ZQUGgNfAw1r1TI7koiIiFQAKmbn8Hg8zJw5k/j4\neBqdXb3fYnImERERqThUzM4xf/58VqxYwYoVK7i3Xj2z44iIiEgFo3vMgM2bNxMUFERsbCw9evTA\n4XCYHUlEREQqoAo9YuZ2u3njjTd4/PHHOXXqFIGBgSplIiIiYpoKPWI2bNgw9u7dy6pVq7jiiivM\njiMiIiIVXIUsZlu3bqVp06Y8/fTTXHHFFdjtFfKPQURERHxMhZrKdLlcvPTSS/Tr1499+/YRHh6u\nUiYiIiI+o8K0kpycHHr27ElgYCCfTJlC+CefEDhmDI4ff8Ti8RQ43pKRYUJKERERqcgqRDE7+N13\nXLV7N8/a7XTcsQNH9+4XdoKgIO8EExERETlH+SxmWVkEbtmC5bPPGP/BByxNS+MnoPNFnMoZHU32\nHXeUdkIRERGRAspHMTMM7Dt2ELh+/Zl/Nm/mQE4O3YDLgE1A4DmHu6tXJ6dtW3LatiW3dWs8Z1f5\nP++pq1UDi9b/FxEREe/z+2IWtGIFVceMwZb6f48YzwZCgIeBfgAOBzktWpDTrh057drhvO46sFao\n9z2IiIiIH/D7Yhb64ot5pSwbGAocBeZdcw0PtW1LWtu25N58M0alSmbGFBERESmW3xcz68mTAOwC\nHggNJaJ+fV5+/XWONm5sbjARERGRC+T3xQzAAH4EegwezEN9+2LRPWEiIiLih/y6mGWdOMFjmZm0\nBh4FDvXogaFSJiIiIn7KP++A93j4ffp0OjdtSo7LRexf2202M1OJiIiIXBK/GzEL2LCB0PHjGf3D\nDzwDxAMWIOeWWzAqVzY5nYiIiMjF81ox83g8zJo1i7179+JwOOjXrx9hYWF5+9euXcvatWux2Wzc\nd9993HjjjcWe09GzJ6PWrycBmH12m7t6ddIHDybjoYe8842IiIiIlBGvTWVu3boVp9NJYmIivXr1\nIikpKW/fiRMnWLVqFS+++CKjRo1iwYIFOJ3OYs/5z/Xr8XBm0VgjKIjTAwdyZONGMvr0gcDA4l4u\nIiIi4tO8NmL2888/c8MNNwAQGRnJnj178vbt3r2bRo0a4XA4cDgchIWFsXfvXq6++uoiz/k8KJ57\nAgAACtZJREFU0NNiISs2ltShQ/HUqeOt+CIiIiJlzmvFLCsri5CQkLzPrVYrbrcbm81GZmZmvn3B\nwcFkZmYWe85ehgGcWdU/pOhDxQfVUZH2W7p2/k3Xz7/p+lUsXpvKDA4OJisrK+9zwzCwnX3XZEhI\nCNnZ2Xn7srKyqKSV+UVERKSC81oxa9SoEdu3bwdg586dXHnllXn7rr76alJSUsjNzSUzM5MDBw4Q\nHh7urSgiIiIifsFiGGfnB0vZX+/K3LdvH4Zh0L9/f7Zv305YWBgtWrRg7dq1fPrpp3g8Hrp27Uqr\nVq28EUNERETEb3itmImIiIjIhfHPlf9FREREyiEVMxEREREf4XOPZPLGEwOkbBR37ZYvX87GjRsB\naNasGQ888IBZUeU8irt+fx0zadIkWrRoQfv27U1KKn9X3LXbvn07ixcvBqBBgwY8+uijWCwWs+LK\n3xR3/ZYuXcpXX32F1Wqla9eutGzZ0sS0cj67du1i/vz5jB07Nt/2b775hvfffx+r1cptt93GHXfc\nUey5fG7EzBtPDJCyUdS1S01NZcOGDYwfP57x48eTnJzM3r17TUwrf1fU9fvLwoULSU9PNyGdFKWo\na5eVlcW8efMYMWIEiYmJXHbZZZw+fdrEtPJ3RV2/jIwMVq1aRWJiIqNGjWLOnDnmBZXzWrJkCTNn\nzizQR1wuF//9738ZNWoU48aN49NPP+XEiRPFns/nillJnxgQEhKS98QA8Q1FXbuaNWuSkJCA1WrF\narXicrlwOBxmRZXzKOr6AWzatAmr1Zp3jPiOoq7dL7/8Qnh4OElJSTz33HNUrVqV0NBQs6LKeRR1\n/QIDA7nsssvIzs4mJydHI50+6PLLL2fo0KEFth84cICwsDAqV66M3W6nUaNGpKSkFHs+nytmhT0x\nALjoJwZI2Sjq2tntdkJDQzEMg6SkJBo0aKDVrH1MUddv3759bNiwgdjYWLPiSRGKunanT5/mp59+\nIi4ujoSEBFauXMnBgwfNiirnUdT1gzN/sX3mmWcYMWIEd999txkRpQitWrXKW0D/XH+/riXtLD53\nj5meGOC/irp2ALm5ucyYMYPg4GD69OljRkQpQlHXb/369aSlpfHCCy9w9OhR7HY7tWvX1uiZjyjq\n2lWpUoWrrrqKatWqARAVFcXvv/+uvxj5kKKu33fffceJEyd48803AUhMTKRx48bFPltazBccHHxR\nncXnRsz0xAD/VdS1MwyDV155hYiICPr27YvV6nM/ehVeUdcvLi6OCRMmMHbsWNq1a0enTp1UynxI\nUdeuYcOG/PHHH5w6dQq3282uXbuoV6+eWVHlPIq6fpUqVSIgIACHw0FAQACVKlUiIyPDrKhyAerW\nrcuhQ4dIT0/H5XKRkpJCZGRksa/zuQVm9cQA/1XUtfN4PEyZMoVrrrkm7/hevXqV6IdUykZx/+39\nZdGiRVSrVk3vyvQhxV27r776iqVLlwJw880306VLF5MTy7mKu36LFi3iu+++w2Kx0LhxY+Li4nSv\nmY85cuQIU6ZMITExkQ0bNpCdnc0dd9yR965Mj8fDbbfdRocOHYo9l88VMxEREZGKSvNJIiIiIj5C\nxUxERETER6iYiYiIiPgIFTMRERERH6FiJiIiIuIjfG6BWRHxL7GxsYSHh+dbm+6qq66iX79+hb5m\n3bp1bNq0iZEjR5ZFxBL75ptvSE5O5pFHHmHbtm3s2rWL7t2759telhYvXkxERAT/+Mc/yvTrioh5\nVMxE5JI9//zz5eL5iy1atMhbs2337t15D2w/d3tZ+vHHH7UYrEgFo2ImIl7z2WefsXbtWlwuF+np\n6XTp0qXAwrSbN2/mgw8+wGKxYLVaiYuLIzo6mszMTN555x327duH2+3muuuu46GHHirwTLpp06YR\nEBDA77//zqlTp2jSpAnx8fHY7XZSUlKYN28eOTk52O12evTowQ033JD3iJvTp08D0KxZM3r06JE3\nktetWzc++eQTPB4PISEhXHHFFWzatInevXszZswY3nrrLex2Ox6PhyeeeIIxY8ZQo0aNEudNT08n\nNTWV5s2bc/vtt/P222+TnZ3N8ePHqV+/PoMHD+azzz5jz549zJ07F6vVSvPmzZk3bx4pKSl4PB7q\n169PfHx8vmfxiYj/UzETkUs2bty4fFOZo0ePJjAwkE8//ZRnn32WKlWqsHPnTsaPH1+gmM2bN49B\ngwYRGRnJ999/z44dO4iOjmbOnDk0bNiQAQMG4PF4mDZtGsuXL6dz584Fvv7u3bsZN24cNpuN8ePH\ns3btWtq0acPkyZMZPnw411xzDX/88Qdjx45l4sSJrF+/ntq1azN69Giys7OZOXNmvocLX3PNNdx5\n552cPn2anj17sm7dOgDq1KlDvXr1+Oabb2jVqhXff/89tWvXpl69ekyfPr3EeXNzc5k8eTIAc+fO\npV27drRt2xaXy8XIkSPZtm0bHTp0YNOmTXTo0IGWLVuyePFibDYbkyZNwmKxsGDBAhYsWKDnzoqU\nMypmInLJCpvK/KtkHDp0iN9//z3fA33/0rp1a1599VWaN29OkyZN+Ne//gXAtm3b2LNnD5999hlw\npswUpl27dgQFBeV9vGXLFmrXrk1YWFjeY8DCw8Np1KgRP/30EzfccAMTJ07kzz//5Prrr6dXr14l\nHnmKiYlh3bp1tGrVis8//5yYmJgLztuoUaO8jx988EGSk5NZsmQJhw4d4vjx4+f9c/r222/JzMwk\nOTkZAJfLRdWqVUuUWUT8h4qZiHjFn3/+yejRo4mJiaFx48a0atWKbdu2FTiuZ8+e3HbbbSQnJ7Nu\n3TqWLVvGxIkT8Xg8PP3003n3WGVkZBT6fMBzpws9Hg9WqxWPx1PgOI/Hg9vt5uqrr2batGkkJyfz\n448/kpCQQEJCQom+r1atWvHf//6X/fv3k5KSwoABA/LOXdK8f5VIgClTpuB2u2ndujXNmzfn2LFj\n532Nx+Ph4YcfplmzZgBkZ2cXWf5ExD9puQwR8Yo9e/YQGhpKt27daNq0aV4pO7cwud1uBgwYQG5u\nLu3bt6dPnz7s27cPp9NJ06ZNWbFiBYZh4HQ6efnll1m9evV5v9bGjRtxOp3k5ubyxRdf0KJFCyIj\nIzl48CC7d+8G4I8//iAlJYXo6Gjmz5/P4sWLadmyJfHx8YSHh7Nv375857TZbLjd7gJfKyAggDZt\n2jB9+nRuuukmAgMDAS4o77m+//577r//flq3bg3Arl278v6MbDYbLpcr7/yrV6/G5XLh8XiYOXMm\nCxYsKPb8IuJfNGImIl7RtGlTPv/8cwYPHozFYiE6OprQ0FAOHz6cd4zNZuPf//43U6ZMwW63Y7FY\neOKJJ3A4HMTHxzNnzhyGDh2Ky+Xi+uuvz5vm/LvAwECee+45MjIyaNWqFbfeeitWq5Wnn36a2bNn\nk5OTg9VqpX///tSpU4dOnToxbdo0hgwZgt1uJyIigjZt2vDVV1/lnfO6665j6tSpzJ49m4YNG+b7\nejExMaxevTrf/V0XkvdcPXv25NVXXyUwMJCQkBCio6Pz/oxuvPFG3n33XVwuF/fffz9JSUkMHz48\n7+b/3r17X9A1ERHfZzEMwzA7hIjIxZo2bRrh4eElKkEiIr5OU5kiIiIiPkIjZiIiIiI+QiNmIiIi\nIj5CxUxERETER6iYiYiIiPgIFTMRERERH6FiJiIiIuIjVMxEREREfMT/B7y04xaqyWhPAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1216c87b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ROC(ytest, svmscores, 'SVM', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Wf/QMAADACiyXXpo7xJw1ZgAAwMosl15crtBtIGBTINDwuUOHHPU+ttyXBgAAOjnLpReb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\nKu3dK/3wh8dp2zaX3O5ghwW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CC2LUUhyppb5bt26d3nzzTUlSnz59dMMNN8hms8WquThC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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d9ba780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_PR(ytest, svmscores, 'SVM', 'b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest Tree Classifiers"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.36 0.37 0.37 75\n",
" 1 0.53 0.52 0.52 102\n",
"\n",
"avg / total 0.46 0.46 0.46 177\n",
"\n",
"##################################################################\n"
]
},
{
"data": {
"image/png": 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u33//vZYtW6bMzEw5nU653W7VrVtX/fr1U61atXzxkQiAwnO5kiRn+XKK/e+/atekOZLH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\nAMSk1SBS22sWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d9e05f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rf = RandomForestClassifier()\n",
"rf_model = rf.fit(xtrain, ytrain)\n",
"rfpred = rf.predict(xtest)\n",
"rfscores = rf.predict_proba(xtest)\n",
"\n",
"plot_confusionmatrix(ytest, rfpred)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SMRMREcklZ86coX///rz00kt06tTJ7DguZz17loJPP+0sZYbNRlLHjsS/8AKO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\noWAmIiIi4iEUzEREREQ8xP8Hqts5nmbYgUEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d843940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ROC(ytest, logscores, 'Random Forest', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Gli5N1DnntNWGDZxpBgCgJSHMGmDx4kSVllp18skeDR3auNmyl15y6r33EpSSEtSDDxbL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\nhIKZiIiIiCG+AXUePkQK4awIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d744d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_PR(ytest, logscores, 'Random Forest', 'b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feed Forward Neural Network"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_features = len(InputDF.columns)\n",
"dropout=0.2\n",
"hidden_1_size = 25\n",
"hidden_2_size = 5\n",
"num_classes = label.nunique()\n",
"NUM_EPOCHS=20\n",
"BATCH_SIZE=1\n",
"lr=0.0001\n",
"np.random.RandomState(52);"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val = (InputDF[:-test_size].values, label[:-test_size].values)\n",
"train = (InputDF[-test_size:].values, label[-test_size:].values)\n",
"NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n",
"NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Model():\n",
" def __init__(self):\n",
" global_step = tf.contrib.framework.get_or_create_global_step()\n",
" self.input_data = tf.placeholder(dtype=tf.float32,shape=[None,num_features])\n",
" self.target_data = tf.placeholder(dtype=tf.int32,shape=[None])\n",
" self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n",
" with tf.variable_scope(\"ff\"):\n",
" droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n",
" \n",
" layer_1 = tf.contrib.layers.fully_connected(\n",
" num_outputs=hidden_1_size,\n",
" inputs=droped_input,\n",
" )\n",
" layer_2 = tf.contrib.layers.fully_connected(\n",
" num_outputs=hidden_2_size,\n",
" inputs=layer_1,\n",
" )\n",
" self.logits = tf.contrib.layers.fully_connected(\n",
" num_outputs=num_classes,\n",
" activation_fn =None,\n",
" inputs=layer_2,\n",
" )\n",
" with tf.variable_scope(\"loss\"):\n",
" \n",
" self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = self.logits, \n",
" labels = self.target_data)\n",
" mask = (1-tf.sign(1-self.target_data)) #Don't give credit for flat days\n",
" mask = tf.cast(mask,tf.float32)\n",
" self.loss = tf.reduce_sum(self.losses)\n",
" \n",
" with tf.name_scope(\"train\"):\n",
" opt = tf.train.AdamOptimizer(lr)\n",
" gvs = opt.compute_gradients(self.loss)\n",
" self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n",
" \n",
" with tf.name_scope(\"predictions\"):\n",
" self.probs = tf.nn.softmax(self.logits)\n",
" self.predictions = tf.argmax(self.probs, 1)\n",
" correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n",
" self.accuracy = tf.reduce_mean(correct_pred)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-110-584c8762f36f>:7: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
"Instructions for updating:\n",
"Use `tf.global_variables_initializer` instead.\n",
"################ done training ################\n",
"################ done testing ################\n"
]
}
],
"source": [
"with tf.Graph().as_default():\n",
" model = Model()\n",
" input_ = train[0]\n",
" target = train[1]\n",
" losses = []\n",
" with tf.Session() as sess:\n",
" init = tf.initialize_all_variables()\n",
" sess.run([init])\n",
" epoch_loss =0\n",
" for e in range(NUM_EPOCHS):\n",
" if epoch_loss >0 and epoch_loss <1:\n",
" break\n",
" epoch_loss =0\n",
" for batch in range(0,NUM_TRAIN_BATCHES):\n",
" \n",
" start = batch*BATCH_SIZE\n",
" end = start + BATCH_SIZE \n",
" feed = {\n",
" model.input_data:input_[start:end],\n",
" model.target_data:target[start:end],\n",
" model.dropout_prob:0.9\n",
" }\n",
" \n",
" _,loss,acc = sess.run(\n",
" [\n",
" model.train_op,\n",
" model.loss,\n",
" model.accuracy,\n",
" ]\n",
" ,feed_dict=feed\n",
" )\n",
" epoch_loss+=loss\n",
" losses.append(epoch_loss)\n",
" #print('step - {0} loss - {1} acc - {2}'.format((1+batch+NUM_TRAIN_BATCHES*e),epoch_loss,acc))\n",
" \n",
" \n",
" print('################ done training ################')\n",
" final_preds =np.array([])\n",
" final_scores =None\n",
" for batch in range(0,NUM_VAL_BATCHES):\n",
" \n",
" start = batch*BATCH_SIZE\n",
" end = start + BATCH_SIZE \n",
" feed = {\n",
" model.input_data:val[0][start:end],\n",
" model.target_data:val[1][start:end],\n",
" model.dropout_prob:1\n",
" }\n",
" \n",
" acc,preds,probs = sess.run(\n",
" [\n",
" model.accuracy,\n",
" model.predictions,\n",
" model.probs\n",
" ]\n",
" ,feed_dict=feed\n",
" )\n",
" #print(acc)\n",
" final_preds = np.concatenate((final_preds,preds),axis=0)\n",
" if final_scores is None:\n",
" final_scores = probs\n",
" else:\n",
" final_scores = np.concatenate((final_scores,probs),axis=0)\n",
" print ('################ done testing ################')\n",
" prediction_conf = final_scores[np.argmax(final_scores, 1)]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Aofy9XUBTZ5UU68R3B2XZ/WULDfd2OQAAoBkhqJ0hq6JcjkVPSQW7dOhoqRTSRorpInvy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\nNNntdtntdlVVVSkgIMBbpfqEhvohSZ9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"text/plain": [
"<matplotlib.figure.Figure at 0x11d9b8588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(np.linspace(0, 1, len(losses)), losses);\n",
"plt.title('Validation loss with epoch')\n",
"plt.ylabel('Validation Loss')\n",
"plt.xlabel('epoch progression');"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.44 0.37 0.40 75\n",
" 1 0.58 0.65 0.61 102\n",
"\n",
"avg / total 0.52 0.53 0.52 177\n",
"\n",
"##################################################################\n"
]
},
{
"data": {
"image/png": 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RMjMz5XK55PF4FBMToz59+qhOnTr++EgEQfGpfEmSq1JFJfz1BWWMfV7yehX7wl/U5M/J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\n0LgAAABj0LgAAABj0LgAAABj/C+w06xI3x4g9gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1260922e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_confusionmatrix(ytest, final_preds)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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41q1bx5QpU0hMTCQiIkLBTERE5B+io6MJb9KEk0eP0oxroSyuRw/ie/Z0d2ni\nIi4byjx48CC1a9cGoEqVKhw5csS5zcfHh6JFi5KYmEhSUpKeTCwiIvIPZ8+epU7FipQ7epQfgBAg\noWNHYoYNc3dp4kIu6zGz2Wz4+/s7l81mM6mpqVgsFgAKFy5M//79cTgctGnTJkNthoaGuqRWyRm6\nfp5L186z6fp5FofDwdGjR6mzbx8Lo6JodH3DE0/gv3gx/l6at5eXuezq+vn5YbPZnMuGYThD2c8/\n/0x0dDRTp04FYPTo0VSrVo1KlSrdts0zZ864qlxxsdDQUF0/D6Vr59l0/TxLVFQU/fr1w3z5Ml/9\n+qszlCXVr8/liRPhwgW31icZl9kfiFw2lFm1alX27NkDwKFDhyhbtqxzW0BAAFarFW9vb6xWKwEB\nAcTHx7uqFBERkVxvz549NG/enGrVqrEyLs75/kt7WBhR8+aBn597C5QcYTIMw3BFw9dnZf75558Y\nhkF4eDh79uyhRIkS1KtXj08//ZSff/4Zk8lEtWrV6NSpU7r3mumnPs+ln9o9l66dZ9P1y/1SU1NJ\nSkriwoULHDlyhH/961+ULF8ek90OwLkdO3BoONrjZLbHzGXBzBX0zcVz6T8Hz6Vr59l0/XK3Cxcu\n0KdPH+6//3769+/vXP/3YHbm2DGwWt1VomRSrhvKFBERkVvbsmULLVq04L777qNv377uLkdyCU3t\nEBERyUGGYWAymThx4gSTJ0/moYcecndJkouox0xERCSHnDlzhg4dOvDTTz/RuXNnhTK5gYKZiIhI\nDti0aRMtW7akadOm1KtXz93lSC6loUwREREXczgcLF26lFmzZqX7CkJTbCw4HDlUmeQ26jETERFx\nkZMnT9KjRw8SEhKYM2fOzUNZaireu3cTOGkShdu1o0TNms5nmEn+ox4zERERF/jyyy9544036N27\nNwEBAWm2WU6dwmfz5mu/vvsOc3T0zRsJCwNv7xyoVnILBTMREZFsdvLkSSIjI5k/fz516tTBFBeH\n9fvv8dmyBd/Nm/E6evS2x9tr1CCxSROChg7NoYolt1AwExGRfM3755+xbt+eLcOHR6Ki2HTkCK/e\ndx87OnbEe+NGrG+/jXXXLkwpKbc8LrV4cZIaNyapSROSHnoIR5EiAASFhoIeEJyvKJiJiEi+5HX4\nMEGRkfitX58t7S0B+gFvAcEbN952X4evL8kNGjjDWEqVKpDOawklf1AwExGRfMV84QJB48fjv3Rp\ntt1kvxTjKj0AAAAgAElEQVT4L7ARqH2LfZJr1rwWxBo3Jvm++8DXN1vOLXmLgpmIiOQLprg4AmfO\nJGDGDMwJCWm22Vq2JKV8+Ttu82BUFMkOB48ULEgTh4Mgq5XYv+9gNpNStSpJjRs7hydFbkfBTERE\n8ja7Hf8lSwiaMAHLpUtpNiU1bEjMsGHYa9W6oyYNw+DTTz9l1KhRvPPOO9z1xBMAaUOZSCYomImI\nSN5kGPiuX0/wO+/cMAvSHhZGzNChJDVtmql7u0aPHs3XX3/NsmXLqFq1ajYVLKJgJiIieZB1xw6C\nR43CunNnmvWpJUsSExGBrX17sFjuuN1Dhw5Rvnx5nnnmGQYMGICfn192lSwC6Mn/IiKSh1gOHybk\npZco0qZNmlDmCA4mZsgQzm/diq1jxzsOZYZhMH/+fNq3b8/BgwepVKmSQpm4hHrMRETE45kvXCBo\nwgT8lyxJM9PS8PYmvksXYvv2xShUKFNtp6SkEB4ezvHjx/niiy+oWLFidpUtcgMFMxER8Vim+HgC\nZs4kcPr0G2ZaJrRpQ+zrr5Natmym24+KiqJQoUK0atWK5s2b46tHXIiLKZiJiIjnsdvxX7r02kzL\nixfTbMrsTMu/MwyDWbNmMXv2bLZs2cKTTz6Z1YpFMkTBTEREPIdh4LthA0HvvIP3kSNpNmV1puV1\nV65c4bXXXuPy5ct8/vnn+Pv7Z7FokYxTMBMREY/gvXMnwaNG4bNjR5r1qSVLEjNoELYOHTI10zJN\nW3/dn1a7dm169eqF1WrNUnsid0rBTEREcjXLkSMEjxmD35dfplnvCAoirk8f4rp1gyzOkHQ4HHzw\nwQfs2bOHuXPn8p///CdL7YlkloKZiIjkSuaLF6/NtFy8+MaZli++SFy/fjgyOdPy7y5evEi/fv2w\n2Wx88MEHWW5PJCsUzEREJFcxxccTMGvWtZmW8fFptiW0bn1tpmW5ctl2vp07d3LPPfcwYMAAvLz0\n36K4l74CRUQkd0hJwf/jjwkaPx7LhQtpNiU9+OC1mZb33JMtp0pNTWXixImUKFGCTp068dhjj2VL\nuyJZpWAmIiK5QsEBA/BftizNOnu1atdmWj78cJZmWv7d2bNn6dOnDxaLhSlTpmRLmyLZRcFMRERy\nBd9Nm5x/Ti1R4to7LbNhpuU/jR07loceeojevXtjyea2RbJKwUxERHIHh8P5x4vr1+MoWjTbmrbb\n7UyaNIlnn32WcePGYTbrVdGSO+krU0REch0jG58fdurUKdq1a8fevXvx9/dXKJNcTV+dIiKSZ9nt\ndp555hkef/xx5s+fT6FseLyGiCtpKFNERPKcpKQkVqxYwdNPP826desICgpyd0kiGaIeMxERyVOO\nHTtG69at2bhxI4mJiQpl4lEUzEREJM84fPgwrVu35umnn+bDDz/EL4uvahLJaRrKFBERj2ez2Th8\n+DA1atTg888/p1KlSu4uSSRT1GMmIiIe7Y8//uCJJ55g8eLFmM1mhTLxaApmIiLisb766ivatWtH\nt27diIyMdHc5IlmmoUwREfE48fHxpKamUqVKFT799FPCwsLcXZJItlAwExGRnJeaiuX4cbz378f7\nwAG89+/HFBeXoUP3799Pjx49eOmll3jhhRdcXKhIzlIwExERlzJdvYr3wYN4/S2EeR08iNlmu+n+\nhrf3LZ/8v2TJEiIjIxk5ciTt27d3ZdkibqFgJiIi2eN6L9j18HX991OnMtyE4eNDbL9+8I/HXCQn\nJ2O1WrFaraxYsUI3+EuepWAmIiJ3zBQTg/eBAxnuBbuZ1GLFsFevTkpYGPbq1bGHhZFSqRJ4e6fZ\n75dffiE8PJzZs2fToUOH7P4oIrmKgpmIiGScw0HIq6/i9+WXGT7E8PYmpXJlZ/iyV69OSvXqOIoU\nuf1xhsHcuXOZPHkyo0ePpnr16lmtXiTXUzATEZEM8/7ll9uGstRixa71fFWvfttesIyw2+3s37+f\n1atXU65cuayULeIxFMxERCTDTPHxzj87goNJbN78jnrBMmLnzp28++67LFq0iPHjx2e5PRFPomAm\nIiKZYq9Zk+jJk7OtPYfDwfTp05k1axZjx47Fx8cn29oW8RQKZiIikiscOnSIb775hi+//JJSpUq5\nuxwRt1AwExERt/r+++/ZuXMnffv25bPPPsNkMrm7JBG30bsyRUTELVJTU5kwYQK9evWiVq1aAApl\nku+px0xERNxi3rx5/PDDD6xbt44SJUq4uxyRXEHBTEREctTmzZsJCQmhc+fOdOnSBYvF4u6SRHIN\nDWWKiEjGpKYSsGiRc/FW77O8lZSUFCIjI+nfvz+JiYlYrVaFMpF/UI+ZiIikzzAoMGwYfqtXO1fZ\nnnzyjpro27cvMTExbNiwgSLZ8LwzkbxIwUxERNIVNGECAQsWOJfjunXD1rFjho7dtm0b9evXZ+jQ\noZQsWRKzWYM1Ireifx0iInJb/h99RNCECc7lhDZtiPnvfyGdGZTJycmMGDGCAQMGcPr0aUqVKqVQ\nJpIO9ZiJiMgt+a5cSYHhw53LiU2bEj1xIqQTsGw2G+3bt6d48eKsX7+ekJAQV5cqkicomImIyE35\nbN5MSL9+mAwDgOQ6dbgyezakc9P/iRMnKFeuHEOGDKFhw4Z6NpnIHVCfsoiI3MB7925CunfHZLcD\nYK9cmcsLFmD4+9/ymMTERAYPHsyLL76I3W6nUaNGCmUid0jBTERErjEMvA4eJGDWLAp37ozZZgMg\nJTSUy0uWYBQqdMtD//zzT1q1asWVK1dYtWoV3t7eOVW1SJ6ioUwRkXzMfOkSPlu34rN5Mz5btmA5\nfz7N9tSQEKKWLsURGnrLNhISEggKCuLll1+mY8eO6iUTyQIFMxGR/CQpCeuOHfhs2YLP5s1Yf/31\nlrs6ChQgatEiUipVuun2hIQEhg4dSkpKClOmTOHpp592VdUi+YaCmYhIXmYYeP3++7Uesa1bsX7/\nPebExFvu7ihQgKRGjUhq0oTEFi1wFC580/0OHDhAz549ueeeexgzZoyrqhfJdxTMRETyINPVqwSN\nHQsbNlDszJlb7md4eZFcty5JjRuT1KQJ9lq14DavSTL+mqF5/PhxwsPD6ZjBh8yKSMYomImI5DGm\nhAQKd+qEdffum25Pueuuaz1iTZqQ3KABRlBQhtqNjY3ljTfeoGnTpjz11FPZWbKI/EXBTEQkL0lO\nJuTll9OEsr8PTyY1bkxqmTJ33Oy+ffvo0aMHjRo1olWrVtlZsYj8TYaC2eXLlzlx4gS1a9cmKipK\nL58VEcmNHA4K/uc/+H777f+vmziRc+3b33Z4MiM++ugjIiIiaN26ddZqFJHbSvc5Zrt372bYsGHM\nmTOHq1ev8p///IcdO3bkRG0iIpJRhkHwiBH4f/GFc1Vs//7w2muZDmXR0dG89tprnD59mgkTJiiU\nieSAdIPZsmXLeOeddwgICCAkJIS3336bTz/9NCdqExGRDAqcPJnAuXOdy/EvvngtmGXSrl27aNGi\nBcHBwRolEclB6Q5lOhyONC+fLV++vCvrERGRO+S/YAHBY8c6l21PPsnVt9+GTD7oNSEhgX79+jFy\n5EhatGiRXWWKSAak22Pm4+PDpUuXnE9yPnDgANZ0XmArIiI5w3fNGgoMGeJcTmzcmCuTJ2dq+DIq\nKooPPvgAPz8/vvnmG4UyETdIN5g999xzjBo1inPnzjF06FDGjRvH888/nxO1iYjIbVi3biWkTx9M\nfz1bLLlOHa58+CFk4ofn7du307x5c6Kjo3E4HHrXpYibpDuUWbVqVUaPHs2hQ4dwOBxUrlyZ4ODg\nnKhNRERuxTAoOGQIpuRkAOyVKhG1YAFGQMAdN7V792569OjBhAkTaNasWXZXKiJ3IN0es+s3/tep\nU4e6desSHBzM0KFDc6I2ERG5lZQUvI4eBcAwmYhasgRHoUJ31MSFCxf48ccfqVOnDps2bVIoE8kF\nbtljNn78eM6ePcv58+cZOHCgc31qaipeXnourYhIrmGxkFqq1B0dsmXLFl577TVefvll7r//fs28\nFMklbpmwOnfuzMWLF5k5cybdunVzrjebzZQuXTpHihMRkey3aNEiJk6cyPvvv0+jRo3cXY6I/M0t\ng1mxYsUoVqwYkyZNwmxOO+KZmJjo8sJERCR7nTlzhoCAAJo0acKjjz5K0aJF3V2SiPxDumOSu3bt\n4tNPPyUxMRHDMHA4HMTFxbFgwYLbHudwOPjwww85ceIE3t7e9OjRgxIlSji379mzh2XLlgFw1113\n0b17d+cjOUREJHtt2rSJgQMHEhkZyWOPPebuckTkFtINZgsXLuSZZ55h48aNtG7dmp9++gk/P790\nG96xYwd2u905o3PBggVEREQAYLPZWLRoESNGjCA4OJiVK1cSGxur2Z4iIi4QGRnJihUrmD17Nvfd\nd5+7yxGR28jQA2YffPBBKleujLe3Ny+99BK7d+9Ot+GDBw9Su3ZtAKpUqcKRI0ec237//XfKlCnD\nggULePPNNylQoIBCmYhINrt69SoAderUYf369QplIh4g3R4zq9WK3W6nRIkSHD9+nBo1amSoYZvN\nhr+/v3PZbDaTmpqKxWIhNjaW3377jbFjx+Lr68ubb75JlSpVCA0NvW2b6W2X3E3Xz3Pp2uVCdrvz\njyZuvEbLly8nPDycH3/8Mc0ELvE8+veXv6QbzOrWrcuYMWPo1asXQ4cO5cCBAxnq3fLz88NmszmX\nDcPA8tcrQoKCgqhYsSIFCxYEICwsjOPHj6f7xXfmzJl0zyu5U2hoqK6fh9K1y6Xsdq5/xzSAs39d\no6SkJN566y2++eYbPvroI8qXL6/r58H0789zZTZQpxvMmjVrRuPGjSlUqBAREREcOHCAhg0bpttw\n1apV2bVrFw8++CCHDh2ibNmyzm0VKlTg5MmTxMTEEBAQwB9//MG//vWvTH0AERG5xuFwABAYGMi6\ndesoUKCAmysSkTuVbjAbOXIkkyZNAq7Nnrzrrrsy1HD9+vXZu3cvw4YNwzAMwsPDWbNmDSVKlKBe\nvXo899xzjB49GoAGDRqkCW4iInmJ16+/4v3bb9napiklJc3yihUrmDNnDqtWrWLw4MHZei4RyTnp\nBrOiRYvy+++/U7ly5RueZ3Y7ZrOZV155Jc26Un97MnXDhg0z1PMmIuKpvA4fJigyEr/16112jgSg\nT2oq306YwPTp0+/o+7SI5D7pBrNTp07x5ptvYrFY8Pb2xjAMTCYT8+fPz4n6REQ8jvnCBYLGj8d/\n6VJMqakuPddvgD0oiHXr1hEYGOjSc4mI66UbzN56662cqENExOOZ4uIInDmTgBkzMCckpNmW+K9/\n4ShcOFvOYxgGC44d41RCAoMbNGDc88+TolAmkidkaChTRERuwTCwnD6Nz6ZNBE2ahOXixTSbkxo2\nJGbYMOy1amXL6eLi4hg8eDC/xcYyffp0YqpWzZZ2RSR3SDeYiYjINaaEBLwOHsT7wAG89+/H68AB\nvA8cwBwTc8O+9rAwYoYOJalpU8jG183Nnj0bX19f1q5dm6G3sIiIZ1EwExH5p796wbz278f7+q8D\nB7AcO4bJMG57aGrJksQMGoStQwf469mNWS/HYP78+dSpU4d+/frpBn+RPCxDwSw5OZlz585RpkwZ\nkpOT8fHxcXVdIiI5wmSzXesFu94D9lcIu1kv2K04ChbEHhZG4iOPEP/ii5CNPVlXr15l4MCB/Pnn\nnzRu3FihTCSPSzeYHTp0iPHjx2M2mxk1ahSDBg3i9ddfp6ruaxART5OcjM/mzXj/9ptzODIjvWDX\nGWYzKRUqkFK9OvawMOx//e4IDc3W4cq/6969O9WqVWPKlCn4+vq65BwiknukG8wWLVrE8OHDef/9\n9ylcuDC9e/dm3rx5REZG5kR9IiLZwzAo0r491t27M7S7o0CBa8GrenVS/gphKVWqYOTAfV2GYbB8\n+XKefPJJ5syZoyf4i+Qj6QazpKQkSpcu7Vy+9957+fjjj11alIhIdjNfunTTUObsBbveA5YDvWC3\nExUVxX/+8x8uX75M06ZNKVKkSI7XICLuk24w8/LyIi4uDtNf36D0MlUR8Uh/vUcSwOHvT8xbb+Vo\nL1hGREdH06JFC5544glmz56N1Wp1d0kiksPSDWZt27Zl5MiRREdHM2nSJPbu3XvDq5ZERDyJERhI\nwrPPursMJ4fDwb59+7jnnntYsGAB1apVc3dJIuIm6QazevXqUbp0afbu3YvD4aBDhw5phjZFRCTz\nLl68SN++fTEMgyVLliiUieRz6c67njRpEpcuXaJ58+a0aNFCoUxEJJvs27ePFi1aUKdOHRYtWqRH\nYYhI+j1m1atXZ+nSpcTExNCsWTMefvhhChYsmBO1iYjkSSkpKcTExFC6dGkmT55Mo0aN3F2SiOQS\n6Qaz5s2b07x5c06dOsW3337LsGHDKFeuHIMGDcqJ+kQkHzLFxGCOisrWNs2XLmVre5l19uxZevfu\nTa1atRgxYoRCmYikkeFXMiUnJ2O32zEMQ93tIpI9UlOxnDjhfO3R9Sfve5065e7KXOLbb7/ltdde\no2vXrvTu3dvd5YhILpRuMFuzZg3ffvstdrudZs2aMXr0aA1lisgdM8XE4H3gwP+/9mj/frwOHsRs\ns+V4LY7ChXP0fCkpKVgsFpKTk5k5cyb3339/jp5fRDxHusHs6NGjdO3alRo1auREPSLi6bKhF8zw\n9ia1RAlwQe+8IySE2By8FePUqVP07NmTXr160aJFixw7r4h4plsGs9OnT1OqVClatWoFXAtof1eh\nQgXXViYiuZ4pNjbty78z0QuWWqwY9rCwa++f/Oup+ymVKoG3twsrzxnr16/n9ddfp2fPnjRv3tzd\n5YiIB7hlMFu4cCFvvPEG48ePv2GbyWRi6tSpLi1MRHIv8/nzBI0fj/8nn2BKScnQMYa3NymVK///\ny7+rVyelenUcefSVQ4ZhsHnzZubOnUvdunXdXY6IeAiTYRjG7Xa4fPkyhf9xP8bJkycpU6aMSwu7\nGb0OynOFhobq+nmov187U1wcgTNmEDBjxm17xVKLFk3z8m9nL1g+eMXQsWPHeOONN5gyZQrFihVz\ndzn6t+fhdP08V2hoaKaOu2WPWVxcHABjxoxhxIgRzvUpKSmMHz+eSZMmZeqEIuKB7Hb8Fy8maOJE\nLP947IS9ShXsd9/t7AGzh4XhKFrUTYW618qVKxk+fDj9+/enaD79OxCRrLllMJs8eTJ79+4FoHv3\n7s71ZrOZBx54wPWViYj7GQYsX06xQYPwOnYszSZ7WBgxw4aR1KQJmExuKjD3iIqKYubMmSxZsoSa\nNWu6uxwR8VC3DGZDhw4FYNq0aYSHh+dYQSKSO1h/+ongUaNg16403yhSQkOJjYjA1q4dWCxuqy+3\n+OOPP/j0008ZMmQIa9euxaSQKiJZkO6szBYtWtwwIxM0K1Mkr/I6fJigyEj81q9Ps94RHExcnz7E\nde0Kfn5uqi73MAyDTz/9lFGjRjFkyBAAhTIRyTLNyhQRAMwXLlybabl0KabU1P/fYLUS16ULsX36\nYBQq5L4Cc5mvv/6a6dOn89lnn1GtWjV3lyMieUS6szJzE81M8VyaWZR7meLiCJw589pMy4SENNsS\n2rXDf/x4zuSD2ZQZtX//fi5dukSjRo1ISkrCL5f3HurfnmfT9fNcmZ2Vme5jtU+fPs3//vc/DMNg\n0qRJ9OnTh19//TVTJxORXMRux3/+fIo1bEjQhAlpQllSw4ZcXLeO6ClToHx599WYixiGwcKFC3n6\n6ae5cuUKZrM514cyEfE86QazWbNmYbVa2b17N5cvX6ZHjx4sXbo0J2oTEVcwDHy//JJizZpRcMiQ\nNI+/sIeFcXnRIi5/8gn2WrXcWGTuM378eBYsWMCKFSto3bq1u8sRkTwq3Xdl2u12HnroIebOnUuD\nBg2oUaMGqX+//0REPIZ1xw6C334b665dadanlixJTEQEtvbtNdPyH/bu3Uu5cuXo1KkTvXv3xtfX\n190liUgelm6Pmd1uJzo6mt27d1OrVi2io6NJTk7OidpEJJt4HT5MSPfuFGnTJk0ocwQHEzN0KOe3\nbsXWsaNC2d8YhsHs2bPp1KkThw4dokSJEgplIuJy6faY/fvf/6ZXr140aNCA0qVL07NnT9q3b58T\ntYlINgicMoWgsWPTzLQ0rFbiX3yR2L59NdPyJgzD4NVXX+X06dOsXr2acuXKubskEcknMjQr0+Fw\nYDZf61yLjY0lKCjI5YXdjGameC7NLHKTxERKVq2a5kXjCW3bEhsRQWrZshlqIr9du1OnTlG6dGm+\n++477rvvPqwePiM1v12/vEbXz3Nl+7syr0tMTGTRokXs2bOH1NRUatWqRZcuXfD398/UCUUk55iS\nk52hzLBaubRypW7qvwWHw8H06dOZPXs2X3/9NQ0bNnR3SSKSD6V7j9n8+fOx2+0MGjSIiIgITCYT\nc+fOzYnaRCQbGT4+CmW3EB0dTefOndm4cSNr166lkIZ3RcRN0g1mhw8fpmfPnpQvX54KFSrw6quv\ncuTIkZyoTUTE5Ww2G35+fjRr1oxly5ZRqlQpd5ckIvlYusEsNTUVh8PhXDYMw3m/mYiIp0pNTWXC\nhAk8++yzWK1WunfvjpdXund3iIi4VLrfhWrWrMmkSZP497//jclk4quvvqJGjRo5UZuIiEucO3eO\n3r17YzKZmDlzpl4+LiK5RrrB7MUXX2T58uUsXboUh8NB7dq1adeuXU7UJiKS7QzD4PTp0zRs2JC+\nffti0bPbRCQXSTeYWSwWOnToQL169bBYLJQtW1Y/XYqIx7Hb7YwdOxZfX1/69+9P3bp13V2SiMgN\n0g1mBw8eZOLEiVgsFhwOB15eXkRERFA2g89AEhFxt9OnTxMeHk5QUBCTJ092dzkiIreUbjCbO3cu\nPXv2pHbt2gDs3LmTWbNmMWrUKJcXJyKSHRYuXMijjz5Kjx49NHlJRHK1DE1Buh7KAOrVq8cnn3zi\nsoJERLJDcnIykZGRtGnThjfeeMPd5YiIZEi6PzpWqlSJ77//3rn8yy+/aBhTRHK148eP06ZNG/78\n8099vxIRj5Juj9kvv/zC//73P+bMmYPZbCYmJgZvb2927NiByWRi/vz5OVGniGSC5eTJ/1/IJ5N2\nHA4HPXv2pEOHDnTr1k2TlUTEo6QbzEaOHJkDZYhItjMMCowY4VxMrlfPjcW4ns1mY+7cubz88sus\nXLnS418+LiL5U7rBrGjRojlRh4hkM7/ly/H54QcADIuFmKFD3VyR6xw+fJgePXpQqVIl7HY7AQEB\n7i5JRCRT9P4RkTzIFB1N8NtvO5fjX36ZlGrV3FiR65w5c4a2bdvy+uuv8/zzz2voUkQ8moKZSB4U\n/O67WC5dAiC1ZEli+/d3c0XZLyEhgZ07d9K4cWM2btxIiRIl3F2SiEiWZeiBPsnJyfz5558YhkFS\nUpKraxKRLPD++Wf8Fy50Ll996y2MPDa0d+DAAR577DHWrFkDoFAmInlGusHs0KFD9OnTh8jISKKi\noujZsye///57TtQmIncqNZUCgwdjMgwAEps1I/Gxx9xcVPb69ttv6dixI7169eK9995zdzkiItkq\n3WC2aNEihg8fTlBQEIULF6Z3797MmzcvB0oTkTvlv3Ah1r17ATB8fbk6alSeeUxGbGws586d4557\n7mHFihV07NjR3SWJiGS7dINZUlISpUuXdi7fe++9pKamurQoEblz5gsXCH73XedybO/epJYr58aK\nss/evXtp0aIFK1euJCQkhEqVKrm7JBERl0g3mHl5eREXF+ec6XTmzBmXFyUidy747bcxx8QAkHLX\nXcSFh7u5ouyxePFinn/+eSIiInj11VfdXY6IiEulOyuzXbt2jBw5kujoaCZNmsTevXt55ZVXcqI2\nEckg63ff4f/5587l6HfeAR8fN1aUdbGxsQQGBlK6dGlWr15N+fLl3V2SiIjLpRvM6tatS6lSpdi7\ndy8Oh4MOHTqkGdoUETdLTqbAkCHOxYTWrUlu3NiNBWXdrl27CA8PZ/LkyTRp0sTd5YiI5Jh0g1lc\nXByBgYE8+OCDN6wTEffzW7UK78OHAXAEBhLz5pturijzHA4HM2fOZMaMGbz77rs88MAD7i5JRCRH\npRvMunfvfsO6kJAQZsyY4ZKCROTOeJ044fxzwvPP4/DQZ3o5HA5MJhPR0dGsXbtWPfMiki+lG8w+\n+eQT559TUlLYtm2bJgCI5FKe+iDZ7du3M2zYMFasWMHgwYPdXY6IiNtk6Mn/13l5edG0aVP2/vWc\nJBGRrEhNTWXSpEn07NmToUOHEhQU5O6SRETcKkP3mF1nGAZHjhwhPj7epUWJSP5w7tw5fvnlF9at\nW6fXKomIkIl7zIKDg+natavLChKRvG/Lli2sW7eOyMhIPvroI3eXIyKSa6QbzCIjI6lQoUJO1CIi\neVxKSgrjxo3js88+Y/Lkye4uR0Qk10n3HrMpU6bkRB0ikg+sXLmSvXv3smHDBho1auTuckREcp10\ne8zKli3Ltm3bqFatGr6+vs71eo6ZiGTUxo0bMZlMtG3blrZt22I239G8IxGRfCPdYLZz5062b99+\nw/q/P0ZDRORmkpOTiYyMZM2aNUybNk2BTEQkHbcMZna7HW9vbxYvXpyT9YhIHjJ06FAuXLjAhg0b\nKFSokLvLERHJ9W4ZzIYNG8a7776bk7WI5GvmM2fw+emnOz7O6+BBF1STNV999RX3338/Q4YMoWDB\ngphMJneXJCLiEW4ZzAzDyMk6RPI1y6lTFGvYEFNKirtLyZLExETeeustvvnmGxYsWEDlypXdXZKI\niEe57VDmsWPHbhnQ9AgNkexj/e67bAllKW78d5mSkkL79u0pVaoUGzZsIDg42G21iIh4qlsGs/Pn\nzzN+/PibBjOTycTUqVNdWphIfpVSpgzJ9957x8fZ77kH2+OPu6Ci9O3bt4+7776b9957j+rVq2vo\nUusYZFcAACAASURBVEQkk24ZzEqXLs17772Xk7WICJDcoAHREye6u4wMsdlsDB8+nJ9++on169dT\no0YNd5ckIuLRNHddxM3MZ87g9+WX7i7jjp0+fZqWLVuSlJTEunXr8Pf3d3dJIiIe75Y9ZmFhYTlZ\nh0i+Y4qJIfCDDwj88ENMiYnO9alFirixqvQZhsGVK1coUqQIAwcOpGXLlhq6FBHJJrcMZnpRuYiL\nJCcTsGABgZP+r737jo+qyv8//pqZJJNGAqIxXyAEBEORLosIK6hRpLgCikjJ4oKIAfQLShWiAhua\nugI+pPgVBUJnUanCroCAyKIgJYuCFCVUaQFD6mRm7u8PdX7EQGiZzEzm/Xw88nDmlnM/ySGPvD3n\nzj2TsVy4UGBX7sMPk9m3r4cKu7bMzEyGDx9OZmYms2fPpp2H7mkTESmtrvnk/5vldDqZOXMmaWlp\nBAYGkpiYSHR0dKFjJkyYQOPGjWnVqpW7ShHxDk4nwStXEjFxIgFpaQV22erWJSMpCZsXrx/53Xff\n8cILL3D//ffz1ltvebocEZFSyW3BbPv27eTn5zN27FgOHDhASkoKQ4cOLXDMokWLyMzMdFcJIl4j\naOtWIpKTCdqzp8B2e0wMl4YPJ+eJJ8BLlysyDAO73U5OTg6DBg2iY8eOni5JRKTUclsw279/Pw0a\nNAAgLi6Ow4cPF9i/bds2zGaz6xiR0ihg/34ixo0jeP36AtudZctyacAAsp59FqxWD1V3bb/88gv/\n+7//S+3atUlMTKRx48aeLklEpFRzWzDLyckp8Ckts9mMw+HAYrFw9OhRtmzZwiuvvMLSpUuvu80K\nFSq4o1QpIX7VfydOwOuvw+zZ4HT+/+1WKwwciHn4cCLLliXSYwVe2zfffEOXLl14/PHHGTlyJFYv\nDpBSNL/63SuF1H/+xW3BLCQkhJycHNd7wzCwWCwAbN68mfT0dMaMGcPZs2cJCAggKirqmqNnJ0+e\ndFe54mYVKlTwi/4zZWQQPm0aYR98gPmyT1oaJhM5nTpxacgQHBUrQnb2r19ebMGCBYwYMYLevXv7\nRd+VVv7yu1daqf98180GarcFsxo1avDtt9/SrFkzDhw4QOXKlV37EhISXK+XLFlC2bJlNaUpPs+U\nmckdrVsXurE/96GHyBgxAnvt2h6q7Pqlp6czbNgwBg4cWOieUBERcT+33W3cpEkTAgMDSUpKYs6c\nOTz77LOsWrWKHTt2uOuSIh4V+N//Fghltjp1OLdwIenz5vlEKPvmm2947LHHiI2NJS4uztPliIj4\nJbeNmJnNZvr06VNgW8WKFQsd17lzZ3eVIFKyHA7XS1vDhpxbscJrP2n5R/n5+YwZM4YJEyYQHx/v\n6XJERPyWb/zVEPExRkiIT4Sys2fPMmrUKAzDYOXKlQplIiIe5v1/OUTELb788ktat25NaGgoZrNZ\nyyqJiHgBt01lioj3OnjwIAMHDmTSpEm0aNHC0+WIiMhvFMxE/MipU6fYtWsXbdu2ZdOmTYSHh3u6\nJBERuYymMkX8xPr162nTpg0//fQTgEKZiIgX0oiZiB9YunQpEyZM4P333+e+++7zdDkiInIVCmYi\npdjx48cxmUzEx8fz8MMPc9ttt3m6JBERKYKmMkVKqTVr1tCuXTt27NhBuXLlFMpERHyARsxESqHx\n48ezfPlyZs2aRaNGjTxdjoiIXCcFM5HLGQaRw4YRunQp2O03dq7T6Z6absCpU6eIjo4mPj6efv36\nERkZ6emSRETkBmgqU+Qy1o0bCZs/H1NeHiaH48a+DMPVjhEWVuK1L1u2jFatWrF//36aNGmiUCYi\n4oM0YiZymfBp0265DUd0NJnPPVcM1Vwfm81GUlISX331FQsXLqRWrVoldm0RESleCmYivwlMTcW6\ndSsAhsXCmS+/xFGx4o03ZDaX2DqZeXl5BAUFUa1aNV5//XU9m0xExMdpKlPkN+HTp7te5zzxBI7Y\nWAgIuPGvEghlhmGwePFi4uPjsdlsvPDCCwplIiKlgEbMRADL0aMEr1rlep+ZmOjBaoqWlZXFq6++\nyn//+19mzpyJ1Wr1dEkiIlJMFMxEgLD/+z9Mv32qMrdFC+x16ni4oqs7d+4cZcqUYfXq1YSGhnq6\nHBERKUaayhS/Z0pPJ3TRItf7rL59PVjNlRmGQUpKCkOGDCE2NpaxY8cqlImIlEIaMRO/FzZnDuac\nHADy77mHvAce8HBFBWVkZDBkyBB+/PFHZsyY4elyRETEjTRiJv4tJ4ewWbNcbzP79gWTyYMFFbZ8\n+XLKly/PypUrqVatmqfLERERN9KImfi10H/+E8v58wDYK1Yk5/HHPVzRrwzDYObMmcTGxpKQkIDJ\ny8KiiIi4h0bMxH85HIS//77rbdbzz0NgoAcL+tWFCxfo1asXy5Yto0aNGgplIiJ+RCNm4reC//Uv\nAo4cAcAZGUl2t26eLeg3Q4YMoUqVKrz//vsEBQV5uhwRESlBCmbinwyjwPJLWT16eGR9y985nU5m\nz57NU089xXvvvUdwcLDHahEREc/RVKb4paBvviFo1y4AjKAgsnr18lgt586dIyEhgRUrVpCTk6NQ\nJiLixxTMxC9dvvxSdqdOOKOiPFJHTk4Ojz/+OPXq1WPp0qVER0d7pA4REfEOmsoUvxNw4ADBn38O\ngGEykfnCCyVeg8Ph4Msvv+TBBx/k448/puLNLJYuIiKljoKZ+J2QTz91vc5t1QpH9eolev2ff/6Z\nF198EYvFQvPmzRXKRETERVOZ4nfMGRmu17Y//7lEr71//37atGlD8+bNWbBgAYFe8HgOERHxHhox\nE79mlNAzwvLz8/n555+pWrUqM2fO5N577y2R64qIiG/RiJmIm504ccL1GAyr1apQJiIiV6VgJuJG\nGzdupG3btrRp04bx48d7uhwREfFymsoUcYO8vDxMJhORkZF8+OGHNG7c2NMliYiID9CImUgxO3Lk\nCB06dOCTTz6hYcOGCmUiInLdNGImpY9hYN2wgeB//xuTzVZod+C337rt0itWrGDkyJG8/PLLPPPM\nM267joiIlE4KZlKqBO7aRURyMtZt20r0uoZhYDKZSEtLY/78+dSrV69Ery8iIqWDpjKlVLAcOUK5\nxETuePzxGwpltmbNbvnahw4dol27dvz000+89NJLCmUiInLTNGImPs18/jzhkycTlpKCyW53bTcC\nAsju3h1b/fpXPdf2pz/huOuuW7r+P//5T8aMGcPw4cOpUqXKLbUlIiKiYCa+yTAImzGDMpMnY87M\nLLAr5/HHyRg27JZD17VkZ2ezbNkylixZQq1atdx6LRER8Q+ayhSfFLx6NZHJyQVCWd5993F2xQou\nvP++W0PZ999/z0svvURQUBDz589XKBMRkWKjYCY+yXLsmOu1vXJlzs+axfmPPybfjU/VNwyDefPm\n8cwzz9CiRQsCAjTgLCIixUt/WcTn5bZtS16rVm6/zs6dO5k9ezaffvop1atXd/v1RETE/yiYiVxD\namoqBw4coFOnTqxdu1YjZSIi4jaayhS5CsMw+Oijj+jevTtWqxVAoUxERNxKf2VErmL69OmsXLmS\nlStX6lEYIiJSIhTMRP7g22+/JSoqioSEBJ577jnXaJmIiIi7aSpT5DdOp5Np06bRq1cvjh07RkRE\nhEKZiIiUKI2YiU8KOHq02Nt88cUXOX78OJ999hkVK1Ys9vZFRESuRcFMfE7o3LmEpaS43ufXqHFL\n7X3//ffUqlWL/v37ExcXR2Bg4K2WKCIiclM0lSk+JXjVKiJffdX1Pu/PfyanffubasvhcDBp0iS6\nd+/OiRMnuOeeexTKRETEozRiJj4jaMsWyr30EibDAMBWvz7pH34IN3EfWGZmJr169cLpdLJmzRqi\no6OLu1wREZEbpmAmPiEwNZXbevXCZLMBkF+tGulz52KEh99wWxcuXKBs2bJ06dKF9u3bY7FYirtc\nERGRm6KpTPF6lsOHuS0hAXNWFgCO6GjSFy7EWb78DbVjt9uZMGECnTp1wul08uSTTyqUiYiIV9GI\nmXg186lTlO/aFcv58wA4y5bl/MKFOG7wU5MnT56kX79+hIaGsnjxYgUyERHxSgpm4rVMFy5Qvnt3\nAk6cAMAZEsL5lBTscXE31I7D4cBms/HYY4/xwgsvYDZroFhERLyTgpl4rchRowj84QcAjIAALnzw\nAfn33nvd59tsNsaNG+f6b9++fd1VqoiISLHQ0IF4LeuWLa7XF99+m7yHHrruc9PS0ujYsSNpaWkM\nGTLEHeWJiIgUO42Yiff67bEYAHkPPHBDp37xxRd06NCB3r17YzKZirsyERERt1AwE48ynz1LwG/T\nlX9kysu7obZyc3MZPXo08fHx/O1vfyuG6kREREqWgpl4hDk9nfDJkwlLScGUn3/L7R06dIi+ffty\n11130aRJk2KoUEREpOQpmEmJMuXkEDZzJuFTp2K+dOm6znGGh2NERhZ5zKhRo+jRowcJCQmauhQR\nEZ+lYCYlw+EgZNEiIt56C8vPPxfYlV+zJs7bbrviaUZwMNndu2OEhBTal52dzaRJk3jxxRdJSUnR\nYzBERMTnKZiJexkG1g0b4M03Kbd3b4Fd+dWqcWnkSHJbtYIbHOX64YcfSExMpE6dOgQEBCiUiYhI\nqaBgJm4TuHs3EcnJWP/znwLbHVFRXBo0iOwuXSDgxv8JXrx4ka5duzJs2DA6d+6sqUsRESk1FMyk\n2FmOHCFi4kRCVqwosN0ZFkZm375k9emDERZ2w+1mZmby+eef07FjRzZu3EhERERxlSwiIuIVNP8j\nxcacnk7E668T9eCDBUKZYbFAv36c+eorMl9++aZC2d69e2ndujX/+c9/cDqdCmUiIlIqacRMbllR\nn7TMaduWjOHDufOBB3CePHlT7W/bto3nn3+ev//973To0KE4ShYREfFKCmZya/LyuP3xxwncv7/g\n5j/9iYykJPIbN77ppn/55RdOnz5Nw4YNWblyJVWqVLnFYkVERLybpjLllgTu318glOVXq0b6Rx9x\n/tNPbymU7dy5k8cee4y1a9ditVoVykRExC9oxExujd3ueplfsyZn//Wvm/qk5eUWLlzI+PHjmThx\nIm3atLnVCkVERHyGgpkUGyMk5JZCWXp6OuHh4dStW5fVq1cTExNTjNWJiIh4P01lilf4+uuvadWq\nFRs3bqROnToKZSIi4pc0YiYeZRgG7777LrNmzeIf//gH8fHxni5JRETEYxTMxGNsNhtBQUFERETw\n2WefUaFCBU+XJCIi4lGayhSP+PLLL2nRogVnzpyhZ8+eCmUiIiJoxExKmN1u55133mHx4sVMnjyZ\nqKgoT5ckIiLiNdwWzJxOJzNnziQtLY3AwEASExOJjo527V+1ahVbt24FoGHDhjz99NPuKkW8SGZm\nJidPnmTt2rXccccdni5HRETEq7htKnP79u3k5+czduxYunXrRkpKimvf6dOn2bJlC8nJySQnJ5Oa\nmkpaWpq7ShEvsHr1anr27ElkZCSTJ09WKBMREbkCt42Y7d+/nwYNGgAQFxfH4cOHXfvKly/PiBEj\nMJt/zYV2u53AwEB3lSIeZLPZmDhxIqtXr2bKlCmYTCZPlyQiIuK13BbMcnJyCA0Ndb03m804HA4s\nFgsBAQFERERgGAZz586latWq13Xzt24Q90JHj7peBgUFFeqjf//73xw/fpydO3dy++23l3R1Ukz0\nu+fb1H++Tf3nX9wWzEJCQsjJyXG9NwwDi8Xiem+z2Zg+fTohISH07t37uto8efJksdcptybw7Fl+\nn5S02Wyc+62P1qxZQ3p6Ot27d2fGjBncfvvt6j8fVaFCBfWdD1P/+Tb1n++62UDttnvMatSowa5d\nuwA4cOAAlStXdu0zDIO33nqL2NhY+vTp45rSFN+Xm5tLUlISo0ePplatWgCavhQREblObhsxa9Kk\nCampqSQlJWEYBv369WPVqlVER0fjdDr5/vvvyc/PZ/fu3QB069aNuLg4d5UjJeStt97i9OnT/Otf\n/yIyMtLT5YiIiPgUtwUzs9lMnz59CmyrWLGi6/X8+fPddWnxgMVAw7w8Bg8eTHBwsEbJREREboLm\nEOWWZOfl8TzwGpDldBISEqJQJiIicpMUzOSmGYbBM2+8QTbwLXBPSIinSxIREfFpCmZywwzDYPPm\nzQBMffll5gFlPFuSiIhIqaC1MuWGZGVlMXz4cPbu3cvHH39M5TvvRBOXIiIixUMjZnLdzp49S+vW\nrbFarXz22Wfcdtttni5JRESkVNGImVyTYRgcO3aMmJgYxo0bxwMPPODpkkREREoljZhJkTIyMkhM\nTGTQoEEACmUiIiJupGAmV/Xdd9/RunVrypcvz9y5c/UYDBERETfTVKYUYhgGubm5hIeHM3LkSNq1\na+fpkkRERPyCgpkUkJ6ezqBBg4iLi+PVV18lNjbW0yWJiIj4DU1lisv27dt57LHHqFq1quueMhER\nESk5GjETDMPAZDKxb98+xo4dS6tWrTxdkoiIiF9SMPNzZ8+eZcCAAfTv358ePXp4uhwRERG/pqlM\nP7ZlyxZat25N/fr1ue+++zxdjoiIiN/TiJmfMgyDDz/8kEmTJtGiRQtPlyMiIiIomPkFU0YG1q1b\nCdqyhdMnTvDa99/zTt26LAsOhoULf/26Seb09GKsVERExL8pmJVGdjuBu3dj3byZ4E2bCNy1C5PD\nwVqgJ9AfuOP4cSzFfV09gFZEROSWKJiVEpa0NKybN//6tWUL5oyMAvtPAP2ARUBLN9WQ19JdLYuI\niPgHBTMf9fv0pHXTJqybNxNw5MgVj0sDVgHP16vHjubNMdWpQ7obRrac0dHYmjQp9nZFRET8iYKZ\nDzFlZxOakkLw2rUE7dyJyeG46rGO6GiWVq/OS3v2kNi7N+cGDy7BSkVERORmKJj5Arud0EWLKPOP\nf2A5c+aKhzhDQrDdfz95LVuS17IlKw4cYPSYMcycN4/GjRuXcMEiIiJyMxTMvJlhYP38cyLGjiXw\n0KFCu2116/4axFq0wNa4MVitHDlyhMzMTOIfeYRmzZtTtmxZDxQuIiIiN0PBzEsF7txJRHIy1q+/\nLrDdER3NpQEDyG3XDmf58gX2LV++nKSkJN544w3q1KmD1WotyZJFRETkFimYeRnLjz8SMWECIatX\nF9juDA8ns39/sp5/HiMkpNB5b7/9Np9++inz58+nXr16JVWuiIiIFCMFMy9hPneO8MmTCZs7F5Pd\n7tpuBASQ9eyzZA4YUGiEDODHH3+kUqVKPPHEE7zwwguUKVOmJMsWERGRYqS1Mj3MlJ1N+OTJRDVr\nRvisWQVCWc5f/sKZjRvJGDPmiqFsyZIltG/fntTUVOLi4hTKREREfJxGzDzFbid08eJfP2l5+nSB\nXXlNm5KRlER+w4ZXPNXpdPLKK6+we/dulixZQq1atUqiYhEREXEzBTN3yM0l5LPPsJw4ceX9Dgch\ny5YRePBggc35cXFkjBhB3iOPXHV5o4yMDCIiImjZsiXjxo0jNDS0uKsXERERD1Ewc4MykyZR5r33\nrvt4x513cmnwYLI7d4aAK3eJYRjMnz+fSZMmsXHjRjp27Fhc5YqIiIiXUDBzg6Bdu67rOGd4OJn9\n+v36ScsiRr4uXbrE0KFDOXjwIIsXL9a9ZCIiIqWUgpmbZbdvjyMmptB2Z7ly5HTqhPP224s83+l0\nYrfbqVy5Mu+88w4hV3hUhoiIiJQOCmZult21K7YHHrjh8wzD4KOPPmLz5s3MmTOHV1991Q3ViYiI\niDdRMPNCFy5cYNCgQZw6dYrp06d7uhwREREpIXqOmRfavn07MTExLFu2jCpVqni6HBERESkhGjHz\nEk6nkxkzZhAeHk6PHj1o1aqVp0sSERGREqZg5gXOnTvHgAEDyMzMZNq0aZ4uR0RERDxEU5nFyTAI\nXrmSgH37bui0N998kzp16rB06VIqVqzopuJERETE22nErJgEbdtGRHJyoWeYOaOjr3i8w+Fg2rRp\ndOjQgfHjx2OxWEqiTBEREfFiGjG7RQEHD1KuZ09uf+qpAqHMGRnJxYkTsd99d6FzTp8+TZcuXdi0\naRNBQUEKZSIiIgJoxOymmc+coczbbxO6cCEmp9O13QgKIqtXLy69+CJGuXKFzrPb7XTu3Jn27dsz\nYMAAhTIRERFxUTC7Gbm53NG2LZZTp1ybDJOJnCef5NLQoTgqVSp0it1uZ9myZTz11FMsX76csmXL\nlmTFIiIi4gMUzG5CwE8/FQhluS1akDFyJPY6da54/IkTJ+jfvz9hYWG0bdtWoUxERESuSPeY3QzD\ncL3Mj4sjfeHCq4ayI0eO0LZtWx599FHmzp1LaBGLlYuIiIh/04jZrbrKPWI2m42DBw9Su3ZtFi9e\nTM2aNUu4MBEREfE1GjFzg7S0NDp06MAHH3yAyWRSKBMREZHromBWzDZv3sxf/vIXnnzySSZNmuTp\nckRERMSHaCqzmOTm5pKfn09sbCxz586lfv36ni5JREREfIyCWRFMGRlYTp4stD3gp58KvD906BB9\n+/alc+fOPP/888TGxpZUiSIiIlKKKJhdRdDWrdzWowfmnJwij1vwyy+80rEjQ4cOJSEhoYSqExER\nkdJIwewqQpYtKzKU5QOBQH54OItmz+aee+4psdpERESkdFIwuwqT3e567YiOxhkZ6Xq/Ny+P7idP\n8mHt2rRPTiZfoUxERESKgT6VeR0yhgzh7IYNnFm/nim9e/NIRgZ9Jk4kdvVq8hs29HR5IiIiUkpo\nxOwKzOfPE3D4cKHtdrudbdu28cknn3D33Xd7oDIREREpzTRidhlTTg7h775LVLNmBO3Y4dq++/x5\nunbtit1u591331UoExEREbdQMANwOAhduJCoP/+ZiIkTMWdmAmAAk6tVo9P06XTp0oWQkBDP1iki\nIiKlmn9PZRoG1vXriRg3jsAffiiwK796db7t1YuUf/6TFXPmULVqVQ8VKSIiIv7Cr4NZ5ODBhC1a\nVGCbIyqKL55+mg3h4bz47LOs7NEDk8nkoQpFRETEn/jtVKYpJ6dAKHOGhXFx0CBG9+xJ18WLqR4X\n9+txCmUiIiJSQvx3xCw/3/XSGRzMma1bmbNmDauXLGH16tVUqlTJg8WJiIiIP/LfYHaZL00msk+c\noHPnznTp0oXAwEBPlyQiIiJ+yG+nMgEcQDLwTG4uGRkZWK1WhTIRERHxGL8eMXseOAxsDw3F8sAD\nni5HRERE/Jxfjpht374dm83G68B6oKLZL38MIiIi4mX8KpHY7XYmTpxIYmIiacePUwU/HzIUERER\nr1Kqc4n5/HnKvvQSQXv2kGcYPJaZidVkYmdoKHd27erp8kREREQKKNXBLOTTTwnetImjQGVgJPAo\nYM7IKHCcERzsgepERERECirVU5n29HQGAw8DecBjFP6GDauVzMTEEq9NRERE5I9K7YjZyZMnSVy8\nmGhgG2Dr14/0fv0KHxgcjKHFyUVERMQLlMpglpubS0hICN3vuYeBP/+MCbgUHIxRrpynSxMRERG5\nqlI1lZmbm8vIkSMZOHAg5cqVo3f9+milSxEREfEVpSaY/fjjj7Rv356zZ88yceJET5cjIiIicsNK\nxVSmYRj88MMPdOvWjR49emAyaZxMREREfI9PBbM7WrUq8D7b6eTlU6doGhpKz9/vH5s/37XffOZM\nSZYnIiIickt8KpgFfved6/V3wDNAA6DrxYsEnjxZ5LlGgE99qyIiIuKHfDat/AN4BegJ17zB31mm\nDLmtW7u/KBEREZFb4LZg5nQ6mTlzJmlpaQQGBpKYmEh0dLRr/7p161i3bh0Wi4Unn3ySe++995pt\n/vjpp4z64AMGdOnChDvvBODsddTiqFoVIyzsZr8VERERkRLhtmC2fft28vPzGTt2LAcOHCAlJYWh\nQ4cCcPHiRdasWcOECRPIz8/ntddeo169egQGBhbZ5qODBtG0aVPKNmuGXQ+FFRERkVLGbcFs//79\nNGjQAIC4uDgOHz7s2nfo0CFq1KhBYGAggYGBREdHk5aWRvXq1Yts85VXXqFjx47uKllERETEo9wW\nzHJycggNDXW9N5vNOBwOLBYL2dnZBfaFhISQnZ19zTb79+/vllqlZFSoUMHTJchNUt/5NvWfb1P/\n+Re3PWA2JCSEnJwc13vDMLBYLACEhoaSm5vr2peTk0OY7gETERERP+e2YFajRg127doFwIEDB6hc\nubJrX/Xq1dm3bx82m43s7GxOnDhBTEyMu0oRERER8QkmwzAMdzT8+6cyjx49imEY9OvXj127dhEd\nHU3jxo1Zt24d69evx+l00rFjR5o2beqOMkRERER8htuCmYiIiIjcmFKziLmIiIiIr1MwExEREfES\nXrckkztWDJCSca2+W7VqFVu3bgWgYcOGPP30054qVa7gWv33+zETJkygcePGtGrVykOVyh9dq+92\n7drF0qVLAahatSrPPfccJtO1FrOTknKt/luxYgVfffUVZrOZjh070qRJEw9WK1dy8OBB5s+fz6hR\nowps37FjBx9//DFms5mHHnqIRx555Jpted2I2eUrBnTr1o2UlBTXvt9XDPj73//OyJEjWbBgAfn5\n+R6sVi5XVN+dPn2aLVu2kJycTHJyMqmpqaSlpXmwWvmjovrvd4sWLSIzM9MD1UlRiuq7nJwc5s2b\nx7Bhwxg7dix33HEHly5d8mC18kdF9V9WVhZr1qxh7NixjBw5ktmzZ3uuULmi5cuXM2PGjEJ5xG63\nM2fOHEaOHMno0aNZv349Fy9evGZ7XhfMrnfFgNDQUNeKAeIdiuq78uXLM2LECMxmM2azGbvdfs0l\nuKRkFdV/ANu2bcNsNruOEe9RVN/98MMPxMTEkJKSwuuvv05kZCQRERGeKlWuoKj+s1qt3HHHHeTm\n5pKXl6eRTi905513Mnjw4ELbT5w4QXR0NOHh4QQEBFCjRg327dt3zfa8LphdbcUA4KZXDJCSa19f\n0QAACCZJREFUUVTfBQQEEBERgWEYpKSkULVqVT3N2ssU1X9Hjx5ly5YtdO7c2VPlSRGK6rtLly7x\n3XffkZCQwIgRI/jss884efKkp0qVKyiq/+DX/7F95ZVXGDZsGG3atPFEiVKEpk2buh6gf7k/9uv1\nZhavu8dMKwb4rqL6DsBmszF9+nRCQkLo3bu3J0qUIhTVf5s3byY9PZ0xY8Zw9uxZAgICiIqK0uiZ\nlyiq78qUKUO1atUoW7YsALVq1eLIkSP6HyMvUlT/7d69m4sXL/Lee+8BMHbsWGrWrHnNtaXF80JC\nQm4qs3jdiJlWDPBdRfWdYRi89dZbxMbG0qdPH8xmr/un5/eK6r+EhATGjRvHqFGjaNmyJe3atVMo\n8yJF9d1dd93FsWPHyMjIwOFwcPDgQSpVquSpUuUKiuq/sLAwgoKCCAwMJCgoiLCwMLKysjxVqtyA\nihUrcurUKTIzM7Hb7ezbt4+4uLhrnud1D5jVigG+q6i+czqdTJkyhbvvvtt1fLdu3a7rH6mUjGv9\n7v1uyZIllC1bVp/K9CLX6ruvvvqKFStWAHD//ffToUMHD1csl7tW/y1ZsoTdu3djMpmoWbMmCQkJ\nutfMy5w5c4YpU6YwduxYtmzZQm5uLo888ojrU5lOp5OHHnqI1q1bX7MtrwtmIiIiIv5K80kiIiIi\nXkLBTERERMRLKJiJiIiIeAkFMxEREREvoWAmIiIi4iW87gGzIuJbOnfuTExMTIFn01WrVo3ExMSr\nnrNx40a2bdvG8OHDS6LE67Zjxw5SU1Pp1asXO3fu5ODBgzzzzDMFtpekpUuXEhsby5/+9KcSva6I\neI6CmYjcsjfeeKNUrL/YuHFj1zPbDh065Fqw/fLtJWnv3r16GKyIn1EwExG32bBhA+vWrcNut5OZ\nmUmHDh0KPZj266+/5pNPPsFkMmE2m0lISKB27dpkZ2cza9Ysjh49isPhoE6dOvz1r38ttCbd1KlT\nCQoK4siRI2RkZFCvXj169uxJQEAA+/btY968eeTl5REQEECXLl1o0KCBa4mbS5cuAdCwYUO6dOni\nGsl76qmn+Pzzz3E6nYSGhvI///M/bNu2jR49evDaa6/x/vvvExAQgNPppG/fvrz22mvcdttt111v\nZmYmp0+fplGjRjz88MN8+OGH5ObmcuHCBapUqcLAgQPZsGEDhw8fZu7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"text/plain": [
"<matplotlib.figure.Figure at 0x126e079e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ROC(ytest, final_scores, 'Feed forward neural net', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1bFmUHA5DV19deUHZuXOj5fPZ1L+/p1Z6Kc45p1QfffSr+vXzKDfXrhtuSNak\nSbEKhNE/536/lJYWp7Fjk+T12nT99YV65539atjQfBKdOpVo+vQDWrbsVw0ZUiSHQ/r4Y7euu66h\nLr20kWbNilZRUc2ks+XLI3XZZY30wgvx8nptuvbaIn3xxV4NG3ZkKDuajAyHbr45SUOGNFR6eqSa\nNvXrb387oI8+2heyUCZJI0YUavr0HH3zTbaeeiqvTkJZfLw5FzAhIaBbby3QF19k67339uuqqwhl\nJ6qU0U7glEQw+01JiVmlX5Iuusinfv286tLFp5wch15/PUaStGCBW6WlNvXu7Q2GhkP5/dKcOWaP\nWmXzjEIlMdHQm2/m6P77zTD5wgvxuummZB08aP1aGnl5Nt14Y7KmTo2Tw2Fo/PhcPffcwUonv7dq\nVapJk3K1enW27r8/T02a+PXDD0498kiiOnduorlzo6vVhoICm9avd2rMmERdd11DZWQ4ddZZJXrv\nvX36299yKz3XR7NyZaT69m2sTz5xKzo6oAceyNNXX+3VtdcWh3z1rNstXXGFR8nJdbdQ4sknD+rN\nN/fru++y9Ze/5KlVq1NvyLK6AgFp7VqnJkyI0+9+10itWzfVokWn+KanwCmIoczfbNjgVHGxXa1a\nlQQ3Nn7kkTz98Y8N9dprsbrxxqLgasw//rHy0PXZZ+Z+haefXqpLLqnd4US7XbrnngJ16lSiu+5K\n0pIlLo0bl6C//z23VttxIjIyHLrppmRlZDiVlOTXa68dUM+ex+9Ratw4oHvuKdCddxboP/9xa8aM\nGK1bF6np02M0dGjl58bvNwu3ZmSUV5LPyIjQTz9FKDu7vDqpy2Vo7Nh8jRpVUK1J6Tk5DtnthoYO\nLdT99+fX+mKFupacbKh///AcSq8LJSXSypVR+vhjV7BO3KHGjElSYuL+3zZOB3AqCFkwCwQCmjFj\nhnbt2iWn06lRo0YpJSUlePu6dev0/vvvS5LOOOMM3XLLLbLVYbXUsmHMiy4qfwG86CKf+vTx6PPP\nXbrvvgStXx+puLiA+vf3VHqMmTPNnrXhwwvrrLZYnz5eLVz4q/r0aaxFi9x6/PG8k66DFQrLlkXp\njjuSlJdnV9u2JXrzzRNfuBAZaZYPad++RL17Nz5i+HbZsijNnRutjIwI7dgRUaEY6qHKtvTp0KFE\nd9+dX62FBW3blio+PqDzz/fpscfyggsEgMN5PDZ98olLH33k0pIlLh08WP5ikZpaqgEDPBowwKNP\nPnHpH//DKouIAAAgAElEQVSI1S23JGv+/H2s5AROESELZmvWrFFJSYnS0tK0bds2zZo1Sw8++KAk\nqbi4WHPmzNGTTz6p+Ph4LViwQPn5+YqPjz/OUUPn66/NsbPD5wA9/HCePv/cpU8/NXvLrryyuNKy\nFDt2OLRsmUsul6EhQ+p2w+KzzvLr8ss9+s9/3Jo7N1r33mudgpaGIb3+eoyefTZegYBNAwYU629/\ny1VsbM0NvxUXS+PHx+uNN2IrXJ+S4g/uqXjWWeUfNbGlT+vWpdq8OYudGHBc48dXfJ1r06ZEAwZ4\nNHCgRx06lO9F2r27T/v32/Xvf0dr2LAGWrBgX62sRgZQt0IWzLZu3apOnTpJklq3bq2MjIzgbT/8\n8IOaN2+uWbNmae/everbt29IQ9mOHQ5NnWpuGF5ZvajSUmn1ajOYXXRRxWGYDh1KNWhQsRYuNNPY\nNddUPul/zhyzt2zQoGJL7Ic3YkSh/vMft95+O0ZjxhRYYuK1zyc99FCi3nvPnAt2zz1muYma7F3c\nvDlCd92VpB9+cCoiwtDdd+fr0ku9OvPM0hoNf5UhlOFYDt2WrVMnnwYO9GjAgOKjzsOz26VJk3K1\nf79DX30VpaFDzXBWNtUCQD1lhMirr75qrF27Nvj1qFGjjNLSUsMwDOPLL780br31VuPAgQNGcXGx\n8cADDxi7d+8+oeM//7xhSIZx333Hv+9DD5n3HT++8ttXrzZvP/vsym/fts0wXC7DaN3aMPz+8utv\nuMF83LRphpGcbF7+5psTehohEwgYRtu2Zpvmzavr1hjGwYOG0a+f2Z7oaMN4772aO/bmzeXHjYoy\nL7dubRjffltz3wM4WXl5hrFggWHs2nXij+vc2fy9Pv98828JQP0Vsh4zt9ut4uLy3iXDMOT4bbwo\nLi5OZ511lhITEyVJ55xzjnbu3KnU1NRjHjMzMzN4OS8vRlKCCgoKlJmZd8zHbd+eKClaOTl5ysw8\ncljvww/NY3XtWqjMzINH3B4TIy1d6lB0tKGsrPL/VouKzOPOmeNVTk6UOnb0qVmzfTqkmXVq2LAY\nPf54giZN8qpHj/111o7sbLtuvjlF69dLDRv6NWtWjs47r6TGfk5790ZIaqyi30aQhw0r1FNP5Sk6\n2rDMuQhnqampFf72UH1dupifT/TH+cYbdl19dUOtWxeh1NSAzjjDLL5sfi5Vx44latu28jlonL/w\nxbkLb8fLNEcTsmDWpk0bfffdd+rRo4e2bdumFi1aBG8788wz9csvvygvL08xMTHavn27Lr300lA1\npcKqu8qsXHnkxP/DHauC//Ll5uOHDy+y1HDWtdcWacKEOK1cGaXt2yNqvfq7JP34o0PXX99Av/wi\nnX56qebO3V/juyE0aBBQRISh2FhDL7yQq4EDK1+cAYSrhg0Dv+30kazt251KT49UenrF+yxe/Ks6\ndqy5LbfqSnGxua/q8uVRWrEiSsnJAb38cm6FoeCTkZNj1+rVkSopkZo29atpU7+aNAnUu/2GEb5C\n9qvYrVs3bdy4UY899pgMw9Do0aO1aNEipaSkqEuXLho6dKjS0tIkSd27d68Q3GpadvbRJzH5/Uef\nX3Yi4uMD+v3vK59/Vlfi4w1dc02x5syJ0cyZ0Xr22WP3LNa0b791asSIBsrNtatbN2nGjH019uJ6\nqOTkgL76aq8SEgJKSKj7+X1AKLRo4dfnn/+qAwfs2rHDoR07zNXG8+a5tXt3hPbscVQ7mBUXS/v2\nObR/v13799uVk2N+PnDg0K/N2wsKbPrd77y69978Gvkny++X0tOd+uqrKH31VZTWrIk8YgX1VVc1\n1KxZOdX65zI316avv47SypWRWrkySlu2HDnh1m431Lhx4LeQ5ldKSuC3zxUvx8cblvrnG/VTyIKZ\n3W7XyJEjK1zXrFmz4OWePXuqZ8+eofr2Fezde/Qes82bncrPt6tFi9KTqpQ+eHCR3G7rhYIRIwo1\nZ06M5s2L1iOP5CsmpnbauGRJlG6/PVkej039+nn073+7dPBg6CYth/seoUBV2GzmPyLJyYHghvSb\nN0do9+4TfynPyHBoyRKzZMfq1ZEqLa164nj//Wj9+99u/elPRfp//y9fqalV/9s2DHMf2rIgtnJl\nlHJzK/7z3KGDTxdf7FO3bj69/HKsNm6M1KBBDfX66zm65JJj13TLy7Pp66/NELZqVaQ2bXLKMMqf\nW1SUoQsu8CkxMaA9exzas8ehvXvtyspyHFFH7nAuV0ApKQGlpFQe4FJS/GrRwk94w0mp9523xcU2\n5eUdvcds1aqy3rKTK+A4fHjh8e9UB9q1K1W3bl6tXh2l+fPdGj489KU8Fi926Y47klRSYtPQoYWa\nMOGgYmJSdfDI6XsAaonPZ44OlIWxHTvKX/7tdkOpqaVq0CCgBg0CwfCXnFz+ddltfr80bVqs3n/f\nHfyn7/rrC/XnPxepsNCmnJyKPWxlPW5lvXD799tVVFTxNbl581L16uXVxRd71bOnr0LP+iWXeDV2\nbKIWL3br+usbaPz4gxo2rPx1rKDApm++idSqVWav2PffOxUIlCejyEhDF1zgVffuPvXo4dUFF/jk\nOmxDhZIS8x/4zEy7srPNgJadXR7WzA+z3Tt32rVz59HfOkeMKNT48bzYofrqfTA71jCmVF6/7GSG\nMXv29Fp665kRI4q0enWUZs2K0Q03hHYe3MKFLt11V5L8fptGjizQE0/k8d8jUEf27bPrv/+V5s1L\n0hdfRKmgoPz1MDExoL59PerXz6Pf/c6rxMSq96ZPmpSrO+/M14svxmvhQrf+8Y9Y/eMfscd/4CHf\n++KLvbrkEvPjWEOi0dGGXn/9gCZMKNXUqXF68MFEbd7sVGxsQCtWRGnjRqf8/vIXmYgIQ507lwex\nLl1Kjjua4XRKzZr51ayZX9LRh4MLCmzBkGaGNzPA7dljDi1v2eLUxo0WqE1UCzwe6eBBuxwO83wy\nR6/m1Psf5bGGMQMB6ZtvyjYuP/Ees1atSmW3Gxo92joFXCszcGCxGjaM15YtTq1eHakLLwzN9i7z\n57t1992JCgRsuuuufD38cD6hDKhFhiFt2xahTz81t3hat84pw5Aksw5jmzYl6tfPo379zJ6jk3kz\nbdXKr1dfPaAxY/I1aVKcNm1yKjk5oKSkQIWetwYN/BV63xo0CCgu7sTmatnt0qOP5uuMM/x65JEE\nvfVWTPA2h8McmuzRw6sePXzq2tWn6OjQTNmIjTXUqlWpWrU68rbvvnNq0KBGIfm+oeL3m0O/ubl2\nHTxo/+2zTQcOHPq1Xbm5tuDX5odNHk95yLfZDCUkGMHz3bhxQP36mUWT4+KsN8XH6up9MDtWj9nW\nrRHKzbWrWbPSSgvPHs+YMQUaNqwoJBPaa1JUlDR0aJFeeSVOM2dGhySYvfuuW/fdlyjDsOm++/J0\nzz0FhDKgFr31VrT+8pd47dpV/rIeGWmoTx/pkktydeml3pDMxWzXrlTTpx+o8eNWZujQIrVoUarX\nXotVmzal6tHDq27dfCEvHm1lhmFO2TlwwHZYmKosZFUMYXl5tgrz706E02koIcEc2i4PbHb99JP5\n+/fRR2498oihyy7z6JpritS7t1eRkTX5zGtOYaFNW7ZEaNMmpzZtcmrzZqd++cWhcePydN11tb+o\nr8rBbPPmzSooKJBhlP8BXHjhhSFpVE06VqmMyvbHPBE2mywfyspcf32RpkyJ1UcfuXXw4MEaXb04\ne3a0Hn7YrEn30EN5GjvW2j2IQH1S9g/Ql1+aE6eSk/3q18+r/v096tXLq7PPbqrMzLrdJq4mXXyx\nTxdfnFPXzQgJr1f65RdHMDzZbNLOnTHKzbVVCD+H92KVlFT/v2BzNXtAiYnmqnbzs/m1+WEccnv5\nddHR5b2eZeGsbD7hDz9EaMECt775JkoffujWhx+6lZgY0AUX+JSUZB4jKcn8aNnSr9/9zltr+0vv\n22dXeroZwNLTzY8dOxyVBtSlS11VDmZ5eTZt2eLU5s0R2rzZqS1bnFq7tnptrFIwmzZtmtavX6+U\nlJQKG42HQzDbu/f4E/8P3x+zPiqbP/HLL2YvYUJCzfzn/MYbZhFbSXr88YMaNcqaiyCA+uq664qV\nm2tX584+9e9vDlGe7N6vqL79++3KyHDorLOO/xqbk2PTt99Gas0a82PjxiNLhUgJxz2Oy2WGpUPD\n0+EhKynpyOvi440a+V1xOBQcppbMzo4RI4r0v/859O9/u/Wvf7m1datTS5e6Kn387Nn71bdv9ed5\nV8YwzJBbFr7KwlhlK28jIgy1bl2i9u3Nj3377JoyJe6Yxy3rWSsLYj//XHMDkFU6Unp6uiZNmiR3\nZbt3W9zReswMo2Ym/oeTmh5anDYtRs88Y75oPPPMQd18M6EMqG2XX+7R5ZdTVLmuNWwYkN1u6Oef\nI9SrVxN17+7V0KFFGjiwWG63+Z6zY4dDa9ZE6ttvI7V6daR+/PHIhQItW5YqOdkMTk2buhQZWXjc\nXqzDV5laxWmn+XXXXQW6664CbdsWoZ07HTpwwOzlO3DArv/8x6WMDKf27z+57jK/X8rIiFB6ulPf\nf29+bN7s1MGDRx43Jiagdu1K1KFD+cfZZ5cqKqr8PgsXmj9Qj8em9evNY5lBzFzgkZ9/5HGjosxw\n165dqdq1K1G7diWSGlbr+VQpmDVo0CAsQ5l09GD2yy8O5eQ41KiRX6efbt0VlVb1yiuxeu45c+P5\n557L1fXX15+hEgA4US1b+rVo0T7NnBmjhQtdWrUqSqtWRSkhIUGdO/u0caNT+/ZVfD9yuQx16mQu\nWOja1afOnX0VVseaWzLVj9IbrVuXqnXrigWCMzMdysg4/irW0lJzvnhmpll3LjPTUeHyDz9EqLj4\nyLDUsKE/GL7atzc/n366v8rDpmWlZQ7XqJH/t/BVqvbtzRB21lmlNbYytUqHadOmjV5++WV17txZ\nkYfM3gvnocxNm8xfhvbtS5ikfgIMQ3rppTi99FKcbDZDL76YWyeTIwHAas47r0QvvZSrv/zFpn//\n2625c6O1cWNkcAivQQO/unXzqUsXs3huhw4llp0QX5uyshxau9Z5ROAq+9i7116hNl1lmjUr1bnn\nmuGr7HOTJoFqvb+3bm1WXAgEbGrTxgxeZgAze8MaNQrt3PIqBbPt27dLkpYuXVrh+nAIZkfrMdu8\n2QxmZncjqsIwpIkT4zRlSpzsdkMvv5yrP/6RUAYAh4qLM3TDDUW64YYipadHKCMjQueeW6IzzmBX\ngMpMnBh/3Ps0bmzua5qaan6UXW7aNKBWrUqUnFxzC9rati3V5s1ZcjqNOhkmrlIwe/LJJyVJfr9f\nhmEoIkwqyXk8OmKrjzKbN5vPoX372t/YO1xNmhSrKVPi5HAYmjLlgAYNYl4LABxLhw6l6tCB95nK\ndO/u04cfuhUXFwgGrrKwdWgAS0nx13rPYl3WX6tSwjp48KD+/ve/Kz09XX6/X+3atdOYMWOUnJwc\n6vadlF9/Pfpyk7KhTHrMqubdd9168cV42e2Gpk07oCuuIJQBAKrvz38u0nXXFdVaqYxwUaUfxz/+\n8Q+dffbZmj59umbMmKFzzjlHM2bMCHXbTlpWVuVPLy/Ppl9+iVBUlKEzz+Q/meP58ssoPfigWafs\nmWcOEsoAADWCUHakKv1I9uzZo8GDBysmJkZxcXEaMmSIsrKyQt22k3a07Zi2bDF7y9q2LWF/r+PY\ntClCt92WpNJSm0aPzteNN7L6EgCAUKlSMPP7/fL5youwer3eCoVmrep4KzIZxjy2zEy7hg9voIIC\nu66+ukiPPJJf100CAKBeq1J/UY8ePfTMM8+oT58+kqTPP/88LFZkllX4tdmMCtstlE38b9eOYcyj\nycuzafjwBsrKcuiii7yaNCmXLmcAAEKsSsHs2muvVYMGDbR+/XoFAgH17t1bffv2DXXbTlrZUGaj\nRoEKw5plpTLat6fHrDI+nzRyZLK2bHGqVasSzZiRU6EqMgAACI1jBrOioiJFR0eroKBAXbt2Vdeu\nXYO3FRYWKjY2NuQNPBllQ5mNG/uDway0VNq61Qxm55xDMDucYUgPPpior76KUqNGfs2enaOkpLpb\nNgwAwKnkmMHsL3/5i5577jndcsstld7+7rvvhqRRNaWsuGzjxuVVen/6KUJer03Nm5cqPp7AcbiX\nXorTvHnRcrsDmjkzRy1asF0VAAC15ZjB7LnnnpNk/QB2NNnZZo9ZSkp5uGAY8+jeecetl14yq/q/\n+uoBnXcePyMAAGpTlaZz5+bm6ttvv5Ukvf3223r66ae1a9eukDbsZPl8Uk6OQ3a7oeTk8h6zTZuY\n+F+ZZcvKa5WlpR3UZZd567hFAACceqoUzKZOnaqsrCylp6dr/fr16tWrl954441Qt+2klFX9b9w4\nIMch5czYI/NI6ekRGjkySX6/TXfema/hw6lVBgBAXahSMMvPz9dVV12ldevWqWfPnurdu3eFumZW\nVDaM2bhxxTlSDGVWtHu3XSNGNFBhoV2//32RHn6YWmUAANSVKgWz0tJSlZaWav369erYsaO8Xq88\nHmtvy1O2CvPQif/79jm0d69DcXEBNW/OpPZDa5V17+7VSy9RqwwAgLpUpbfhrl276tZbb1VcXJzO\nPPNMjRs3Tj179gx1205K2T6ZTZocOvHfnF92zjklCoONC0LK55Nuuy1ZW7c6dfbZ1CoDAMAKqlRg\ndsiQIbr00kuVnJwsSRo7dqxatmwZ0oadrLIesyZNAvL/ls3K9sg81YcxDUN64IFELV9eXqssMZHS\nIQAA1LVjBrMvv/xSvXr10qJFi4647fvvv9dVV10VsoadrEPnmO3ZY4a0/HzzulN9ReaLL8bpgw/M\nWmWzZuUwrAsAgEUcM5hlZWVJkn7++edaaUxNKu8xKw9mZU71FZkffBAtu93QtGkH1LHjqf2zAADA\nSo4ZzIYMGSJJGj16tDZv3qx27dqpoKBAmzdvVrdu3WqlgdVVVvW/SZNAhevtdkNt2hBGJkw4qH79\nqFUGAICVVGny/zvvvKN58+ZJkrxerxYsWKD58+eHtGEn62jlMs46q1Rud120qO65XOY8srvuytf1\n11OrDAAAq6lSMFuzZo0effRRSVKDBg301FNPaeXKlSFt2MkoLZX277fLZjPUqFHFHrNTeRhz/PiD\nmjgxVw89RK0yAACsqEqrMktLSxURUX7XiIgI2Sxcb+LXX+0yDJsaNfIr4rBn2L79qTvxv3t3n7p3\nt3ZhYAAATmVVCmZt2rTRK6+8or59+0qSvvjiC7Vq1SqkDTsZ5fPLjlxteCr3mAEAAGurUjC7+eab\n9e6772rmzJmy2+0699xzNXjw4FC3rdr27i2bXxY44jaCGQAAsKoqBTOXy6URI0aooKBAsbGxoW7T\nSTtaj1nDhv5KwxoAAIAVVGnyf2Zmpu655x7dd999ysnJ0T333KPdu3eHum3VdrRSGe3asRUTAACw\nrioFszfeeEM33XSTEhISlJycrAEDBuj1118PdduqrXwo0+wxc/xWX/ZUnvgPAACsr0rBLD8/Xx07\ndgx+ffnll6uoyLp1sLKyKvaY/eEPRRoypEjDhxfWZbMAAACOqUpzzGw2m3w+X7BERm5urgIB687V\nKusxK5tjduaZfk2alFuXTQIAADiuKgWz/v37Ky0tTQcPHtTcuXO1YsUKXX311aFuW7WV7ZN5eNV/\nAAAAK6tSMOvbt69SUlK0du1alZaW6vbbb68wtGklfr9ZYFaqvFwGAACAVVUpmD399NN64okn1K5d\nu1C356Tt22dXIGBTgwZ+OZ113RoAAICqq9Lk/8LCQnk8nlC3pUaUD2PSWwYAAMJLlQvM3nnnnWrR\nooVcLlfw+oceeihkDauurCwza6akML8MAACEl+MGs59//lldunTReeedp+Tk5Npo00mhxwwAAISr\nYwazzz//XLNmzVLTpk2VnZ2tMWPGqFOnTrXVtmo5vLgsAABAuDhmMFu8eLFefPFFJScna9u2bfrn\nP/9p+WBWVlyWoUwAABBujjv5v2z4snXr1srLywt5g05WeY8ZQ5kAACC8HDOY2Q7b8dtRtumkhVFc\nFgAAhKsqlcsoc3hQs6LyoUx6zAAAQHg55hyzXbt2acSIEcGvvV6vRowYIcMwZLPZNHPmzJA38EQE\nAuVV/xs1oscMAACEl2MGs8mTJ9dWO2rE/v12+f02JSX5FRVV160BAAA4MccMZo0aNaqtdtSI7Gyz\nt6xJE4YxAQBA+DmhOWZWx8R/AAAQzupVMMvONoMZPWYAACAc1bNgVjaUSY8ZAAAIP/UqmLFPJgAA\nCGf1KpjRYwYAAMJZPQtmzDEDAADhq14Fs7J9MukxAwAA4ajeBDPDoFwGAAAIb/UmmB04YFdJiU0J\nCQG5XHXdGgAAgBNXb4IZE/8BAEC4q0fBjFIZAAAgvNWjYGY+FeaXAQCAcFWPgpnZY5aSQjADAADh\nqd4Es7JSGQxlAgCAcFVvgll5cVl6zAAAQHiqh8GMHjMAABCe6k0wKx/KpMcMAACEp3oRzA6t+k+P\nGQAACFf1Ipjl5trk9doUFxdQdLRR180BAAColnoRzNgjEwAA1Af1IpiVb8fEMCYAAAhf9SSYUSoD\nAACEv4hQHTgQCGjGjBnatWuXnE6nRo0apZSUlCPuM3HiRHXp0kX9+/ev9vcqH8qkxwwAAISvkPWY\nrVmzRiUlJUpLS9PQoUM1a9asI+7zzjvvqKCg4KS/V/lQJj1mAAAgfIUsmG3dulWdOnWSJLVu3VoZ\nGRkVbv/6669lt9uD9zkZFJcFAAD1QciGMouLixUdHR382m63y+/3y+Fw6Oeff9by5ct177336v33\n36/yMVNTU4OX4+PNz7GxscrNNS+3b5+k1NSkGmk/at6h5w/hhXMX3jh/4Ytzd+oJWTBzu90qLi4O\nfm0YhhwOs2fryy+/VE5Ojp5++mn9+uuvioiIUOPGjY/be5aZmRm8nJcXIylBBQUF+uUXl6QI2e3Z\nysxkONOKUlNTK5w/hA/OXXjj/IUvzl14q26oDlkwa9Omjb777jv16NFD27ZtU4sWLYK3XX/99cHL\n7733nhITE6s9pGkYlMsAAAD1Q8iCWbdu3bRx40Y99thjMgxDo0eP1qJFi5SSkqIuXbrU2PcpKLDJ\n47ErJiag2Fiq/gMAgPAVsmBmt9s1cuTICtc1a9bsiPsNGTLkpL5PVhalMgAAQP0Q9gVm9+6lVAYA\nAKgf6kEwo+o/AACoH8I+mO3bZz4FhjIBAEC4C/tgFgjYJEkpKfSYAQCA8Bb2wawMPWYAACDc1aNg\nRo8ZAAAIb/UmmKWk0GMGAADCW70JZvSYAQCAcFcvgpnLFVBcHFX/AQBAeKsXwSwlJSCbra5bAQAA\ncHLqRTBjGBMAANQH9SSYMfEfAACEv3oRzNiOCQAA1Af1JJjRYwYAAMJfPQlm9JgBAIDwVy+CGZP/\nAQBAfVAvghlDmQAAoD6oJ8GMHjMAABD+wj6YRUUZSkig6j8AAAh/YR/MGjf2U/UfAADUC2EfzJhf\nBgAA6ouwD2asyAQAAPVF2AYz+28tT0khmAEAgPohoq4bUF0DB3q0caNTw4YV1XVTAAAAakTYBrPm\nzf2aMiW3rpsBAABQY8J2KBMAAKC+IZgBAABYBMEMAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAA\nACyCYAYAAGARBDMAAACLIJgBAABYBMEMAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAAACyCYAYA\nAGARBDMAAACLIJgBAABYBMEMAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAAACyCYAYAAGARBDMA\nAACLIJgBAABYBMEMAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAAACyCYAYAAGARBDMAAACLIJgB\nAABYBMEMAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAAACyCYAYAAGARBDMAAACLIJgBAABYBMEM\nAADAIghmAAAAFkEwAwAAsAiCGQAAgEUQzAAAACyCYAYAAGARBDMAAACLIJgBAABYRESoDhwIBDRj\nxgzt2rVLTqdTo0aNUkpKSvD2RYsWaeXKlZKk888/X4MHDw5VUwAAAMJCyHrM1qxZo5KSEqWlpWno\n0KGaNWtW8Lbs7GwtX75czz77rJ599llt3LhRu3btClVTAAAAwkLIesy2bt2qTp06SZJat26tjIyM\n4G0NGjTQuHHjZLebubC0tFROpzNUTQEAAAgLIQtmxcXFio6ODn5tt9vl9/vlcDgUERGh+Ph4GYah\n2bNn64wzzlBqaupxj1mV+8C6OH/hi3MX3jh/4Ytzd+oJWTBzu90qLi4Ofm0YhhwOR/Brn8+nV199\nVW63W7feemuVjpmZmVnj7UTtSE1N5fyFKc5deOP8hS/OXXirbqgO2RyzNm3aaN26dZKkbdu2qUWL\nFsHbDMPQ888/r5YtW2rkyJHBIU0AAIBTWch6zLp166aNGzfqsccek2EYGj16tBYtWqSUlBQFAgFt\n3rxZJSUlWr9+vSRp6NChat26daiaAwAAYHkhC2Z2u10jR46scF2zZs2Cl99+++1QfWsAAICwxBgi\nAACARRDMAAAALIJgBgAAYBEEMwAAAIsgmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDM\nAAAALIJgBgAAYBEEMwAAAIsgmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJg\nBgAAYBEEMwAAAIsgmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEE\nMwAAAIsgmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEEMwAAAIsg\nmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEEMwAAAIsgmAEAAFgE\nwbqo8PgAAAjJSURBVAwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEEMwAAAIsg\nmAEAAFgEwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEEMwAAAIsgmAEAAFgE\nwQwAAMAiCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBEEMwAAAIsgmAEAAFgEwQwAAMAi\nCGYAAAAWQTADAACwCIIZAACARRDMAAAALIJgBgAAYBERoTpwIBDQjBkztGvXLjmdTo0aNUopKSnB\n25csWaIlS5bI4XDommuuUefO/7+9O4uNqf/jOP6e0xadWhqUBrVUacWS8hdbJEIQywVzoRc1iYs2\nEuqKRmljN0jEhSDWCLWEBolG2htNkBJiaUOkKA0VVUU1WtPRZea5eGLy8PR/BnnGnOHzuptzTs98\nTz7T5Du/M+f3+1+wShEREREJC0EbMbt9+zatra24XC7S09PJz8/372toaKC4uJgtW7aQl5fH6dOn\naW1tDVYpIiIiImEhaI3Zo0ePSE1NBWD48OE8e/bMv+/p06ckJycTFRWF3W4nPj6eFy9eBKsUERER\nkbAQtFuZzc3N2O12/2vDMGhvbyciIgK32/3VvujoaNxud8Bz9uvXLyi1yq+h/MKXsgtvyi98Kbs/\nT9BGzKKjo2lubva/9vl8REREAGC32/F4PP59zc3NxMTEBKsUERERkbAQtMYsOTmZsrIyAJ48ecLA\ngQP9+5KSkqioqKClpQW3282rV69ISEgIVikiIiIiYcHm8/l8wTjxl6cyq6ur8fl8LF++nLKyMuLj\n4xk/fjyXL1+mpKQEr9eLw+Fg0qRJwShDREREJGwErTETERERkR+jCWZFRERELEKNmYiIiIhFBG26\njJ+lFQPCV6DsLl26xI0bNwAYO3YsixYtClWp0oFA+X05ZseOHYwfP57Zs2eHqFL5VqDsysrKOHfu\nHABDhgwhIyMDm80WqnLlG4HyKyws5Pr16xiGgcPhYMKECSGsVjpSWVnJqVOn2Lhx41fb79y5w/nz\n5zEMg+nTpzNz5syA57LciJlWDAhfZtm9efOG0tJStm7dytatW7l//74mFbYYs/y+OHPmDE1NTSGo\nTsyYZdfc3MzJkyfJycnB5XIRFxdHY2NjCKuVb5nl9+nTJ4qLi3G5XOTl5XHs2LHQFSodunjxIgcO\nHPhXP9LW1sbx48fJy8tj06ZNlJSU0NDQEPB8lmvMtGJA+DLLrlevXuTm5mIYBoZh0NbWRlRUVKhK\nlQ6Y5Qdw8+ZNDMPwHyPWYZbd48ePSUhIID8/n/Xr19OjRw+6d+8eqlKlA2b5de7cmbi4ODweD58/\nf9ZIpwX17duX7Ozsf21/9eoV8fHxdO3alcjISJKTk6moqAh4Pss1Zv9vxQDgp1cMkF/DLLvIyEi6\nd++Oz+cjPz+fIUOGaEZrizHLr7q6mtLSUtLS0kJVnpgwy66xsZGHDx/idDrJzc2lqKiImpqaUJUq\nHTDLD/7+Yrty5UpycnKYO3duKEoUE5MmTfJPoP9P3+b6vT2L5X5jphUDwpdZdgAtLS3s37+f6Oho\nMjMzQ1GimDDL79q1a9TX17N582bevn1LZGQkffr00eiZRZhl161bN4YOHUpsbCwAI0aM4Pnz5/pi\nZCFm+ZWXl9PQ0MDevXsBcLlcpKSkkJSUFJJa5ftFR0f/VM9iuREzrRgQvsyy8/l87Ny5k0GDBrF0\n6VIMw3IfvT+eWX5Op5Nt27axceNGpk2bxvz589WUWYhZdomJibx8+ZKPHz/S3t5OZWUlAwYMCFWp\n0gGz/GJiYujUqRNRUVF06tSJmJgYPn36FKpS5Qf079+f169f09TURFtbGxUVFQwfPjzg31luglmt\nGBC+zLLzer3s3r2bYcOG+Y9PT0//rg+p/BqB/ve+KCgoIDY2Vk9lWkig7K5fv05hYSEAkydPZuHC\nhSGuWP4pUH4FBQWUl5djs9lISUnB6XTqt2YWU1dXx+7du3G5XJSWluLxeJg5c6b/qUyv18v06dOZ\nM2dOwHNZrjETERER+VPpfpKIiIiIRagxExEREbEINWYiIiIiFqHGTERERMQi1JiJiIiIWITlJpgV\nEfkRaWlpJCQkYBgGNpuNz58/Y7fbyczMZOjQof/pe9XV1bFq1SpOnDhBQUEBjY2NZGRk/KfvISJ/\nNjVmIhL2NmzY8NX6j4WFhRw9ehSXyxXCqkREfpwaMxH5rbS3t/Pu3Tu6du3q33bhwgVu3bqF1+sl\nLi6OzMxMevbsSUNDA4cOHaKmpgabzcasWbOYN28eT5484dSpU7S2tvLhwwfGjBnDsmXLQnhVIvKn\nUGMmImFv06ZN2Gw2GhsbiYqKYty4cSxfvhyAq1evUl1dzbZt24iIiODy5cscPHiQtWvXcuTIEfr1\n68fq1atxu92sW7eOcePGUVRURFpaGiNHjsTj8ZCVlUVVVdVXzZ6ISDCoMRORsPflVmZVVRXbt29n\n1KhR9OjRA4C7d+/y7Nkz1qxZA/y9/E1LSwsADx48wOl0AmC329m1axcAK1as4N69e1y4cIGamhpa\nWlrweDxqzEQk6NSYichvIzExkSVLlrBv3z4GDx5Mnz598Hq9LFiwwL+2Z2trq38RaMP4+sH0N2/e\n0K1bN1wuFwMHDiQ1NZUpU6ZQWVmJVq8TkV9B02WIyG9l6tSpJCUlcfz4cQBSU1MpKSnB7XYDcPbs\nWfbs2QPA6NGjuXLlCgBut5vNmzdTW1vL06dPWbx4MRMnTuT9+/fU1tbi9XpDcj0i8mfRiJmI/HYy\nMjLIzs6mvLycGTNmUF9fT15eHjabjd69e5OVleU/7vDhw2RnZ+Pz+XA4HCQmJuJwOMjJyaFLly70\n7NmTlJQUamtr6du3b4ivTER+dzafxudFRERELEG3MkVEREQsQo2ZiIiIiEWoMRMRERGxCDVmIiIi\nIhahxkxERETEItSYiYiIiFiEGjMRERERi1BjJiIiImIRfwEKletMoPPuvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d74a668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_PR(ytest, final_probs, 'Feed forward neural net', 'b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recursive Neural Nets"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"RNN_HIDDEN_SIZE=4\n",
"FIRST_LAYER_SIZE=50\n",
"SECOND_LAYER_SIZE=10\n",
"NUM_LAYERS=2\n",
"BATCH_SIZE=1\n",
"NUM_EPOCHS=25\n",
"lr=0.0003\n",
"NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n",
"NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)\n",
"ATTN_LENGTH=30\n",
"beta=0\n",
"np.random.RandomState(52);"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class RNNModel():\n",
" def __init__(self):\n",
" global_step = tf.contrib.framework.get_or_create_global_step()\n",
" self.input_data = tf.placeholder(dtype=tf.float32,shape=[BATCH_SIZE,num_features])\n",
" self.target_data = tf.placeholder(dtype=tf.int32,shape=[BATCH_SIZE])\n",
" self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n",
" \n",
" def makeGRUCells():\n",
" base_cell = rnn.GRUCell(num_units=RNN_HIDDEN_SIZE,) \n",
" layered_cell = rnn.MultiRNNCell([base_cell] * NUM_LAYERS,state_is_tuple=False) \n",
" attn_cell =tf.contrib.rnn.AttentionCellWrapper(cell=layered_cell,attn_length=ATTN_LENGTH,state_is_tuple=False)\n",
" return attn_cell\n",
" \n",
" self.gru_cell = makeGRUCells()\n",
" self.zero_state = self.gru_cell.zero_state(1, tf.float32)\n",
" \n",
" self.start_state = tf.placeholder(dtype=tf.float32,shape=[1,self.gru_cell.state_size])\n",
" \n",
" \n",
"\n",
" with tf.variable_scope(\"ff\",initializer=xavier_initializer(uniform=False)):\n",
" droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n",
" \n",
" layer_1 = tf.contrib.layers.fully_connected(\n",
" num_outputs=FIRST_LAYER_SIZE,\n",
" inputs=droped_input,\n",
" \n",
" )\n",
" layer_2 = tf.contrib.layers.fully_connected(\n",
" num_outputs=RNN_HIDDEN_SIZE,\n",
" inputs=layer_1,\n",
" \n",
" )\n",
" \n",
" \n",
" split_inputs = tf.reshape(droped_input,shape=[1,BATCH_SIZE,num_features],name=\"reshape_l1\") # Each item in the batch is a time step, iterate through them\n",
" split_inputs = tf.unstack(split_inputs,axis=1,name=\"unpack_l1\")\n",
" states =[]\n",
" outputs =[]\n",
" with tf.variable_scope(\"rnn\",initializer=xavier_initializer(uniform=False)) as scope:\n",
" state = self.start_state\n",
" for i, inp in enumerate(split_inputs):\n",
" if i >0:\n",
" scope.reuse_variables()\n",
" \n",
" output, state = self.gru_cell(inp, state)\n",
" states.append(state)\n",
" outputs.append(output)\n",
" self.end_state = states[-1]\n",
" outputs = tf.stack(outputs,axis=1) # Pack them back into a single tensor\n",
" outputs = tf.reshape(outputs,shape=[BATCH_SIZE,RNN_HIDDEN_SIZE])\n",
" self.logits = tf.contrib.layers.fully_connected(\n",
" num_outputs=num_classes,\n",
" inputs=outputs,\n",
" activation_fn=None\n",
" )\n",
"\n",
" \n",
" with tf.variable_scope(\"loss\"):\n",
" self.penalties = tf.reduce_sum([beta*tf.nn.l2_loss(var) for var in tf.trainable_variables()])\n",
"\n",
" \n",
" self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = self.logits,\n",
" labels = self.target_data)\n",
" self.loss = tf.reduce_sum(self.losses + beta*self.penalties)\n",
" \n",
" with tf.name_scope(\"train_step\"):\n",
" opt = tf.train.AdamOptimizer(lr)\n",
" gvs = opt.compute_gradients(self.loss)\n",
" self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n",
" \n",
" with tf.name_scope(\"predictions\"):\n",
" self.probs = tf.nn.softmax(self.logits)\n",
" self.predictions = tf.argmax(self.probs, 1)\n",
" correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n",
" self.accuracy = tf.reduce_mean(correct_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training the RNN"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x1292dfbe0>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
"################ done training ################\n",
"################ done testing ################\n"
]
}
],
"source": [
"with tf.Graph().as_default():\n",
" model = RNNModel()\n",
" input_ = train[0]\n",
" target = train[1]\n",
" losses = []\n",
" with tf.Session() as sess:\n",
" init = tf.global_variables_initializer()\n",
" sess.run([init])\n",
" loss = 2000\n",
" \n",
" for e in range(NUM_EPOCHS):\n",
" state = sess.run(model.zero_state)\n",
" epoch_loss =0\n",
" for batch in range(0,NUM_TRAIN_BATCHES):\n",
" start = batch*BATCH_SIZE\n",
" end = start + BATCH_SIZE \n",
" feed = {\n",
" model.input_data:input_[start:end],\n",
" model.target_data:target[start:end],\n",
" model.dropout_prob:0.5,\n",
" model.start_state:state\n",
" }\n",
" _,loss,acc,state = sess.run(\n",
" [\n",
" model.train_op,\n",
" model.loss,\n",
" model.accuracy,\n",
" model.end_state\n",
" ]\n",
" ,feed_dict=feed\n",
" )\n",
" epoch_loss+=loss\n",
" losses.append(epoch_loss)\n",
" #print('step - {0} loss - {1} acc - {2}'.format((e),epoch_loss,acc))\n",
" print('################ done training ################')\n",
" \n",
" final_preds =np.array([])\n",
" final_scores = None\n",
" for batch in range(0,NUM_VAL_BATCHES):\n",
" start = batch*BATCH_SIZE\n",
" end = start + BATCH_SIZE \n",
" feed = {\n",
" model.input_data:val[0][start:end],\n",
" model.target_data:val[1][start:end],\n",
" model.dropout_prob:1,\n",
" model.start_state:state\n",
" }\n",
" acc,preds,state, probs = sess.run(\n",
" [\n",
" model.accuracy,\n",
" model.predictions,\n",
" model.end_state,\n",
" model.probs\n",
" ]\n",
" ,feed_dict=feed\n",
" )\n",
" #print(acc)\n",
" assert len(preds) == BATCH_SIZE\n",
" final_preds = np.concatenate((final_preds,preds),axis=0)\n",
" if final_scores is None:\n",
" final_scores = probs\n",
" else:\n",
" final_scores = np.concatenate((final_scores,probs),axis=0)\n",
" print('################ done testing ################')"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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wFEENAADAUAQ1AGhCnGWlcm7fJmdZqb9LAXARBPq7AADAT+esqpQjb6ZUtEOqKJPCbFJc\nvKzp42RpFezv8gBcIHrUAKAJcOTNlDatl8oPS06n63XTetdxAI0WQQ0AGjlnWamrJ60uRTsYBgUa\nMYIaADR2B0tcw511OVIuHTrg23oAXDQENQBo7KJjXM+k1eXScCmqtW/rAXDRENQAoJGz2CKluPi6\nT8bFu84DaJQIagDQBFjTx0mJKVJ4hGS1ul4TU1zHATRaLM8BAE2ApVWwAsZkuCYOHDogRbWmJw1o\nAghqANCEWGyREgENaDIY+gQAADAUQQ0AAMBQBDUAAABDEdQAAAAM5dXJBOXl5Zo4caIyMjLUpk0b\nSdL8+fNlt9uVmpoqSSooKNCSJUskSe3bt9fIkSNlsVjc9ygpKdGLL74oi8Wi2NhYjRw5UlYr+RIA\nADR9Xks81dXVys3NVVBQkCSpoqJC06ZN0+eff+6+prKyUosWLdKECROUnZ2tqKgoHTlypMZ9Xn31\nVQ0aNEhTpkyR0+nUhg0bvFUyAACAUbwW1BYuXKi+ffsqIiJCklRVVaW0tDT17NnTfc0333yj2NhY\nLViwQE899ZTCw8MVFhZW4z6FhYVKSEiQJCUnJ2vz5s3eKhkAAMAoXhn6XLNmjcLCwpSUlKRly5ZJ\nkqKjoxUdHa2CggL3dUeOHNG2bds0Y8YMtWrVSk899ZSuuuoq2e32Gvc7PRQaHBysY8eONaiGs+/h\nbb5+P3hGm5iF9jAPbWIW2sM8JrSJV4La6tWrJUlbtmxRUVGRcnJyNGHCBNlsNTcNvvTSS3XllVe6\nj1999dUqKiqq8YM583m1yspKhYaGNqiG4uLin/oxGsxut/v0/eAZbWIW2sM8tIlZaA/z+LJN6guE\nXglqWVlZ7q8zMzM1atSoWiFNkjp06KBvv/1WFRUVCg0N1Y4dO3TbbbfVuCYuLk7btm1Tly5dVFBQ\noK5du3qjZAAAAOP4dQupsLAwDR48WNnZ2ZKkX/ziF7riiiv03XffaeXKlUpPT9ewYcM0d+5cVVdX\nq02bNurRo4c/SwYAAPAZi9PpdPq7CG9g6LN5o03MQnuYhzYxC+1hHlOGPlmQDAAAwFAENQAAAEMR\n1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQ\nAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEEN\nAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUA\nAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAA\nAAxFUAMAADBUoDdvXl5erokTJyojI0Nt2rSRJM2fP192u12pqamSpHnz5umbb75RcHCwJOnxxx9X\nSEiI+x7r1q3TokWLFBkZKUl68MEHlZCQ4M2yAQAAjOC1oFZdXa3c3FwFBQVJkioqKpSTk6P9+/fL\nbre7r9u9e7cmT56ssLCwOu+ze/duDRkyRD169PBWqQAAAEbyWlBbuHCh+vbtq2XLlkmSqqqqlJaW\npoKCAvc1DodDJSUlys3NVXl5uW655RbdeuutNe5TWFio3bt3691331XHjh01ZMgQBQQEeKtsAAAA\nY3jlGbU1a9YoLCxMSUlJ7mPR0dGKj4+vcd3x48fVr18//e53v9OkSZP0wQcfaM+ePTWu6d69u0aM\nGKGsrCxVVVXpn//8pzdKBgAAMI7F6XQ6L/ZNn376adfNLRYVFRXp8ssv14QJE2Sz2bR48WLZbDal\npqbK4XDo+PHj7ufTFi1apCuuuEK9evVy3+vHH39UaGioJOmLL77QunXr9Oijj17sktGEnSo9pOr9\n3ynw8rYKiIzydzkAADSYV4Y+s7Ky3F9nZmZq1KhRstlsta4rLi7W7NmzNX36dDkcDn399dfq3bu3\n+7zT6dT48eM1depURUZGauvWrerQoUODaiguLv7pH6SB7Ha7T98Pntntdu0r3CVH3kypaIdUUSaF\n2aS4eFnTx8nSKtjfJTYr/B0xD21iFtrDPL5skzOf3T+bV2d9etK2bVvddNNNmjx5sgICAtSrVy/F\nxsZq69at+vrrr/XAAw/okUce0XPPPaegoCC1bdtWt912mz9LRiPiyJspbVr/nwPlh6VN6+XIm6mA\nMRn+KwwAgAbyytCnCehRa95at2yh4t8NdoWzs4VHyJrxF1lskb4vrJni74h5aBOz0B7mMaVHjQVv\n0SRV7//ONdxZlyPl0qEDvi0IAIALQFBDkxR4eVvXM2l1uTRcimrt24IAALgABDU0SQGRUVJcfN0n\n4+IZ9gQANAoENTRZ1vRxUmKKFB4hWa2u18QU13EAABoBv876BLzJ0ipYAWMy5CwrdT2TFtWanjQA\nQKNCUEOTZ7FFSgQ0AEAjxNAnAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEA\nABiKoAYAAGAoghoAAIChCGoAADRyzrJSObdvc22ZhyaFLaQAAGiknFWVcuTNlIp2SBVlUphNiouX\nNX2cLK2C/V0eLgJ61AAAaKQceTOlTeul8sOS0+l63bTedRxNAkENQIMxvAKYw1lW6upJq0vRDv6e\nNhEMfQLwiOEVwEAHS1x/H+typFw6dECyRfq2Jlx09KgB8IjhFcBA0TGuX5rqcmm4FNXat/XAKwhq\nAOrF8ApgJostUoqLr/tkXLzrPBo9ghqA+jVkeAWAX1jTx0mJKVJ4hGS1ul4TU1zH0STwjBqA+p0e\nXik/XPscwyuAX1laBStgTIarZ/vQASmqNT1pTQw9agDqxfAKYD6LLVKW+AT+PjZBBDUAHjG8AgD+\nwdAnAI8YXgEA/yCoAWgwiy2SdZkAwIcY+gQAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAA+4Swr\nlXP7NraeOw/M+gQAAF7lrKqUI2+ma9/gijLXbidx8bKmj5OlVbC/yzMaPWoAAMCrHHkzpU3rXVvR\nOZ2u103rXcdRL4IaAADwGmdZqasnrS5FOxgG9YCgBgAAvOdgiWu4sy5Hyl27neCcCGoAAMB7omNc\nz6TV5dJwKaq1b+tpZAhqAADAayy2SCkuvu6TcfHsG+wBQQ0AAHiVNX2clJgihUdIVqvrNTHFdRz1\nYnkOAADgVZZWwQoYk+GaOHDogBTVmp60BvJqUCsvL9fEiROVkZGhNm3aSJLmz58vu92u1NRUSdK8\nefP0zTffKDjYtY7K448/rpCQEPc9tm/frvnz5ysgIEDdu3dXWlqaN0sGAABeYrFFSgS08+K1oFZd\nXa3c3FwFBQVJkioqKpSTk6P9+/fLbre7r9u9e7cmT56ssLCwOu/z17/+VePGjVPr1q317LPPqrCw\nUB06dPBW2QAAAMbw2jNqCxcuVN++fRURESFJqqqqUlpamnr27Om+xuFwqKSkRLm5uXryySf1r3/9\nq8Y9jh07purqasXExMhisSgxMVFbt271VskAAABG8UqP2po1axQWFqakpCQtW7ZMkhQdHa3o6GgV\nFBS4rzt+/Lj69eun/v37y+FwKCsrS1deeaXatWsnSaqsrHQPiUpSq1atdPDgwQbVcGavnS/4+v3g\nGW1iFtrDPLSJWWgP85jQJl4JaqtXr5YkbdmyRUVFRcrJydGECRNks9VcR6Vly5a688471bJlS0lS\n165dtWfPHndQCw4OVmVlpfv6qqqqGs+v1ae4uPhifJQGsdvtPn0/eEabmIX2MA9tYhbawzy+bJP6\nAqFXhj6zsrKUlZWlzMxMxcXFacyYMbVCmuQKU0899ZQcDoeqq6v19ddfq3379u7zISEhCgwMVElJ\niZxOpzZt2qSrr77aGyUDAAAYx6/Lc7Rt21Y33XSTJk+erICAAPXq1UuxsbHaunWrvv76az3wwAMa\nNWqUXnjhBTkcDnXv3l3x8edYNA8AAKCJsTidTqe/i/AGhj6bN9rELLSHefzdJs6yUtcekNExrKcl\n/7cHajNl6JMFbwEAPuOsqpQjb6ZUtMO1UXeYTYqLlzV9nCytgj3fAGhm2EIKAOAzjryZ0qb1Uvlh\nyel0vW5a7zoOoBaCGgDAJ5xlpa6etLoU7XCdB1ADQQ0A4BsHS1zDnXU5Uu7aAxJADQQ1AIBvRMe4\nnkmry6XhUlRr39YDNAIENQCAT1hskVLcOZZYiotn9idQB4IaAMBnrOnjpMQUKTxCslpdr4kpruMA\namF5DgCAz1haBStgTIZr4sChA1JUa3rSgHoQ1AAAPmexRUoENMAjhj4BAAAMRVADAAAwFEENAADA\nUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABD\nEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAATYKz\nrFTO7dvkLCv1dynARRPo7wIAAPgpnFWVcuTNlIp2SBVlUphNiouXNX2cLK2C/V0e8JPQowYAaNQc\neTOlTeul8sOS0+l63bTedRxo5AhqAIBGy1lW6upJq0vRDoZB0egR1AAAjdfBEtdwZ12OlEuHDvi2\nHuAiI6gBABqv6BjXM2l1uTRcimrt23qAi4ygBgBotCy2SCkuvu6TcfGu80AjRlADADRq1vRxUmKK\nFB4hWa2u18QU13GgkWN5DgBAo2ZpFayAMRmuiQOHDkhRrelJQ5NBUAMANAkWW6REQEMTw9AnjMLK\n4gAA/Ac9ajACK4sDAFCbV3vUysvL9eijj2rfvn3uY/Pnz9cHH3xQ4zqHw6Fp06bVOi5JhYWF+s1v\nfqPMzExlZmbqk08+8WbJ8BNWFgcAoDav9ahVV1crNzdXQUFBkqSKigrl5ORo//79stvtNa59/fXX\ndfTo0Trvs3v3bvXv318DBgzwVqnws4asLM6DwQCA5qhBPWoHDrhWdv7iiy+0ZMkSHTt2zOP3LFy4\nUH379lVERIQkqaqqSmlpaerZs2eN6z799FNZrVYlJSXVeZ/CwkJ98cUXevrppzVnzhxVVlY2pGQ0\nJqwsDgBAnTz2qOXm5kqS7rzzTs2dO1eJiYl66aWXNH78+HN+z5o1axQWFqakpCQtW7ZMkhQdHa3o\n6GgVFBS4r9u7d6/y8/M1duxYLVmypM57dezYUbfddps6dOigpUuX6s0339SwYcM8frCze+28zdfv\n15ScatlCJRGRcvzwfa1zVtvPFNM1UQGRUed9X9rELLSHeWgTs9Ae5jGhTTwGtcLCQk2bNk3Lli1T\n7969NXjwYE2cOLHe71m9erUkacuWLSoqKlJOTo4mTJggm63mNh9r167VDz/8oClTpujQoUMKDAxU\ndHR0jd61lJQUhYaGur+eN29egz5YcXFxg667GOx2u0/frylyxHaQ6ghqjtgOOnD8pHSeP1/axCy0\nh3loE7PQHubxZZvUFwg9BjWn0ymr1aotW7bo3nvvlSQdP3683u/Jyspyf52ZmalRo0bVCmmSNHTo\nUPfXixcvls1mqzUEmp2drREjRqhjx47asmWLOnTo4KlkNELW9HH/mfV5pNy1R9//zfoEAKC58hjU\nWrdurT/96U86cOCAEhIS9Pzzz6tdu3ZeLeq7777TypUrlZ6ervT0dM2bN0+BgYGy2WwaPXq0V98b\n/sHK4gAA1GZxOp3O+i6oqqrS+vXr1blzZ0VHR+uDDz5Q79691bJlS1/VeEEY+mzeaBOz0B7moU3M\nQnuYp9EMfbZq1UqdOnVSdHS0vvjiC1VUVOjUqVMXtcDGzFlWquM/HJDTGkgPEAAAuKi8MuuzOThz\nJf2DFeVSWDgr6QMAgIvK4zpqhYWFSk9P1/r169W7d2899thj+v772rPzmpuaK+k7WEkfAABcdB6D\n2pmzPrt27SrJ86zPpq4hK+n/1PuzMTkAADBy1qfxGrKS/gU8r8bG5AAA4Eweg9pjjz2m9evX6+qr\nr1ZgYKA6d+6s3r17+6I2c0XHuEJU+eHa5y4Nl6JaX9Bt3cOpp50xnBowJuMCiwUAAI1Vg2Z9Xn75\n5VqzZo3it7B+AAAgAElEQVROnTql7t27G780h7dZbJFSXHzNUHVaXPwFzf5kY3IAAHA2j8+orV27\nVn/5y1/0448/6tixY3r++ee1atUqX9RmNGv6OCkxRQqPkKxW12tiyoWvpM/G5AAA4Cwee9RWrFih\nadOmKSIiQpJ0zz33KDs7W3369PF6cSY7cyX9y5yn9L0l4Kf1eHlpOBUAADReDZr1eTqkSdLPfvYz\nWa0ev63ZsNgi1bJL0k8elnQPp9blAodTAQBA4+YxcV1yySX67LPP3H9ev369QkNDvVpUc3XRh1MB\nAECj5nHoc8SIEfrzn/+sefPmub4hMLDZ70rgLWxMDgAAzuQxqMXGxmr27NkqLi6Ww+FQmzZtFBAQ\n4Ivami2LLfKC1mEDAABNS4MeNrNarWrbtq2uuOIKBQQE6NFHH/V2XQAAAM3eBc0K+PHHHy92HQAA\nADjLBQU1i8VysesAAADAWVhnAxeMzeMBAPCuc04mePbZZ+vsOXM6nTpx4oRXi4LZ2DweAADfOGdQ\n69Gjxzm/qb5zaPrYPB4AAN84Z1C7+eabfVgGGgs2jwcAwHd4Rg3nh83jAQDwGYIazs/pzePr0ow2\nj2ciBQDAFzzuTACcyb15/JnPqJ3WDDaPZyIFAMCXPAa1srIy/fOf/9TRo0fldDrdx0eMGOHVwmAu\na/q4/4SVI+WunrT/CytNHRMpAAC+5DGovfDCC2rZsqXi4uJY6BaSmu/m8UykAAD4mseg9sMPP2jW\nrFm+qAWNTLPbPL4hEyma088DAOB1HicTXHbZZaqqqvJFLYDZmEgBAPAxjz1qERERevzxx5WQkKCg\noCD3cZ5RQ3PT3CdSAAB8z2NQi4qKUlRUlC9qAYzXnCdSAAB8z2NQS0tLU1VVlQoLC1VdXa34+HgF\nB7MMAZqn5jqRAgDgHx6D2s6dOzVjxgyFh4fL4XCotLRUEydOVKdOnXxRH2CkZjeRAgDgFx6D2sKF\nC/W73/1OXbt2lSRt3bpVCxYsUHZ2tteLAwAAaM48zvqsrKx0hzRJ6tq1q44fP+7VogBcHGx1BQCN\nm8ceNYvFokOHDrknFBw8eFBWK1uEAiZjqysAaBo8BrX7779fkydPVrdu3SRJmzdv1siRI71eGIAL\nx1ZXANA0eAxqKSkpatu2rbZu3SqHw6F7771Xbdu29UVtAC4AW10BQNNxzjHMrVu3SpLWrVunb7/9\nVuHh4YqIiNC+ffu0bt06nxUI4Dw1ZKsrAECjcM4etfz8fHXt2lUrV66s8/z111/vtaIA/ASnt7oq\nP1z7HFtdAUCjcs6g9sgjj0iShgwZoo4dO9Y4t3nzZu9WBeCCsdUVADQd5xz63L17twoLC/Xiiy+6\nvy4sLNSOHTuUl5fnyxoBnCdr+jgpMUUKj5CsVtdrYgpbXQFAI3POHrUPPvhAmzdv1uHDh/Xcc8+5\njwcEBCglJcUnxQG4MGx1BQBNwzmD2m9+8xtJ0uuvv65BgwZd0M3Ly8s1ceJEZWRkqE2bNpKk+fPn\ny263KzU11X2dw+HQs88+q+uuu67GcUkqKSnRiy++KIvFotjYWI0cOZJ13IAGYqsrAGjcPC7PMWjQ\nIO3evVtVVVVyOp1yOBwqKSlRnz596v2+6upq5ebmKigoSJJUUVGhnJwc7d+/X3a7vca1r7/+uo4e\nPVrnfV599VUNGjRIXbp0UW5urjZs2ECPHgAAaBY8BrWXX35ZGzZs0MmTJxUREaGSkhJ17tzZY1Bb\nuHCh+vbtq2XLlkmSqqqqlJaWpoKCghrXffrpp7JarUpKSqrzPoWFhUpISJAkJScna9OmTQQ1AADQ\nLHgMalu2bFFOTo7y8vL0wAMPqLS0VMuXL6/3e9asWaOwsDAlJSW5g1p0dLSio6NrBLW9e/cqPz9f\nY8eO1ZIlS855P4vFIkkKDg7WsWPHGvTBzu618zZfvx88o03MQnuYhzYxC+1hHhPaxGNQs9lsatWq\nldq0aaO9e/cqJSVFr7zySr3fs3r1akmukFdUVKScnBxNmDBBNputxnVr167VDz/8oClTpujQoUMK\nDAxUdHR0jd610yFNcm0QHxoa2qAPVlxc3KDrLga73e7T94NntIlZaA/z0CZmoT3M48s2qS8Qegxq\ngYGB+vLLL9W2bVsVFBSoa9euqqqqqvd7srKy3F9nZmZq1KhRtUKaJA0dOtT99eLFi2Wz2WoNgcbF\nxWnbtm3q0qWL+/0BAACaA4/TJ4cMGaJVq1YpOTlZe/bs0ciRI9WzZ0+vFvXdd9+512obNmyYFi9e\nrMmTJ6u6ulo9evTw6nsDAACYwuJ0Op3n8w3Hjh1TSEiIt+q5aBj6bN5oE7PQHuahTcxCe5jH+KHP\nl156qd6bPvbYYxdeEXzOWVbq2qw7OoaFTwEAaCTOGdRiY2MlSd98842+//573XTTTQoICNAnn3yi\n1q3Z1LmxcFZVypE3UyraIVWUuTbrjouXNX2cLK2C/V0eAACoxzmD2oABAyRJ69evV1ZWllq2bClJ\nuu2222pMFoDZHHkza27OXX5Y2rRejryZChiT4b/CAACARx4nE5SXl6tFixbuP1ssFh05csSrReHi\ncJaVunrS6lK0w3UeAAAYy+PyHN26dVN2drZuuukmOZ1OrV27Vtddd50vasNPdbDENdxZlyPlrs26\neV4NAABjeQxqI0aM0Pvvv6/1613DZzfccIPH7aNgiOgY1zNp5Ydrn7s0XIriWUMAAEx2zqB2ehmO\nyspK9erVS7169apx7pJLLvFJgbhwFlukFBdf8xm10+Limf2JJotZzgCainMGtaysLE2fPl0jR46s\n8/wbb7zhtaJw8VjTx/1n1ueRcldP2v/N+gSaGmY5A2hqznvB28aCBW9rcpaVup5Ji2rdLHoYGkOb\nNCe+ao9TOVPr7kFOTGGW81n4O2IW2sM8xi94u2LFinpv2r9//wuvCD5nsUUycQBNWkNmOTeHX1IA\nNC3nDGp79+71ZR0A8NMwyxlAE3TOoMYWUQAaFWY5A2iCPC7PsX37di1btkxVVVVyOp1yOBw6ePCg\n5syZ44v6AKBBmOUMXFzMnjaDx6D28ssvq1evXlq3bp369u2r9evX6/rrr/dFbQBwXpjlDPx0zJ42\ni8egZrFYdM899+jIkSOy2+0aO3asJk6c6IvaAOC8WFoFK2BMRrOb5QxcTOwRbRaPe322atVKktS6\ndWt9++23CgoKktXq8dsAwG8stkhZ4hMIacB5Yo9o83hMXPHx8Zo1a5a6du2qd955RwsWLFBAQIAv\nagMAAL7UkNnT8CmPQW348OG66667ZLfb9fDDD8vhcOj3v/+9L2oDAAC+dHr2dF2YPe0X5wxqM2bM\n0JYtW2SxWHTVVVdJkq655ho9/PDD9a6gCwAAGif37Om6MHvaL845maBz586aN2+eJCk1NVU333yz\ngoOZ7QEAQFPG7GmznDOoDRgwQAMGDNCXX36pVatW6a233lKPHj10++23KzY21pc1AgD8iPW0mhdm\nT5vF4/IcCQkJSkhI0NGjR7V27Vrl5OQoJCRETz/9tC/qAwD4CetpNW/sEW2GBq+zERgYqJYtWyok\nJERHjhzxZk0AAAO419MqPyw5nTXW0wLgGx571L7++mv961//0meffabu3bsrLS1NCQkJvqgNAOAn\nDVlPi+EwwPvOGdTefvttrV69WsePH9ett96qv/zlL4qIiPBlbQAAf2nIeloENcDrzhnUNm7cqEGD\nBiklJYWdCACguTm9nlb54drnWE8L8JlzBjUmCwBA8+VeT+vMPR9PYz0twGfoKgMA1MmaPk5KTJHC\nIySr1fWamMJ6WoAPeZxMAABonlhPC/A/ghoAoF6spwX4D0OfAAAAhiKoAQAAGIqgBgAAYCiCGgAA\n5+AsK5Vz+zbXhArAD5hMAADAWdiQHqagRw0AgLOwIT1MQVADAOAMDdmQHvAVghoAAGdqyIb0gI8Q\n1AAAONPpDenrwob08DGCGgAAZ3BvSF8XNqSHjxHUAAA4CxvSwxReXZ6jvLxcEydOVEZGhtq0aSNJ\nmj9/vux2u1JTUyVJK1eu1EcffSRJeuCBB3TttdfWuEdhYaGmT5+uyy+/XJKUmpqqG264wZtlAwCa\nOTakhym8FtSqq6uVm5uroKAgSVJFRYVycnK0f/9+2e1297EPPvhAf/7zn3Xy5EmNHTtW11xzjSwW\ni/s+u3fvVv/+/TVgwABvlQoAQJ3YkB7+5rWgtnDhQvXt21fLli2TJFVVVSktLU0FBQXua8LCwjRj\nxgwFBATo0KFDCgkJqRHSJFePWnFxsTZs2KCYmBg9/PDDCg5msUEAAND0eSWorVmzRmFhYUpKSnIH\ntejoaEVHR9cIapIUEBCglStXavHixbrjjjtq3atjx4667bbb1KFDBy1dulRvvvmmhg0b5rGG0712\nvuLr94NntIlZaA/z0CZmoT3MY0KbeCWorV69WpK0ZcsWFRUVKScnRxMmTJDNVvd05379+qlPnz6a\nNm2atm7dqq5du7rPpaSkKDQ01P31vHnzGlRDcXHxT/wUDWe32336fvCMNjEL7WEe2sQstId5fNkm\n9QVCr8z6zMrKUlZWljIzMxUXF6cxY8bUGdKKi4v13HPPyel0KiAgQC1atJDVWrOk7Oxs7dy5U5Ir\n+HXo0MEbJQMAABjHr5uy2+12tWvXThkZGZKk5ORkJSQk6LvvvtPKlSuVnp6u9PR0zZs3T4GBgbLZ\nbBo9erQ/SwYAAPAZi9PpdPq7CG9g6LN5a4xt4iwrdW1dEx3T5JYBaIzt0dTRJmahPcxjytCnX3vU\nAEjOqko58ma6NoGuKHNtXRMXL2v6OFlaMcMZAJozdiYA/MyRN1PatF4qPyw5na7XTetdxwEAzRpB\nDfAjZ1mpqyetLkU7XOcBAM0WQQ3wp4MlruHOuhwpd21dAwBotghqgD9Fx7ieSavLpeFSVGvf1gMA\nMApBDfAjiy1Siouv+2RcfJOb/YmanGWlcm7fxhA3gHNi1ifgZ9b0cf+Z9Xmk3NWT9n+zPtE0MdMX\nMJuzrFTHfzggpzXQ778wE9QAP7O0ClbAmAxXr8qhA1JUa7//hwHe5Z7pe9oZM30DxmT4rzCgmTvz\nl6iDFeVSWLjff4li6BMwhMUWKUt8AiGtiWOmL2CumsslOYxYLomgBgC+xExfwEim/hJFUAMAX2Km\nL2AmQ3+JIqgBgA8x0xcwlKG/RBHUAMDHrOnjpMQUKTxCslpdr4kpzPQF/MjUX6KY9QkAPsZMX8BM\nJi6XRFADAD+x2CIlAhpgjDN/ibrMeUrfWwL8/ksUQQ0AAOAMFlukWtrtshQX+7sUnlEDAAAwFUEN\nAADAUAQ1AADQaDnLSuXcvq3J7urBM2oAAKDROXNfTlWUudZA8/O+nN5AjxoAAGh0au7L6TRiX05v\nIKgBAIBGxdR9Ob2BoAYAABoXQ/fl9AaCGgAAaFwM3ZfTGwhqAACgUTF1X05vIKgBAIBGx5o+TkpM\nkcIjJKvV9ZqY4td9Ob2B5TkAAECjc+a+nDp0QIpq3aR60k4jqAEAgEbLYouUmmBAO42hTwAAAEMR\n1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAKABnGWl\ncm7f5tquBgB8hC2kAPiVs6xUOlgiRccYuU+fs6pSjryZUtEOqaJMCrNJcfGypo+TpVWwv8sD0MQR\n1AD4RWMJQI68mdKm9f85UH5Y2rRejryZChiT4b/CADQLXh36LC8v16OPPqp9+/a5j82fP18ffPCB\n+88rV67UE088oSeeeEKff/55rXuUlJToySef1FNPPaW//vWvcjgc3iwZgI+4A1D5YcnprBGATOEs\nK3UFyboU7WAYFIDXeS2oVVdXKzc3V0FBQZKkiooKTZs2rUYYq6io0AcffKBnnnlGTz31lPLy8uR0\nOmvc59VXX9WgQYM0ZcoUOZ1ObdiwwVslA/CRRhOADpa4evvqcqRcOnTAt/UAaHa8FtQWLlyovn37\nKiIiQpJUVVWltLQ09ezZ031NWFiYZsyYocDAQJWVlSkkJEQWi6XGfQoLC5WQkCBJSk5O1ubNm71V\nMgBfaSwBKDrGNSRbl0vDpajWvq0HQLPjlaC2Zs0ahYWFKSkpyX0sOjpa8fHxta4NCAjQypUrNXny\nZPXo0aPO+50Ob8HBwTp27Jg3SgbgS40kAFlskVJc7f9uSZLi4o2c/ACgafHKZILVq1dLkrZs2aKi\noiLl5ORowoQJstnq/g9zv3791KdPH02bNk1bt25V165d3efO7GGrrKxUaGhog2qw2+0/4ROcP1+/\nHzyjTcxSoz3sdh3q3E1V69bWuq5V526KSujmw8rq53jyOZXOyNCJHV/KUfaDrLafKSg+QZH/NVXW\n4BB/l/eT8HfELLSHeUxoE68EtaysLPfXmZmZGjVqVJ0hrbi4WH/72980btw4BQQEqEWLFrJaa3by\nxcXFadu2berSpYsKCgpqhLj6FBcX/7QPcR7sdrtP3w+e0SZmqas9nEN/K1VVuZ5VO1Lu6kmLi9eJ\nob81r+3Sx0tlpbIeOiBFtdZJW6RKDpdJh88xfNsI8HfELLSHeXzZJvUFQr8uz2G329WuXTtlZLim\nuCcnJyshIUHfffedVq5cqfT0dA0bNkxz585VdXW12rRpc87hUQCNi6VVsALGZLgmDvxfADJ5KNFi\ni5QMrg9A02Rxnj3NsomgR615o03MQnuYhzYxC+1hHlN61NhCCgAAwFAENQAAAEMR1AAAAAxFUAMA\nADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAA\nwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAA\nQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAM\nRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAU\nQQ0AAMBQgd68eXl5uSZOnKiMjAy1adNGkjR//nzZ7XalpqZKklasWKFPPvlEkpScnKy0tLQa9ygs\nLNT06dN1+eWXS5JSU1N1ww03eLNsAAAAI3gtqFVXVys3N1dBQUGSpIqKCuXk5Gj//v2y2+2SpAMH\nDig/P1/Tpk2TJD399NNKSUlRu3bt3PfZvXu3+vfvrwEDBnirVAAAACN5LagtXLhQffv21bJlyyRJ\nVVVVSktLU0FBgfuayMhITZo0SVarawS2urpaLVq0qHGfwsJCFRcXa8OGDYqJidHDDz+s4OBgb5UN\nAABgDK8EtTVr1igsLExJSUnuoBYdHa3o6OgaQS0wMFBhYWFyOp1auHCh2rdv7+5tO61jx4667bbb\n1KFDBy1dulRvvvmmhg0b5rGGs+/jbb5+P3hGm5iF9jAPbWIW2sM8JrSJV4La6tWrJUlbtmxRUVGR\ncnJyNGHCBNlstlrXnjhxQnPmzFFwcLDS09NrnU9JSVFoaKj763nz5jWohuLi4p/wCc6P3W736fvB\nM9rELLSHeWgTs9Ae5vFlm9QXCL0S1LKystxfZ2ZmatSoUXWGNKfTqRkzZqhLly6655576rxXdna2\nRowYoY4dO2rLli3q0KGDN0oGAAAwjldnfXry2Wef6csvv9TJkye1ceNGSdLgwYMVEhKilStXKj09\nXenp6Zo3b54CAwNls9k0evRof5YMAADgMxan0+n0dxHewNBn80abmIX2MA9tYhbawzymDH2y4C0A\nAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAA\nAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAA\nGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABg\nKIIaAACAoQhqAAAAhiKoAQAAGIqgBgCADznLSuXcvk3OslJ/l4JGINDfBQAA0Bw4qyrlyJspFe2Q\nKsqkMJsUFy9r+jh/lwaD0aMGAIAPOPJmSpvWS+WHJafT9bppves4cA4ENQAAvMxZVurqSatL0Q6d\nKj3k24LQaBDUAADwtoMlruHOuhwpV3XJPt/Wg0aDoAYAgLdFx7ieSavLpeEKjGnj23rQaBDUAADw\nMostUoqLr/tkXLwCIqN8WxAaDYIaAAA+YE0fJyWmSOERktXqek1MYdYn6sXyHAAA+IClVbACxmS4\nJhYcOiBFtXb1tAH1IKgBAOBDFlukREBDA3k1qJWXl2vixInKyMhQmzauByXnz58vu92u1NRUSdKK\nFSv0ySefSJKSk5OVlpZW4x4lJSV68cUXZbFYFBsbq5EjR8pqZcQWAAA0fV5LPNXV1crNzVVQUJAk\nqaKiQtOmTdPnn3/uvubAgQPKz8/X1KlTNXXqVG3evFl79uypcZ9XX31VgwYN0pQpU+R0OrVhwwZv\nlQwAAGAUrwW1hQsXqm/fvoqIiJAkVVVVKS0tTT179nRfExkZqUmTJslqtcpqtaq6ulotWrSocZ/C\nwkIlJCRIcvW4bd682VslAwAAGMUrQ59r1qxRWFiYkpKStGzZMklSdHS0oqOjVVBQ8J83DwxUWFiY\nnE6nFi5cqPbt28tut9e6n8VikSQFBwfr2LFjDaqhrvt4k6/fD57RJmahPcxDm5iF9jCPCW3ilaC2\nevVqSdKWLVtUVFSknJwcTZgwQTZb7cX+Tpw4oTlz5ig4OFjp6em1zp8OaZJUWVmp0NDQBtVQXFx8\ngdWfP7vd7tP3g2e0iVloD/PQJmahPczjyzapLxB6JahlZWW5v87MzNSoUaPqDGlOp1MzZsxQly5d\ndM8999R5r7i4OG3btk1dunRRQUGBunbt6o2SAQAAjOPX5Tk+++wzffnllzp58qQ2btwoSRo8eLBC\nQkK0cuVKpaena9iwYZo7d66qq6vVpk0b9ejRw58lAwAA+IzF6XQ6/V2ENzD02bzRJmahPcxDm5iF\n9jCPKUOfLEgGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIZqsuuoAQAA\nNHb0qAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgqEB/F9BYOBwO5eXlac+ePWrR\nooUeeeQRxcTEuM+vWrVKq1atUkBAgO677z5de+21fqy2efDUJitWrNAnn3wiSUpOTlZaWpq/Sm0W\nPLXH6WueffZZXXfddUpNTfVTpc2HpzYpKCjQkiVLJEnt27fXyJEjZbFY/FVus+CpTZYvX66PP/5Y\nVqtV9957r1JSUvxYbfOxY8cOvfbaa8rMzKxxfMOGDXrrrbdktVp1yy23qE+fPj6vjR61Bvrss890\n8uRJZWdna/DgwVqwYIH7XFlZmd577z0988wzmjx5sv72t7/p5MmTfqy2eaivTQ4cOKD8/HxNnTpV\nU6dO1ebNm7Vnzx4/Vtv01dcep73++us6evSoH6prnuprk8rKSi1atEgTJkxQdna2oqKidOTIET9W\n2zzU1yY//vij3nvvPWVnZ2vy5MmaP3++/wptRt5++229/PLLtf6/XV1drVdffVWTJ09WVlaWPvzw\nQ5WVlfm8PoJaA3399ddKSkqSJF111VXatWuX+9zOnTvVqVMntWjRQiEhIYqJiSEU+EB9bRIZGalJ\nkybJarXKarWqurpaLVq08FepzUJ97SFJn376qaxWq/saeF99bfLNN98oNjZWCxYs0FNPPaXw8HCF\nhYX5q9Rmo742admypaKiolRVVaXjx4/Tu+kjrVu31vjx42sd37dvn2JiYnTJJZcoMDBQnTp10ldf\nfeXz+ghqDVRZWamQkBD3n61Wq06dOiVJOnbsWI1zwcHBOnbsmM9rbG7qa5PAwECFhYXJ6XRqwYIF\nat++vex2u79KbRbqa4+9e/cqPz9fDz74oL/Ka5bqa5MjR45o27ZtGjp0qCZNmqR3331XxcXF/iq1\n2aivTSTXL5ljx47VhAkTdMcdd/ijxGanR48eCggIqHX87Lby1//beUatgYKDg1VZWen+s9PpdDds\nSEiIqqqq3OcqKysVGhrq8xqbm/raRJJOnDihOXPmKDg4WOnp6f4osVmprz3Wrl2rH374QVOmTNGh\nQ4cUGBio6Ohoete8rL42ufTSS3XllVfKZrNJkq6++moVFRXxC42X1dcmGzduVFlZmXJyciRJ2dnZ\n6ty5szp27OiXWpu74OBgI/7fTo9aA3Xq1EkFBQWSpO3bt+uKK65wn+vYsaO++uornThxQseOHdO+\nffsUGxvrr1KbjfraxOl0asaMGWrXrp1Gjx4tq5V/1b2tvvYYOnSopk2bpszMTPXu3Vt33XUXIc0H\n6muTDh066Ntvv1VFRYVOnTqlHTt2qG3btv4qtdmor01CQ0MVFBSkFi1aKCgoSKGhofrxxx/9VWqz\n16ZNG+3fv19Hjx5VdXW1vvrqK1111VU+r4NN2Rvo9EydvXv3yul06rHHHlNBQYFiYmJ03XXXadWq\nVfrwww/lcDh07733qkePHv4uucmrr00cDodmz56t+Ph49/WDBw/2y1+y5sLT35HTFi9eLJvNxqxP\nH/DUJh9//LGWL18uSfrFL36he+65x88VN32e2mTx4sXauHGjLBaLOnfurKFDh/Ksmg8cPHhQs2fP\nVnZ2tvLz81VVVaU+ffq4Z306HA7dcsst6tevn89rI6gBAAAYivEgAAAAQxHUAAAADEVQAwAAMBRB\nDQAAwFAENQAAAEMR1AA0Kdu2bdO4ceP8XYZXvfHGG/roo4/8XQYAH2BnAgBoZH75y1/6uwQAPkJQ\nAyA8528AAAaLSURBVOAzGzZs0NKlS1VdXa2WLVvqV7/6la666iotXrxYJSUlKi0tVdn/b+/+Qpr6\n3ziAv8/ZmjWxxKRiTVxeZIa6ygvtIpKBV0WFYf8QylVqKtRCi0AKl1Q3wUC9kNqWoPRHKCKDJHNd\nFKiRjOm0LHIp/mPWZH/MTd3zu+jn+da3/H77/sHffvC87vacc57znDMYD8/Z4TM1hcTERBQXF0Op\nVGJ4eBgWiwU+nw+CIGD37t3YuXMnAKC9vR0tLS0QRRExMTEoLS0FAMzMzMBkMmFkZASzs7MoKipC\nSkrKd7U4nU40NTUhPj4eo6OjUCgUKCkpgVqtRl1dHfx+PyYmJrBt2zbk5ubi5s2b+PjxIwBg69at\nOHz4MGQyGbq7u9HU1ARRFKHRaNDT0wOj0Yi+vj60t7cjGAxCqVTi0qVLaG9vR2trK4gIMTEx0Ov1\nWL9+Pd68eYOGhgaEw2EIgoB9+/YhKytr0XhdXR0SEhKwZ88e9Pf3o7GxEcFgEHK5HIcOHcKWLVvw\n/PlzdHV1QRAEjI+PQ6FQoLS0lFcfYOz/DTHG2BIYHR2ls2fPktfrJSKioaEhOnnyJH358oXu3r1L\nxcXF5PF4aH5+nkwmEzU0NNDc3ByVlZVRR0cHERF9+vSJioqK6O3btzQ4OEh6vZ7cbjcREbW0tFB9\nfT319vbSwYMHaWBggIiIHj16RFVVVT/U09vbSwcOHKC+vj4iImptbaXz588TEVFtbS0ZjUZp35qa\nGrJYLBQOhykUClF1dTU9ePCAvF4vFRQU0ODgIBER2Ww2ysvLo4mJCbLZbHTs2DEKBAJEROR0Ouni\nxYs0MzNDRER2u53OnDlDRERVVVX04sULIiJyuVx048aNP4zX1tbSw4cPyev10okTJ6RrHRoaIr1e\nL53/6NGjNDk5SUREZrOZampq/sE3yBj7X+CJGmNsSTgcDkxNTcFoNEqxhWkPAGRlZUkLhOt0Oty6\ndQs6nQ6hUAiZmZkAgLi4OGRmZsJut0OpVEKr1SI+Ph4AsGvXLgBfJ2Vr166Vlg/TaDSw2Ww/rUmj\n0UiTNp1OB7PZDJ/PB+DrmowL7HY7Ll++DEEQsGzZMuTk5ODx48dQqVRQq9XQaDQAgOzsbFitVum4\nxMREKJVKAEB3dzfGx8dRWVkpbff7/fD7/di+fTvMZjNev36NtLQ0HDlyBAAWjS949+4d1q1bJ11r\nQkICkpOT4XQ6IQgCkpKSsHr1agDAhg0b0NnZ+WdfE2MswnCjxhhbEuFwGKmpqTAYDFJscnIScXFx\n6Orqgkwmk+JEBFEUpUd+3yIizM3NQRTF77aFQiG43W4AgFz+20/bH62TKIq/vU9F/11NbyG2fPny\n77Z9myccDmN+fh6iKErH/SzntznC4TB27NiB/Px86bPH40F0dDRycnKQkZEBh8MBu92O5uZmmEym\nRePf5vy9hdrkcjkUCsUv3QfGWOTitz4ZY0siLS0NDocDIyMjAL5OmCoqKhAKhQAAr169wvT0NMLh\nMNra2pCRkQGVSgWZTCZNgj5//ozOzk6kp6cjNTUVPT098Hg8AICnT5+isbHxL9Xkcrmk/521tbUh\nOTkZ0dHRP+yn1Wrx5MkTEBFmZ2fx7NkzpKenY9OmTRgbG5NydHR0IBAI/LQp0mq1ePny5Xf1LkwX\nKysr4XK5kJ2djcLCQgQCAUxNTS0aX7Bx40aMjo7i/fv3AIDh4WH09/dj8+bNf+k+MMYiF0/UGGNL\nQq1Wo7CwUJoIiaKIc+fOSVOn2NhYXL16FV6vFykpKcjNzYVcLkdFRQWsViuam5sxPz+P/fv3IzU1\nFQCQn5+PK1euSMefOnUKY2Njv1xTbGwsbt++DbfbjVWrVqGsrOyn+xUUFMBisaC8vBxzc3PQarVS\nfadPn0ZtbS1EUURSUhJkMhmioqJ+yKHVarF3715UV1dDEASsWLEC5eXlEAQB+fn5sFqtuHPnDgRB\nQF5eHtasWbNofMHKlSthMBhgsVgQDAYhiiJKSkqgUqkwMDDwy/eBMRa5BPr93J4xxpbYvXv34PP5\ncPz48SU7p9PphMViwfXr1/92junpady/fx95eXmIiorChw8fcO3aNdTX1/OjRsbYv4Inaowx9jcp\nlUrI5XJcuHABMpkMcrkcBoOBmzTG2L+GJ2qMMcYYYxGKXyZgjDHGGItQ3KgxxhhjjEUobtQYY4wx\nxiIUN2qMMcYYYxGKGzXGGGOMsQjFjRpjjDHGWIT6Dxe65ebR4Qj7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1273995c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(np.linspace(0, 1, len(losses)), losses);\n",
"plt.title('Validation loss with epoch')\n",
"plt.ylabel('Validation Loss')\n",
"plt.xlabel('epoch progression');"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.51 0.29 0.37 75\n",
" 1 0.60 0.79 0.69 102\n",
"\n",
"avg / total 0.57 0.58 0.55 177\n",
"\n",
"##################################################################\n"
]
},
{
"data": {
"image/png": 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YoHPnzvlfLygoUGxsbLnrULgAAGCbEFzHJSMjw//n9PR0DRo0SKtWrVJ2drYS\nExO1ZcsWtWzZstx1KFwAAEBIPPnkk1qyZIk8Ho8SEhLUoUOHcj9D4QIAgG1CfK+i9PR0/5+/m8RU\nBMO5AADAGCQuAADYxmvuvYooXAAAsE2IW0U3glYRAAAwBokLAAC2MfgmiyQuAADAGCQuAADYJgQX\noKssJC4AAMAYJC4AANjG4FNFFC4AANjG4Ou40CoCAADGIHEBAMAyPoNbRSQuAADAGCQuAADYxuAZ\nFwoXAABsQ6sIAAAg8EhcAACwjI97FQEAAAQeiQsAALYx+F5FFC4AANjG4FYRhQuumxMWptbLZyim\nQYJckRHaM2ORjq/9VJLUfO7zurh7nw4u/V2IdwncmtxuRxNHNlN87Sh5vdLsBbt0MLdAkvSrgXfp\nYG6+/vDRkRDvEqh8zLjguiX856MqPnVWf3/gP/V5j0FqOW+SIm6vqZ98sExxPTqFenvALe2edrXk\ndjsaOvZLvfa7A3q6349Uo1q45qa30r0pt4V6e7jZ+XyB+QkCEhdctyPvfqQjv/+T/7HPUyJ3lVjt\nmTpfd3S5L4Q7A259h74pkNvlyHGk2Bi3PB6foqPd+u1b+9UhuVaotwcEDIULrlvJxXxJkrtKrJL/\n+/9q1+RXVLA/VwX7cylcgAArKCxRfFyU3lr0E1WvFq6xU3boyLFCHTlWSOGCcpl8HDoghUtGRoaK\ni4tLPefz+eQ4jqZNmxaIr0SIRN0Zr+R3M3Vg8Vs6/Lu1od4OYI3He96pzzef0ZLX96n27ZGaN721\n+j+bpaJic0+LABURkMKlT58+WrJkiZ577jm53e5AfAVuAhG1b1P7D3+rHSOm6NSGf4R6O4BVLuQV\nq6Tk2yLl/IVihbldcrkcSRQuqADuVVRakyZNdN999+ngwYNKSUkJxFfgJtB43BCF1aymJhOGqcmE\nYZKkz3sMkrfwUoh3Btz63v5Drp4f0UyZs5IUHuZo6ap9KrxkbvyP4PIZfK8ix+e7Oa9Csy68Wai3\nAFine/Eu3fvIplBvA7DO3z74j6B+X97i5wOybpUhMwOy7ncxnAsAgG0MbhVxHRcAAGAMEhcAAGxj\n8IwLhQsAAJbx0SoCAAAIPBIXAABsY/CVc0lcAACAMUhcAACwzE16CbcKoXABAMA2tIoAAAACj8QF\nAADLcBwaAAAgCEhcAACwjcFXziVxAQAAxiBxAQDAMibPuFC4AABgGR/HoQEAAAKPxAUAANsY3Coi\ncQEAAMbZ2AMcAAAIWElEQVQgcQEAwDI+g49DU7gAAGAZk08V0SoCAADGIHEBAMA2HIcGAAAIPBIX\nAAAsY/KMC4ULAACW4cq5AAAAQUDiAgCAZXw+c1tFJC4AAMAYJC4AANiGGRcAAIDAI3EBAMAyHIcG\nAADGMLlwoVUEAACMQeICAIBluAAdAABAEJC4AABgmVDNuHi9Xi1evFhHjhyRy+XS0KFDJUmZmZly\nHEf16tVTWlqaXK5r5yoULgAAWCZUraKsrCxJ0tSpU5Wdna3XX39dPp9PqampSkxM1NKlS5WVlaWU\nlJRrrkGrCAAABEVKSooGDx4sSTpx4oSqV6+unJwctWjRQpLUtm1bbdu2rcw1KFwAALCMz+sLyE9F\nuN1uLViwQK+99po6dOggSXIcR5IUHR2t/Pz8Mj9PqwgAAATVs88+q7Nnz2r8+PEqKiryP19QUKDY\n2NgyP0viAgCAbXy+wPyU4y9/+YvWrFkjSYqIiJDjOGrUqJGys7MlSVu2bFHz5s3LXIPEBQAAy4Rq\nODclJUULFy7U5MmT5fF49NRTTykhIUFLliyRx+NRQkKCv310LRQuAAAgKKKiojRq1Kgrns/IyKjw\nGhQuAABYhnsVAQAABAGJCwAAluFeRQAAAEFA4gIAgGVMnnGhcAEAwDImFy60igAAgDFIXAAAsAzD\nuQAAAEFA4gIAgGVMnnGhcAEAwDLeEnMLF1pFAADAGCQuAABYhuFcAACAICBxAQDAMgznAgAAY5hc\nuNAqAgAAxiBxAQDAMiQuAAAAQUDiAgCAZTgODQAAEAQkLgAAWMbkGRcKFwAALMO9igAAAIKAxAUA\nAMuY3CoicQEAAMYgcQEAwDImH4emcAEAwDK0igAAAIKAxAUAAMtwHBoAACAISFwAALCMyTMuFC4A\nAFjG5FNFtIoAAIAxSFwAALCMj+FcAACAwCNxAQDAMhyHBgAACAISFwAALMNxaAAAYAxaRQAAAEFA\n4gIAgGV8JVyADgAAIOBIXAAAsAzDuQAAwBgM5wIAAAQBiQsAAJbhXkUAAABBQOICAIBlvB5zExcK\nFwAALOMrNrdwoVUEAACMQeICAIBlTG4VkbgAAABjkLgAAGAZZlwAAACCgMQFAADLmDzjQuECAIBl\nfMXeUG/hutEqAgAAxiBxAQDAMia3ikhcAACAMRyfz2du2QUAAH6wdeHNArJu9+JdAVn3uyhcAACA\nMWgVAQAAY1C4AAAAY1C4AAAAY1C4AAAAY1C4AAAAY1C4AAAAY3DlXFQKr9er5cuX68CBAwoPD9eQ\nIUMUHx8f6m0B1tizZ4/efPNNpaenh3orQECRuKBSfPHFFyouLtb06dPVp08fvf7666HeEmCNP/zh\nD1q8eLGKi4tDvRUg4ChcUCm++uorJSUlSZKaNm2qvXv3hnhHgD3i4uL03HPPhXobQFBQuKBSFBQU\nKCYmxv/Y5XKppKQkhDsC7NGhQwe53e5QbwMICgoXVIro6GgVFBT4H/t8Pv4hBQBUOgoXVIpmzZpp\ny5YtkqTdu3erfv36Id4RAOBWxKkiVIqUlBRt27ZNEydOlM/n07Bhw0K9JQDALYi7QwMAAGPQKgIA\nAMagcAEAAMagcAEAAMagcAEAAMagcAEAAMagcAEMcfz4cT3xxBMaM2ZMqZ9PP/30htadNWuWNm7c\nKEkaM2aMLl68eM335ufnKyMjw/+4vPcDQGXjOi6AQSIiIvTiiy/6H58+fVqjR4/WXXfdpQYNGtzw\n+t9d+2ry8vL09ddfV/j9AFDZKFwAg9WqVUvx8fHaunWrXn31VV26dEkxMTGaPHmyPv30U/3pT3+S\nz+dT1apVNWDAACUkJOj06dPKzMzUmTNndMcdd+jcuXP+9R5//HEtX75c1apV05o1a7Rp0ya53W7F\nx8frmWee0aJFi1RUVKQxY8Zo9uzZSk1N9b//3Xff1WeffSa32606deooLS1NNWrUUHp6upo2bapd\nu3bp5MmTatWqlZ5++mm5XAS+AH44ChfAYLt379bRo0dVVFSkQ4cOKTMzUzExMfrXv/6lTZs2acqU\nKYqMjNTWrVs1d+5cvfzyy3r11VfVpEkTpaam6ujRoxozZswV62ZlZWnjxo2aPn26qlSpopUrV+qj\njz7S0KFDNXr06CuSlg0bNujLL7/UzJkzFRUVpbfffluZmZmaMGGCJOno0aOaPHmyCgsLNXLkSP3r\nX/9Sy5Ytg/J3BODWQuECGORy2iFJXq9XVatW1fDhw3Xu3Dk1aNDAf4fuzZs36+jRo5o4caL/s3l5\necrLy9P27dvVr18/SVJ8fPxVC4ht27bpnnvuUZUqVSRJ/fv3l/TtnM3VbNmyRffff7+ioqIkSd26\nddOgQYPk8XgkSe3atZPL5VJMTIzi4+OVl5dXGX8dACxE4QIY5PszLpdt3LjRXzRI3xY1//7v/66+\nffv6H585c0axsbFyHKfUZ692F+/vP3fx4sUyh3C9Xm+pdX0+n0pKSnT5jiIRERH+177//QDwQ9Bk\nBm5Bbdq00WeffaYzZ85IktavX68pU6b4X/vzn/8sSTp58qSys7Ov+HyrVq30+eefKz8/X5L0zjvv\naO3atXK73fJ6vfr+Lc6SkpK0YcMGFRYWSpL++Mc/qnnz5goPDw/Y7wjATiQuwC2oTZs26tmzp6ZN\nmybHcRQdHa3nnntOjuNo4MCBWrhwoUaOHKlatWqpYcOGV3z+xz/+sXJzczVp0iRJUr169TR48GBF\nRkaqcePGGjVqlL8QkqROnTrp1KlTGj9+vHw+n+Li4jR8+PBg/boALMLdoQEAgDFoFQEAAGNQuAAA\nAGNQuAAAAGNQuAAAAGNQuAAAAGNQuAAAAGNQuAAAAGNQuAAAAGP8P5FUyjx6sNS7AAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d9b8b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_confusionmatrix(ytest, final_preds)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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N1WqlQ4cOeHt7s3fv3tT8/fv3591338Xb25vixYvTsGFDDMNg3Lhx9OrVC4vFwsCBA8mR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\n8f8A9do9feRPrzgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d75bc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ROC(ytest, final_scores, 'Feed forward neural net', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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G9flsKiy0ye0u+X22dKlLL7yQoAULDiS2nBy7wmERzACgBuEtvRzmzjU/DM8/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125c878d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_PR(ytest, final_scores, 'Feed forward neural net', 'b')"
]
},
{
"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",
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"cell_type": "code",
"execution_count": null,
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": []
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{
"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
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"outputs": [],
"source": []
},
{
"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": 2
}
| mit |
VascoVisser/euler | notebooks/0018.ipynb | 1 | 1669 | {
"metadata": {
"name": "",
"signature": "sha256:70c7fe68ef82e54771b30318e600ee3dcb86b946a4deee90c0ce62008164efb3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"raw_triangle=\"\"\"75\n",
"95 64\n",
"17 47 82\n",
"18 35 87 10\n",
"20 04 82 47 65\n",
"19 01 23 75 03 34\n",
"88 02 77 73 07 63 67\n",
"99 65 04 28 06 16 70 92\n",
"41 41 26 56 83 40 80 70 33\n",
"41 48 72 33 47 32 37 16 94 29\n",
"53 71 44 65 25 43 91 52 97 51 14\n",
"70 11 33 28 77 73 17 78 39 68 17 57\n",
"91 71 52 38 17 14 91 43 58 50 27 29 48\n",
"63 66 04 68 89 53 67 30 73 16 69 87 40 31\n",
"04 62 98 27 23 09 70 98 73 93 38 53 60 04 23\"\"\"\n",
"\n",
"triangle = [[int(i) for i in l.split(' ')] for l in raw_triangle.split('\\n')]\n",
"line_bottom = triangle[-1]\n",
"for line_top in triangle[-2::-1]:\n",
" c1 = (x[0] + x[1] for x in zip(line_bottom, line_top))\n",
" c2 = (x[0] + x[1] for x in zip(line_bottom[1:], line_top))\n",
" line_bottom = [max(x[0], x[1]) for x in zip(c1, c2)]\n",
"\n",
"print line_bottom"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1074]\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
davidrpugh/Pyreto | examples/clauset-shalizi-newman-2009.ipynb | 1 | 37799 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import pyreto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Forest fires </h2>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fire_size = pd.read_csv(\"http://tuvalu.santafe.edu/~aaronc/powerlaws/data/fires.txt\", names=['acres'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>acres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>203785.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>89.563111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2098.732181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>412050.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" acres\n",
"count 203785.000000\n",
"mean 89.563111\n",
"std 2098.732181\n",
"min 0.100000\n",
"25% 0.100000\n",
"50% 0.200000\n",
"75% 2.000000\n",
"max 412050.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fire_size.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check that I get same estimate for alpha given reported xmin...\n",
"desired_alpha, desired_xmin = 2.2, 6324\n",
"result1 = pyreto.distributions.Pareto.fit(fire_size.acres, xmin=desired_xmin)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check that I get the same estimates for both alpha and xmin using brute force minimization\n",
"result2 = pyreto.distributions.Pareto.fit(fire_size.acres, xmin=None, quantile=0.999, method='brute')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.testing.assert_almost_equal(result2.params['alpha'], desired_alpha, decimal=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.testing.assert_almost_equal(result2.xmin, desired_xmin, decimal=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check that I get the same estimates for both alpha and xmin using bounded minimization\n",
"result3 = pyreto.distributions.Pareto.fit(fire_size.acres, xmin=None, quantile=0.999, method='bounded')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.testing.assert_almost_equal(result3.params['alpha'], desired_alpha, decimal=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 1 decimals\n ACTUAL: 7149.9999509662648\n DESIRED: 6324",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-b77cff7068bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 1 decimals\n ACTUAL: 7149.9999509662648\n DESIRED: 6324"
]
}
],
"source": [
"np.testing.assert_almost_equal(result3.xmin, desired_xmin, decimal=1)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pvalue, Ds = pyreto.distributions.Pareto.test_goodness_of_fit(42, result3, fire_size.acres, method='bounded')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# pareto distribution should be rejected...\n",
"assert pvalue <= 0.10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Weblinks </h2>"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weblinks_histogram = pd.read_csv('http://tuvalu.santafe.edu/~aaronc/powerlaws/data/weblinks.hist', sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>degree</th>\n",
" <th>frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.448000e+04</td>\n",
" <td>1.448000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.549990e+04</td>\n",
" <td>1.910143e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.657173e+04</td>\n",
" <td>1.022556e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.619750e+03</td>\n",
" <td>1.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.468500e+03</td>\n",
" <td>2.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.475050e+04</td>\n",
" <td>1.100000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.199466e+06</td>\n",
" <td>1.066498e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" degree frequency\n",
"count 1.448000e+04 1.448000e+04\n",
"mean 1.549990e+04 1.910143e+04\n",
"std 3.657173e+04 1.022556e+06\n",
"min 0.000000e+00 1.000000e+00\n",
"25% 3.619750e+03 1.000000e+00\n",
"50% 7.468500e+03 2.000000e+00\n",
"75% 1.475050e+04 1.100000e+01\n",
"max 1.199466e+06 1.066498e+08"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weblinks_histogram.describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# convert histogram data into degree series..\n",
"raw_counts = np.repeat(weblinks_histogram.degree.values, weblinks_histogram.frequency.values)\n",
"weblinks = pd.Series(raw_counts, name='count')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 2.765887e+08\n",
"mean 7.990814e+00\n",
"std 3.054431e+02\n",
"min 0.000000e+00\n",
"25% 1.000000e+00\n",
"50% 1.000000e+00\n",
"75% 4.000000e+00\n",
"max 1.199466e+06\n",
"Name: count, dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weblinks.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check that I get same estimate for alpha given reported xmin...\n",
"desired_alpha, desired_xmin = 2.336, 3684\n",
"result1 = pyreto.distributions.Pareto.fit(weblinks, xmin=desired_xmin)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.testing.assert_almost_equal(result1.params['alpha'], desired_alpha, decimal=3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 3 decimals\n ACTUAL: 2.3261940228265701\n DESIRED: 2.336",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-f7e786fc530c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# check that I get the same estimates for both alpha and xmin using bounded minimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mresult2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyreto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPareto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweblinks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9999\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bounded'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_scaling_exponent_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-4-6899f550d2ad>\u001b[0m in \u001b[0;36mtest_scaling_exponent_estimation\u001b[0;34m(desired_alpha, result, decimal)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_exponent_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 3 decimals\n ACTUAL: 2.3261940228265701\n DESIRED: 2.336"
]
}
],
"source": [
"# check that I get the same estimates for both alpha and xmin using bounded minimization\n",
"result2 = pyreto.distributions.Pareto.fit(weblinks, xmin=None, quantile=0.9999, method='bounded')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.testing.assert_almost_equal(result2.params['alpha'], desired_alpha, decimal=3)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 1 decimals\n ACTUAL: 3213.9999650697919\n DESIRED: 3684",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-f11ed5f3e503>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-16-aac319d7d71d>\u001b[0m in \u001b[0;36mtest_scaling_threshold_estimation\u001b[0;34m(desired_xmin, result, decimal)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 1 decimals\n ACTUAL: 3213.9999650697919\n DESIRED: 3684"
]
}
],
"source": [
"test_scaling_threshold_estimation(desired_xmin, result2, decimal=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Cities </h2>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cities = pd.read_csv('http://tuvalu.santafe.edu/~aaronc/powerlaws/data/cities.txt', names=['population'])\n",
"cities.population /= 1e3 # CSN units are in thousands of persons"
]
},
{
"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>population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>19447.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>9.002051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>77.825051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.001000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.369500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.089000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.135500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>8008.654000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" population\n",
"count 19447.000000\n",
"mean 9.002051\n",
"std 77.825051\n",
"min 0.001000\n",
"25% 0.369500\n",
"50% 1.089000\n",
"75% 4.135500\n",
"max 8008.654000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cities.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# check that I get same estimate for alpha given reported xmin...\n",
"desired_alpha, desired_xmin = 2.37, 52.46\n",
"result1 = pyreto.distributions.Pareto.fit(cities.population, xmin=desired_xmin)\n",
"test_scaling_exponent_estimation(desired_alpha, result1, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n ACTUAL: 2.3639368738287363\n DESIRED: 2.37",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-5b14153ed2ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# check that I get the same estimates for both alpha and xmin using brute force minimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mresult2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyreto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPareto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcities\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.99\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'brute'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_scaling_exponent_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-5-aac319d7d71d>\u001b[0m in \u001b[0;36mtest_scaling_exponent_estimation\u001b[0;34m(desired_alpha, result, decimal)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_exponent_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_alpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n ACTUAL: 2.3639368738287363\n DESIRED: 2.37"
]
}
],
"source": [
"# check that I get the same estimates for both alpha and xmin using brute force minimization\n",
"result2 = pyreto.distributions.Pareto.fit(cities.population, xmin=None, quantile=0.99, method='brute')\n",
"test_scaling_exponent_estimation(desired_alpha, result2, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n ACTUAL: 51.442999999999998\n DESIRED: 52.46",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-068cfd665dcb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-5-aac319d7d71d>\u001b[0m in \u001b[0;36mtest_scaling_threshold_estimation\u001b[0;34m(desired_xmin, result, decimal)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n ACTUAL: 51.442999999999998\n DESIRED: 52.46"
]
}
],
"source": [
"test_scaling_threshold_estimation(desired_xmin, result2, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# check that I get the same estimates for both alpha and xmin using bounded minimization\n",
"result3 = pyreto.distributions.Pareto.fit(cities.population, xmin=None, quantile=0.99, method='bounded')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_scaling_exponent_estimation(desired_alpha, result3, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n ACTUAL: 70.075391716136934\n DESIRED: 52.46",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-3c50a6c928ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-5-aac319d7d71d>\u001b[0m in \u001b[0;36mtest_scaling_threshold_estimation\u001b[0;34m(desired_xmin, result, decimal)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_scaling_threshold_estimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_xmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/drpugh/anaconda/envs/pyreto-dev/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\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[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n ACTUAL: 70.075391716136934\n DESIRED: 52.46"
]
}
],
"source": [
"test_scaling_threshold_estimation(desired_xmin, result3, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# using brute force minmization to find xmin makes this test take a while!\n",
"pvalue, Ds = pyreto.distributions.Pareto.test_goodness_of_fit(42, result2, cities.population, quantile=0.99,\n",
" method='brute')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-68fb2dca5c54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# pareto distribution should not be rejected...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"# pareto distribution should not be rejected...\n",
"assert pvalue > 0.10"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.026783583288262355"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pvalue"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pyreto.distributions.Pareto.test_goodness_of_fit??"
]
},
{
"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
}
| mit |
google/or-tools | examples/notebook/constraint_solver/vrp_with_time_limit.ipynb | 1 | 7103 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# vrp_with_time_limit"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/constraint_solver/vrp_with_time_limit.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/ortools/constraint_solver/samples/vrp_with_time_limit.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"# Copyright 2010-2021 Google LLC\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# [START program]\n",
"\"\"\"Vehicles Routing Problem (VRP).\"\"\"\n",
"\n",
"# [START import]\n",
"from ortools.constraint_solver import routing_enums_pb2\n",
"from ortools.constraint_solver import pywrapcp\n",
"# [END import]\n",
"\n",
"\n",
"# [START solution_printer]\n",
"def print_solution(manager, routing, solution):\n",
" \"\"\"Prints solution on console.\"\"\"\n",
" print(f'Objective: {solution.ObjectiveValue()}')\n",
" max_route_distance = 0\n",
" for vehicle_id in range(manager.GetNumberOfVehicles()):\n",
" index = routing.Start(vehicle_id)\n",
" plan_output = 'Route for vehicle {}:\\n'.format(vehicle_id)\n",
" route_distance = 0\n",
" while not routing.IsEnd(index):\n",
" plan_output += ' {} -> '.format(manager.IndexToNode(index))\n",
" previous_index = index\n",
" index = solution.Value(routing.NextVar(index))\n",
" route_distance += routing.GetArcCostForVehicle(\n",
" previous_index, index, vehicle_id)\n",
" plan_output += '{}\\n'.format(manager.IndexToNode(index))\n",
" plan_output += 'Distance of the route: {}m\\n'.format(route_distance)\n",
" print(plan_output)\n",
" max_route_distance = max(route_distance, max_route_distance)\n",
" print('Maximum of the route distances: {}m'.format(max_route_distance))\n",
" # [END solution_printer]\n",
"\n",
"\n",
"\"\"\"Solve the CVRP problem.\"\"\"\n",
"# Instantiate the data problem.\n",
"# [START data]\n",
"num_locations = 20\n",
"num_vehicles = 5\n",
"depot = 0\n",
"# [END data]\n",
"\n",
"# Create the routing index manager.\n",
"# [START index_manager]\n",
"manager = pywrapcp.RoutingIndexManager(num_locations, num_vehicles, depot)\n",
"# [END index_manager]\n",
"\n",
"# Create Routing Model.\n",
"# [START routing_model]\n",
"routing = pywrapcp.RoutingModel(manager)\n",
"\n",
"# [END routing_model]\n",
"\n",
"# Create and register a transit callback.\n",
"# [START transit_callback]\n",
"def distance_callback(from_index, to_index):\n",
" # pylint: disable=unused-argument\n",
" \"\"\"Returns the distance between the two nodes.\"\"\"\n",
" return 1\n",
"\n",
"transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
"# [END transit_callback]\n",
"\n",
"# Define cost of each arc.\n",
"# [START arc_cost]\n",
"routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
"# [END arc_cost]\n",
"\n",
"# Add Distance constraint.\n",
"# [START distance_constraint]\n",
"dimension_name = 'Distance'\n",
"routing.AddDimension(\n",
" transit_callback_index,\n",
" 0, # no slack\n",
" 3000, # vehicle maximum travel distance\n",
" True, # start cumul to zero\n",
" dimension_name)\n",
"distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
"distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
"# [END distance_constraint]\n",
"\n",
"# Setting first solution heuristic.\n",
"# [START parameters]\n",
"search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
"search_parameters.first_solution_strategy = (\n",
" routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
"search_parameters.local_search_metaheuristic = (\n",
" routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
"search_parameters.log_search = True\n",
"search_parameters.time_limit.FromSeconds(5)\n",
"# [END parameters]\n",
"\n",
"# Solve the problem.\n",
"# [START solve]\n",
"solution = routing.SolveWithParameters(search_parameters)\n",
"# [END solve]\n",
"\n",
"# Print solution on console.\n",
"# [START print_solution]\n",
"if solution:\n",
" print_solution(manager, routing, solution)\n",
"# [END print_solution]\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
0ppen/introhacking | Code From Book.ipynb | 1 | 31118 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code from Book"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Getting Started"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"! whoami"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash \n",
"echo $0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! echo 'hello'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"os.__file__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%timeit 9999 in range(10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%javascript\n",
"var d = 10\n",
"alert(\"d = \" + d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Thinking in Binary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"! file my_image.jpg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import magic\n",
"print magic.from_file(\"my_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if magic.from_file(\"upload.jpg\", mime=True) == \"image/jpeg\":\n",
" continue_uploading(\"upload.jpg\")\n",
"else:\n",
" alert(\"Sorry! This file type is not allowed\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import imghdr\n",
"print imghdr.what(\"path/to/my/file.ext\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import binascii\n",
"\n",
"def spoof_file(file, magic_number):\n",
" magic_number = binascii.unhexlify(magic_number)\n",
" with open(file, \"r+b\") as f:\n",
" old = f.read()\n",
" f.seek(0)\n",
" f.write(magic_number + old)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"! xxd -b my_file.docx | less"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"! du -h my_file.docx"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def to_ascii_bytes(string):\n",
" return \" \".join(format(ord(char), '08b') for char in string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"string = \"my ascii string\"\n",
"\"\".join(hex(ord(char))[2:] for char in string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hex_string = \"6d7920617363696920737472696e67\"\n",
"print hex_string.decode(\"hex\")\n",
"print \"\".join(chr(int(hex_string[i:i+2], 16)) for i in range(0, len(hex_string), 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# adapted from https://code.activestate.com/recipes/142812-hex-dumper/\n",
"def hexdump(string, length=8):\n",
" result = []\n",
" digits = 4 if isinstance(string, unicode) else 2\n",
"\n",
" for i in xrange(0, len(string), length):\n",
" s = string[i:i + length]\n",
" hexa = \"\".join(\"{:0{}X}\".format(ord(x), digits) for x in s)\n",
" text = \"\".join(x if 0x20 <= ord(x) < 0x7F else '.' for x in s)\n",
" result.append(\"{:04X} {:{}} {}\".format(i, hexa, length * (digits + 1), text))\n",
"\n",
" return '\\n'.join(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print hexdump(\"The quick brown fox jumps over the lazy dog\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import struct\n",
"\n",
"num = 0x103e4\n",
"struct.pack(\"I\", 0x103e4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"string = '\\xe4\\x03\\x01\\x00'\n",
"struct.unpack(\"i\", string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bytes = '\\x01\\xc2'\n",
"struct.pack(\"<h\", struct.unpack(\">h\", bytes)[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import base64\n",
"\n",
"base64.b64encode('encodings are fun...')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print base64.b64decode(_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"string = \"hello\\x00\"\n",
"binary_string = ' '.join('{:08b}'.format(ord(char)) for char in string)\n",
"\" \".join(binary_string[i:i+6] for i in range(0, len(binary_string), 6))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bin_string = '011010 000110 010101 101100 011011 000110 111100 000000'\n",
"[int(b, 2) for b in bin_string.split()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"echo -n hello | base64\n",
"echo aGVsbG8= | base64 --decode && echo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"u'◑ \\u2020'.encode('utf8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'\\xe2\\x97\\x91 \\xe2\\x80\\xa0'.decode('utf8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unicode('\\xe2\\x97\\x91 \\xe2\\x80\\xa0', encoding='utf8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"utf8_string = 'Åêíòü'\n",
"utf8_string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unicode_string = utf8_string.decode('utf8')\n",
"unicode_string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unicode_string.encode('mac roman')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'Åêíòü'.decode('utf8').encode('ascii') # Raises UnicodeEncodeError"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"! chardetect uni.txt another_file.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"file = \"\"\"潍楪慢敫椠桴慧扲敬整瑸琠慨⁴獩琠敨爠獥汵⁴景琠硥⁴敢湩敤潣敤獵湩湡甠楮瑮湥敤档\n",
"牡捡整湥潣楤杮楷桴挠浯汰瑥汥⁹湵敲慬整湯獥景整牦浯愠搠晩敦敲瑮眠楲楴杮猠獹整‧⠊慔敫\n",
"牦浯攠楷楫数楤牯⥧\"\"\"\n",
"\n",
"print file.decode('utf8').encode('utf16')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ftfy\n",
"ftfy.fix_text(u\"“Mojibake“ can be fixed.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 0b1111\n",
"y = 0b1010\n",
"bin(int(\"{:b}{:b}\".format(x, y), 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bin(x << 4 | y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Cryptography"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import string\n",
"\n",
"r = random.SystemRandom()\n",
"\n",
"# Get a random integer between 0 and 20\n",
"r.randint(0, 20)\n",
"\n",
"# Get a random number between 0 and 1\n",
"r.random()\n",
"\n",
"# Generate a random 40-bit number\n",
"r.getrandbits(40)\n",
"\n",
"# Choose a random item from a string or list\n",
"chars = string.printable\n",
"r.choice(chars)\n",
"\n",
"# Randomize the order of a sequence\n",
"seq = ['a', 'b', 'c', 'd', 'e']\n",
"r.shuffle(seq)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"ALLIGATOR\".encode('rot13')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"NYYVTNGBE\".encode('rot13')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plaintext = \"A secret-ish message!\"\n",
"\"\".join(chr((ord(c) + 20) % 256) for c in plaintext)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ciphertext = 'U4\\x87yw\\x86y\\x88A}\\x87|4\\x81y\\x87\\x87u{y5'\n",
"\"\".join(chr((ord(c) - 20) % 256) for c in ciphertext)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plaintext = 0b110100001101001\n",
"one_time_pad = 0b110000011100001\n",
"bin(plaintext ^ one_time_pad)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"decrypted = 0b100010001000 ^ one_time_pad\n",
"format(decrypted, 'x').decode('hex')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import binascii\n",
"\n",
"# ASCII-encoded plaintext\n",
"plaintext = \"this is a secret message\"\n",
"plaintext_bits = int(binascii.hexlify(plaintext), 16)\n",
"\n",
"print \"plaintext (ascii):\", plaintext\n",
"print \"plaintext (hex):\", plaintext_bits\n",
"\n",
"# Generate the one-time pad\n",
"onetime_pad = int(binascii.hexlify(os.urandom(len(plaintext))), 16)\n",
"\n",
"print \"one-time pad: (hex):\", onetime_pad\n",
"\n",
"# Encrypt plaintext using XOR operation with one-time pad\n",
"ciphertext_bits = plaintext_bits ^ onetime_pad\n",
"\n",
"print \"encrypted text (hex):\", ciphertext_bits\n",
"\n",
"# Decrypt using XOR operation with one-time pad\n",
"decrypted_text = ciphertext_bits ^ onetime_pad\n",
"decrypted_text = binascii.unhexlify(hex(decrypted_text)[2:-1])\n",
"\n",
"print \"decrypted text (ascii):\", decrypted_text"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import binascii\n",
"\n",
"p1 = \"this is the part where you run away\"\n",
"p2 = \"from bad cryptography practices.\"\n",
"\n",
"# pad plaintexts with spaces to ensure equal length\n",
"p1 = p1.ljust(len(p2))\n",
"p2 = p2.ljust(len(p1))\n",
" \n",
"p1 = int(binascii.hexlify(p1), 16)\n",
"p2 = int(binascii.hexlify(p2), 16)\n",
"\n",
"# get random one-time pad\n",
"otp = random.SystemRandom().getrandbits(p1.bit_length())\n",
"\n",
"# encrypt\n",
"c1 = p1 ^ otp\n",
"c2 = p2 ^ otp # otp reuse...not good!\n",
"\n",
"print \"c1 ^ c2 == p1 ^ p2 ?\", c1 ^ c2 == p1 ^ p2\n",
"print \"c1 ^ c2 =\", hex(c1 ^ c2)\n",
"\n",
"# the crib\n",
"crib = \" the \"\n",
"crib = int(binascii.hexlify(crib), 16)\n",
"\n",
"xored = c1 ^ c2\n",
"\n",
"print \"crib =\", hex(crib)\n",
"\n",
"cbl = crib.bit_length()\n",
"xbl = xored.bit_length()\n",
"\n",
"print\n",
"mask = (2**(cbl + 1) - 1)\n",
"fill = len(str(xbl / 8))\n",
"\n",
"# crib dragging\n",
"for s in range(0, xbl - cbl + 8, 8):\n",
" xor = (xored ^ (crib << s)) & (mask << s)\n",
" out = binascii.unhexlify(hex(xor)[2:-1])\n",
" \n",
" print \"{:>{}} {}\".format(s/8, fill, out)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cryptography.fernet import Fernet\n",
"key = Fernet.generate_key()\n",
"f = Fernet(key)\n",
"ciphertext = f.encrypt(\"this is my plaintext\")\n",
"decrypted = f.decrypt(ciphertext)\n",
"print decrypted"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"from cryptography.hazmat.primitives import padding\n",
"from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\n",
"from cryptography.hazmat.backends import default_backend\n",
"\n",
"pt = \"my plaintext\"\n",
"\n",
"backend = default_backend()\n",
"key = os.urandom(32)\n",
"iv = os.urandom(16)\n",
"\n",
"padder = padding.PKCS7(128).padder()\n",
"pt = padder.update(pt) + padder.finalize()\n",
"\n",
"cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)\n",
"encryptor = cipher.encryptor()\n",
"ct = encryptor.update(pt) + encryptor.finalize()\n",
"decryptor = cipher.decryptor()\n",
"out = decryptor.update(ct) + decryptor.finalize()\n",
"\n",
"unpadder = padding.PKCS7(128).unpadder()\n",
"out = unpadder.update(out) + unpadder.finalize()\n",
"print out\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nonce = os.urandom(64/8)\n",
"nonce"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import hashlib\n",
"hashlib.md5(\"hash me please\").hexdigest()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! md5 -s 'hash me please'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hashlib.sha1(\"hash me please\").hexdigest()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! echo 'hash me please' | openssl dgst -sha1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m1 = binascii.unhexlify(\"d131dd02c5e6eec4693d9a0698aff95c2fcab58712467eab4004583eb8fb7f8955ad340609f4b30283e488832571415a085125e8f7cdc99fd91dbdf280373c5bd8823e3156348f5bae6dacd436c919c6dd53e2b487da03fd02396306d248cda0e99f33420f577ee8ce54b67080a80d1ec69821bcb6a8839396f9652b6ff72a70\")\n",
"\n",
"m2 = binascii.unhexlify(\"d131dd02c5e6eec4693d9a0698aff95c2fcab50712467eab4004583eb8fb7f8955ad340609f4b30283e4888325f1415a085125e8f7cdc99fd91dbd7280373c5bd8823e3156348f5bae6dacd436c919c6dd53e23487da03fd02396306d248cda0e99f33420f577ee8ce54b67080280d1ec69821bcb6a8839396f965ab6ff72a70\")\n",
"\n",
"hashlib.md5(m1).hexdigest() == hashlib.md5(m1).hexdigest() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"from cryptography.hazmat.primitives.kdf.scrypt import Scrypt\n",
"from cryptography.hazmat.backends import default_backend\n",
"\n",
"backend = default_backend()\n",
"salt = os.urandom(16)\n",
"\n",
"kdf = Scrypt(salt=salt, length=64, n=2**14, r=8, p=1, backend=backend)\n",
"key = kdf.derive(\"your favorite password\")\n",
"key"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kdf = Scrypt(salt=salt, length=64, n=2**14, r=8, p=1, backend=backend)\n",
"kdf.verify(\"your favorite password\", key)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import hmac\n",
"import hashlib\n",
"\n",
"secret_key = \"my secret key\"\n",
"ciphertext = \"my ciphertext\"\n",
"\n",
"# generate HMAC\n",
"h = hmac.new(key=secret_key, msg=ciphertext, digestmod=hashlib.sha256)\n",
"print h.hexdigest()\n",
"\n",
"# verify HMAC\n",
"hmac.compare_digest(h.hexdigest(), h.hexdigest())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p = 9576890767\n",
"q = 1299827\n",
"n = p * q\n",
"print n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"e = 65537\n",
"phi = (p - 1) * (q - 1)\n",
"phi % e != 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sympy\n",
"\n",
"d = sympy.numbers.igcdex(e, phi)[0]\n",
"print d"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m = 12345\n",
"c = pow(m, e, n)\n",
"print c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pow(c, d, n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m = 0\n",
"while pow(m, e, n) != c:\n",
" m += 1\n",
"print m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cryptography.hazmat.backends import default_backend\n",
"from cryptography.hazmat.primitives.asymmetric import rsa\n",
"from cryptography.hazmat.primitives import serialization\n",
"\n",
"private_key = rsa.generate_private_key(public_exponent=65537, key_size=2048, backend=default_backend())\n",
"\n",
"public_key = private_key.public_key()\n",
"\n",
"private_pem = private_key.private_bytes(encoding=serialization.Encoding.PEM, \n",
" format=serialization.PrivateFormat.PKCS8, \n",
" encryption_algorithm=serialization.BestAvailableEncryption('your password here'))\n",
"\n",
"public_pem = public_key.public_bytes(encoding=serialization.Encoding.PEM, \n",
" format=serialization.PublicFormat.SubjectPublicKeyInfo)\n",
"\n",
"print public_pem\n",
"print private_pem "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from cryptography.hazmat.primitives import hashes\n",
"from cryptography.hazmat.primitives.asymmetric import padding\n",
"import base64\n",
"\n",
"with open(\"path/to/public_key.pem\", \"rb\") as key_file:\n",
" public_key = serialization.load_pem_public_key(key_file.read(), \n",
" backend=default_backend())\n",
"\n",
"message = \"your secret message\"\n",
"ciphertext = public_key.encrypt(message, \n",
" padding.OAEP(mgf=padding.MGF1(algorithm=hashes.SHA256()), \n",
" algorithm=hashes.SHA256(), \n",
" label=None))\n",
"b64_ciphertext = base64.urlsafe_b64encode(ciphertext)\n",
"print b64_ciphertext\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plaintext = private_key.decrypt(ciphertext, \n",
" padding.OAEP(mgf=padding.MGF1(algorithm=hashes.SHA256()), \n",
" algorithm=hashes.SHA256(), \n",
" label=None))\n",
"print plaintext"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cryptography.hazmat.primitives import hashes\n",
"from cryptography.hazmat.primitives.asymmetric import padding\n",
"\n",
"signer = private_key.signer(padding.PSS(mgf=padding.MGF1(hashes.SHA256()), \n",
" salt_length=padding.PSS.MAX_LENGTH), \n",
" hashes.SHA256())\n",
"message = \"A message of arbitrary length\"\n",
"signer.update(message)\n",
"signature = signer.finalize()\n",
"signature"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"public_key = private_key.public_key()\n",
"verifier = public_key.verifier(signature, \n",
" padding.PSS(mgf=padding.MGF1(hashes.SHA256()), \n",
" salt_length=padding.PSS.MAX_LENGTH), \n",
" hashes.SHA256())\n",
"verifier.update(message)\n",
"verifier.verify()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Networking"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import requests\n",
"r = requests.get('https://www.google.com/imghp')\n",
"r.content[:200]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r.status_code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r.headers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(r.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r.apparent_encoding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r.elapsed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r.request.headers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"custom_headers = {\"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36\"}\n",
"r = requests.get(\"https://www.google.com/imghp\", headers=custom_headers)\n",
"r.request.headers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import requests\n",
"import logging\n",
"import http.client\n",
"\n",
"# Enable logging\n",
"http.client.HTTPConnection.debuglevel = 1\n",
"\n",
"logging.basicConfig()\n",
"logging.getLogger().setLevel(logging.DEBUG)\n",
"requests_log = logging.getLogger(\"requests.packages.urllib3\")\n",
"requests_log.setLevel(logging.DEBUG)\n",
"requests_log.propagate = True\n",
"r = requests.get('https://www.google.com/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import urlparse\n",
"simple_url = \"http://www.example.com/path/to/my/page\"\n",
"parsed = urlparse.urlparse(simple_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"parsed.scheme"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"parsed.hostname"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"parsed.path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"url_with_query = \"http://www.example.com/?page=1&key=Anvn4mo24\"\n",
"query = urlparse.urlparse(url_with_query).query\n",
"urlparse.parse_qs(query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import urllib\n",
"url = 'https://www.example.com/%5EA-url-with-%-and-%5E?page=page+with%20spaces'\n",
"urllib.unquote(url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"chars = '!@#$%^%$#)'\n",
"urllib.quote(chars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"urllib.unquote_plus(url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"urllib.quote_plus('one two')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"\n",
"http_client.HTTPConnection.debuglevel = 0 # Logging off\n",
"r = requests.get(\"http://www.google.com\")\n",
"soup = BeautifulSoup(r.content, \"lxml\")\n",
"\n",
"soup.find_all('p')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soup.find_all('a')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for link in soup.find_all('a'):\n",
" print link.text, link[\"href\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import dryscrape\n",
"from bs4 import BeautifulSoup\n",
"session = dryscrape.Session()\n",
"session.visit(\"http://www.google.com\")\n",
"r = session.body()\n",
"soup = BeautifulSoup(r, \"lxml\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from selenium import webdriver\n",
"driver = webdriver.Chrome(\"/path/to/chromedriver\")\n",
"driver.get(\"http://www.google.com\")\n",
"html = driver.page_source\n",
"driver.save_screenshot(\"screenshot.png\")\n",
"driver.quit()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import smtplib\n",
"\n",
"server = smtplib.SMTP('localhost', port=1025)\n",
"server.set_debuglevel(True)\n",
"server.sendmail(\"me@localhost\", \"you@localhost\", \"This is an email message\")\n",
"server.quit()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! host google.com"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! host 172.217.11.14"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
diminishedprime/.org | programmey_stuff/chord_thingy/Chord Thingy.ipynb | 1 | 24402 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def modBase1(num, base):\n",
" return ((num - 1) % base) + 1\n",
"modBase1(13, 12)\n",
"modBase1(15, 12)\n",
"modBase1(14, 12)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'A': 10,\n",
" 'A#': 11,\n",
" 'Ab': 9,\n",
" 'B': 12,\n",
" 'B#': 1,\n",
" 'Bb': 11,\n",
" 'C': 1,\n",
" 'C#': 2,\n",
" 'Cb': 12,\n",
" 'D': 3,\n",
" 'D#': 4,\n",
" 'Db': 2,\n",
" 'E': 5,\n",
" 'E#': 6,\n",
" 'Eb': 4,\n",
" 'F': 6,\n",
" 'F#': 7,\n",
" 'Fb': 5,\n",
" 'G': 8,\n",
" 'G#': 9,\n",
" 'Gb': 7}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
"\n",
"note_to_num_hash = {}\n",
"num_to_note_hash = {}\n",
"num_to_note_hash['normal'] = {}\n",
"num_to_note_hash['flat'] = {}\n",
"num_to_note_hash['sharp'] = {}\n",
"#normals \n",
"note_to_num_hash['C'] = 1\n",
"num_to_note_hash['normal'][1] = 'C'\n",
"note_to_num_hash['D'] = 3\n",
"num_to_note_hash['normal'][3] = 'D'\n",
"note_to_num_hash['E'] = 5\n",
"num_to_note_hash['normal'][5] = 'E'\n",
"note_to_num_hash['F'] = 6\n",
"num_to_note_hash['normal'][6] = 'F'\n",
"note_to_num_hash['G'] = 8\n",
"num_to_note_hash['normal'][8] = 'G'\n",
"note_to_num_hash['A'] = 10\n",
"num_to_note_hash['normal'][10] = 'A'\n",
"note_to_num_hash['B'] = 12\n",
"num_to_note_hash['normal'][12] = 'B'\n",
"\n",
"#sharps # zero index for the mod\n",
"note_to_num_hash['C#'] = modBase1(note_to_num_hash['C'] + 1, 12)\n",
"num_to_note_hash['sharp'][ modBase1(note_to_num_hash['C'] + 1, 12)] = 'C#'\n",
"note_to_num_hash['D#'] = modBase1(note_to_num_hash['D'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['D'] + 1, 12)] = 'D#'\n",
"note_to_num_hash['E#'] = modBase1(note_to_num_hash['E'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['E'] + 1, 12)] = 'E#'\n",
"note_to_num_hash['F#'] = modBase1(note_to_num_hash['F'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['F'] + 1, 12)] = 'F#'\n",
"note_to_num_hash['G#'] = modBase1(note_to_num_hash['G'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['G'] + 1, 12)] = 'G#'\n",
"note_to_num_hash['A#'] = modBase1(note_to_num_hash['A'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['A'] + 1, 12)] = 'A#'\n",
"note_to_num_hash['B#'] = modBase1(note_to_num_hash['B'] + 1, 12)\n",
"num_to_note_hash['sharp'][modBase1(note_to_num_hash['B'] + 1, 12)] = 'B#'\n",
"\n",
"#flats\n",
"note_to_num_hash['Cb'] = modBase1(note_to_num_hash['C'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['C'] - 1, 12)] = 'Cb'\n",
"note_to_num_hash['Db'] = modBase1(note_to_num_hash['D'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['D'] - 1, 12)] = 'Db'\n",
"note_to_num_hash['Eb'] = modBase1(note_to_num_hash['E'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['E'] - 1, 12)] = 'Eb'\n",
"note_to_num_hash['Fb'] = modBase1(note_to_num_hash['F'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['F'] - 1, 12)] = 'Fb'\n",
"note_to_num_hash['Gb'] = modBase1(note_to_num_hash['G'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['G'] - 1, 12)] = 'Gb'\n",
"note_to_num_hash['Ab'] = modBase1(note_to_num_hash['A'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['A'] - 1, 12)] = 'Ab'\n",
"note_to_num_hash['Bb'] = modBase1(note_to_num_hash['B'] - 1, 12)\n",
"num_to_note_hash['flat'][modBase1(note_to_num_hash['B'] - 1, 12)] = 'Bb'\n",
"\n",
"note_to_num_hash"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"5\n"
]
}
],
"source": [
"def note_to_num(note):\n",
" return note_to_num_hash[note]\n",
"\n",
"print(note_to_num('C#'))\n",
"print(note_to_num('Fb'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D#\n",
"Eb\n",
"E#\n"
]
}
],
"source": [
"def num_to_note(num, kind='normal'):\n",
" normalized_num = ((num - 1) % 12) + 1\n",
" normal = num_to_note_hash['normal'].get(normalized_num)\n",
" sharp = num_to_note_hash['sharp'].get(normalized_num)\n",
" flat = num_to_note_hash['flat'].get(normalized_num)\n",
" if (kind == 'sharp' and sharp != None):\n",
" return sharp\n",
" elif (kind == 'flat' and flat != None):\n",
" return flat\n",
" # We default to sharp if it's not a normal note\n",
" elif (normal == None):\n",
" return sharp\n",
" else:\n",
" return normal\n",
" \n",
"print(num_to_note(4, 'sharp'))\n",
"print(num_to_note(4, 'flat'))\n",
"print(num_to_note(6, 'sharp'))\n",
"#print(num_to_note(4))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['C', 'E', 'G']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def notes_for_nums(nums, kind='normal'):\n",
" return list(map(\n",
" lambda num: num_to_note(num, kind),\n",
" nums\n",
" ))\n",
"notes_for_nums([1, 5, 8])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"intervals = {}\n",
"# returns the number of halfsteps between the base note and the other note of the interval\n",
"intervals['prime'] = 0\n",
"intervals['minor 2'] = 1\n",
"intervals['major 2'] = 2\n",
"intervals['minor 3'] = 3\n",
"intervals['major 3'] = 4\n",
"intervals['perfect 4'] = 5\n",
"intervals['tritone'] = 6\n",
"intervals['perfect 5'] = 7\n",
"intervals['minor 6'] = 8\n",
"intervals['major 6'] = 9\n",
"intervals['minor 7'] = 10\n",
"intervals['major 7'] = 11\n",
"intervals['octave'] = 12\n",
"intervals['minor 9'] = 13\n",
"intervals['major 9'] = 14\n",
"# min 10 - 14\n",
"# maj 10- 15\n",
"intervals['minor 11'] = 16\n",
"intervals['major 11'] = 17\n",
"# min 12 "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scales = {}\n",
"scales['Major'] = ['major 2', 'major 2', 'minor 2', 'major 2', 'major 2', 'major 2', 'major 2', 'minor 2']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"chord_quality = {}\n",
"chord_quality[''] = ['prime', 'major 3', 'perfect 5']\n",
"chord_quality['maj7'] = ['prime', 'major 3', 'perfect 5', 'major 7']\n",
"chord_quality['-7'] = ['prime', 'minor 3', 'perfect 5', 'minor 7']\n",
"chord_quality['7'] = ['prime', 'major 3', 'perfect 5', 'minor 7']\n",
"chord_quality['7b9'] = ['prime', 'major 3','minor 7', 'minor 9']\n",
"chord_quality['6'] = ['prime', 'major 3', 'perfect 5', 'major 6']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
"[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2]\n",
"[6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5]\n"
]
}
],
"source": [
"def chromatic_scale_from(note=1):\n",
" return list(map(lambda x: modBase1(x, 12), range(note, note+12)))\n",
"\n",
"print(chromatic_scale_from(1))\n",
"print(chromatic_scale_from(3))\n",
"print(chromatic_scale_from(30))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def lookup_multi(keys, lookupable):\n",
" return list(map(\n",
" lambda key: lookupable[key]\n",
" , keys\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 7, 10]\n",
"['D', 'F#', 'A']\n",
"[4, 8, 11, 15]\n",
"['Eb', 'G', 'Bb', 'D']\n",
"[12, 15, 19, 22]\n"
]
}
],
"source": [
"def nums_for_quality(quality='', base_note='C'):\n",
" quality_intervals = chord_quality[quality]\n",
" nums = lookup_multi(quality_intervals, intervals)\n",
" adjusted = list(map(lambda x: x + note_to_num(base_note)\n",
" , nums))\n",
" return adjusted\n",
"\n",
"print(nums_for_quality(base_note='D'))\n",
"print(notes_for_nums(nums_for_quality(base_note='D')))\n",
"print(nums_for_quality(base_note='Eb', quality='maj7'))\n",
"print(notes_for_nums(nums_for_quality(base_note='Eb', quality='maj7'), kind='flat'))\n",
"print(nums_for_quality(base_note='Cb', quality='-7'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" - [1, 5, 8]\n",
"maj7 - [1, 5, 8, 12]\n",
"-7 - [1, 4, 8, 11]\n",
"7 - [1, 5, 8, 11]\n",
"7b9 - [1, 5, 11, 14]\n",
"6 - [1, 5, 8, 10]\n"
]
}
],
"source": [
"for quality in chord_quality:\n",
" print(quality + ' - ' + str(nums_for_quality(quality=quality)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4, 8, 11, 15]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nums_for_quality(base_note='Eb', quality='maj7')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]\n",
"[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n",
"[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]\n",
"[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
"[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]\n",
"[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]\n"
]
}
],
"source": [
"def string_for(note='E', frets=18):\n",
" baseNum = note_to_num(note)\n",
" nums = list(range(baseNum, baseNum + frets))\n",
" return nums\n",
"\n",
"print(string_for(note='E'))\n",
"print(string_for(note='B'))\n",
"print(string_for(note='G'))\n",
"print(string_for(note='D'))\n",
"print(string_for(note='A'))\n",
"print(string_for(note='E'))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def contains(my_list, val):\n",
" if val in my_list:\n",
" return True\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"E|-----|-F#--|-----|-----|--A--|-----|-----|-----|-----|-----|-D#--|-----|-----|-----|--G--|-----|-----\n"
]
}
],
"source": [
"def print_string(nums, lookup_fn, formatter_fn):\n",
" base_print = str(num_to_note(nums[0]))\n",
" # first string gets printed a bit differntly\n",
" firstString = True\n",
" for num in nums[1:]:\n",
" base_print = base_print + '|'\n",
" for_num = lookup_fn(num)\n",
" base_print = base_print + formatter_fn(for_num)\n",
" return base_print\n",
"\n",
"c7nums = nums_for_quality('maj7', base_note='D')\n",
"\n",
"print(print_string(string_for('E')\n",
" , lambda num: num_to_note(num) if contains(c7nums, num % 13) else ''\n",
" , lambda val: '{:-^5}'.format(val)\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['C', 'E', 'G', 'B']\n"
]
},
{
"data": {
"text/plain": [
"['E|-----|-----|--G--|-----|-----|-----|--B--|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----',\n",
" 'B|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|--B--|--C--|-----|-----|-----|--E--',\n",
" 'G|-----|-----|-----|--B--|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|--B--|--C--',\n",
" 'D|-----|--E--|-----|-----|--G--|-----|-----|-----|--B--|--C--|-----|-----|-----|--E--|-----|-----|--G--',\n",
" 'A|-----|--B--|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|--B--|--C--|-----|-----',\n",
" 'E|-----|-----|--G--|-----|-----|-----|--B--|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def strings_for_chord(base_note='C', quality='', kind='normal', tuning=['E', 'A', 'D', 'G', 'B', 'E']):\n",
" chord_nums = [modBase1(num, 12) for num in nums_for_quality(quality=quality, base_note=base_note)]\n",
" print(notes_for_nums(chord_nums, kind=kind))\n",
" lookup_fn = lambda num: num_to_note(num, kind=kind) if contains(chord_nums, modBase1(num, 12)) else ''\n",
" formatter_fn = lambda val: '{:-^5}'.format(val)\n",
" strings = []\n",
" for note in reversed(tuning):\n",
" strings.append(print_string(string_for(note), lookup_fn, formatter_fn))\n",
" return strings\n",
"\n",
"strings_for_chord(quality='maj7')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" o o oo o \n"
]
}
],
"source": [
"space = '{: ^5}'.format('')\n",
"dot = '{: ^5}'.format('o')\n",
"doubleDot = '{: ^5}'.format('oo')\n",
"\n",
"def dotsFor(frets=18):\n",
" acc = ' '\n",
" for i in range(1, frets):\n",
" if (\n",
" False\n",
" #or i == 3\n",
" or i == 5\n",
" or i == 7\n",
" or i == 15\n",
" ):\n",
" acc = acc + dot\n",
" elif (i == 12):\n",
" acc = acc + doubleDot\n",
" else:\n",
" acc = acc + space\n",
" acc = acc + ' '\n",
" return acc\n",
"print(dotsFor())"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 \n"
]
}
],
"source": [
"def numsFor(frets=18):\n",
" acc = ' '\n",
" for i in range(1, frets):\n",
" acc = acc + '{: ^6}'.format(i)\n",
" return acc\n",
"print(numsFor())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['C', 'E', 'G']\n"
]
},
{
"data": {
"text/plain": [
"['E|-----|-----|--G--|-----|-----|-----|-----|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----',\n",
" 'B|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|-----|--C--|-----|-----|-----|--E--',\n",
" 'G|-----|-----|-----|-----|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|-----|--C--',\n",
" 'D|-----|--E--|-----|-----|--G--|-----|-----|-----|-----|--C--|-----|-----|-----|--E--|-----|-----|--G--',\n",
" 'A|-----|-----|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----|-----|-----|--C--|-----|-----',\n",
" 'E|-----|-----|--G--|-----|-----|-----|-----|--C--|-----|-----|-----|--E--|-----|-----|--G--|-----|-----']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c7nums = nums_for_quality('maj7', base_note='D')\n",
"\n",
"\n",
"def print_fretboard(strings, frets=18, dots=True, numbers=True):\n",
" guitar = ''\n",
" if (dots):\n",
" strings.append(dotsFor(frets))\n",
" if (numbers):\n",
" strings.insert(0, numsFor(frets))\n",
" for string in strings:\n",
" guitar = guitar + string + '\\n'\n",
" return guitar\n",
"strings_for_chord()\n",
"#print(print_fretboard(strings_for_chord()))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['D', 'F#', 'A', 'C#']\n",
" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 \n",
"E|-----|-F#--|-----|-----|--A--|-----|-----|-----|-C#--|--D--|-----|-----|-----|-F#--|-----|-----|--A--\n",
"B|-----|-C#--|--D--|-----|-----|-----|-F#--|-----|-----|--A--|-----|-----|-----|-C#--|--D--|-----|-----\n",
"G|-----|--A--|-----|-----|-----|-C#--|--D--|-----|-----|-----|-F#--|-----|-----|--A--|-----|-----|-----\n",
"D|-----|-----|-----|-F#--|-----|-----|--A--|-----|-----|-----|-C#--|--D--|-----|-----|-----|-F#--|-----\n",
"A|-----|-----|-----|-C#--|--D--|-----|-----|-----|-F#--|-----|-----|--A--|-----|-----|-----|-C#--|--D--\n",
"E|-----|-F#--|-----|-----|--A--|-----|-----|-----|-C#--|--D--|-----|-----|-----|-F#--|-----|-----|--A--\n",
" o o oo o \n",
"\n"
]
}
],
"source": [
"print(print_fretboard(strings_for_chord(base_note='D', quality='maj7')))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Eb', 'G', 'Db', 'Fb']\n",
" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 \n",
"E|-----|-----|--G--|-----|-----|-----|-----|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--|-----|-----\n",
"B|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--|-----|-----|-----|-----|-----|-Db--|-----|-Eb--|-Fb--\n",
"G|-----|-----|-----|-----|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--|-----|-----|-----|-----|-----\n",
"D|-Eb--|-Fb--|-----|-----|--G--|-----|-----|-----|-----|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--\n",
"A|-----|-----|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--|-----|-----|-----|-----|-----|-Db--|-----\n",
"E|-----|-----|--G--|-----|-----|-----|-----|-----|-Db--|-----|-Eb--|-Fb--|-----|-----|--G--|-----|-----\n",
" o o oo o \n",
"\n"
]
}
],
"source": [
"print(print_fretboard(strings_for_chord(base_note='Eb', quality='7b9', kind='flat')))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['C', 'Fb', 'Bb', 'Db']\n",
" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 \n",
"E|-----|-----|-----|-----|-----|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----|-----|-----\n",
"B|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----|-----|-----|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--\n",
"G|-----|-----|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----|-----|-----|-Bb--|-----|--C--\n",
"D|-----|-Fb--|-----|-----|-----|-----|-----|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----\n",
"A|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----|-----|-----|-Bb--|-----|--C--|-Db--|-----\n",
"E|-----|-----|-----|-----|-----|-Bb--|-----|--C--|-Db--|-----|-----|-Fb--|-----|-----|-----|-----|-----\n",
" o o oo o \n",
"\n"
]
}
],
"source": [
"print(print_fretboard(strings_for_chord(base_note='C', quality='7b9', kind='flat')))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['F', 'A', 'C', 'D']\n",
" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 \n",
"E|--F--|-----|-----|-----|--A--|-----|-----|--C--|-----|--D--|-----|-----|--F--|-----|-----|-----|--A--\n",
"B|--C--|-----|--D--|-----|-----|--F--|-----|-----|-----|--A--|-----|-----|--C--|-----|--D--|-----|-----\n",
"G|-----|--A--|-----|-----|--C--|-----|--D--|-----|-----|--F--|-----|-----|-----|--A--|-----|-----|--C--\n",
"D|-----|-----|--F--|-----|-----|-----|--A--|-----|-----|--C--|-----|--D--|-----|-----|--F--|-----|-----\n",
"A|-----|-----|--C--|-----|--D--|-----|-----|--F--|-----|-----|-----|--A--|-----|-----|--C--|-----|--D--\n",
"E|--F--|-----|-----|-----|--A--|-----|-----|--C--|-----|--D--|-----|-----|--F--|-----|-----|-----|--A--\n",
" o o oo o \n",
"\n"
]
}
],
"source": [
"print(print_fretboard(strings_for_chord(base_note='F', quality='6', kind='flat')))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: {'chord-part': 'root'}, 5: {'chord-part': '3rd'}, 8: {'chord-part': '5th'}}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 5, 8]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nums_for_quality(base_note='C', quality='')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
equalitie/BotHound | ipython/Clustering.ipynb | 2 | 942285 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clustering\n",
"===\n",
"*Bothound project*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialization"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 82,
=======
"execution_count": 1,
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
"metadata": {
"code_folding": [
0
],
"collapsed": true
},
"outputs": [],
"source": [
"# import\n",
"import numpy as np\n",
"import sklearn\n",
"from sklearn.cluster import KMeans\n",
"from scipy.spatial.distance import cdist,pdist\n",
"from scipy.signal import argrelextrema\n",
"%matplotlib inline\n",
"from pylab import *\n",
"from numpy import *\n",
"from mpl_toolkits.mplot3d import axes3d\n",
<<<<<<< HEAD
"from matplotlib import pyplot as plt\n",
"\n",
"db_host = '127.0.0.1'\n",
"db_name = 'bothound'\n",
"db_user = 'root'\n",
"db_password = '7k32uW+C!JMFXTRT'\n",
=======
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"code_folding": [
17
],
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named bothound_tools",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-9a758ec0dbc9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# %load \"../src/bothound_tools.py\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mbothound_tools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBothoundTools\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mImportError\u001b[0m: No module named bothound_tools"
]
}
],
"source": [
"# %load \"../src/bothound_tools.py\"\n",
"from bothound_tools import BothoundTools"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
"\n",
"features = [\n",
" \"request_interval\", #1\n",
" #\"ua_change_rate\",#2\n",
" \"html2image_ratio\",#3\n",
" \"variance_request_interval\",#4\n",
" #\"payload_average\",#5\n",
" #\"error_rate\",#6\n",
" #\"request_depth\",#7\n",
" #\"request_depth_std\",#8\n",
" \"session_length\",#9\n",
" #\"percentage_cons_requests\",#10\n",
" ]\n",
"columns = []\n",
"for f in features:\n",
" columns.append((f, 'float'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read From Database"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 83,
=======
"execution_count": 6,
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
<<<<<<< HEAD
"(191231, 4)\n"
=======
"(21664, 4)\n"
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
]
}
],
"source": [
"import MySQLdb\n",
"id_incident = 18\n",
"\n",
<<<<<<< HEAD
"db = MySQLdb.connect(host = db_host, user = db_user, passwd = db_password, db = db_name)\n",
=======
"#db = MySQLdb.connect(host = '127.0.0.1', user = 'root', passwd = '7k32uW+C!JMFXTRT', db = 'bothound')\n",
"db = MySQLdb.connect(host = '127.0.0.1', user = 'root', passwd = 'mazhur1n', port = 3306, db = 'bothound')\n",
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
"cur = db.cursor(MySQLdb.cursors.DictCursor)\n",
"cur.execute(\"select * from sessions WHERE id_incident = {0}\".format(id_incident))\n",
"sessions = [dict(elem) for elem in cur.fetchall()]\n",
"db.close()\n",
"values = []\n",
"for s in sessions:\n",
" row = []\n",
" for f in features:\n",
" row.append(s[f])\n",
" values.append(row)\n",
"X = np.array(values)\n",
"print X.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PCA"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"code_folding": [
0
],
"collapsed": false
},
"outputs": [],
"source": [
"# perform PCA dimensionality reduction\n",
"pca = sklearn.decomposition.RandomizedPCA(n_components=3).fit(X)\n",
"X = pca.transform(X)\n",
"\n",
"#totss = sum(pdist(X)**2)/X.shape[0] # The total sum of squares\n",
"#print \"totl ss\", totss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Means"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 333"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 84,
=======
"execution_count": 7,
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
"metadata": {
"code_folding": [],
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
<<<<<<< HEAD
"[191231]\n",
"[185100, 6131]\n",
"[131137, 6001, 54093]\n",
"[120425, 4278, 52765, 13763]\n",
"[116930, 52765, 3319, 3660, 14557]\n",
"[111897, 2747, 52765, 2845, 7386, 13591]\n",
"[109158, 1828, 52765, 8424, 2640, 13276, 3140]\n",
"[52765, 108570, 1811, 7139, 2631, 3042, 13129, 2144]\n",
"[107180, 1426, 52765, 3044, 7270, 2526, 13079, 2147, 1794]\n",
"[52765, 13153, 1386, 5077, 1622, 102797, 2537, 2145, 2519, 7230]\n",
"Num clusters: 4\n",
"[120425, 4278, 52765, 13763]\n"
=======
"[21664]\n",
"[21516, 148]\n",
"[12791, 142, 8731]\n",
"[8733, 12729, 200, 2]\n",
"[8720, 2, 12519, 48, 375]\n",
"[8555, 954, 170, 2, 20, 11963]\n",
"[8555, 11842, 70, 2, 976, 203, 16]\n",
"[9410, 8555, 69, 2, 708, 2717, 16, 187]\n",
"[9338, 8555, 93, 2, 36, 740, 10, 2689, 201]\n",
"[9284, 13, 8555, 114, 2, 2671, 230, 7, 57, 731]\n",
"Num clusters: 6\n",
"[8555, 954, 170, 2, 20, 11963]\n"
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
]
},
{
"data": {
<<<<<<< HEAD
"text/plain": [
"<Container object of 4 artists>"
=======
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DD/D666Er5Xfega22SjqNcy7titIfvpntSzhw+xpwJDAjM95tXVbqmt1++8Gp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\ntm3DsYDp08P9p5+G29dfwzb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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc50b124350>"
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
<<<<<<< HEAD
"image/png": 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CvvqqkUIRQgjHqaTJyMDAoHjVqlVL9+3bN9nd3f1UTU2NRlVVlVbDY6qftNlI\nLJa7KiGEvLPkFoSDBw9+1LRp04pdu3ZNMzU1fZCXlydYsGDBenWEayw9ekiuMoqLa5z9cbG9kIuZ\nAG7mokyKoUyK42ouZckdy6h169b5n3/++UbpfNu2bXPepD4EQHJPgnTAO2dnttMQQgg3Neh5COrU\nGH0IAFBQIHnecm4uoK/fCMEIIYTDVNKH8LYwMZGcHRw+zHYSQgjhpnoLwsCBA88DwMKFC9epL45q\nNdbjNbnYXsjFTAA3c1EmxVAmxXE1l7Lq7UPIz89vffny5b5hYWEeXl5eoQzD8Hg83j9tN7Ufdfkm\nGDECCAgAMjMBS0u20xBCCLfU24dw+PDhcTt37vS7dOnSB7169bpae3lUVJTrq3YsEonMvb299zx8\n+LAVj8dj/P39twcGBm6WXSc6Otpl1KhRJzp06HAPADw9PY8uW7bs65cCNlIfgtTcuYCBAbByZaPt\nkhBCOKchfQhyh0NdsWLFV8oOocowDPLz802Tk5OFDMOguLhYv1OnTndv375tLbtOVFSUy8iRI8Ne\ntR80cPjr+qSkMIy5OcPU1DTqbgkhhFOgiuGvv/rqq/+dOHFi1Lx5876dP3/+hpMnT45UpNCYmpo+\nEAqFKQCgr69fYm1t/ef9+/fb1FGQlKtgr8nODnjvPeDChYbvg4vthVzMBHAzF2VSDGVSHFdzKUvu\nfQiLFy9ec+XKld6TJk36lWEY3ubNmwMvX77cd/Xq1UsUPUh2drZFcnKyvaOjY4Ls6zwej7l8+XJf\nOzu76wKBIG/Dhg3zbWxsbtfefurUqbCwsAAAGBkZQSgUwsXFBcC/b4Qy8x98AISEuGDQoIZtn5KS\n8lrHV8W8FFfySOdTUlI4lYfevzd7not/T7LYzBMdHY2Qv6+akX5eKk3eKUS3bt1Sq6urNaTz1dXV\nGt26dUtV9BSkuLhYv2fPnlePHz8+uvay58+fG5SWluoyDIPw8PBhHTt2TKu9Dhq5yYhhGObRI4Yx\nNGSYoqJG3zUhhHACVNFkxOPxGNknpBUVFRnJXm30KlVVVVqenp5HJ0+evG/06NG/1V5uYGBQrKur\nWwYAw4YNi6iqqtIqLCw0VriaNdB77wEDBwKHDqn6SIQQ8uaQWxCWLFmyukePHklTp04N8fHx2d2z\nZ89rS5cuXSVvO4ZheH5+fjttbGxuz507d1Nd6xQUFJgwf/chJCYmOjAMwzM2Ni5U/sdQnnQoi4ao\nfZrIBVzbAXBmAAAgAElEQVTMBHAzF2VSDGVSHFdzKUtuH8KECRMOODs7x1y5cqU3j8dj1qxZs7h1\n69b58ra7dOnSB/v27Ztsa2t7w97ePhkAVq1atTQnJ6ctAAQEBGw7cuTIh1u3bp2pqalZraurWxYa\nGur1+j+SYoYOBWbMAO7elQxpQQgh77p3ZiyjuixYAGhqAqtXq2T3hBDCGpU9U5lNqiwIt24Bbm5A\nTg6goaGSQxBCCCtocDslde0KCATA778rtx0X2wu5mAngZi7KpBjKpDiu5lKW3D4EAHj69GnznJyc\ntjU1Nf98j34TxzKqy9Spks7loUPZTkIIIeyS22T05ZdfrgwJCZnaoUOHe3w+/5+HUMoby6ixqLLJ\nCACePgXatweysoDmzVV2GEIIUSuV9CF06tQp7ebNm920tbUrXytdA6m6IACAlxfQvz/wyScqPQwh\nhKiNSvoQunbteuvp06dv9XdnZZ+TwMX2Qi5mAriZizIphjIpjqu5lCW3D2Hp0qWr7O3tk7t163az\nSZMmLwDJt/awsDAP1cdTj8GDAT8/yVVHXbuynYYQQtght8nI2tr6z5kzZ27t1q3bTWkfAo/HY5yd\nnWPUElANTUYAsGQJUF0NrF+v8kMRQojKqaQPoXfv3leuXLnS+7WSvQZ1FYS7dwEXF8k9CVpaKj8c\nIYSolEr6EJycnOKWLFmy+o8//ng/KSmph3RqeExu6txZcrXR2bPy1+VieyEXMwHczEWZFEOZFMfV\nXMqS24eQlJTUg8fjMfHx8X1kX1fXZafqJB3wzt2d7SSEEKJ+7/TQFbU9ewa0awdkZEiGyCaEkDeV\nSvoQVqxYsfzvD2We7HMQvvrqq/81MKdS1FkQAGDyZMDBAQgMVNshCSGk0amkD0FPT69UT0+vVF9f\nv4TP54vDw8OHZ2dnWzQ4Jccpck8CF9sLuZgJ4GYuyqQYyqQ4ruZSltw+hPnz52+QnV+wYMF6Nze3\nc6qLxK4BA4AnT4Dr1wE7O7bTEEKI+ijdh1BYWGjs4OCQmJGRYaWiTC9Rd5MRAHz1FfD8ObCpzue8\nEUII9zWkyUjuGUL37t1Tpf8Wi8X8hw8ftlJX/wFbfHyA998H1q0DtLXZTkMIIeohtw/h5MmTI6XT\n2bNnh+Tn57eePXv2D+oIxxZLS8DaGjh9uu7lXGwv5GImgJu5KJNiKJPiuJpLWXILQk1NjYapqekD\nCwuL7PT09I4//fTTJ0VFRUbqCMcmZQe8I4SQN53cPgQ7O7vr165d65mdnW0xfPjw8FGjRp24detW\n1/Dw8OGv2k4kEpl7e3vvefjwYSsej8f4+/tvDwwM3Fx7vcDAwM0RERHDdHV1y0JCQqba29snvxSQ\nhT4EACgpAczMJENamJio/fCEEPJaVHLZKZ/PF2tqalYfO3Zs7OzZs39Yv379gvz8/NbyttPS0qr6\n7rvvPrt161bX+Pj4Plu2bPn0zz//tJZdJzw8fHhGRoZVenp6x+3bt/vPnDlzqzLhVUlfHxg9Gvj1\nV7aTEEKIesgtCNra2pX79++fuGfPHm93d/dTAFBVVSV3+DdTU9MHQqEwBQD09fVLrK2t/7x//34b\n2XXCwsI8fHx8dgOAo6NjQlFRkVFBQQFnvo9Lh7KofYLCxfZCLmYCuJmLMimGMimOq7mUJfcqo127\ndk3btm1bwBdffPFN+/bts7KystpPmTJlrzIHyc7OtkhOTrZ3dHRMkH09Ly9PYG5uLpLOm5mZ5ebm\n5pqZmJgUyK43depUWFhYAACMjIwgFArh4uIC4N83QhXzTk7A48fR2LED8Pf/d3lKSopajq/MvBRX\n8kjnU1JSOJWH3r83e56Lf0+y2MwTHR2NkL87PqWfl0pjGEalU3FxsX7Pnj2vHj9+fHTtZe7u7icv\nXrz4gXR+4MCBkdeuXeshu44kIntWrGCYTz9lNQIhhCjt789OpT6v5TYZvY6qqiotT0/Po5MnT943\nevTo32ovFwgEeSKRyFw6n5ubayYQCPJUmUlZ3t5AaCjw4gXbSQghRLVUVhAYhuH5+fnttLGxuT13\n7tw67/n18PAI27NnjzcAxMfH9zEyMiqq3VzENgsLyRAWYWH/vlb7NJELuJgJ4GYuyqQYyqQ4ruZS\nltw+hIa6dOnSB/v27Ztsa2t7Q3op6apVq5bm5OS0BYCAgIBtw4cPDw8PDx9uZWWVoaenVxocHOyr\nqjyvw9dXck/CuHFsJyGEENWRex/C3bt3O2/YsGF+dna2RXV1tSYguTfgwoULA9QSkKX7EGSVlUnu\nSbh5E2jTRv76hBDCNpU8D8HW1vbGzJkzt/bo0SNJQ0Oj5u8DMT179rz2GlkVD8iBggAAM2YAVlbA\nokVsJyGEEPlUcmOalpZW1cyZM7c6Ojom9OrV62qvXr2uqqsYcIl0KAuG4WZ7IRczAdzMRZkUQ5kU\nx9VcypJbEEaOHHlyy5Ytn+bn57cuLCw0lk7qCMclffsCNTVAQoL8dQkh5E0kt8nIwsIiW/bRmVJZ\nWVntVZZKBleajABg1SogJwf4+We2kxBCyKuppA+BbVwqCLm5gK0tkJcH6OiwnYYQQurXqH0I58+f\nHwgAR48e9Tx27NjY2tPrhn0TmZkBvXoxGDQoAFwpUlJcbcPkYi7KpBjKpDiu5lJWvfchxMbG9h84\ncOD5kydPjqyryWjs2LHHVBuNm7p2PYvNmwtx7Ng5eHoOYTsOIYQ0GmoyUtD27fvw/fehqKy0Q0bG\n12jffhl0dK5jzhwv+PtPZjseIYS8RCXPVK6oqGh69OhRz9o3pr3tz1WubcaMSWjevAXmzYsFwENh\noRi//DKLzhIIIW8NuZedjho16kRYWJiHlpZWlb6+fom+vn6Jnp5eqTrCcQmPxwOPx0NRUQVatx6H\n58/L8eKF5DUu4GobJhdzUSbFUCbFcTWXsuSeIeTl5QnOnj1LX4MBZGSIEBw8FMbG2li+vBK7dokw\naRLbqQghpHHI7UPw9/ffPmvWrB9tbW1vqCnTS7jSh1BbTg5gbw/cuAEIBGynIYSQlzXqfQjdu3dP\nBYCamhqN9PT0ju3bt89q0qTJi78PxNy4ccP2tRMrEpCjBQEAli4F8vMlj9kkhBAuadSCkJ2dbfH3\nTpnaO+XxeEy7du3+amhQZXCxIERHR8PFxQXPnwOdOgFnzgBCITcycQ0Xc1EmxVAmxXExV6NeZWRh\nYZENAFOmTNm7d+/eKbLL6nrtXdSsGbB8OTBvHhAZCXCkf5kQQhpEbh+Cvb19cnJysr10vrq6WtPW\n1vbG7du3bVSeDtw8Q5BVXS0ZzmLdOsDdne00hBAi0ahDV6xatWqpgYFBcWpqancDA4Ni6dSqVauH\nHh4eYfVt967R1ATWrwcWLACqqthOQwghDVdvQVi6dOmq4uJig/nz528oLi42kE6FhYXGa9asWazO\nkFxT+5rj4cMlVxr98gs7eQDuXgfNxVyUSTGUSXFczaWsevsQ7ty506VLly53xo0bdzgpKalH7eU9\nevRIUm20NwePB2zYAAwdCkyaJOlbIISQN029fQgzZszYsWPHjhkuLi7RdQ1uFxUV5Spv59OmTdt1\n+vTpEa1atXqYmpravfby6Ohol1GjRp3o0KHDPQDw9PQ8umzZsq9fCsjxPgRZvr6AqSmwejXbSQgh\n7zrOPQ8hLi7OSV9fv8Tb23tPfQVh48aNn4eFhXnUG/ANKgh5eZIO5qQkoF07ttMQQt5lKnmmcr9+\n/S5+8cUX35w5c2ZocXGxgTI7d3JyimvevPnTV62jbGAuqK+9UCAAZs2S3LCmblxtw+RiLsqkGMqk\nOK7mUpbcsYz27NnjHRcX53T06FHP+fPnb2jatGlFv379Lm7atGnu6x6cx+Mxly9f7mtnZ3ddIBDk\nbdiwYb6Njc3t2utNnToVFhYWAAAjIyMIhcJ/bgKRvhHqnE9JSal3eZ8+0diyBbhyxQW9e6svnxQb\nv49XzaekpHAqj7z3j615Ka7k4eo8F/+eZLGZJzo6GiEhIQDwz+elshRqMrp//36b2NjY/rGxsf2j\noqJc27Ztm6PogHfZ2dkWI0eOPFlXk1FxcbGBhoZGja6ubllERMSwOXPmfJ+WltbppYBvUJOR1M6d\nwO7dQEwM3axGCGGHSpqMLC0tM8eMGXO8oKDAxM/Pb+etW7e6NtbopwYGBsW6urplADBs2LCIqqoq\nrcLCQuPG2Debpk4FioqA335jOwkhhChObkEIDAzcbG5uLjpw4MCEzZs3B4aEhEzNyMiwaoyDFxQU\nmEgrWGJiogPDMDxjY+PCxti3KtU+TaxNQ0NyGerChUBlJTcysYWLuSiTYiiT4riaS1ly+xDmzJnz\n/Zw5c74vKSnRDw4O9g0KCgrKy8sT1NTUaMjbdsKECQdiYmKcHz9+/J65ubloxYoVy6uqqrQAICAg\nYNuRI0c+3Lp160xNTc1qXV3dstDQUK/G+KG4wM0NsLICtm4F5sxhOw0hhMgntw9h3rx538bFxTmV\nlJTo9+3b97KTk1Ncv379LlpaWmaqJeAb2IcgdesW4OoK3L0LNG/OdhpCyLtEJfchHD58eFz//v1j\nTUxMCl4rXQO9yQUBAAICAAMDSRMSIYSoi0o6lceNG3eYrWLAVcq0F65YAYSEAPfuqSwOAO62YXIx\nF2VSDGVSHFdzKUtuQSCvx9QUmDsXWPxODwdICHkTqHToisbwpjcZAUBZGdC5M3DwINC3L9tpCCHv\nApU0GQGSMYmCg4N9AeDRo0cts7Ky2jck4LtKVxf45hvJk9Xe8NpGCHmLyS0IQUFBQevWrVu4evXq\nJQBQWVmpPXny5H2qj8ZdDWkvnDwZePECOHy48fMA3G3D5GIuyqQYyqQ4ruZSltyCcPz48TEnTpwY\npaenVwoAAoEgT9lB7gjA5wPffivpS3jxgu00hBDyX3L7EBwcHBITExMdpM9WLi0t1Xv//ff/uHHj\nhq1aAr4FfQiyPDyA/v2B+fPZTkIIeZup7LLTgICAbUVFRUbbt2/3Hzhw4Pnp06ez+LDIN9u6dcDa\ntcDjx2wnIYSQl8ktCAsWLFjv6el51NPT82haWlqnlStXfhkYGLhZHeG46nXaC7t0AT76CPjf/xov\nD8DdNkwu5qJMiqFMiuNqLmXJHcsIANzc3M65ubmdU3WYd8Xy5YC1teRhOp06yV+fEELUQW4fgoGB\nQXHt1wwNDZ/17t37yrfffjtP+jxkVXnb+hCk1q4F4uOB48fZTkIIeRupZCyjZcuWfW1ubi6aMGHC\nAQAIDQ31yszMtLS3t0/++eefP46OjnZpeGQFAr6lBaGiQtJ8tHs34OzMdhpCyNumIQUBDMO8cure\nvfuN2q/Z2dmlMAwDW1vb6/K2f91JEpFboqKiGmU/+/czTM+eDFNT8/r7aqxMjY2LuSiTYiiT4riY\n6+/PTqU+b+V2Kuvq6pYdPHjwI7FYzBeLxfxDhw6Nb9q0acXfFejt++quRl5ekofp7N/PdhJCCFGg\nySgzM9Nyzpw538fHx/cBgD59+sRv2rRprkAgyLt27VrPfv36XVRpwLe0yUjq4kVg4kTJMxN0dNhO\nQwh5W6ikD4Ftb3tBAABPT6BXL2DJEraTEELeFiq5Ma28vFznxx9/nPXJJ5/8NG3atF3SqeEx33yN\nfc3x2rWSYS0ePmz4Prh6HTQXc1EmxVAmxXE1l7LkFoQpU6bsLSgoMDlz5sxQZ2fnGJFIZK6vr1+i\njnDvCisrYMoUyf0JhBDCGnm9ztIriqRXG1VWVmo5ODgkyNvO19d3V6tWrQq6deuWWt86s2fP3mxl\nZZVua2t7PSkpyb6udcDBq4xU4ckThmnZkmFu3WI7CSHkbQBVXGWkra1dCUhuRktNTe1eVFRk9OjR\no5bytvP19Q0+c+bM0PqWh4eHD8/IyLBKT0/vuH37dv+ZM2duVaKOvXWMjSV9CAsXsp2EEPKuklsQ\n/P39txcWFhp//fXXyzw8PMJsbGxuL1y4cJ287ZycnOKaN2/+tL7lYWFhHj4+PrsBwNHRMaGoqMio\noKDARLn47FBVe+EnnwB//gmcP6/8tlxtw+RiLsqkGMqkOK7mUtYrxzISi8V8AwODYmNj40JnZ+eY\nxnxSWl5ensDc3FwknTczM8vNzc01MzExKai97tSpU2FhYQEAMDIyglAohIuLC4B/3wh1zqekpKhk\n/02aAN7e0QgIAO7edYGGhuLbS7Hx+3jVfEpKCqfyqPL9e515Ka7k4eo8F/+eZLGZJzo6GiEhIQDw\nz+el0uS1KfXo0eOasu1Q0ikrK8uivj4Ed3f3kxcvXvxAOj9w4MDIa9eu9ai9Ht6RPgQpsZhhPviA\nYXbtYjsJIeRNBlX0IQwePPj3DRs2zBeJROaFhYXG0qlh5edfAoEgTyQSmUvnc3NzzQQCQd7r7vdN\nx+NJLkH98kugtJTtNISQd4ncghAaGuq1ZcuWT/v37x/bs2fPa9LpdQ/s4eERtmfPHm8AiI+P72Nk\nZFRUV3MRF9U+TWxsjo6Ak5OkMChK1Zkaiou5KJNiKJPiuJpLWXKfh5CdnW3RkB1PmDDhQExMjPPj\nx4/fMzc3F61YsWJ5VVWVFgAEBARsGz58eHh4ePhwKyurDD09vdLg4GDfhhznbbVqleTu5RkzgNat\n2U5DCHkXyB26orS0VG/jxo2f5+TktN2xY8eM9PT0jnfv3u3s7u5+Si0B34GhK+qzcCFQWAj8Qg8s\nJYQoSSVDV/j6+gZra2tXXr58uS8AtGnT5v4XX3zxTUNDEsUtXQqcPAncuMF2EkLIu0BuQcjMzLRc\ntGjRWukNanp6eu98V6e62guNjCSdy/PnA/JOkrjahsnFXJRJMZRJcVzNpSy5BaFJkyYvysvL/xmY\nOTMz07JJkyYvVBuLSAUEAH/9BZw9y3YSQsjbTm4fwrlz59y++eabL27fvm0zePDg3y9duvRBSEjI\nVFdX1yi1BHyH+xCkTpwAvvgCSEkBNOVeBkAIISp8HsLjx4/fkz4gx9HRMaFly5aPGphRaVQQJM1F\nrq6SB+n4+7OdhhDyJlBJp/LIkSNPnjt3zs3V1TXK3d39lDqLAVepu71QerPa8uVAcTE3MimKi7ko\nk2Iok+K4mktZcgvCvHnzvo2Li3OysbG5/eGHHx45cuTIhxUVFU3VEY78q2dPYPBgycN0CCFEFRR+\nhGZ1dbVmVFSU644dO2acOXNm6PPnz5upOBsAajKSJRIBQqGkL8HcXP76hJB3l0qajADJYzSPHj3q\n+fPPP3985cqV3tJhq4l6mZsDH38MLFvGdhJCyNtIbkEYP378oS5duty5cOHCgFmzZv2YmZlp+cMP\nP8xWRziuYrO9cPFi4Nw5ICnp5de52obJxVyUSTGUSXFczaUsuQVh2rRpu+7du9dh27ZtAa6urlGX\nLl364NNPP92ijnDkvwwMgKAgYN48+TerEUKIMhTqQ0hKSupx4MCBCYcOHRrfvn37LE9Pz6OzZ8/+\nQQ35qA+hDtXVgJ0dsHo14OHBdhpCCBc1pA+h3tuc7t692/nAgQMTDh48+FHLli0fjRs37jDDMLzo\n6GiX105KXoumJrBhAzB3LjBsGKClxXYiQsjboN4mI2tr6z+TkpJ6nD17dkhsbGz/2bNn/6ChoVGj\nznBcxYX2wqFDgbZtge3bJfNcyFQXLuaiTIqhTIrjai5l1VsQjh07NlZHR6e8f//+sR9//PHP58+f\nH6js6QdRHR5Pcpbwv/8Bz56xnYYQ8jaQ24dQUlKif+LEiVEHDhyYEBUV5ert7b1nzJgxx93c3M6p\nJSD1IbySnx/w3nt0wxoh5GUqG8tIqrCw0PjIkSMfhoaGel24cGGA0gkbgArCq92/D3TrxsDLaz22\nbFkAHo9O4gghKrwxTcrY2LjQ399/u7qKAVdxqb2wTRtg8OCz2L79Co4dU8tJm1K49LuSokyKoUyK\n42ouZSlVEAi3bN++D127uiMpKQ41NZ/gs89i0bWrO7Zv38d2NELIm4hhGJVNERERQzt37nzHysoq\nfc2aNYtqL4+KinJp1qzZM6FQmCwUCpNXrly5rPY6koikLmKxmDl0KJwxN1/MAAyjpbWY+f77CEYs\nFrMdjRDCsr8/O5X6zFbZ41Zqamo0Zs2a9WNkZOQggUCQ17t37yseHh5h1tbWf8qu5+zsHBMWFka3\nVzUAj8cDj8dDUVEFrK0/x717YixbxsPFizwsXSoZCI8QQhSlsiajxMREBysrqwwLC4tsLS2tKi8v\nr9ATJ06Mqr0e8wZeysql9sKMDBGCg4diy5aR+PXXYZg3TwQHB2D4cGDECODyZXbzcel3JUWZFEOZ\nFMfVXMpS2RlCXl6ewNzcXCSdNzMzy01ISHCUXYfH4zGXL1/ua2dnd10gEORt2LBhvo2Nze3a+5o6\ndSosLCwAAEZGRhAKhXBxcQHw7xuhzvmUlBRWjy8736dPR0h5eg5BixbRAKJx754LgoMBT89omJgA\n69e7YNAgICZGvflSUlJY/f1w/f2r/UHClTxcnefi35MsNvNER0cjJCQEAP75vFSWUpedKuPo0aOe\nZ86cGbpjx44ZALBv377JCQkJjrIjpRYXFxtoaGjU6OrqlkVERAybM2fO92lpaZ1eCkiXnb6Wqirg\nwAHJuEcGBsDSpZLxj/h0OQEhbzWVX3aqDIFAkCcSif55jItIJDI3MzPLlV3HwMCgWFdXtwwAhg0b\nFlFVVaVVWFhorKpM7yItLcDbG7h1C1i0CFi5ErC1BX79VTJIHiGESKmsIPTq1etqenp6x+zsbIvK\nykrtgwcPfuTh4REmu05BQYGJtIIlJiY6MAzDMzY2LlRVpsZS+zSRC+Rl4vMBT0/g6lXJkBfbtgGd\nO0vGQnrxgr1cbKBMiqFMiuNqLmWprA9BU1Oz+scff5w1ZMiQszU1NRp+fn47ra2t/9y2bVsAAAQE\nBGw7cuTIh1u3bp2pqalZraurWxYaGuqlqjxEgseTDIw3dCgQFwesWiUZD2nePMDfH9DTYzshIYQt\nKutDaCzUh6B6165J+hhiY4HAQGDWLMDIiO1UhJDXwak+BPLm6NkTOHIEiIkB0tMBS0tgyRLg4UO2\nkxFC1IkKQgNwsb2wMTJZWwO7d0v6GZ49A7p0kZwxiETyt1VlrsZGmRRDmRTH1VzKooJA/qN9e+Cn\nnyRXJjVpInlcp5+f5OxBLpEI+PxzoFcvyaPdXF0lHRcdOgAzZwJZWSrPTwhpGOpDIHI9eQL88AOw\nZQswcKDkXgZb2zpWTEiQ3B795En9OzM2BsLDAUfH+tchhLw26kMgKtGiBRAUBNy7J+lvGDJEcnNb\nfLzMSgkJwOjRkmIweDBw/jxQXAwwjOSGh4QEyXgahYXAqFGSHmxCCKdQQWgALrYXqiOTgQGwYIGk\nMAwdCnh5Sc4YLh4QgRkxAnjwAOjdGzh1ChgwANDXl+TS0AAcHIDjxyXLCwoANzfJmQIL3tX3T1mU\nSXFczaUsKghEaTo6wCefSPoUvL2B9E+/A+/JEzwUDoY49iKgrQ1AMrT6jh0H8E+Tn7Y2cPGipC/h\nxQtg8uRXNy8RQtSK+hDIa2N69QLv2jX4W55HvO4ALFkCjB8PHD9+BtOmnUVw8FB4eg6R2YCRnCFE\nRko6JL75hr3whLylVP5MZTZQQXgDaGoCNTVgnhfjzEV9zJ69Dzk5oTA2tkNBwdfo2HEZtLSuY84c\nL/j7T5ZsExkp6WsQCoHkZHbzE/IWok5lNeFieyGrmWpqAAA8A30MGwakpU3CF198imfPxABiIBKJ\n0aHDLFRVTcLZs0BGBlDV633Jtjdvqj0uvX+KoUyK42ouZalsLCPyDqqpATQ0wOfz0LUrD1paFWjR\nYgsKCwUwMuLhxg0ejh8HMjMBnbz7uA2gBHqY/7Hk7mjZSV+f7R+GkHcPNRmR19ehg+SGs4QEydVE\nANas2YGOHdti7Fg3HDt2DunpIixePF2yPsNAPHos+GG/4b7rRBz3/BWZmfhnundPckVT7SIhnVq1\nktzr1hAMw2Dp0vVYtWoBeA3dCSFvAOpDIOyYORP4+WfJfQbHj/9zlVGdKisl165u3iw5DbhzBxAI\nXlqFYYD8fLxUJGSnFy8kNaiuYtG2raRLoz5HjtTT0U3IW4YKgppER0f/8wg7rmA1U1aWZKiKwkLJ\nfQYX/7309KVclZWAkxOQmChZHhoKjBmj9OGePau/WDx4AJib/7dQJCXtw+HDoaipsUN6+iB07Bj5\n345uljAMg8mTP8a+fT9z6qyF/s4Vx8VcDSkI1IdAXl/79pKbzEaNAq5cAebOlYxzIfvhxjCSM4PE\nREmbz6FDgLNzgw5naAj06CGZanvxAsjOfrlIREcDGRmTkJnZAjU1sQB4yM0Vw8FhFm7eHIKvvpLs\ns1kzyX9lJ+lrenoNb6aS5+jRszh+vBDHjp3jzFmL9B4SZ2dnThUpolp0hkAaT2ys5P6CFy+AQYMk\nz+x8/33g/n1g4ULgt98kZwbnzjW4GLyOw4clzUXvvcdDQYEYvr7D0LHjEDx7JjnreP4c//y79vyL\nF5LiULtoyJuXfa1ZM8lN21Lbt+/D99+HoqrKDunp9VyeyxIuNq1R/49yqMmIsC88HJg0CSgq+u8y\nfX1gz54GNRM1hld2dMtRVSUZmqm+gqHIfHExoKsrWyAYVFScQVpaLMrLV8PAYAkGDXKGvf0Q6Ory\noKMjWV9H59/pVfOv6jtRFBUp5XGxUDEMAz6fTwVBHbjYXsipTE+eABs3AuHhiE5NhYu+vmQU1HXr\n/tOBzAa2fldiMVBa+nLBOHXqDL777ix0dHJRWirAmDHDYGk5BOXlQFkZUF6O//y7vnk+X7kCUte8\njg6D69fPYO/eWDx6NAQmJmcxb54zhg0bgqZNeWjaVDIkunTS1lZdU5rUy0WKW/0/gKRQeXvvxN69\n0zlTqI4cOYNx44ZRH4I6pKSkcOfD92+cytSihWQ4im++QcqmTXCZO5ftRC9h63fF50supzUwAMzM\nJG5spxoAABAYSURBVK/FxIiwd+9Q5OTcRtu2Nn+ftSi/b4aRnMUoWjxk//3smaQzXjLPQ3o6D4WF\nFdDSCsKjR0L89BMPwcE8VFRIms5kp6oqSVGoXShkp/qWKbqNqekkjBnTAjt2xAK4juJiMebPn4V+\n/YYgPV1yZqSpKWmOk/679mt8fuMXLtlCVV7eF0uWxOKrr35gtVDJZmoIlRaEM2fODJ07d+6mmpoa\njenTp/+yaNGitbXXCQwM3BwRETFMV1e3LCQkZKq9vT3nxzEoqqs5hGVczARwMxeXMi1ePAMAEBT0\nx2t9u+TxJB/M2tqS5qjXsWaNCAsXDkVqqiG6d3//lUVKLJZcPFZXsXjxov7X61r29Gl9y3jIyeHh\nyZMKaGoexMOHjti0iYcdO3ioqZGMri471X6tpkaSU5HCodw6k2Bg0AI3bsQCeIa8PMmFCgkJQ3D1\nqmR9DQ1JMXrVv+UtV2ZdgWASRo+WFk/lqawg1NTUaMyaNevHyMjIQQKBIK93795XPDw8wqytrf+U\nrhMeHj48IyPDKj09vWNCQoLjzJkzt8bHx/dRVSZCiHzSIpWaKr9I8fmSb/NNm6o205o1InTsqFiR\nqotY/HKhUKSQ1Fdc/p3n4eJFHq5fr4CBQTSqqhxhY8ODvb2kUEkL0av+LRZLzrIUXVf+fnnIy5Oc\n4TWEygpCYmKig5WVVYaFhUU2AHh5eYWeOHFilGxBCAsL8/Dx8dkNAI6OjglFRUVGBQUFJiYmJgWq\nytUYsrOz2Y7wH1zMBHAzF2VSDJcySYvUyZMHEBQUpPT2fL5k0tJq3Fx374qwb99QnDz5FCNHDkN6\nugjTFbtOQWWkxfPDDzcpvzHDMCqZDh8+/OH06dN3SOf37t07edasWT/IruPu7n7y0qVLfaXzAwcO\njLx69WpP2XUAMDTRRBNNNCk/Kfu5rbIzBB6PxyiyXu1e8NrbKdtLTgghpGFUNvy1QCDIE4lE5tJ5\nkUhkbmZmlvuqdXJzc80EAkGeqjIRQgipn8oKQq9eva6mp6d3zM7OtqisrNQ+ePDgRx4eHmGy63h4\neITt2bPHGwDi4+P7GBkZFXG9/4AQQt5WKmsy0tTUrP7xxx9nDRky5GxNTY2Gn5/fTmtr6z+3bdsW\nAAABAQHbhg8fHh4eHj7cysoqQ09PrzQ4ONhXVXkIIYTIoapO5dedfH19d7Vq1aqgW7duqWxnkU45\nOTnmLi4uUTY2Nre6du168/vvvw9kOxPDMCgvL2/q4OCQYGdnl2JtbX178eLFq9nOxDAMqqurNYRC\nYbK7u/tJtrNIp3bt2mV37979hlAoTO7du3ci23kYhsHTp0+NPD09j3Tp0uVPa2vr23/88UcfNvPc\nuXOns1AoTJZOzZo1e8aFv/VVq1YtsbGxudWtW7fUCRMm7K+oqGjCdiaGYbBp06Y53bp1S+3atevN\nTZs2zWEjQ12fl0+ePDEeNGjQ7x07dkwbPHjwuadPnxrJ2w/rv8z6ptjYWKekpCR7LhWE/Px80+Tk\nZCHDMCguLtbv1KnT3du3b1uznYthGJSWluoyDIOqqipNR0fH+Li4uH5sZ/r2228/nzhx4q8jR44M\nYzuLdLKwsMh68uSJMds5ZCdvb+/dO3funCZ9/4qKigzZziSdampq+Kampvk5OTnmbObIysqyaN++\n/T1pERg/fvzBkJAQH7Z/P6mpqd26deuWWl5e3rS6ulpj0KBBv2dkZFiqO0ddn5cLFixYt3bt2oUM\nw2DNmjWLFi1atEbefjj7TGUnJ6e45s2bP2U7hyxTU9MHQqEwBQD09fVLrK2t/7x//34btnMBgK6u\nbhkAVFZWatfU1GgYGxsXspknNzfXLDw8fPj06dN/YTh2pRiX8jx79swwLi7Oadq0absASVOroaHh\nM7ZzSUVGRg6ytLTMNDc3F7GZo1mzZs+1tLSqysrKdKurqzXLysp0uXAByp07d7o4OjomNG3atEJD\nQ6PG2dk55tixY2PVnaOuz0vZ+7x8fHx2//bbb6Pl7YezBYHrsrOzLZKTk+0dHR0T2M4CAGKxmC8U\nClNMTEwKXF1do2xsbG6zmeezzz77bv369Qv4fL6YzRy18Xg8ZtCgQZG9evW6umPHjhls58nKymrf\nsmXLR76+vsE9evRImjFjxo6ysjJdtnNJhYaGek2cOHE/2zmMjY0L582b923btm1z2rRpc9/IyKho\n0KBBkWzn6tat2824uDinwsJC47KyMt3Tp0+PyM3NNWM7FwDI3uRrYmJSUFBQYCJvGyoIDVBSUqL/\n4YcfHvn+++/n6Ovrl7CdBwD4fL44JSVFmJubaxYbG9s/Ojraha0sp06dcm/VqtVDe3v7ZC59GweA\nS5cufZCcnGwfERExbMuWLZ/GxcU5sZmnurpaMykpqccnn3zyU1JSUg89Pb3SNWvWNGB4u8ZXWVmp\nffLkyZHjxo07zHaWzMxMy02bNs3Nzs62uH//fpuSkhL9X3/9dRLbubp06XJn0aJFa93c3M4NGzYs\nwt7ePplrX4IAyRchRe4No4KgpKqqKi1PT8+jkydP3jd69Ojf2M5Tm6Gh4bMRI0acvnr1ai+2Mly+\nfLlvWFiYR/v27bMmTJhw4MKFCwO8vb33sJVHVuvWrfMBoGXLlo/GjBlzPDEx0YHNPGZmZrlmZma5\nvXv3vgIAH3744ZGkpKQ6ngWnfhEREcN69ux5rWXLlo/YznL16tVeffv2vdyiRYsnmpqa1WPHjj12\n+fLlvmznAoBp06btunr1aq+YmBhnIyOjos6dO99lOxMgOSt48OCBKQDk5+e3btWq1UN521BBUALD\nMDw/P7+dNjY2t+fOnduAgUJU4/Hjx+8VFRUZAUB5ebnO77//PpjNUWNXrVq1VCQSmWdlZbUPDQ31\nGjBgwAXp/SZsKisr0y0uLjYAgNLSUr1z5865de/ePZXNTKampg/Mzc1FaWlpnQBJm33Xrl1vsZlJ\n6sCBAxMmTJhwgO0cgOSbeHx8fJ/y8nIdhmF4kZGRg9huFpV6+PBhKwDIyclpe/z48TFcaGIDJPd5\n7d692wcAdu/e7aPQF1i2e+nrm7y8vA60bt36vra29gszMzPRrl27fNnOFBcX14/H44nt7OxSpJfk\nRUREDGU7140bN7rb29sn2dnZpXTv3v3GunXrFrCdSTpFR0c7c+Uqo3v37rW3s7NLsbOzS+natevN\nVatWLWE7E8MwSElJsevVq9cVW1vb62PGjDnGhauMSkpK9Fq0aPH4+fPnBmxnkU5r165dKL3s1Nvb\ne3dlZaUW25kYhoGTk1OsjY3NLTs7u5QLFy64spFB+nmppaVVKf28fPLkifHAgQMjlbnslPNPTCOE\nEKIe1GRECCEEABUEQgghf6OCQAghBAAVBEIIIX+jgkA4gc/ni+fPn79BOr9hw4b5K1asWN4Y+546\ndWrI0aNHPRtjX69y+PDhcTY2NrcHDhx4XpW5/vrrr3YHDhyYoHxCQl6NCgLhBG1t7crjx4+PefLk\nSQtA8SfuKeJ19lVdXa3wEPE7d+70++WXX6afP39+oKK5GpItKyur/f79+ycqs40yPwd5d1FBIJyg\npaVV5e/vv/277777rPay2t+kpcOFREdHuzg7O8eMHj36N0tLy8zFixev2bt37xQHB4dEW1vbG/fu\n3esg3SYyMnJQ7969r3Tu3Pnu6dOnRwBATU2NxoIFC9Y7ODgk2tnZXd++fbu/dL9OTk5xo0aNOlHX\nTWIHDhyYYGtre6N79+6pixcvXgMA//vf/766dOnSB9OmTdu1cOHCdbW3Wbt27SJbW9sbQqEwZenS\npatqL7ewsMguLCw0BiR35bq6ukYBQExMjLO9vX2yvb19cs+ePa/9v727D2njDAMA/uTSnemiQSco\nimjQdBU0krhEXN1m4nAxqMhWFgaCrVU3NjprsR+BOeJo/nCbo4tKi9syh1AQP9B91M2KnLa1jGYu\nMjvQajTdELuhzUzVxDTm2R/2IJPWpn81rM8PXsh9vLl7OLjncnd53rW1tUij0dh05cqVl5VKpd1i\nsRwLBALMo+LIzMy8sbGx8WxxcfFFhUIxKZfLp7q7uw2Pe5zI/9yT/lMHNWqICJGRkXfdbneUVCpd\nWF1dlTQ3N9c3NjaaEBEOHz7c0dvbezB4XUQEjuM00dHRrtu3b8dvbm6yiYmJiyaTqRERwWKx1NbV\n1Z1FRDh06NA3er1+EBFhdnZWlpSU9KfX641ob29/22w2f4CI4PV6I1QqlW1hYUHKcZxGLBavOZ3O\nlJ37ubi4mJicnHxreXk51u/3CwsKCkYGBgbKEBE0Gg03MTGRvbPP4OCg/sCBA+Mej0eEuD3+AR9X\nX1/fG4j/Lctts9lUGo2GQ0QoLS397tq1ay8ibpc49/v9wtHR0fzgMSZCjaO3t/dgTU3NF3y/1dVV\nyZM+7tTCq9EvBBI2oqKi7lZUVHS2tLTUhtpHrVbb4uPj/2JZ1ieTyeZ0Ot0QwHYVSqfTKQXYvjVj\nMBi6AQBkMtlcamrq/PT0dPqlS5de6+zsrFAqlfbc3Nyf79y589zc3JwMACAnJ+d6SkrKrZ3bs9ls\naq1Wy8XGxq4IhcKt8vLyC5cvX36FX44PKOY3MjLy6pEjR74WiUReAIDo6Oh/Qo0vLy9v/Pjx42db\nW1vfd7lcMUKhcGvnNkKNIysr67fh4eFCo9HYdPXq1ZckEok71P0gTwdKCCSs1NXVfW61WqvW19fF\n/Lw9e/b4A4EAA7Bd5tvn87H8soiIiE3+M8MwAX6aYZjAbvfN+Xv3bW1tR+12u9JutysdDkcaX1JZ\nLBavP6xf8AkZEQXBzwEe9kzgQYkiWHCMXq9XxM8/ffr0x1artcrj8ezNy8sbn5mZ2f+g/qHEsW/f\nvlm73a6Uy+VTDQ0N5jNnzny42z6Rpw8lBBJWYmJiXAaDodtqtVbxJ1epVOqcmJh4AWB70I979+49\n8zjfiYiCnp6eNxFR4HA40ubn51PT09OndTrd0Llz597jE8fNmzeff9RYBGq12jY2Npa/srISu7W1\nJezq6norPz9/bLc+hYWFwx0dHZUej2cvAIDL5YrZuY5UKnXyFWqDn5c4HI60jIyM30+dOvWJWq22\nzczM7JdIJG6+SB8AQKhxLC0tJYhEIm95efmFEydONIdLVVUSPujNAxIWgq+s6+vrP2trazvKT9fU\n1HxZVlb2rUKhmCwqKvopeAyKh12RB7/BIxAIMDk5+Y+cnJzrbrdb0t7e/g7Lsr7q6uqvnE6nNDs7\n+1dEFMTFxf3d39//+m5v/yQkJCw1NTUZtVoth4iCkpKSH0pLS7/fLTadTjc0OTmpUKlUv7As6ysu\nLr5oNpsbgtcxmUwfVVVVWSUSiVuj0Yzy27dYLMc4jtMyDBPIzMy8odfrfxQIBCgUCrcUCsVkZWVl\nR21tbUsocUxNTclPnjz5KcMwAZZlfefPn39396NCnjZU3I4QQggA0C0jQggh91FCIIQQAgCUEAgh\nhNxHCYEQQggAUEIghBByHyUEQgghAADwL4Lw2db72IMXAAAAAElFTkSuQmCC\n",
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"<matplotlib.figure.Figure at 0x4bcd59d0>"
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5087352d0>"
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
<<<<<<< HEAD
"image/png": 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01vl8PpVKpfIREalUKp/P51MREXV1dS3PyMhokMeLoujxer1qpVI5IoqiR+5Xq9Ver9er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\nwEoAAAAASUVORK5CYII=\n",
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"<matplotlib.figure.Figure at 0x42a7ff10>"
=======
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s0safAtTiOqhpfdrDKlA9NgCwfEJSADiFo1roq1ZdWWyhT5VPaV3RtBYfh6vjsCmqadYg\nqaS41mbxNWw6QDVI6mycZpBUhHVQAeAsCEkB4BiKVVmpYjSpYvVVcS29wWAQo9Fo0kK/tbWl7RNq\nqhieHjZIKlXEp9e79BpokNTZOSxAjZi/Dmoy3b5vHVQAWIyQFACOUFznL63xF1HNUDQirmuhTwOX\ntre3DVw6Y+5bqiKtl3lYgDpdST89SEqAejYWWQc1tfBPK1adWgcVAA4SkgLAlOLApWIL/bVr16LT\n6cTW1taat/D48jyPq1evTga6ZFkWvV4vdnZ2tNAvkZCBJkgB6izTa6CmgW/TAeq8Nn5OZ9nroFqj\nFoAmEZIC0HiLttBX5SRx1sCliGcriM6dO2fgEnBmFhkkVaxCTRduUpX+YVWo61b19YatgwoAhxOS\nAtBI8wYuVSkMLSoOXBoMBtFqtQ4MXLp8+XJlK2CBelgkQC2+NheXOYmIQ1v4V/WaXbX3hkUtax3U\ndBHOOqgAVJGQFIBGKLbQp5PvpIpT6I8zcKmqQaMTa2iOYtA53cpfrHIsXtyaDlDntfB7LTm9RddB\nnRegWgcVgCoQkgJQS9MVSaPRaPK1k1YetVqttQaOBi4BTVQMTw8bJJXa+ItDpAySOnsnXQc1/Yx1\nUAEoCyEpALVRtxb6NPAkfRi4BHC91N59WIA6/f4wPUhKgHo2TrsOajE8nW7h9/gAsGxCUgAq66gW\n+qqdRM0auNTtdiPLMgOXGqyqyyVAGaQAdZbpjoN0YWo6QE1/pu/VJr4ci6yDWlxaYTpENUgKgGUT\nkgJQKdOhaNWrRQ8buJRlWeVuD0BVLDJIqliFmud59Pv92N/fP/Cz01Wo6XdzOstYB3U6QBVwA3AY\nISkApTavhT6iHgOXxuPxpFp0a2trbsXTaRQHODk5rJZ1r4MLTTUrQB2NRrGxsTGpKi2+P6ULXsVB\nUvNa+L0On551UAE4C0JSAEqleOKZKkaTqp7AGLgEzSXkrp/i+9D0ha3iOpvFQVKpXTzP85nhqTbx\n5TnOOqjj8Tj6/X5sbm5OfqZYhWodVIBmEZICsHbpJLL4Z0Q5Q9FFAg8Dl4CiMr2GcbaK4ekig6SK\nQ6QMkjp70wFqu92O/f39yWNlHVSAZhOSArByxYFLVWqhn9f6bOASx6GyEJrrqEFSxQB1OkSdHiQ1\nXYXK6R21DmpxyZxp1kEFqD4hKQBnrthCv7e3d+CEoYzVooswcAmAZToqQC2Gp6ljoRigFsNTAery\nLTJIKq2DOutn51Whpq8DsH5CUgDOxLyBS1evXp1UV1ZJsfL16aefXsnAJVRdAkTMHiSVFAPU9Gdq\nFR+Px9cFqMW/p9/N6RxnHdR5g6SsgwqwfkJSAJai2EKfwtGkeGJXpYP96Rb6iGfb6QxcWo2q3r+C\nXWCVFglQixcuU4ia3qcPWwO1qq/Di5oOLM/CYQFq2gbroAKUg5AUgBOZPukajUaTr1X15OqogUv7\n+/uTz8EsVdvngXorvhdPdzwUKxxTFWoxqMvz/NA1UL3eLYd1UAHKQ0gKwMLmtdBXORRtysClNHSq\nLrcHgNMphqezloyZbuFP7/vTg6TmVaFyeme1DqrHB2A2ISkAcx3VQl/FE6HTDFxqtVoH7gMAmqNp\nF5pOO0jqsCpUTu+k66CmCuFiBap1UAGeJSQFYKJYlVA88YlYbii6yjUbi21q6QTOwCUAOLlFB0nN\nC1APq0Ll9I4ToKbPWQcVQEgK0HjzWugjYu4J0Gms4uB6uoW+3W5HlmUGLgHAGVs0QJ3Vxh8xe5BU\nFYc/ltUig6RmrYM6vcTCvCpUgCoTkgI0TB1b6I8auLTsoBcAOL5FAtTpi7epCjVidoBa1WOXsjrt\nOqjF8NQ6qEDVCEkBGmA6FDVwCQAok+LxyGGDpNJxTDoOSMHqYWugOiZYjkXa+FOAah1UoIqEpAA1\nVKwWXUUL/SqcZuASAFBtqUrxsAB1Vgv/dJt48e+rXCO97k46SCqxDipQBkJSgBootqelitGkytWi\nBi4BAEdJAeosRw2SiojY3d2dW4XK6Z10HdSkGKCmx9pjBJwFISlARc0buFT2ULTVas2s3Ei3p8wD\nl+ZtexVUdduruM0AlMdh66AOh8PY39+PLMsOHIcUK1DnVaGu+5ikThZZB/WwAHW6ld9jBJyUkBSg\nItJV9uL6W0lVW+gNXOIwTm4AOEspnOt2rz8tLlagzmrjj5g9SCodu3gPW47TroOaji2LrfxlLygA\n1kdIClBS81ron3766bhw4UIlr5Cn29Tv92Nvb8/AJQCglA6rQE3HM9NdPakKNWJ2gCqcW65FAtS9\nvb0Yj8cz7/fiECnroAIRQlKAUjlOC31VDt6mBy6lK/oGLgEAVVQ8JjtskFSqQk0BagpWZ4Wnwrnl\nKt6PKQgtKhYiHLUOqrVqoTmEpABrVJxCnw6mk3nVC2U/MDtq4NK1a9diY2Mjer3eujcVAGDp0nCh\nwwLUWS38qV38sCpUlsM6qMAsQlKAFSqunZQOjJOqtmBND1waDAbR6XQOHbhkGA8A0EQpQJ1luoU/\nrd0+HaDOq0LlesV1Shd10nVQ088Uw1ProEK1CEkBzti8FvqIw69il9l4PD5QLXqcgUtVvL1JVSfE\ns1r2EQBOYpF1UItVqOkYrLjm5rwqVJZjkQC1eMx/2DqoaS1+lcJQHkJSgCU7qoW+igdBxfW0BoOB\ngUswh+cB1JcLIPVTpcf0uAFqsY0/wiCpVTgsQI24fh3U6RB11jqoAlRYLSEpwBJMh6KHDVw6rVVV\nM0630LdaLQOXKq5KJ4MAZeS9r37q8JguEqBOdzalNv6IMEhqRY5aB7W4rv+0eWugqhSG5RKSApxA\nsVp0Vgv9Ye3mZXXUwKV562dRDQ6gy+Uka6QBwHEVg7nDBkmli/zpon861hWgrsYig6SsgwpnT0gK\nsIDiFfh08JhU9QDkJAOXgNPxnAKgTNIgqcMC1Fkt/MVBUvPa+FmO06yDWnxcrIMKRxOSAswxb+BS\nVUPRiOsHLkVEZFkWGxsbRw5cWhbDjzgO+woArEcKUGeZbuHP83zSiVQMUOdVoa5K3Ts3rIMKyyUk\nBfi2urbQTw9cyrJssraodYwoM/smAJTTcQdJpYBuPB4f+NlZQSrLYx1UOB4hKdBYVW2hP6oSc7qF\nvt1uR7fbja2tLS30p6QKlnWwzwFQJccdJJWOXYuDpOa18DuOXZ7jrIM662fnVaGmr0MVCUmBRqlj\nC/2sgUupWtTAJai2Kr4mAcA8hw2SKq6vWTxenw5QDZI6e9ZBpamEpECtFVvox+NxXLt2LXq93oGD\nqqopLgmQgtE0hX5nZ2dyIAIAAFVRDE8PGySV2vjT8X063p8Vnqafc2y8PMcJUK2DStUISYFamW7f\nGY1Gk6+1Wq0YDAaTkLRKigOXRqNR7O7uRq/XW+nApWXSPrwe7ncA+A7hWbWkQVKHBajF84DUJn7t\n2rVD10C1DyzPIoOkZq2DOh1ypwB1ugoVzpqQFKi8eS30EYevtVNmhw1c6nQ6sbm5GRsbG+vezBOp\n4uNRB+53AKCuUoBalOd5XL16NXZ2dq6rQk0t/MVwbl4bP8tz2nVQi+GpQVKcBSEpUDnTLfTHHbhU\n1mq6WQOX0rqixYFLg8HAgQAAACxgkUFSxSrU1CI+Ho8P/OysIJXlWaSNPwWoxSrwPM+j3W5HnueT\nghLroHJSQlKgEqZD0ZMOXCrTG+RhA5e2t7cr10LfBKbbAwDUxyIBarEKNYWoxUFS81r4y3TeUXWL\nBKi7u7tzl9CwDiqLEpICpXRUC30VA8Ti1WkDl6C5PM8BoPyKAdqsVv6IuG6QVKpCTdWN81r4HQss\nz/RQqOnzxHnroEZEvOc974lPfOITK9lOqkFICpRC8SptqhhNqnw1tjhwKb0pZ1lW2YFLwNFUGwNA\nvRXD00UGSaXzm3TOY5DU8s2rIj3sPv2Lv/iLs94sKkZICqxNuuJa/DPi7EPRswwwplvoR6PRpIV+\nc3NzKesXVb3lu+rbz+rYVwCAKpo1SCqZDlCnQ1QB6uo4zmSakBRYmeLApXW10J/FgcX0FPo0cGl7\ne/vAwCUAoJqKF3KBaphXWbhuRwWoxfA0z/PJGqjFAHVeGz9wOkJS4MzUtYU+HaykjzRJsdfraaGn\n1FwtP3tVfE0DaKqyhmg01yKDpIpVqOlcZDweH/jZ6SA1/W4Ocp8wTUgKLNW8gUtVD0Wnq0XTwKVz\n584ZuEQl2EcBAKprkQC1eC6WQtRUqHJYC3/VjxMVArAsQlLgVIot9OnNOKnqFPqIuG4KfavVmqwr\n2u12K3u7OB1rZAIAUDbFoHO6lT8duxZb+FMBSDqXO6yFv0oB6nG3tUq3jdUQkgLHMn2FcjQaTb5W\nhSuR80KuwwYubW1tlWqdnzoEdVXffgAAqIJieDprLdTpFv7iECmDpGgaISlwpDq20EcYuLQu7lcA\nAM6aNWcXc9QgqWKAOh2iGiRF3QhJgevMaqHf3d2Ndrsdm5ublQ1G05v7/v5+7O7uGrgEAAAwx1EB\najE8TcNtiwFqMTwVoFIFQlIgIuK6UHS6WnT671Uwa+BSq9WKbrdr4BIAAMAJLTJIqliFmtZAHY/H\n1wWoxb+n330cqoZZFiEpNNS8FvqI+g5cyrIsrl27Fp1OJ7rdar/8WdMTAAAoo0UC1OL5aDqHS0OA\nD1sDdVlhqPMpZql2SgAsrPhGlCpGk0XecMo6LKhKA5eWpW63p0rK+jyoM/c3AGfFewysXvG887BB\nUqkKNXUGpvPZWeFp+rnjGAwGkWXZ6W8QtSIkhRpLbyrFPyMMXGK9BI3rUcX73XMZgLPmvQbKJa2D\neliAWiz+SX+PiLh69eqhVahFQlJmEZJCjRQHLtWphT4tAp4+DFwCAAAOU7WLwxxt3iCp0WgUe3t7\nsbW1NXOQ1MMPPxz/7t/9u/ie7/meycett94aERHXrl2L7e3tVd8USkpIChV22hb6spo1cKnb7UaW\nZaceuFTFajoAAOD4qnguxMlMD38qeuc73xmvf/3r48/+7M/iq1/9anz1q1+N3/u934vPf/7zceON\nN8ZNN90UFy9ejBe96EVx8eLFuHjxYnz84x+Pz372s3HLLbfEH//xH0dExDe/+c24//7747HHHos7\n7rgjHn744bjhhhsiIuLBBx+Mhx56KLrdbvziL/5ivOlNb4qIiM9//vPxEz/xE7G3txf33Xdf/If/\n8B9Wd6dwbEJSqJh5A5fOOhQ963DxqIFLDnC+Q9ALAACwmF6vF3fddVfcddddk889+eST8a/+1b+K\nj3/843Hp0qX4yle+El/5ylfiz/7sz+K//tf/Gv/7f//veOaZZ+Kpp56K173udfGRj3wk/vN//s9x\n7733xgc+8IH4+Z//+XjwwQfjgx/8YHzxi1+Mhx9+OB599NF44okn4t57740vf/nL0Wq14r3vfW/8\nyq/8SrzmNa+J++67L/77f//v8eY3v3mN9waHEZJCyRVb6FM4mlS9hb44cGk8Hk+qRbe2tma2UQBU\ngYsYAADlNhgMotvtRqfTidtuuy1uu+22+P7v//4D3/O1r30t7rvvvviFX/iFeP7znx+f/OQn4zOf\n+UxERLzrXe+KN7zhDfHBD34wfuM3fiPe/va3R7fbjTvuuCPuuuuu+NznPhe33357PPPMM/Ga17wm\nIiL+wT/4B/Hrv/7rQtISE5JCyRRb6FO1aFLlFvoIA5d4lkpYAKomz3PHKQAldZJzi0UGN7Vareh2\nu/H6178+IiK+/vWvx8033xwREbfcckt8/etfj4iIS5cuxete97rJz916661x6dKl6Ha78YIXvGDy\n+Re84AVx6dKlY28rqyMkhRJYVwv9cbRarQNVrIso48AlAR2nYf/hKGV4vQYAaJrjHoMNh8Po9Xor\n/T8pPyEprMFRLfRlCUaP6ywHLnGQoA4AAOZTAc5hFqkknXbzzTfHX/7lX8bNN98cTz75ZDzvec+L\niGcrRx9//PHJ9z3xxBNx6623zv085VXNxQyhYlIoOhwOo9/vx/7+fuzv78dwOJy8eadJfFULSMfj\ncezv78eVK1fi8uXLceXKlRiPx7G5uRnPfe5z48KFC7G1taWdfoncj5yEKlgAOEiIBs2V1iQ9TDqP\nT9761reIlgewAAAgAElEQVTGr/7qr0ZExIc//OH4oR/6ocnnP/GJT0S/348///M/j6985Svx2te+\nNm655Za44YYb4nOf+1zkeR4f+chHJj9DOakkhTOSWuhTtWjxxdXAJQAAAFiPoypJ3/GOd8T/+B//\nI77xjW/EbbfdFv/m3/yb+Jf/8l/G3//7fz8eeuihuP322+Phhx+OiIi777473va2t8Xdd98dWZbF\nL//yL08uwPzSL/1S/MRP/ETs7e3FfffdF295y1tWcvs4GSEpLMmsFvrBYBD7+/tx/vz5ylWIFo1G\no0kw2u/3Kz1w6SRrq7J8KhphcZ4vAADznaQq/Kg1ST/+8Y/P/Pwjjzwy8/MPPPBAPPDAA9d9/lWv\nelV84QtfONa2sT5CUjiF6VB0euBSqhatUogYMXvgUqfTiXa7HefPn69sFSzlULXnA+uT9pUmt0M2\n9XYDAJylk6xJSv0JSeEYitWixSn0EdVvoT9q4FK/34/BYFDZ21g3KsvWq8mhHQAAVJ2QlFmEpHCI\nFIoW1xZNFplCX+ZBKWk5gPTRarUiy7LY3NyMLMuuu11lvi1NI5xbH/c9AEA1uKjNYVJhEBTZI2BK\nCkVTMDrdQl/VN1oDl54l7AVg1ap67AAAVXCS87vhcKiSlOsISWm8WQOXkiq30EfEdS30VR64BAAA\nALMc99xWuz2zCElpnNO20B/HqqsWZw1cyrIser1e7OzsVDrwpT5U866P+x1Wy3MOys1zFJpLSMos\nQlIaoc4t9EcNXKpq4Mt8HgtOoqqvcwBwlrw/QjMJSZlFSEot1bmF/rgDlzhIwAism9dpAID1Gg6H\nsbW1te7NoGSEpNRCCr1SKDoajSZfW2e16DICOQOXoFzS81rQBQBQXo7XmuMkj7VKUmYRklJZ81ro\nI6LyLfTT1aKdTqcUA5dUYAIATSRsAagX0+2ZRUhKZRzVQl/1YLTsA5eqet/WVdUD66pvP6tVtXDC\n/g0AUG4qSZlFSEqpTYeiVRu4NK/dfpUDlzioDmuSVn3fqPr2s1pV21+qtr0AAE0kJGUWISmlUqwW\nndVCX4aKypMycAkAAACW6yRdR9rtmUVIylqlUDStKzoej+PKlSuxtbU1WXuzquFhaqGPiHj66acr\nP3CpDhWY0HSewwAA1NFJQtJuVyTGQfYIVm7ewKUUiKarQFULR+cNXIqItQ9cAnChAwAOqtqa1xxP\nnueV7kTkbGm3ZxYhKSuT1uCsWwv9cDg8dODSU089JSAtkboERXW4DVVVl30IoHKGw2h/4Qsxvuuu\niHPn1r01AFSYdntmEZKyMmnwUhWrRBMDlyiDqu9jQkaazv4PJ9P5/d+P7m//doxe8YoYvuMd694c\nACpMJSmzCElZuaMCnrIFKAYuPatsjwtAlYzH4+j3+5P3koiIdrt93UeVLyTCWRu/6EUxfsELYvy9\n37vuTQGgRE6ydIaQlFmEpDAlz/MDLfSnHbhUXGcVgGZInQcpGB2Px5PlWIrvI8X1rIsdFyk0LQ44\nFKDSdPkLXhCDn/qp6Pzu70b7j/4oxvfcs+5NAqCitNszi5CUxps3cCnLMgOXakhFLHBW8jyfhJ2X\nL1+OVqsVvV7vuveS3d3duWtyF0PR4sfu7u5kAEWqOJ2uQIUmaF26FN1Pfzqi14t9ISkAJ6SSlFmE\npJTOKkKs6YFLERFZlsXGxsZk4BIHCRfLw2MB5VFcpzpdZGu1WnHhwoVjdx5ExKRaNL0PDYfDiIjY\n2tq6LjxN/3exylT7PnWXP//5Mfz+74/8ppvWvSlAyenm4zBCUmYRktIIswYuZVk2WVv0LKtwBFpw\nUNWfE1Xeftt9erOWZCleZMvzPJ555pkTBaRHabVa0el0rvvdxerTo9r3i1WoAlQqqd2O0ZvfvO6t\nAKBETnKsqN2eWYSk1NZ0C3273Z6sK6qFHmiaqr7mlWG7Uxt9Wl80vZ/MWpJlNBqtfPumq0+Lptv3\nR6PRJDzVvg8A1IXBTSyDkJSVWfRF66RVWvOqe9KJrBb65ah620qVqwCB1UndB/1+P4bDYXS73ej1\neica4LdORwWo2vepg/af/EnE3l6MX/WqM/s/HDvUU9WPa4GTE5Iyi5CUypo1cClNod/Z2ZmsC7du\ndQnlynBf8h112KegTOZdaNvc3Iwsy2r5Gqh9n1oYDCL76Ecj8jz277wz4rnPXfcWAVAB6SI4FNkj\nWJnjnDjNC4AMXAKBNSzLvGVZynShbR2071MpWRbDN74xWvv7Ec95zpn+V/ZjqBZFBRzGmqTMIiSl\ndIoHoOscuAScLS1urMNoNJqsLVp8T1lmG32dT8q071M23Y9+NNpf/nIMfuqnIuxLwBTvMc1wkvMK\n7fbMIiSldFLL45UrV2oxcKku7fYR37ktVXsMiur0eFRVlfefxD5UHcU2+n6/H3meR6/XO7M2+jrs\n3yelfZ91aF25Eq3BIGJ/f92bAkDFOM5gmpCUtUtTg1NVz2g0ik6nE5ubmwYuAaXjYKr8ZrXR93q9\nOHfuXCXb6Kseymvf5ywNfvInI65dO/NWewCg/oSkrNysFvriwKX9/f1ot9uxsbGx7k2FUlINCwel\nkG1WG30ZL7YJ+L5D+z6n1us9+wEAcEpCUlbq2rVrMRwOIyIm64p2u90DJ0f9fn9dm3dmBFrAutUt\nXE9t9CkYjYjaT6NvGu37AMAiqr4kHOUhJGWl0tqih7XK1e1Evk4v1nV4bNLj4Y0UqieFYf1+P4bD\nYeXb6BdRx9t0Wtr3gWVxPFhvHl/guISkrEyr1Yosy0rX9gisXh2GgHH20kWZvb29GA6HMR6Po9vt\nRq/Xi52dHe8nXEf7PgAAJyUkBagoISN1VBzmV2yh3t7ejm63a5/nxLTvAwBwGCEpK7XIyUSr1Yrx\neLyCrVmNOrSoJ3W6LVXmpHy9PA+Wb7qNvtPpRJZlcf78+XjmmWcm61fDWdC+DwDVddLjcu/VzOKM\ng5VS+UYZaPWG9crzfNLq3O/3YzweR5ZlM9voPU9Zp2W070fEZA1d1acAcDa8v7IMQlIA4MwV2+j7\n/f5knWpt9FTVIu37qTNG+z4AQPkJSSmdurWy1u32ACxqPB5Hv9+fhKPdbjeyLIsLFy5cFyxBXRSr\nSPM8j36/H1tbWxGhfR9glXSOAcclJAUWJvAtlyof+NmXVm8V93lqo0/B6GFt9NBEy2jfnw5Pq/o+\nAABQNkJSoHHqENA5KaYsUht9CkZbrVb0ej1t9HBMi7Tvpz+1769elS9MMlvVjwWBZ53k9dnzn3mE\npKzMoi9cdQiwiup2ewBShVuxjb7X68XW1pY2eliyo6pPG9u+PxhE67HHIr/zzghV6pxCLZ4PwLEM\nh8PodsVhXM9eARyLwJema+KFjzzPYzgcTkLR1Ea/sbER586dc4I5pWn7B+vT5Pb97qc+FZ3f//0Y\nvuUtMXrjG+d+X/sP/zDyCxciv+uuFW4dAGU2GAwiy7J1bwYlJCQFFlaVEyfg9Kbb6Nvt9tqm0Vcp\ndPQ6SVnUsn3/W9+Kzp/8SYxe+coYP//50T5/PvK/8Tfmfnvr//2/yP7bf4u814v+v/23K9xQAMos\ndULBNHsFpVO3Kq263Z46qMNjUvXbUPXtr6tUadbv92M4HEaWZZFlmTZ6qJEqt+93P/3p6PzhH0Y8\n80yM3vSm6L/61c9+4fLlaF2+HPkddxy8Pd/93TH6m38z8ptuWsn2AeWRjjPXfnGHUhoOh9Hr9da9\nGZSQkBQAGqrYRt/v9yPP8+j1erG5uRlZlpXixKIM2wBNUfb2/dErXhGtp5+O8UtfeuDzvYceitZf\n/VX03/OeyG+//Ttf6HZj+Pf+XmQf+1i0/uqvYnj//RFeUwBq5SSDm7TbM4+QlFJSYVZOqv+g+lJr\nbfpIbfTnzp2LTqcjlARmKkP7fn7xYgwuXrzu8+M774xWpxP5c5973dfaX/1qdH/rtyJarRjffXeM\nX/7y491wAGpHuz3z2CsonTqeoAsWgXVJoUVaW/Ty5cuTNvrt7e2ZFWMAiypD+/7wh3947tfGr3xl\nDH7kR6J9+XKMb7312LcPgPpRSco8QlJW7iTl8FXWpNtaJYJrTqoKFdWpjT4Fo3meR7fbjXa7HTfc\ncIPXJWAlStG+3+nE8N3vXtItAqAORqORkJSZhKSszHEOasseQFBtdQiIqhDUHaXq2182s9roe73e\npI1+OBzG7u5uLfZ/oPrK0L6/dN/6VsRwGHHjjevdDhbStMINqCtrkrJMQlJKx8FKedUhmKMcPM9P\nL8/zSeXVYDCI0WgU3W43er2eNnqgssrQvn8i43H0/v2/j9b+fuw/8EDEDTec/f8JzCUE5zBCUuYR\nkrJSTXyjEiwCyzLdRh8RkWVZqabRA5yVUrTvz9+4yG+6KeLKlYiNjeX8TgDOhMFNzGOvoHSEiqyC\nfYyqSK2m/X4/hsNhdDod0+gBpizavh9//ucx6nRi/5Zbltu+32rF4Kd/esm3CoCzMBwOo9frrXsz\nKCEhKbCwugTYdQmV6vBYcL1iG32/34/xeDxpo9/Z2dFGD3AMB6pPn346Nh56KKLbjf1//a8j73QW\nbt9Pn9PCC1B92u2ZR0gKZ6wuwSLl4gStXvI8PzB0KeLZNvrt7e3odruNf7y9hgJLsbMT4+/5nmg9\n/ni0/vqvI265ZeH2/fRx9erV1bTvA7AQg5tYJiEppSNUhPqr8vN8Wds+Ho8na4umdZGyLIvz58+f\n2aCRqt7nAEvR7cb4ZS+L7le/Gt3f+q0Y/KN/NPdbp9v3+/1+5HkevV7vQPt+WhIl/Xtp7fsAnBkh\nKfMISYHGqXJAR3XNaqPPsmxlbfRVPTmv6nYD5TR66Uuj9dhjMX7Vq07080cNj1q0fX86SF2Wzq//\nemQf/Wj0P/CBGL/2tUv7vVA1lsbgMMPhUEjKTEJSOGN1CuTqdFtgFYpt9P1+P1qtVvR6PW30AOvy\nnOfE8B3vOJNffVSAWmzbTxfNxuPxUtv3u488Eu2vfS26n/lM9IWkh3JMC82lkpR5hKSUlqt/MJ/A\nurzSiW+xjb7X68WFCxeum7oMQDNMt+8nxerTZbTv99/3vuj+1m+dWRBcN841oPqsScoyCUlZqUWC\nHQcrQJWkNvq0vuiq2+gpl/Qe5kIflFv2kY9E69Kl6P/Tfxpx4cLatmPp7ft33hmDn/7pNdwSgOoY\nDofR7YrDuJ69As5YnSr+Wq1WjMfjdW/GqdXpMamyKj8GeZ5Hv9+fBKPtdts0ehZ23NegKj9XoKxa\nTz4Zraeeitbly5GvMSQ9TBna9wHqaDgcxs7Ozro3gxJS3kIpCbGg3qp4kjYajWJvby/6/X7s7e3F\n3t5edLvduHDhQtxwww2xvb0dWZZV8rZRXvYnOBv997438p2dyD70oYgrV9a9OceW2vezLIuNjY3Y\n3NyM7e3t2NnZia2trej1etHpdCZrY+/u7sbVq1fj6tWrsbu7G/v7+zEYDGI4HE6qU6FO7NMcRrs9\n86gkBYAZ8jyP4XA4WVs0tdGnSlFXnwEq7Ny5iO3tiGvX1r0lS7X09v1v/xuqyL7bDCcJxIWkzCMk\nZaWaWCHaxNvM2bNfnY00MCN9pDb6nZ2d6HQ60Wq1Ynd3130PUHWtVvTf976I8ThiY2PdW7MS2veB\nujrua9FwOBSSMpOQlFISAJVTnR6XutwOTi+dCPb7/ckBU5ZlsbW1Vatp9HV6/gIshRPkidS+P/2+\nV6w+TX+m8DQNqJsOTgWoQNmpJGUeISkrZ+Iv62b/a7ZiG32/3488z6PX68Xm5qY1RQGgQPs+UEcq\nSZlHSAoroHoLDlp1VeO8Nvpz585N2ugpJxWwwJnq96P9hS/E+CUveXadUhZW9fZ9hRvQXCpJmUdI\nykoteiBSp5NiB1+chTo9R85COjnr9/uT6b2pjX57e3vmCR3AsnmdLr/O7/1edH/nd2J0zz0xvP/+\ndW9ObWjfB1YlVa4fh5CUeYSkwMLqFMzV5XbwHamNPgWjERFZli29jb7VasV4PF7K7wLqT7BTbuO7\n7orxo4/G+GUvW/emNIL2fVZFpTCH0W7PPEJSSkuIxVmpU9jbdLPa6Hu9njZ6Ss9rEJRD/l3fFYN/\n8k8ivF+sXdXb94HqUEnKPEJSSqlOBzQCOViePM8nJ0aDwSBGo1F0u93o9Xra6AE4lvaf/ElkH/1o\njF7/+hj+4A8e+r2O5dbrLNr3geZSSco8QlJgYQJflmnRfSnP8wPVohHPttFvbW1Ft9t1ogPAyYzH\nEXn+7J8L8H5TPqdp30+P5/7+vvZ9qLCTLK0wHA6j2xWHcT17BaVVtzDOujjlUvX9q+qB9VHPhVQJ\n0u/3YzgcRqfTMY3+lKq+zwCLaz3xRMTubuR33bXuTSm18fd9X+z/zM+Yal9TRwWoxYuv2vehWbTb\nM4+QlFKq0wFInW5LXXhMyqfYRt/v92M8HkeWZdHr9WJnZ0cbPcCi8jyyj30sYjCI/nvfG3HTTeve\nonI7f37dW8AaFEPQjY2NyedP077v+LJcFKhwGCEp8whJWSlvVECS5/lkEn2/349WqxVZlsX29nap\n2+hVZAKl1mrF6JWvjNYzz0TccMO6twZKa9Z7+Wna96eDU+37UF5CUuYRklJKQohy8rhwWuPxOPr9\n/qRatNvtRpZlceHCBScS1IoKFtZp9OY3r3sToBKO8zp9VICawtMUoGrfh/IyuIl5hKSsVFMPBFK4\n2NTbz9moQmCd2uhTxWhqo09rjO7s7Kx7E6mAKuzrRV7rgbPU+sY3ovvRj8b45S+P0RvfuO7NIZ59\n3e90OtHpdA58Xvs+nL2TnGerJGUeISnQOHWoiC3zbSgOQ0ht9L1e70Ab/e7ubmm3n3JxkghwUOvx\nx6P9ta9FRAhJS+647fvFQFX7PpwdISnzCEkppTIHQE3mcWGe1FaWPrrdbvR6vbhw4cJ1VRUANNgz\nz0T20Y/G+PbbY3Tffevemkoav+IVMXjXu2L8wheue1M4hUXb94vDLbXvw3Jot2ceISmsgHCRupnX\nRr+xsWEaPQBztS5fjtaTT0Z7MIjRujemqlqtGL/yleveCs7QUe37xRBV+/5sljrjMCpJmUdICjSS\n0Pr4Uht9Ckbb7XYlptHzLBdrgDLIX/jCGPzDfxj5DTese1Ogcoph52Hrn2rfp0lOGojb95lFSEop\nOZnnLNXhDbHVasV4PD7z/ye1d/X7/UlbSpZlsbW11dg2eq9PAKeT33HHujcBauek7fsRMbN1X4BK\nnTmWZx4hKaxAXUKVutwO5svzPIbD4WRt0dRGv7m5GVmWLe1geVUhLwBA0y2jfX/67wJUoI6EpAAN\nlw6I00dqo9/Z2YlOp+MgGACoHWtWHq99P7XwF9v35w2QgrKznzKPkJSVWvTFSMUiZ63p+1dx6JI2\negCYYW8vsg99KPIbb4z9H/7hdW8NrJT2faCJhKSwInUI5eoSXjfxAK3YRt/v9yPP8+j1ektvoweA\numg980y0H3ss8q9/XdUhFFShfT9Vu1J/Xp9ZJiEprIAXbZZtkcB6Vht9r9eLc+fOaaMHgCPk3/3d\n0X/PeyK2tyPC8RwcRfs+UHVCUkrJUBc4vnTgOauNfnt729X0JapDRXWVuL+hYfI8Op/6VEREjH7g\nByLWGJLkd9zx7F/29ta2DUuV5xGDQUSvt+4toWG071Mm9h3mEZICjVSX0CW10adgNCLOZBr9slV5\n6Yay3qeLqlpLUpW2FViS3d3o/M//GRERoze8YVLJyellH/lItP/X/4r+T//0dwJgWLOzaN8HOAkh\nKaxAlQOhonTAUbWQZVqVtz3iYBv95cuXtdGzEPsFUBnb2zF8+9snf2eJ+v2I8ThiOFz3lsCRTtO+\nHxGTz2vfr686nGNTLkJSSqkuoSIsQ7HtqN/vHzjYO3/+vDZ6KKFlvod5P6QRrlyJ1je+Efntt0dE\nxPh7v3fNG1RPg5/8yYirVyMuXFj3psCpHNW+v7e3N/ke7fv157FjWYSkACWU5/mBoUsRMVlbtNvt\nxmAwiP39fQEplNAyD9Qd9NMU2cc/Hq0nnojBj/945Hfdte7Nqa9OR0D6bS5A1Vd67+x2u9Htfify\nOE37vvdjaAYhKSt1nDeXOh24qIxlEelArd/vx3A4jE6nE1mWTapFHZxBPXguw/XGt90W7d3dyG+8\n8ez+k8EgOp/9bIxf8pLIb7nl2D/eeuqpaO3tTapdqT6vx81ymvb96eBU+351pccaZhGSUkrebMor\nBb5VfozKElrPaqPPsix6vV7s7OzUukq0LI8BAOUwestbYvSWt5zp/9H+/Oej+5u/GeNHH43Be95z\n7J/f/E//KTpXrkT/fe87UcgKlNdR7fspOC0ev2vfr6bRaHSgwhiK7BkAK1Rso+/3+9FqtaLX603a\n6B1MlZ+AF6Caxi9+cYxe/vIYv+IVJ/r50cWL0frrv45cuzo0SqvVik6nM7f6VPv++pykeGcwGESW\nZWe0RVSdkJTSqlMIIVRptvF4HP1+fxKOdrvdyLIsLly4cN3B1qLsUwBwTM99bgx//MdP/OP9H/ux\nyJ1YA9+mfb+a0vkYzGLPoJS8OZSXcO5oqQ0nBaNNaqOn/Kq+XAYAQNlp3y+vwWAQvV5v3ZtBSQlJ\ngcY5i6A3tdGnYFQbPWVkPwQAmqDMF4W176/XcDjUbs9cQlJKq27VinW7PcTkqm+xjb7X68XW1taJ\n2+ibxHOCRdlXgDJpf+EL0Xr66Ri9/vXr3hSgRrTvH99J1yTVbs889gzW4qgXs7q9mNft9jRVnucx\nHA4noWhqo9/Y2Ihz586t/HGucnDkOcGiLPEBlE32iU9EDIcxvvPOyJ///HVvDtAA2veXx+AmDiMk\nZaWa+kJcJ00LLKbb6NvtdmRZtvY2es8lAFid4gX+4X33Reub34z8llvWvFWcRqq+g6rTvn88w+HQ\nmqTMJSSllOoYxNXt9lTZUftXuvra7/cna9ZkWaaNnoio5+sTAIsb/Z2/s+5NADiS9v3ZVJJyGCEp\nK1eHF9bjEqqUW7GNvt/vR57n0ev1YnNzM7Isa+Q+CwAA1FNd2vdPco4tJOUwQlLgWOoU+O7v70/W\nF01t9OfOnYtOpyMYBQCAiirzdPuyq1r7vsFNLJM9g1KqUxCX1O32VNFoNJqsLRoR0e/3J+uLVnFN\nqirvU3V8jleF+x1otPE4ooLv+QDrVpf2/dFopJKUuYSksAKuYq5HaqNPwWixjf7KlStrmUi/LFXd\nbtbLfgM0WetrX4vsoYdi/OpXx/Ctb1335gDURpXa97XbcxghKVArqe2j2Ebf6/W00QMrpWIXyqe1\nvx+t0Sji6tV1bwpAY5StfV9IymGEpJSSVtzyKttjU7waORgMYjQaRbfbjV6vV9k2esqtbM8BysfF\nGCin8UteEvv//J9H3HDDujcFoPGW0b6fvnc0Gi1cfSok5TBCUlgBocpyTbfRR0RkWWYaPdSU109g\naW68cd1bQEkY7FNvjh2qbdH2/eFwGHmex/7+/sz2/S996UuR53lcvHgxtra2IiJiOBwKSZlLSMrK\nHScwdPBCktov+v1+DIfD6HQ6p5pGn/bDqu5fdQjeq779ALBU43HEcBjR6617S6AWqnqcz+Gm2/dH\no1Fsbm7ObN//1Kc+Fb/2a78Wjz/+eDzvec+LF73oRfG85z0vxuNxfPrTn44Xv/jF8cIXvlD3IRNC\nUkrJG1p5rSqcK7bR9/v9GI/Hkzb6nZ0db2QV5znOouwrQFNk//E/Rvvxx6P/z/5Z5N/1XeveHIBK\nmdW+//73vz/e//73x2AwiMceeyy+9KUvxWc+85n4whe+EA8++GB86UtfiqeeeiouXrwYL37xi+Ml\nL3lJvPjFL578/cMf/nD8yq/8SrTb7fi+7/u++NCHPhRXr16N+++/Px577LG444474uGHH44bvr2M\ny4MPPhgPPfRQdLvd+MVf/MV405vetLb7g5MRksIK1KHqbxXyPD8wdCni2Tb67e3t6Ha7whIAoJTa\nf/iH0X7ssRj+4A9GbGyc7JcMh89Wk45Gy904gJpa9Bw7y7K4ePFiXLx4MZ5++um455574h//438c\nERFXrlyJL3/5y/GlL30p/vRP/zQ+/elPxy/90i/Fo48+Gvv7+3HlypXo9Xpx//33x6/92q/FF7/4\nxbj33nvjAx/4QPz8z/98PPjgg/HBD34wvvjFL8bDDz8cjz76aDzxxBNx7733xpe//GXnsBUjJAXW\najweT9YWHQwG0e12I8uyOH/+/MKLbwMArFP3kUei9dRTMX7pS2P8kpec6HcM3vOeiP39iHPnlrx1\nAPV13PPF4XAY29vbk3+fO3cu7rnnnrjnnnsOfN+lS5fib//tvx1Xr16Ndrsdu7u7ceutt8aDDz4Y\nn/nMZyIi4l3vele84Q1viA9+8IPxG7/xG/H2t789ut1u3HHHHXHXXXfF5z73ufhbf+tvnf5GsjJC\nUkqr6mtGMtusNvosy1beRl+H6t6qbz+rV4f9HqCMBj/6o9F+4okY33XXyX9Jlj37sU6pinVq0vQB\neR6tJ56I/NZbIyx/BFTMYDCI3gJrP996663x/ve/P2677bbY3t6ON73pTXHvvffGX/7lX8bNN98c\nERG33HJLfP3rX4+IZ0PV173udQd+/tKlS2dzIzgz3tVYuSaGnnUKJk5yW/I8j36/H1evXo3Lly/H\nlStXIs/z2N7ejuc85zlx7ty52NjYsM7oMTTxeVQWdXo+A7Ac+cWLMXrDG6odGg4G0fu5n4vez/1c\nxLeXPZql8zu/E71f+IXo/vZvr3DjYHGO0zjMYDBYaLr95cuX45Of/GQ89thj8Rd/8Rdx9erV+NjH\nPnbdeZjzsnpRSUqpeYOrrlQtWmyj7/V6ceHChclC2jSXoBGAKmnEe9Z4HK0Ujo7Hc78tv+mmiG7X\ncClKT3jFLKPRaKGQ9JFHHok777wzbrzxxoiI+JEf+ZH47Gc/GzfffPOkmvTJJ5+M5z3veRHxbOXo\n4wsJFfoAACAASURBVI8/Pvn5J554Im699dazuRGcGSEpa7FIG703tWpJbfRpfdF1tdEDACzV1aux\n8clPRvtlL4t4+cvXvTVnZ2Mj9n/mZyZ/n2f86lfH/qtfvaKNApjvJMvzpQKeo9x2223xB3/wB7G3\ntxcbGxvxu7/7u/Ga17wmzp07F7/6q78a/+Jf/Iv48Ic/HD/0Qz8UERFvfetb453vfGe8733vi0uX\nLsVXvvKVeO1rX3ui28X6CElZuaaGn3WqQEi3JU2jT8Fou92uzDR6lYwAUE3d//Jfov2Nb0T/3e+O\nKAzfOIn2//k/0f3N34zhD/xAjF/60tnf86d/Gu0/+qNoP/VUDOsckkZEbG2tewtWxuwDaKZF2+1f\n+9rXxo/92I/FPffcE1mWxT333BPvfve745lnnom3ve1t8dBDD8Xtt98eDz/8cERE3H333fG2t70t\n7r777siyLH75l3/Za0wFCUkptbqEWHV6cczzPIbDYXzrW9+K4XA4aaPf2trSRr8GDvABaJr2k09G\nXL0asbs7OyTd3Y3sIx+J/Lu+K4Y/+qOH/q7WY49F65vfjNb//b8Rc0LS8cteFsM3vjE63/u94R0X\noNqGw+FCIWlExM/+7M/Gz/7szx743I033hiPPPLIzO9/4IEH4oEHHjj1NrI+QlJKS/BTDikUTWuL\njkaj6HQ6sbW1FVmWeZzWxP0OwMrt7UX2oQ9FfuFCDN/5zrVtRv8974nW/n7ETTfN/HrrypVoXboU\nraeeOvJ3jf7u3438jjtifOed87+p14v+G94QGxsb4XIwQLUtWklKMwlJgeuMx+MDQ5dSG/3Ozk7s\n7+9Hu92OXq+37s2kwqq83EEKqFXxrkaV9xWonb29aD31VLSuXYvI84h1vQaePx/5+fNzv5x/93fH\n4P+zd+dxcpV1vvg/Z+1aujvp7CH70tkJhGwIKmEngICC7C6ooKOIyk/x6nhn7nWuI3PvzOs6vNRx\nHIflXmfUqCyiV3BhDcgegkAkCdlICEnI0ul0uuuc8zzP74/KqVR3V1fXcqrqnFOf9+uVV5Lqquc8\n1XXq1Dmfep7v84lPZEebDtdP04ScO7cGnSSiRuE5WvOo5LUuZyQpNR+GpBRqcbkwjsJFvr8aveM4\nuQ8Oy7IGTaN3XTf0z6UUUXhNiILG/T48Sn0dGMpTqIwcCffTn4ay7cYFpCXS//IXmE89Be+CCyBO\nP73R3Ymew4eh79iRrdMa8teaiJpbJQs3MSSloTAkpdDixWBt5U+jdxwHSinYto1EIsFp9EREVWIY\nTXGlxoxpdBdKojo6oEwTauTIoe908CC0vj6oVApaJgM1blz9Ohhy1o9/DH3jRrjXXw/JleyJKEYY\nklIxDEmJmshQ0+hbW1thGAaD0YjxRwXydSOKFr5niWpPrlwJZ+XKovexf/ADaD09ULoOTSlkvvjF\nIeucho22YwcgBNSMGTVpXy5YAK27G2ry5Jq0T0TUKJxuT8UwJKXQitO00EY9F6UUpJRwHAeu6/ab\nRp9KpaDresXtEhFRYQxBiaJBTpsGbf9+IJkEjhwBUqlGd6k0mQzs734XUAqZv/kboEh91kqJVasg\nVq0KvF0iokbjSFIqhiEp1R0vHmvLn0bvB6MAYFlWYNPo4/L6xSmEjzKOhCUiCgEhYP34xwAA9/rr\nAaM51nD3rr220V2ojG1DLloEeF50gt0Q4jkIUfRV8j5mSErFMCQlioFC0+ht2+Y0egot7pNERCHi\nutD27Mn+2/PCH5IKAUgJNOtFrqbB/ehHG90LotBiAE7FMCSlYhiSUt2V+oEVp5F+QT8XpVRuNXrX\ndSGEgGmasG27qmn0FD1xeY9EDevBElEs9PRA27MHasYMOJ/8ZHYV85aWRvdqWNb3vw+tuxvOLbcA\nra2N7k5s6Rs3Qn/1VXgXXMARq0QUG/61M1Eh3DOIIkIp1W+0KJCdRp9MJmGaZt3CmjiF11HHgI4o\nmqSUkFIyaG8CYf+8NH/1K+hbt8K77DLIBQsa3Z2SaZ53fDQp1Yz5m99A274dctIkyGEWwSIiigqO\nJKViGJIShZg/jd5xHHieB8MwuBp9QBj2UrPifl9/+aP/HceBPBbsOI4DTdOg6/qgPzy+x0eYX0s1\nbRrU4cNQY8c2uitlcW6+OVsWIJkMrlEpYTz3HOSUKVCTJgXXboS5F18M47XXIBcvbnRXiIgKYk1S\nChpDUgqtOIZYwx3EC11IW5YF27aRTqc5jZ6IqhLmsKaYKH4W5C+i5zgOgOzo/1QqlfuZYRhQSuVG\nlvrHfyklNE3LBagAcvf3bycKgjjtNIjTTmt0N8pnWYHXI9X/8heYv/oV5PjxcL/whUE/j+JxqFqq\nsxNeZ2eju0FEFCjP8xiS0pAYkhLVwXDBqD+F3h9V5F9I13MaPVG9sa4nlSJK+4cffgoh0NPTA03T\nCi6i55dMGRiE5reTH576j3EcB0qpIUeeRul3RRQ2cto0iIULIYuEgnyPERFFH0eSUjEMSYkaQEoJ\nx3Fy4ahpmrAsC+3t7aGfZhnHEb5RxdeCqPH8L7r8Y7p/DE8kEkgkEhW1OTA8dRwHyWQy9573w1O/\nJItf39TfNqfuE1UgnYZ33XWN7gURBYBfwlMx/jkTUSEMSSm04hQA+c/j6NGj8DyP0+hDIE77F9Uf\n95/mppTq90WXYRiwbRvJZBKGYeDw4cMwDKMm29Y0DYZhDGp/YHjqeV7uNtY9JSIiIiIaHkNSohoZ\nOI3ex2n0RETRk7+Qnj9Nyy+NEoYvuoqFp8XqnuYHppy6T0TNhKMNiaKP72MKGkNSqrtyDmJRG6nl\nX3zmT6O3bRvt7e04fPgwkslkKC6mq8ERdETULPzSKI7jQAiRO6ZHaQZAqXVP80efsu4pUUzt3w/r\ngQcgTj0VcsGCRveGiIgodBiSUmhFIYzzV6P3Rxb50+hbWloidRFNRERZ/jHdcZzcMT2RSMCyrFgF\nhEOFp8DgqfsD654Wqn0ap98NUVwZL78M/cUXgb4+hqREREQFMCQlKlOhRTpKWY0+CqFvM4nD6xH1\n5xD1/kcVf+f9Ffqyy7btqkujRPn3XE7dUz88Zd1TogIOHIB28CDUrFnHb1MKcBygpaXu3RGnnQa4\nLuRJJ9V920REYRHlczSqPYakFGphOYD50+gdx4HnebladP4iHUREUcDAKkspBc/z+tWMDiIYjbtq\n657mj0Dl1P1o0/btg7lmDeSJJ0K8//2N7k5o2XfdBW3fPjif+QzU9OkAAPPnP4exbh2cm26CmjGj\nvh1KJiEuuKC+2yRqANapbB58rSloDEkptBp5sMu/gM6fRh/HKZfl4ug/Iooi/7juT6X3ZwG0trbC\nMIymPq5Xq9S6p0KI3L8BsO5phGn790M7eBDaW281uiuhJufMgWZZUKNGHb/RdbOjSYVoXMeIiJoY\nS+JRMQxJiY7x6675f/wL6HQ6HcgFNMNFovjg+zkaCpVH8RfTq+UsAIZ8WdXWPS00+pTCQc6bB/f6\n66HGjWt0V0LN+8AHBt92zTXwLrkEaGtrQI+IiIioGIakFGq1DiHy69BxGn1ziUvIFYfnQDSccvZz\nP3Dzy6MYhpGbSs+RA+FRat3T/PCUdU/DxZ8+Hgo9PTBeeAFi8WKgo6PRvSlO1wcHpMfKfsC2698f\nIiIiymFISqFVi4uegXXolFKwbZvT6MsQl3AxDqK+v3JfolKUsp9LKXPT6P0vvGzbRjqdZjAaMcOF\np/7fxeqeMjxtPsYzz8B44glo+/fD+9CH6rZd/ZVXoO3fD7FqFVDp/pbJwP5f/wvQNDi33QZYVqB9\npMJ4/kEUfXwfUy0wJKW6q/dFS6Fp9LZt170OHQMhIqLg5M8EEEJEqm502PsXRn54OlCxRaMA1j1t\nFnLxYmj79kGcckpdt2vedx+0TAaysxNq8uTyHtzbC/P3v4fIX/me6o7HAqLo4/uYgsSQlEKr0lDR\nv1jKv3g2TROWZXG6JRFRRCmlcgGY4ziQUnImAJW0aJT/d7G6pwxPo02NHQvv6qvrvl3vooug7d8P\ndcIJZT9Wf/VVGE89Be2tt7IjSDUtNKNIt2pb8a/2v+J94n24yLuo0d0hKhtXPKehcNASDYchKcVC\n/qrFrusCQKRGFVH9aZqWG2lE1EyidHLoB6NCCHR1dQFA7gsv0zR5bKchVbpoFKfu01AKHTvlsmUV\ntydPPBFi167savdHjgCjRlXTvUB1aV3IIIN92r5Gd4WIKFBCCK49QkUxJKXIGrg4R6Om0ZcqLtPt\n4/I84oCvRWNF8XcftuNiIYW+9AIQ2mM7RU+pi0ax7ikNFOjrnUhAdXTA/H//D+jrg/eRjwTXdpVO\nlidjrDMWY9XYwNo0nnoK2vbt8K64ggtUEVHDuK4LKySj9imcGJJSaA0MgPKnWuZPo+fiHETRFcWg\nEYhG2BglSqncl175taPb2togpURvby9Mk6csVFvFwtOh6p7mj1jl1H0ql5wyBTh6FGrcuEZ3ZZBJ\natLQP5QS2r59UOPHl9ye+dBDQFcXxPLlUJ2dAfSQiJpdJWUVXNflOSUVxb2DQk0plbtozp9Gn0wm\nOdWSKOL4/m1uA4/vhmHAtm0kk8l+IRXLYlCjlVL31J+uP7Duqf+Frz/jheEp5dPfeQdIpaDv2AHR\n6M6Uwbz3Xhhr18K7+mqIU08t6THuRz4CbdcuqNmza9w7IqKhua4Lm6PZqQiGpBQ6/jT6TCYDIQT6\n+vpgWRba2toiPbUtLlOj+TyIqFL5ZVL86U5cVI+iqpS6p57n9Rt5mh+ecup+c9L/8hdoW7dCnHsu\nxMknA11dkIsWNbpbZVFtbYCmQbW2lvwYOWcOMGdODXtFdBzP8WkoQghOt6eiGJJSw/jD4wutWGxZ\nFkzThJQS7e3tje4qERFVSEoJx3HgOA7LpORhIBZv/tR9IHtBlkwmAQyue+p5Xu421j1tDuavfw3t\n3Xehpk2DXLAAYvXqRnepbOLcc6G/+SaMRx+FnDcP4NRVCiEeO6kQf/YS0VD4iUZ15wejruvmFufQ\nNA22bfdbsdjzvH6LdhBRfxwNS2ElhMgFo/4XX4lEApZl8aKlQpXU3aLwqbTuaX5gyrqn0eZdeCG0\nbdsgo1yX03Whb9kCKAVkMoGFpNrevYCUUBMmBNJeMTymEkVfJddBXLiJhsOQlBoik8nA8zxYloX2\n9vam+TaHgRYRxZE/I8CfRi+lHPTFV7XtNzNeyMdfqXVP80ef+lP3a7polFIwfv97wDAgzj47mDar\nZDz1VLY/JdbCDBu5YAGwYEGju1GdlhY4X/kKICWQTgfTZiYD+/bbAaWQ+bu/A8qYyk9EzauShZtY\nk5SKYUhKDeFPOyt2UIvbKLm4XOT6zyPq38DHbf+Koii/BlHue1D99hej8UulAAg0GPVF+ThDVK1S\n6p76fwbWPR1q9GlZjhyB8cILgKZBvPe9QEtLQM+sNBISOvKee3c3jD/8AQCy9TwTibr2h44rZ2X7\nklgW5IwZ0FyXrysR1Yw/UItoKAxJiYiImkQQIzr9MimO40DXdViWhdbWVhiGwUAzT1RDdIqOYlP3\n88NTIUQuPC277mlbG7wPfhAwjLoHpA+YD+A1/TV8wv0EJqgJx/tzwQWApkUqSNP/8hfob74J75xz\n6v57jAxdh3vzzUBXF2ucElHNcLo9DYefQEREEcYghmrNryHtT6XXdR22bTdVqZRyMSymRqq27mn+\n6FNN0yDnz2/I8ziCI/DgoQ99/W6XK1c2pD/VMB96CNqePZBTp0KeeGKjuxNa5gMPwPjjH+Fef30k\nX2cKj6jPeKPacV0XJr+IoSK4d1BoRXk6ayFxez7UeDz5o1rxp+46jgPP82AYRm4qfTOvSE8UZaXW\nPfVHnkopAaD2dU+HcKV3JY7gCDrQUdPt1IN34YXQ3nwTcu7cgj83nn4aasQIyIULq9/YoUOAbQOp\nVPVt1Zmy7ewoYY7yogDwPDn+KgnDPc9jTVIqiiEpEZXND3yjfvLB0JroOCllbrSoPxXJtm2k02kG\no0QxNlx4mh+gDlX3dODo0yBYsGIRkAKAnDMHmDOn4M+0bdtgff/7UOPHI/NP/1Tdhg4dQss//ANU\nKgXnG9/IBo4RIi68MLtAWBklCbT9+6FGjOAUfSIqCafb03D4aUJETSnqAW9cMKhuLH+6reM4EELA\nsiy0tLSgtbWV75Ea475PYZc/WnS4uqf54WnZdU+bnL5lCzTHgQxilXjLgkoms6FhVH/fZQSk+vr1\nsH74Q8jly+F+/OO16xMRxQZDUhoOQ1IKrbhNT4/b8yGqFi+Y688fFdbb2wvHcSClhG3bSCQSsCyL\nrwkRlWRg3dMX9RexV9uLs72zYUgjF6IWq3saqvBUShh/+APUuHGQJ59c+D5KwfztbwFNg/AXjwpi\n04sXw7v4YohTT62+sXQazn/9r8f7JgS0vXuhJk6svu0QUqkUYBhQFQTMcZgRRUTlY0hKw2FISg3B\nwDDa+PqFA1+HxonS714pBSFEbkV6ALn6oqZp8iKRiKr2pPkketCDRXIRpmhTBv282KJRQOPqnvq0\nnTthPvkkVCIBp0BIqpQCenthPPUUNF2HOOOMwGp+qjFj4H70o4G0BaBfeGvedx+MZ56B++EPh28h\nJCmBKku5qM5OZL7znarbIaJoquRc3PM8hqRUFENSCj1+00u1EKWQi6hcSil4nperMQpkg1F/tGg6\niGmdRETHfMD9AN7V3sVkNbngz0tZNMr/u1jd01qFp2ryZHhnngk1duzQd0ql4F1zTe7fUaA6OrK1\nOkeMaHRX+tG2boV9xx0Qp54K76qrqmuMASkVwOvH5lHJwk1c3Z6K4d5BoRW3DzZN03IjJoiIgqaU\nyi265DgOdF2Hbdtoa2vLBQu9vb38coCIAjdLzcIsNavsxw0VngJ1rnuq6xBnnQUAeE5/Dq1oxQK5\nYNDd5KJFlbXfIOLss7MLISG7wJH5wAMQy5dDnnhiQ/ul9fYCngetu7uh/SCi5sPp9jQchqREREQR\npZTqtyK9YRiwbRvt7e2DFlqJMga7RDGlFLRdu6AmTBi0OvnAuqe5hxw6BOOhh+AtXAh3zpxA657u\n0/bhQetB6ErHf3P+GzTU4At7IWDddReg69nFhuo0ElLfsAH6668DUjY8JJULFsD527+FGjmyof0g\noubDkJSGw5CUQo/TJcKHU9XDI8qvA/ejyvgjqvxw1LIsWJaFVCpVcDRW1PH4TxRf+vPPw3z4YYil\nSyEuvLCkxxibN8N84w0YmQz0vLCvWN3T/BGrxabuj1aj8R7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4118Pz3VrNuVc37gR+oYN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LK/JwNPssZew9ne2Zzmn9brc/KunYCePgQ9vaz7bS+/HO+ss6CqqusNu5iS7y9ahPXuu5gTTsCb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\nQZy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68P3rs197eyGTSbJFrcV/5RXQmuCOO3BfeonU66/jXL5M+Mu/jL7jjuQ5UZRkDo+T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\nHxHIVP39qIEB3GPH+FR3G3hr0a0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XML292Joa1P79qP5+yGSwly+jd+7E3bcPurpQnkf8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Ry0M2fAAPj/7t+1aYsQBNEIJEmJViFJSiQaSnC1H855ydRW0zQxPDycqkFvmqHv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l3mySiRhy7bBu/IHsTgoGg0lbN3PNNiX+xCgWxOwhdJxU7y2SxTv4IYdbPoTN1QW9Y8cO\nLFy4EI2Njbjppptw6aWXumbNQgghDoAiKSGEmEk8Hsfq1atVt+mXX36JI488EjU1NaitrcXYsWMR\niURM2SBS/LIO7TRku1rqrSDZoA0/O3i8jBD6c2lDTjfblHVDZFINoctUcNe6oDmp3rvo1Q1gvuBu\nJ3JERK4u6G3btmHevHlYv349brvtNkyZMsXV3xtCCLEJiqSEEGIliqKgublZHQj10UcfIRAIYOzY\nsaitrUVtbS0GDRpk+kAoil/Z4YaWerNhJq430TrthNBvphOLblOSDdlkVGbigibeRJvD7eZ7jpkR\nERs3bsTcuXOxY8cO3HnnnTj77LMd//4JIcTBUCQlhJB8oigK2tvbsXz5ctTX1+ODDz7Av/71L5x4\n4omqaHrKKaeY0hqVTPzyigvDLOLxuOq0E2KSn9vTUolfnE7sXOx0QdNtSrLF6J7Tq1cv9QBQZDRy\nUj0R6N1zFEXpscZxUpu+LI7menC1evVq3HPPPejs7MTs2bNRW1tr8tUSQogvoUhKCCF2k0gksGHD\nBtTV1aG+vh7r169H37591VzTiooK9O7d27SBUEZtj17P/NIii0mJRIKZdklI5vyi4O4MnOq0o9uU\nZEN3dzei0Sji8bhaG04Wv4hzSBYpI3/k854Tj8dNy89tamrC3LlzUVxcjNmzZ2P06NEmXy0hhPga\niqSEEOI0FEXBzp071Rb95cuXIxaLYdSoUWq26eDBg01r0dcKGIFAwNPT0OWWeieJSW4iVc6g3wR3\nO5Ez7XId+JEP6DYlyUjmtEslfnnxeUVyJ1mGu1w7ZndHiGdkZ2dnzgexiqJg2bJlWLBgAcrLyzF7\n9mycdNJJpl0rIYQQFYqkhBDiBjo7O7Fq1Sq1RX/79u0YMmSIKpqedtppCIVCOf87RpsJ2b3jVkFR\nbL7lAQlOFpPchtFUa63zy4214zTykTeaT+g29TdCTOrq6srIaZdK/OJhDTEim1zcTF5biKOKouQ0\nXExRFLz22mtYuHAhTj31VMyYMQNDhgzJ+HUIIYSkDUVSQghxI4lEAl9++SXq6+tRX1+PNWvWoLCw\nEJWVlRg3bhyqq6vRt29f01r09dw7bsin1Do53C4muQntkI14PE4BI0fszBvNJ3Sb+gMrJtUbTUTn\nYQ1JhuiOMMrFTWf4pVzPgUAgpy6V7u5u/PWvf8VDDz2Eb37zm7j99ttRVlaW69s0nc7OTpx55pno\n6upCPB7HJZdcgv/8z/9Ea2srpk6dii1btmDYsGFYunQp+vbta/flEkJIOlAkJYTkh2uvvRavvPIK\nysrK8OmnnwIA7rrrLjz88MM48sgjAQC/+c1vcO655wIA5syZgyVLlqCgoAALFizApEmTAACrVq3C\ntGnTEI1GMXnyZMyfP9+eN+QwFEXB3r170djYiLq6OjQ2NmL//v0YOXKk6jY9/vjjTRGk3JBPyZZ6\nZ6IVMOLxONtl08CpeaP5JNlgn3QEDOIc8lnPqQ5rnH7QR+xDO/zSyKkcCARMq+euri489dRTePzx\nx3Heeefh5ptvxoABAyx4d+bR3t6O4uJidHd341vf+hbuvfde/OUvf8HAgQMxY8YM/Pa3v0Vrayvu\nvvtuuy+VEELSgSIpISQ/1NXVoaSkBFdddVUPkbS0tBS33XZbj7+7bt06XHHFFVi+fDm2b9+OCRMm\nYOPGjQgEAqipqcHChQtRVVWFyZMnY/r06TjnnHPseEuOJx6PY/Xq1airq0NDQwO++OILlJWVoba2\nFjU1NRg7diwikUjOG0NtPqW8CdW2y1oNW+rdRbJ2Wa1T2Y9o80bD4TAne/8fqdymfq8dJyIfXvXq\n1UsVk/KNla3WxNvoHfQBQCAQQCgUQigUyqp2Ojo68Pjjj+OZZ57BJZdcghtuuAGlpaVWvAXLaG9v\nx5lnnokHHngAP/rRj/Duu++irKwMLS0tGD9+PNavX2/3JRJCSDoY3sC5AieEmMoZZ5yBLVu2HPZ5\nvQOZF198EZdffjkKCgowbNgwDB8+HE1NTRg6dCj279+PqqoqAMBVV12FF154gSKpAQUFswK6WQAA\nIABJREFUBRg7dizGjh2Lm2++GYqioLm5GfX19XjppZdw1113IRgMYuzYsapwOmjQoIwX98KBEw6H\n1c/JGwnRhmWV60uvpb60tJRt3C5AHhIm6kd2fXV2dqK9vd13jkF5EnIoFEJJSQnrWUMgEEBBQUEP\nkU0W3P1aO05EiP1dXV0IhULo3bu3rYdXcu1EIpHDDvqi0ShzcYkuonbEM0ocXgnhXVs70WgUO3bs\nwPDhw3Vrft++fXjkkUfw8ssv4+qrr8a7776LwsJCG95Z9iQSCVRUVOAf//gHbrzxRlRVVWHHjh1q\nPMBRRx2Fr7/+2uarJISQ3KFISgjJCwsXLsQf//hHVFZW4p577kHfvn3R3NyMcePGqX+nvLwczc3N\nKCgowODBg9XPDx48GM3NzXZctisJBAIYPHgwpk6diqlTp0JRFLS3t2P58uWoq6vDE088gV27duHE\nE09EbW0tamtrcfLJJ2e1mZVdFUDPtrV4PI5oNAogN+eOtmUzHA7nnGdH7CcYDCIYDFpaO05EbxJy\ncXGx699XPtGrHdnxpa0duk2tRTup3qmHV0YHfdraURTFE0MMSXZonf1GYr9cOxs2bMC0adOwb98+\njB07FlVVVaiqqsLJJ5+Mp59+Gu+88w6uv/561NXVmTJ80w6CwSA++ugj7Nu3D//2b/+Gzz777LDf\nC/6eEEK8AEVSQojl/OxnP8P/+3//D4FAAP/xH/+B22+/HY888ojdl+UbAoEAevfujfHjx2P8+PEA\nDm4C1q9fj7q6OixcuBDr169Hv3791FzTysrKrIQb2TEokF1fwn2hFS/0NtTCnSpcScXFxWxB9jDp\n1o5bholpYd6oddBtmn/0xP6ioiLXfT+T1Y6ey52Zyt5FFkfTcfbLtVNbW4v169ejpaUFjY2NqK+v\nx+9+9zusXr0a/fv3x9lnn439+/fj448/xujRo3sI9W6jT58+GD9+PF5//XWUlZWpbtKWlhZ19gAh\nhLgZ7jYJIZYzaNAg9f9fd911mDJlCoCDztFt27ap/2379u0oLy83/Dwxj2AwiJEjR2LkyJH4yU9+\nAkVRsHPnTtTX1+Odd97B7373O8RiMYwePVoVTsvLy7PaFCZzfcViMdX1JYQLAIjFYmypJ2nXjpOG\niWnRupIo9ucHuk2twYpJ9U4jmctdHN7ZlcdNzEfrhM4l9qSzsxPLli3Dxo0b8atf/QrnnHMONmzY\ngMbGRnz44Yd45JFHsGnTJowaNUqNP6qpqcGwYcMc/Tu0c+dOhEIh9O3bFx0dHXjzzTcxa9YsXHjh\nhXjssccwc+ZMPP7447joootM/7fFwTohhOQLDm4ihJjO5s2bMWXKFKxevRoA0NLSgqOOOgoAMG/e\nPCxfvhxPPvkk1q5diyuvvBKNjY1obm7GxIkT1cFNtbW1uPfee1FVVYXzzz8ft9xyC84991w735bv\niEajWLVqFerr6/HBBx+gubkZQ4YMUVv0Tz31VFPaxoR4ITYpAg71IanQZgyKj3ScylYTj8fVnF4x\nXIwiirPQTkMXTmW6TQ+HTuie6N13APQQTZ12YEN6ImeLhsPhnAZcbtiwAXPnzsXOnTsxY8YMjB8/\n3vC12trasGLFCjQ2NqriaXd3N5555hm128dprF69GldffTUSiQQSiQSmTp2Kf//3f8fu3btx2WWX\nYdu2bRg6dCiWLl2Kfv36WXIN+/btQ58+fSx5bUKIL+F0e0JIfrjiiiuwbNky7Nq1C2VlZbjrrrvw\nzjvv4OOPP0YwGMSwYcOwaNEiNeh9zpw5WLx4MUKhEBYsWIBJkyYBAFauXIlp06YhGo1i8uTJWLBg\ngZ1vi+CgoPDll1+irq4ODQ0NWL16NYqKilBVVYXa2lpUV1ejb9++GW0yhCtHnuotHANiA0rxgmSC\nEN1lAQPIj3ih14IcDodZpy5B6xiMx+O+z6d0yqR6pyNqRxbdhdtUWzt+qh8nIg/M+//s3Xlc1XXa\n//HXOSwHENxAUVyATFFcWGQ5mJY26l1Wtk3qaGWNluU0amqK1WROU2rdqJhbZbRMTjN4l2WLTXc2\nkpzD5pqG5r7gghsiopz1+/vD+/v9HRBM4SDb9Xw8fDRz2D5f+HAO532uz3XV9D5627ZtpKSk4HQ6\nmTVrFgkJCTf8ORRFoaCggBYtWjT5ELCkpISMjAzi4+O15wnfffcdK1aswGAwMHbsWG6//Xb8/f3r\neKVCiEZAQlIhhBDupSgKxcXFZGdnYzKZyMnJ4eLFi0RGRmpH9MPDw6+qnnM4HHz//fcsX76c8ePH\nM3ToULy9va9ZZVdZeAH1+5i1qB9+K7xwDd1r8jXUIEmn0zX5KrvGpKlWm7r2hPb29i73Apa4PlW9\nYCOPWzef6wtYiqLUuE1EdnY2CxYsoHnz5syaNYvevXu7ecVNi9PpRK/Xc+DAAQ4cOMDgwYOBKyfR\nnnjiCf70pz9RXFzMDz/8QO/evZk2bVodr1gI0QhISCqEEKL22e12fv75Z63a9ODBg7Rr1w6j0Ujv\n3r3ZuXMn77//PgaDgWeffZaRI0fi5+dXra/lGppWPGbtjuBLNF4Vwwu73V6twSwV+416e3tLlV0j\nV/EFG4fDgdPpbDTVpq5tItRwVO5H3aOq9iCuw+gaa+heV9Tvt9qDuCbhqKIo/Pjjj6SmphIWFkZy\ncjK33nqru5fcpCiKws8//4yPjw8REREAHD58mO+++46HH36Yn376idTUVDIyMrDb7WzatIk33niD\nDz/8kNatW9fx6oUQDVyVDwTyl7wQQgi38fT0JDY2ltjYWCZNmoSiKGRmZjJv3jz++te/EhkZSY8e\nPejSpQtBQUGUlpZWeyKyXq8vNyHWNfiyWq04HI5yE9NlIrFQVZxo7Vptqu6fioNZXIOviv1GazLo\nQzQsrvcpqmtNQ28I1aaNZVJ9faceta/scUsGirmX64AxnU6Hj49Ptb+PTqeTb7/9lqVLlxIVFcUH\nH3xAp06damHVTY9Op2PHjh38+uuveHp6YjAY6N+/P+vWraN79+7Exsai0+k4evQonTp1wt/fn+bN\nm3Py5EkJSYUQtUZCUiGEEG7nWnFhNpsZN24cy5Yto3Pnzly6dInc3FxMJhMff/wxZ8+epWvXrtqk\n14iIiGod67ze4KuxVHwJ93ENvtQAo7LgS90riqLUeAqyaDyuNQ3dNXysb/c9TWFSfX1X8XELKPe4\n1RBD97pUccCYr69vtVsa2O12PvvsM1auXMntt9/O6tWradu2bS2suulQ7xtd/8bz9/cnJSWFuLg4\n5s+fT1JSEt9++y1ms5n27dtz2223MWfOHFauXMmpU6e4fPky3bt3r8OrEEI0dnLcXgghhNuUlpby\nySefsHjxYvR6PZMmTWLMmDHXPFLvdDrZtWuXdkT/119/pVWrVlpf0759++Ln5+eWJ4RV9ResWC0o\nTz6FyvVJtxqmAuX64rqGF7J3RGXqU29TmVTfsFTV4qFib9Om/IKN656u6YAxq9XKP/7xDz7++GPu\nuece/vznP9faxPamqqysjK+//pp77rmHoqIiUlJSaNu2Lc899xzNmjXDbDbzzjvv8NRTT9GtWzd+\n//vf06pVK/bt28dLL73E6NGjURRF7rOEEDUhPUmFEELULkVR6NOnD126dGHSpEkMGjSo2n2/Tp8+\njclkwmw2s2nTJmw2G9HR0SQkJJCUlERISIhb/jiueNRRBmsIVWVTvSvuBdeKr8r6C0roLqpSF71N\nXfe09NBt2FxD98r6KjeVx66Ke1q9n66OS5cu8eGHH7J69WpGjhzJ008/LVPUa8jpdJZ7DLRarSxc\nuJCPP/6Y6OhoQkNDefDBB2nXrh0zZ87k0UcfZdiwYQC88MIL+Pr6MnPmTHx8fNixYwfR0dF1eTlC\niMZFQlIhhBC17+LFi7XypKKsrIzNmzdjNpsxm80cP36c0NBQjEYjRqORXr16ueXJfsXBGq6T0KVi\np2lQj9er/UZvZKq3hO6iJmqr2lSdVG+z2W54T4uGoWJ7Gddq08Y4zLDi0LyahKPFxcW89957fPPN\nNzz55JM8+eSTGAwGN6+4aTtz5gxffvklnTt35vjx4zz22GPk5OQwYcIE4uPjeeedd3j99dfx8vLi\nmWeeASA3N5f//Oc/TJs2rVybA3VIpxBC1JCEpEIIIRoPp9PJgQMHtCP6O3fuxM/Pj/j4eIxGIwkJ\nCTRv3txt1aau1V6uFTvSH65xqGxwjbe3d41/plVNs644lKWxBBfCvWpabaruaYfDIZPqmyDXYYbq\n/oGG/aKNazjq5eWFwWCo9p4+c+YMy5YtIyMjg2effZbRo0dLZbUbKYpCaWkpb731FsXFxQQFBTF9\n+nR0Oh0vv/wyGzdu5OGHH+aXX35h9OjR3HrrrSxevJi1a9fSs2dPVq1aRfPmzev6MoQQjZeEpEII\nIRovRVEoLi4mKysLk8lETk4OpaWl9OzZUzuiHxYW5paAoGJwIb0pGy61j53VatWmTtf24Jqqqk1d\nQwvZP6Iqv1VtqtfrtSPI7gz8RcN3PS/a1NcX/Vwr/Gsa+J84cYLU1FS2bdvG5MmTefDBB+XFg1rU\nvn177r77btLS0gD49ddfefnll1m9ejUAAwYMICQkhI8//piysjL27t1LXFyc9vFOp1N+PkKI2iAh\nqRBCiKbFbrezfft2TCYTJpOJw4cP065dO4xGI4mJiURHR7vtSF1lvSmlWrD+up5+ozeL6zHZii0e\nGuMxWeFeri/a2Gw2LXRXe+PK/hHXcq0WIfXhRb+K1dAGg6Haazl48CCLFi3i4MGDTJs2jbvuuqve\nhcENzbWOvtvtdjw9PUlNTeWbb77h+++/B+DChQu0bNmSLVu2sGXLFtavX0+fPn0YN24cQUFB2sdL\nOCqEqGUSkgohhGjaFEWhoKBAC023bt2Kp6cnffv2JTExEaPRSGBgYK0OhHId6FMfq3UaO7UayWaz\nadVI9bG3WcVjshWHssj+EarKJtWr1aTu7m0qmoaqBtLdrP2j3v+p4WhNq6F37dpFSkoKxcXFzJw5\nk9tvv93NKxZWqxVvb+9yt6nT5+12Oz169GDVqlUkJCQAsHz5cr7//nsuXrzIokWL6NmzZ10sWwjR\ntElIKoQQQrhS+2Xl5uZiMpnIysri3LlzdOvWTQtNIyIi3HpEv7KhGhUnoQv3qthvtCH2ZpT9Iyq6\nkUn119PbVKpNRVVu1v5xva9WFAWDwVCj9idbt24lJSUFnU7HrFmzyh3hFu7x1VdfkZaWRmRkJPfc\ncw/9+vUr93a10vTVV18lPz+f9PR04MrP+tKlSzRr1kx7X6kcFULcZBKSCiGEEL/F4XCwa9curdp0\nz549tG7dmsTERBITE4mLi8PX19ftA6Gk2sv9FEXBZrNhsVgAavyEu76prUnoon5TJ9WrlVvVrYa+\n1v6RamXxW9y5f9x9X20ymVi4cCGtW7dm1qxZ9bpK0el0EhcXR8eOHVm7di1FRUWMHDmSw4cPExYW\nRnp6Oi1atKjrZVYaYP7nP/9hyZIlJCcnk5uby2effUZqaiq9e/fW3ketJr1w4QK9evUiKyuLDh06\nlHu7+oKfEELcZBKSCiGEEDdKURROnz6thaabNm3C4XAQFRVFYmIiSUlJtG/f3m2haVUDoRrqJOK6\nUJ/6jd5MMlCscavtSfVVVQtWvP+RSi9RmWtVu1fVW9k1HNXpdBgMhmpXxCuKwg8//MDixYvp0qUL\nycnJ3HLLLe68xFqxcOFCNm/ezIULF1i7di0zZ84kMDCQGTNmMH/+fIqKipg3b15dL1Pz448/EhAQ\nQHx8PK+++irt27fHz8+PN998k/vvv59Zs2aVqw6F/19NWlJSQkBAQB2tXAghriIhqRBCCOEOZWVl\nbNq0CbPZjNls5sSJE4SFhWmhac+ePas89nqjKoYWrgOhZCBLeQ2l3+jNVFVvwYqhlwSn9VPFVhE3\ne1K9VJuKmnA9LaH+A7T7ZbvdjoeHBz4+PtV+AcfpdPLVV1+xfPly+vbty/Tp08tVKtZnBQUFPPnk\nk7z00kssWLCAtWvX0r17dzIyMggODubkyZMMHDiQ3bt31/VSeeedd/jf//1ftm7dSvfu3fnmm29Y\nvHgxL7/8Ms8++yyTJ08mJCSEy5cv43A48Pf3B678fHQ6nfazlSP1Qoh6pMoHHfc8ixNCCCGaCB8f\nH/r370///v2BK3/079+/H5PJRFpaGr/88gt+fn7Ex8eTlJREQkICAQEB1XoCqNfryw1DcB3oY7Va\ncTgc6HS6JhtaqE/C1e+Ft7c3AQEB8iTs/+j1evR6PV5eXsDVA8XKysoAqVaub+pLq4jK9o/rCzdW\nq1WqTUWVdDodXl5e2v5xOp2UlZVhs9nQ6XTo9XocDgdlZWXlHsNcQ7Wq2O12Vq9ezfvvv8+gQYP4\n7LPPaNOmzc24LLd5/vnneeuttyguLtZuKywsJDg4GIB27dpx6tSpm7aeqo6+b9iwgQ8++IC///3v\nnDp1ikcffZSsrCwiIyO57777GDVqFCEhIeTl5bFq1SqeeuoprcWBel+Qk5PDr7/+ygMPPEDz5s1v\n2jUJIUR1SEgqhBBC1IBer6dr16507dqVJ554AkVROH/+PNnZ2WRmZvL2229TWlpKr169SEhIICkp\nidDQ0GoFCTqdDk9PT61SteIRRzW0cA0sGuNAn8pCJD8/v0Z3ne5W2f5xrfay2WzlqpVdg3dR+1wn\n1asVdvXp99f1BRmVa7Wp2g5Aqk2Fq4pDxvz9/bU95PrCjc1mo6ysjKKiIqZOnUp8fLzWC1w9pm2x\nWFi1ahWffPIJw4cPZ926dfWiZ+eN+uabbwgODiY6OpoNGzZU+X438/dG/f0uLS3l+++/Z8iQIfj7\n+3P06FH69eun/Z0zYsQIVq9ezauvvsq+ffuYOHEigYGBFBYWMnr06HI9YH/66SeWLFlCSUkJqamp\nEpAKIRoEOW4vhBBC1DKbzca2bdswm82YTCYOHz5MSEgIiYmJGI1GoqKiMBgMbvlaVR2RrTgFvSGG\nFk213+jNVLHaVD0iW3GStXzP3adiiKTu64ZIepsKldPp1FqgeHl5XVcLFEVRuHjxIuvWrSMnJ4dN\nmzbxyy+/EB4eTseOHdm7dy8jR47kpZde0o50N0Qvvvgin3zyCZ6enly+fJmSkhIefPBBNm3axIYN\nG7Tj9oMGDWLXrl21soaKR+FtNhuvvvoq69atIy4uDqfTyZ///Gf27t1LRkYGr776KoGBgWzbto1+\n/fqRmZlJbGwsJ0+eZPv27fzXf/2X9rnPnz/P008/jZeXF3/961/p0qVLrVyDEELUgPQkFUIIIeoL\nRVE4evSoNhBq+/bteHp60rdvXxITE0lMTCQwMNBtA6EqC70a0hFr6Tdad1yrldU99FsDWcT1qbiv\nDQZDo/w+Sm/TpkVtxaCGozXZ1+fPn2fFihXk5eURHBzM5cuXycvLo6ioSOsDbjQaSUhIoFWrVm6+\nkpsjIyODlJQU1q5dy4wZMwgMDGTmzJm1NrhJnTivUvuEnjhxgm+++YbHHnuMrVu3Mn78eGJjY1m5\nciWPPPIIQ4cO5dlnn2X9+vVMmTKFJ554ghdeeEE7fQBov9s6nY5z587RunVrt65dCCHcSEJSIYQQ\n1TNu3Di+/vprgoOD+fnnnwEoKipi5MiRHD58mLCwMNLT07Ujb3PnziUtLQ1PT09SU1MZOnQoAFu2\nbOGJJ56grKyMYcOGsWjRojq7pvpGURRKS0vJzc0lMzOTrKwsioqKiIiI0KpNu3Xr5pYApeIRa9fQ\nqz5VelXWb9TdE71F9bj2xlX/K6HX9avtSfX13bUmoden+yBxY9TQ326313hfnz59miVLlmA2m5k4\ncSIjR44sNxCxsLCQnJwcsrKyyM7OZtOmTXTs2BGj0YjRaGTIkCENYro9lA9Jz507x4gRIzh69Cih\noaGkp6fTsmVLt3wd9Tm/er9cWFjIn//8Z9q3b8+oUaNISkri4sWL/OUvfyE7O5vnn3+eV155hfT0\ndKxWK++//z4bN24kKiqKAQMGsHLlSjZt2uSWtQkhRB2QkFQIIUT1ZGZm4u/vz+OPP66FpDNnziQw\nMJAZM2aUq3bIz89nzJgx5OXlUVBQwODBg9m7dy86nY7ExESWLFlCfHw8w4YNY/LkyeWOZ4nyHA4H\n+fn5WrXp3r17ad26tRaa9u3bF19fX7dVm7oej3UNvVwrBW9G6FVfhtaI63c9oVd96q1ZF+p6Un19\n51ptKsF7w+Ia+td0Xx87dozU1FR27NjBlClTuP/++68raLXb7fzyyy9kZ2eTnZ3NwIEDGTt2bLXW\n0Bi5Vo/u3r2bU6dOUVBQwP79++nQoQMrV65kzZo1eHh4MHPmTN58800CAwO5/fbb8fPz47vvvsNq\ntXL69Glat27NG2+8QUBAADNmzKjjKxNCiGqTkFQIIUT1HT58mPvuu08LSbt3705GRobWN2vgwIHs\n3r2befPmodPpmDlzJgB33303r776KqGhodx5553k5+cD8M9//pOMjAyWL19eZ9fU0CiKwqlTp7TQ\ndPPmzTgcDqKiorTqmfbt27stNHXtK2i32wGuOmLtzsCiYr9Rb2/vJh+sNWTXOmJ9s4P3uiShf/VI\ntWn9plaTl5WVuSX0379/PwsXLqSgoIDp06czZMgQ+R2podWrV3Ps2DGmTJkCwC+//MJnn31Geno6\nPj4+tG3blrVr1+Lp6cnTTz9Nx44defzxxxk7dix/+9vf2LZtG4cPH6awsJDly5fTrFkz1qxZw/Tp\n07n//vt59dVXG+TQLCGE+D9VPsjIdHshhBA37NSpUwQHBwPQrl07Tp06BVypAklKStLer0OHDhw7\ndgxPT086duyo3d6xY0eOHTt2cxfdwOl0OoKDg3nooYd46KGHUBSFsrIyNm/ejMlkYvr06Zw4cYLw\n8HCtV1tkZGS5I4o38rWqmmJtt9spKytz2xR0tX+d1WrFy8uLZs2aSb/RRkCv16PX6/Hy8gLKB+/q\nHoLaDd7rUsVJ9b6+vo3q+mqb632Qt7c3UD54t1qtUm1aB1wrohVFqXHo/8svv7BgwQIuXrxIcnIy\nt912m5tX3LSUlpbi6+uLXq9n4MCBNGvWDKfTSVlZGX/4wx/o2bMnO3fu5PPPP2f16tXs3LmT6Oho\nnn76aSZMmEBycjLPPPMMr732Gnq9nhUrVhAWFqZ9/kGDBvHzzz836KFZQgjxWyQkFUIIUWPypPTm\n0+l0+Pr60r9/f/r37w9cCRH27duHyWRi5cqV5Ofn06xZM+Lj40lKSiI+Pp6AgIBq/bwqC73UwMJm\ns2mhl+vx6qoCC/VjXfsyBgQESFVYI3a9wbter7+qUrAh3b9UnFQvob/7VBW8q3vIarVKtWktqVgR\n7ePjU6NK/82bN7NgwQK8vLyYNWsWMTEx7lxuk3PkyBHmz5/P5s2biY+PZ9asWYSEhDB79mxKSkpY\nsGABv//97/niiy8AGDp0KOvWrWPTpk1069aNuLg4goODef3115kzZw733nsvAQEB2udXXxRtqMOx\nhBDiRkhIKoQQ4oYFBwdTWFioHbdv27YtcKVy9OjRo9r7FRQU0KFDhypvF+6l1+vp1q0b3bp148kn\nn0RRFIqKisjOziYzM5PU1FQuXbpEr169tGrTzp07VytE0Ol0eHp64unpicFguO7AQq1CgitHj/38\n/BpUCCbc51rBe8VqU9c9VB/3i+vQGi8vL/z9/SWcq2WVVZu69leWatOacw1HdTpdjcJRRVHIzMxk\n0aJFtGnThjfeeIMePXrUwqqblgMHDjBhwgQSExP59ttvmTZtGhMnTuSLL75g6NChjBs3jjlz5vDc\nc8/x5ZdfkpmZSf/+/bn99tvZuHEjsbGxxMbG8vbbb9OsWTMAAgICtMf0ii9uCSFEYyc9SYUQQvym\nQ4cOcd9997Fjxw7gyuCm1q1bM3PmzEoHN+Xk5HDs2DGGDBmiDW4yGo0sXryY+Ph47rnnHiZNmsRd\nd91Vx1fW9NhsNrZt24bJZMJsNnPkyBFCQkK0gVBRUVFa4FBTamBht9ux2WzadF0PDw+8vLwksBC/\nybU3rvrPHW0e3KWpT6qv76S3afVUbBdhMBiq/QKFoij8+9//ZsmSJURERDBjxgzCw8NrYdVNy1//\n+lfat2/PU089RXZ2NkajEYDjx48zePBgsrKyaNGiBXfffTd33HEHycnJzJ07l61bt5Kens758+eZ\nOnUqzz77LPHx8XV8NUIIcdPJ4CYhhBDVM3r0aDZs2MDZs2cJDg5mzpw5PPDAAzzyyCMcPXqU0NBQ\n0tPTadmyJQBz587l/fffx8vLi9TUVIYOHQpcOV73xBNPUFZWxrBhw0hNTa3LyxL/R1EUjhw5ooWm\n27dvx9PTk759+2I0GklMTKR169bVenJcsd+oOtijsoFQ9b1SUNQPFatNHQ4HQLn9U9t7SCbVN2yu\n1abqPpJq0ysURcFisWgD9AwGQ7X6WsOV+/+1a9eyYsUKEhISmD59Ou3bt3fzipsW1yn1q1atYtmy\nZZhMpnK3//jjj3z00Ud89NFHAGzcuJGJEyeyadMmzp49S1xcHF9//TWxsbHYbDatkl8IIZoYCUmF\nEEII8dsUReHixYvk5uaSmZlJdnY258+fJyIiQqs27dq1a5WVV06nk4yMDPbt28fIkSN/s7ruWpWC\nrlPQhaiMa6WgGni5HhF15x6SSfWNk1SbXt1LV60crQ6bzca//vUvPvjgA4YMGcLkyZMJDAx084qb\nHvWxUWWxWBg2bBjjxo1j9OjRWK1WvL29+fDDD9myZQuLFy/W3jcpKYnhw4cza9YsfvrpJ/r166e1\nO3E6nY16bwshRBUkJBVCCCFE9TgcDvLz88nMzMRsNrN3714CAwO10LRv377odDpWr17N0qVLKSsr\nY+rUqTz22GM3HCCplYKuwalr78GmXOUlrk/FPVTTSkF3Hj0WDUNTqTZ1Op1YLBbXcJ8lAAAgAElE\nQVStotBgMFQ7MCsrK+Pvf/87//jHP3jwwQeZOHEizZs3d/OKmxan0wmg/UxKSkpYv349vXr14tZb\nb+XDDz/kgw8+ICMjQ/uYAQMGsHjxYmJiYtizZw/dunXjxx9/5OTJk4wePbpOrkMIIeohCUmFEEII\n4R6KolBYWIjJZGL9+vWsW7eOoqIiunbtyn333cfo0aNp3769WwKEqqq8XCu8ajJlWTR+11MpWNke\ncg2QalpdJxq2xlZtWnHQWE3C0YsXL5KWlsaaNWsYM2YM48ePx8/Pz80rblqcTic6nU67T7JarRQW\nFjJs2DC6du1Kfn4+eXl5+Pr6MmjQIKZNm8YDDzzA/v37ef7553nyySdZuXIlFouFL774An9//zq+\nIiGEqHckJBVCCCGE++zdu5fU1FT+8Y9/MHz4cJ555hlsNhsmk4msrCxOnjxJeHg4RqMRo9FIZGRk\ntXvbVVTxeLXD4UCv118VeElwKqpS1R5Sgy71dm9v7xoFSKLxaojVpq7haE0HjRUVFbFixQr+93//\nl6eeeorHHnvMbUP/mqqKR+qLi4t58cUXycvLY/Dgwdx9990MGDCAESNGEBoayltvvcU777zD559/\nzr///W8yMjIYNGgQAwcO5Nlnn+WRRx6pw6sRQoh6TUJSIYQQQtSMoihs3LiRBQsWYDKZmDBhAn/6\n058qHcbhdDrZt2+fdkQ/Pz8ff39/4uPjSUpKIi4ujoCAALdVm1Y2zEcGQonrpR6pt1qt2hFXoFzw\nLntIXEt9rjZVB405HI4aDxorLCxk6dKlZGVl8dxzzzFixAipsK6hkpISNmzYwF133aUNUnr11Vc5\nePAg0dHRhIeH89Zbb/Hggw8yffp08vPzGT58OFlZWfj5+REbG8vy5cvp2LEjx48fZ+DAgdrnrhi8\nCiGEACQkFUIIIURN5ObmMnHiREpKSnj++ed5/PHHb+hIpaIoFBUVkZWVhclkIjc3l8uXL9OrVy+t\nt2nnzp3dNmDHtcrLdZhPXYcVon5xnVSvKIpWXafT6a4KvCpWLKt7SIJTUZXKqk11Ol258L229pD6\n4lFZWRlOp7PG4ejRo0dZtGgRu3btYurUqdx3332y92vIbrdrJyz++Mc/EhISwrZt23juuec4dOgQ\nb731FpmZmbRv3560tDS2bdvGK6+8QlBQEA899BDh4eGkpKTwr3/9i8jISHr37q19bglHhRDimiQk\nFUIIIUT1HThwgPz8fIYNG+a2cNFms7F161ZMJhNms5kjR47QoUMH7Yh+nz593HZ881pHY10noMuT\n/qbBdVK9TqfDYDD8Zm9b14pldQ+BVCyL63etatOK90U1+Rquwb/BYMDLy6va+3Lfvn0sWLCAEydO\n8MILL/C73/1O9ngNvf322/zud78jMjISuNJz9PXXX+fNN99k+vTpvPbaawD06NGD9957j/79+7Np\n0yZWrVpFdHQ0Y8eOJScnh3fffZf33ntPXvATQogbJyGpEEIIIeo3RVE4fPiwFppu374db29v+vbt\ni9FoJCEhgdatW7t1IJRrtSlwVVghYUDj4u5J9a57SP2n7iHXSkEhqvJb1abXG767Bv8APj4+NRpq\nt2PHDlJSUrBYLCQnJ5OUlFStz3MzhIWF0aJFC/R6PV5eXuTm5lJUVMTIkSM5fPgwYWFhpKen06JF\ni7peKgATJ07k8uXLzJ49m2eeeQaLxcLYsWP59NNPmTZtGoMHD0av1/P666+Tl5fHF198QWlpKStW\nrGD37t2kpqZedZJDURR5vBJCiOsnIakQQgghGhZFUbh48SI5OTmYTCays7M5f/48ERERGI1GEhMT\n6dq1q9tCqMqOV0vg1TjcrEn1VfXHvdHASzRdN1ptWp2q6GvJy8tjwYIF+Pj48OKLLxIVFeXOy6sV\nt9xyC5s3b6ZVq1babTNnziQwMJAZM2Ywf/58ioqKmDdvXh2u8v87ePAgo0aNomvXrjz22GP88ssv\nlJaWcuDAAQBmz55NWFgYVquVbt26sWrVKm677Tb2799P8+bNadOmjRaKyrF6IYSoFglJhRBCCNHw\nORwOfvnlF20g1L59+wgKCtL6msbGxuLj41OrA6FuRj9B4R6u07y9vLxu+qR618CrYn9cdx2vFo1f\nxWpT9b5I7Z2rVkWrQ3+q8/kzMjJITU0lJCSE5ORkIiIi3HkJtSo8PJxNmzYRGBio3da9e3cyMjII\nDg7m5MmTDBw4kN27d9fhKst75pln2L59O1lZWZw+fZqPPvqI0tJS1q9fz4svvkhYWBhBQUGsXLmS\nVq1aMWHCBO1jpWpUCCFqTEJSIYQQQjQ+iqJw8uRJ7Yj+5s2bURSF6OhorbdpcHCwW4/oX2t6dU0q\nuIR7qOG2Os1bHcZUX4JIdX2urR7U/rgSvovfoigKZWVlWK1WrSrZ6XRWK3xXFIXvvvuOt99+m549\nezJjxgxCQ0Nv0pW4zy233ELLli3x8PBgwoQJjB8/nlatWlFUVKS9T+vWrTl37lwdrrK8/Px8Hnjg\nAX766SfatWvH559/zt69ezlw4AC+vr588MEHvPbaa0yaNKmulyqEEI2RhKRCCCHqJ/VxSAIB4Q5q\ngJCXl4fZbMZsNlNYWMgtt9yihaaRkZFuO57oWuGl/lcGQtWNa02qr8+uJ3yXalPhdDqxWq1YrdZK\nW0ZUDN8dDgfjxo3DYDCQkJBAYmIi0dHRGAwGHA4Ha9as4d1336Vfv35MnTqVdu3a1eHV1cyJEydo\n3749p0+fZujQoSxevJj777+/XCgaGBjI2bNn63CVV3vmmWfw9/fnv//7vzl27BipqakEBgbyhz/8\ngcDAQJo1awbI30lCCFELJCQVQgjRMDidTgAJBITbOJ1O9u7di8lkwmQykZ+fT0BAAAkJCRiNRuLj\n4/H396/1gVAyAb12uLsnY31Q8Yi+hO9Nl2s/3RtpGaEoCr/++ivZ2dnk5OSQl5fH4cOH6datG4WF\nhQwYMIDZs2drE9Ybizlz5uDv78/KlSvZsGGDdtx+0KBB7Nq1q66XV862bdt4+OGHycnJISgoiF9/\n/bVcn231915+z4UQwu0kJBVCCFH/nDhxgoyMDIxGI2FhYXW9HNFEKIrCuXPnyMrKwmQykZubS1lZ\nGb1799Z6m3bu3NltT0yvNQFdelJWn6IoWCwW7dhxTSfV12cVw3eHw4GiKFfto8Z47U2Vaz/dmraM\nuHz5Mh9//DGfffYZRqMRX19ftmzZQnZ2Nv7+/iQlJWn/oqOj8fb2dvPV1J5Lly7hdDrx9/entLSU\noUOHMnv2bNavX0/r1q2ZOXNmvRvc5Or5559n1KhRJCYmardJz1EhhKh1EpIKIYSof9LS0pgwYQJj\nxozh0KFDvPTSS/z444/ceeedDBgwAB8fn6s+RlEUFEWRUEm4lc1mY8uWLdoR/SNHjtCxY0eMRiOJ\niYn06dPHbcFBZcdidTqd9KS8TjdrUn19V/GIvlp1VvGIvuyjhqViOGowGKr9MywpKWHlypWsXbuW\nxx9/nD/+8Y/4+vpqb1cUhb1795KVlaX927dvH9HR0SQlJdG/f38eeOABd11arTh48CAPPvggOp0O\nu93OmDFjSE5O5ty5c4wYMYKjR48SGhpKeno6LVu2rOvlCiGEqB8kJBVCCFH/jB8/nrKyMj755BOW\nLl3KsWPH6NWrF2vWrKFPnz785S9/QVEUCgoKsFqtdOnSpa6XLJoIRVE4fPgwmZmZmM1mtm/fjsFg\noG/fvlpw2qpVq1odCOUadjX04+PuUNeT6us7NXx3PaYP0uqhoVD76TocDgwGQ4366Z49e5YVK1bw\n448/8vTTT/Poo4/i5XV9k+9LSkrIy8sjKyuL48ePs3Tp0mqtQVw/p9Mp92VCCHFzSUgqhBCiflEU\nhTvvvJPXXnuNbt26MWLECEpKSpg7dy6nTp0iMzOTOXPmYDabWbVqFdu3bycxMZHY2FjuuOMOYmJi\ntCcVlR1Nk0EHwp0URaGkpIScnBxMJhPZ2dkUFxfTvXt37Yj+rbfe6rYnulX1pFTDLjU0bez7u7JJ\n9TWprGtqrtXqwbVqWdSNisPGDAYDXl5e1d7fJ0+e5O233yYvL49Jkybx8MMPN8kqayGEEOI3SEgq\nhBCifiksLOTuu+9mw4YN7N27l/nz53PfffexdetWtm/fjq+vL2+88QYvvPACTzzxBH/4wx948803\neffdd1m5ciXe3t58+umnPP300/Tu3ZtDhw7Rpk0bbRqsK+nvJWqDw+Fg586d2kCo/fv3ExQUpFWa\nxsbG4uPj47Zq04qhKTTeKkF3h0fiiqr2keseakz7qL5y9/4+cuQIixYtYs+ePUydOpV77rlHfoZC\nCCFE1ap8kPS8masQQgghVJs2baK0tJTmzZvTrFkzNm3aRHp6Oo899pj2Ptu2bcPT05N7770XgJiY\nGMLDw4mOjuarr77Cz89P6zH21FNPMWzYMJ5//nnOnj1LXl4enTp1omfPnuh0Oq2CCq5UV6lDT1xv\nF+JGeHh4EBUVRVRUFBMnTkRRFE6cOIHJZOLbb7/lb3/7G4qiEBMTo1WbBgcHVyu80Ol0eHp6aj04\n1d68atBltVpxOp1XhaYNrUqwMU6qr08q20euVcuu+0gGi7mfu/f3nj17SElJ4cyZM7zwwgsMGjRI\nfleEEEKIGpCQVAghRJ2Ijo5m0aJFAHTu3JlHH32U0aNHc8cddxAVFcWtt95KWVkZR44cISAgAKfT\nyaVLl2jevDktW7bkwIEDBAUFERISAsCFCxfo378/paWlpKSkAJCTk0Pr1q1Zvnw5QUFBnDp1ipYt\nW5YbwPPjjz/i5eVFv379GtREX1H/6HQ6QkJCeOSRR3jkkUdQFIXLly+Tl5eH2Wzmn//8J6dOneKW\nW27BaDRiNBrp0aNHtUJ69ai9656tGJra7Xb0ev1VYVd9DFEqTqr39fWVisabwHVgmMp1sFjFfSSD\nxapHURSsVisWiwW9Xl/j/b19+3ZSUlJwOBwkJyeXm4wuhBBCiOqTkFQIIUSd6NChAx06dEBRFPz8\n/Jg0aRJr1qwhIyODHTt2MGvWLIKDg7nlllvIzc3Fw8ODhQsXcscdd2C32ykuLiY8PBwPDw+OHj2K\nw+EgNDSUtLQ0PvnkEz799FNefvllZs2axc8//0xUVBRTp05l586dGAwGpkyZwrBhw1i6dCmjR4+u\nNCBVj+lfunQJPz+/Ovgu3VxhYWG0aNECvV6Pl5cXubm5FBUVMXLkSA4fPkxYWBjp6em0aNECgLlz\n55KWloanpyepqakMHTq0jq+gftHpdPj5+XHHHXdwxx13AFeqmPfs2YPJZGLFihXs2rWL5s2bk5CQ\ngNFoJD4+nmbNmlW72tTLy0sb0KJWCdrtdux2O2VlZQBXVQnWZdhVcVJ9s2bNpLK7jrlWm8LVg8Ua\nS9XyzeAajnp4eODn56d9X6sjOzubBQsW0Lx5c2bPnk3v3r3duFohhBBCSEgqhBCiTqgBpBrQBAUF\n8dRTT/HUU0+Ve7+HHnqIiRMn0qdPHwwGA5GRkXh6elJWVqY92fzss88IDg7Gbrdz6NAh/uu//otl\ny5aRn59PWVkZERERWpi6bds2tm/fzunTp8nNzWXDhg3s27ePNWvWkJaWhq+vr/a11bW9+eabxMXF\nce+99zbq/qZ6vZ4NGzbQqlUr7bZ58+YxePBgZsyYwfz585k7dy7z5s0jPz+f9PR0du3aRUFBAYMH\nD2bv3r2N9nvjLnq9nu7du9O9e3fGjRuHoiicO3eOrKwsNm7cSEpKChaLhT59+pCQkEBSUhKdOnWq\ndmhasUrQNewqKyurs0E+FSfV+/v7S8hWT7nuI/XFJNcj+upQrYZStXwzOJ1OrFYrVqu1xuG/oij8\n5z//ITU1lc6dO7Nw4UK6du3q5hULIYQQAiQkFUIIUUcqe/Ls2itUNXbsWMaOHcuePXt48cUXiYiI\nAK4c0f/LX/7Cnj17eP/993n44YcJCQnh5MmTPPTQQzz88MMAnDlzBm9vbw4fPsylS5dYtGgRf/zj\nH4mKimLv3r3cdtttzJ49mzNnzpQLSF3Nnj270nW79jNtDOGpWjHm6ssvvyQjIwO48rMYOHAg8+bN\nY+3atYwaNQpPT0/CwsLo2rUrubm5cuzzBul0OgIDA7n33nu13rtWq5UtW7ZgNpt5+eWXOXr0KJ06\nddIGQvXp00erFr1Rer1eqxSG8oN8bDabVm2qBl3uPFpd2aT6gICABv970xRVto9cq5YtFgtOp7Pc\nPqrrquWbwZ2V0U6nk2+//ZalS5fSp08f0tLS6NSpk5tXLIQQQghXEpIKIYSoNyqrJHM4HOh0Ok6c\nOMGJEyfo0aMHAMnJyYwaNYqDBw/idDq56667ALjzzjv5+uuvadu2Ld26ddP6mfbu3ZsffviB2bNn\nM3nyZFasWMFPP/1ESEgIcXFx6PV6nE6ntgY19NyyZQtTp07lq6++wmKx8OOPPzJ06FBatmxZ7snv\nmjVriIqKIjQ0tEbHKeuSTqdjyJAheHh4MGHCBMaPH09hYSHBwcEAtGvXjlOnTgFw7NgxkpKStI/t\n0KEDx44dq5N1Nzbe3t5az9KpU6eiKAqHDh3CZDLxj3/8g+TkZAwGA3FxcVpw2rJlS7cNhPqto9U3\nOmimsknefn5+jT4wa0qut2pZr9dfdUS/MewD13C0ppXRDoeDzz77jPfee48BAwawevVq2rZt6+YV\nCyGEEKIyDfNZnBBCiCZDfdJ922238f7772tBjk6nIywsjLCwMAYNGqS9/6hRoygsLGTu3LkUFBQw\nb948IiMj+fLLLxk0aBCvvfYat912G8ePH2f37t306NEDm82GwWAo96RW/Rrbt28nPDycgIAAPvzw\nQ6ZMmcKMGTNIT09n2rRpTJw4kby8PD7//HOtFYArNeRtCEeJTSYT7du35/Tp0wwdOpSIiIirAozG\nEGg0NDqdjvDwcMLDw3n00UdRFIWSkhJycnLIzMzk3Xffpbi4mB49emhH9Lt06VKtPVfZ0WrXgVAW\ni4VLly5d19FqtR+j1WqVSfVN0LWqliv2yHUNThvS/nBtG+Ht7V2jcNRqtfLpp5/y0UcfMWzYML7+\n+utyrU+EEEIIUfskJBVCCNEgeHp60r1796uOtavHw9Unps2aNePFF1/U3m6xWDh37hz//ve/Wbx4\nMU6nkxEjRhAWFsbFixcJCwvDYDBU+XXz8vK06tX169fzxBNPMHfuXHr37s3GjRs5e/Ys//M//8Pn\nn39Ofn4+U6ZM4fHHHy/X69GVul7Xfqz1Rfv27QFo06YNDzzwALm5uQQHB2vVpCdPntQqmjp06MDR\no0e1jy0oKKBDhw51su6mRqfT0bx5c4YMGcKQIUOAK2HNjh07MJlMzJ8/nwMHDtCmTRut0jQ2NhaD\nwVDrA6HUEFQ9vi+T6oWq4kAoQNtH6n6pqx65N8rhcGjVsTVtG3Hp0iU++ugj0tPTGTFiBD/88AP+\n/v5uXrEQQgghrodOUZRrvf2abxRCCCHqGzXAqap688yZM7Rq1QoPDw/Wrl3Lc889R1JSEv/6178q\n/Xx33303kydP5q677iIkJIQffviByMhINm7cyLvvvsvSpUv59NNPKSgoIC4ujq5du+Lt7U16ejpf\nffUVt956K9OnTycqKqrK9daH8OjSpUs4nU78/f0pLS1l6NChzJ49m/Xr19O6dWtmzpzJ/PnzKSoq\n0gY3jRkzhpycHI4dO8aQIUNkcFM9oigKx48fx2w2YzKZ2LJlCzqdjpiYGBITEzEajbRt29ZtPy+n\n04nNZtOO5wPa0WrXalMhrqVitanD4QAoV2lal4G72jbC4XBgMBjw9vau9louXLjAe++9x9dff80T\nTzzBk08+iY+Pj5tXLIQQQohKVPngLZWkQgghGhX1uLArNTj18PAgKChIu3348OEMHTqU/fv3A1ee\nALtWOR09epTi4mJ69uzJ+fPnsVqtREZGAnD27Fn0ej0BAQH8/PPPxMXFcf/99wMwYMAApk+fzuTJ\nk1m7di3r1q0jKiqK5cuXY7FY8Pf3Jy4ujh49emjtA9S1u6654m21qbCwkAcffBCdTofdbmfMmDEM\nHTqUuLg4RowYQVpaGqGhoaSnpwMQGRnJiBEjiIyMxMvLi2XLlklAWo/odDo6dOjAI488wiOPPIKi\nKFy+fJm8vDytt+np06fp0qULiYmJJCUl0b1792oNmak4qV6tWHXta6q2nahYISh7Rri6Vo9ch8NR\nrkduxXYPtcXdPXXPnDnDsmXLyMjI4NlnnyUzM7Pag9iEEEII4V5SSSqEEKLJqqqKUz3yuXnzZlau\nXMny5cv55z//yfTp0ykoKODChQt89NFHnD59mhdffJE//vGPPP7449x1110cP36cLl26EBkZSfv2\n7QkPDyc9PZ3Dhw/zyiuv8NNPP9GvXz/atm3LihUr+Pzzz4mNjQXg/PnztGzZ8qr1qAOlSktLURRF\njmIKt3A6nezZs4fMzExMJhO7d++mRYsWJCQkYDQaiYuLo1mzZpX+jjidTjZs2MA333zDK6+8go+P\nzzWr6ioOhHI4HOWmn1dnIJRomtRqU3Uf2e12rUeuOwN4NRxV20kYDAa8vLyq/XlPnDjB4sWL2bJl\nC5MnT+ahhx6S6mohhBCibkglqRBCCFGVt99+G4fDwe23305sbKxWTde3b1/69u0LQK9evViyZAkA\nRUVF5Ofn07t3b0pLS/H399f6eW7dupXY2FhMJhNZWVnk5uYyZcoUvLy8KCgoYPTo0UyaNAm48qT5\n+PHjWkgaExPDt99+C0BOTg4xMTFERUVpT6RzcnL46KOPeOGFF+jVq9fN+waJRkmv19O9e3e6d+/O\n+PHjURSFs2fPYjabycjI4L//+7+xWq306dNHGwjVrl07vvjiCxYtWsTFixeZNGkS/v7+v1mBWtlA\nKNcKQdeBUGpwqoamEpwKVxV7m1YM4F2rTV1D+OsNJBVFwWazYbFY0Ol0+Pj41CjAP3ToEIsWLWL/\n/v1MmzaNRYsW1ds9XVxczPjx49m5cyd6vZ60tDS6devGyJEjOXz4MGFhYaSnp9OiRYu6XqoQQghR\nKyQkFUII0WSpT1RHjBiB2WzmvffeY9OmTQQEBPD6668THx+vPRHv1asXvXr1QlEUQkNDmTlzJt7e\n3gQGBuLr68vgwYNJS0ujZcuWdOzYkePHj5OUlERSUhIA2dnZ+Pj4cMcddwBXnohHR0ezceNG7r33\nXt5//30SEhLo0aMH27dv5+jRo6xevRqHw8GaNWvw9fXl3Llz+Pn50blz57r5holGTafTERQUxPDh\nwxk+fDhwZfDZ1q1b2bBhA6NGjWL//v2EhoYydOhQ7r//fqKioqp1RB+uPf3cZrM1iunnovZdTwDv\ncDi0alPXI/oVW5xYrVYsFgt6vb7GA8d2795NSkoK58+fZ8aMGdp9f302efJkhg0bxurVq7Hb7ZSW\nlvLGG28wePBgZsyYwfz585k7dy7z5s2r66UKIYQQtUKO2wshhBAVXLhwAYfDQatWrbTb1CPvVdm/\nfz96vZ7w8HDmzp3Lv/71L4KDg4mPjyc5OZnPP/+cnTt3MnnyZG0K/Lx587h48SLDhw8nNTWVKVOm\nEB8fz+7duykpKaFt27asWrWKiIgI7r//fpYsWUJhYSFz586t9e+BEHCl9+6yZctYsmQJRqORadOm\n0alTJ+2I/o4dO/Dx8SEuLg6j0UhCQgItW7Z0S5CpKIp25FmtEqxJhaBoutRqU3UvVWz3oA4eU3uh\nuvamvlFbt24lJSUFnU5HcnIy8fHxbryS2nPhwgViYmK0Ht2q7t27k5GRQXBwMCdPnmTgwIHs3r27\njlYphBBCuIUctxdCCCGuV/Pmza+67beCmC5dumj/e9asWfz5z3/GbDZTXFyMv78/+/fvp0WLFuU+\nd+vWrbFYLMydO5fbb7+d+Ph4PvzwQzIzMyksLOTkyZOcP3+ev/zlL9hsNq3fqRC17dChQyxcuJC/\n//3vPPjgg2zYsIEePXpobw8PD+exxx5DURQuXLhATk4OmZmZrFixgpKSEnr06KEd0b/llluqFWSq\nR+3V6kCgXGhqtVrL9aOsqkJQCNdqU5U6jEk9Vg9oYakaxt/IXjKbzSxcuJBWrVrx2muv0bNnz1q5\nltpy8OBBgoKCePLJJ9m+fTtxcXEsWrSIwsJCgoODAWjXrh2nTp2q45UKIYQQtUcqSYUQQgg3q2og\n1IULF8qFpLt27aJ379488MADLF26lODgYG6//XYmTJjAmDFjKCoq4tFHH+Wll14iODiYN998kz/8\n4Q8MHDjwJl6NaEqKi4uZOHEi3333HePHj2fSpEla5fP1stvt7Ny5k8zMTMxmMwcOHKBt27YkJiZi\nNBqJiYnBYDC4rdrUtULQbrcDXDX9XEJToXI6nVgslnKVox4eHuXaPbjupfz8fDIyMsoNM1MpisL6\n9etZvHgx4eHhJCcnN9gXsjZv3ozRaCQrK4u4uDief/55AgICWLJkCefOndPeLzAwkLNnz9bhSoUQ\nQogak0pSIYQQ4mZRA5mKYWnFCtVbb72VTz75hJiYGK1S5/e//z3r1q0jKCiINWvWcPbsWXr37k1O\nTg6KotCtW7ebdyGiyQkICGDAgAEsW7as2sNZPD09iY6OJjo6mueeew5FUTh+/Dgmk4mvvvqKOXPm\noNfriYmJ0YLTNm3aVCvIrKxC0HWIT1lZGQ6HQ3sf1+nnomlRq49tNhteXl74+/uX2wcVB0LBlb3k\n6+tLYWEhL730Ert37yYiIoL4+HhatmyphacffvghHTt2rIvLcpuOHTvSqVMn4uLiAHj44YeZN28e\nwcHBWjXpyZMnadu2bR2vVAghhKg9UkkqhBBC1CNnzpxhwYIFFBYW0qZNGzZt2sQPP/zAokWL2Lt3\nL0uXLq3rJQpRI4qicOnSJfLy8jCZTGRlZXHmzBluvfVWEhMTSUpKIiIionk84ZAAABW4SURBVNoD\noSr7eq7VgQ6HA0CrMlVDU6k2bZzU4U12ux1vb2+8vb2rHZJfuHCBtLQ0MjMzKSgo4MSJExgMBpKS\nkujXrx/9+vUjJiamXIuIhuSOO+7gvffeo1u3bsyZM4dLly4BV1rDzJw5k/nz51NUVCSDm4QQQjR0\nVf7RJyGpEEIIUYd+ayDU2bNnCQwMZPXq1Zw9e5ZnnnnmJq5OiJvD6XTy66+/akf0d+/eTYsWLbS+\npn379qVZs2ZuPaLvGppWHAjl6ekpoWkDp/YcdTgceHt716jFg8ViYdWqVfz9739n+PDhPPfcc7Ro\n0QJFUThw4ABZWVmYzWbMZjN79+4lJiZGC06TkpJo166dm6+udmzfvp3x48djs9m45ZZb+OCDD3A4\nHIwYMYKjR48SGhpKeno6LVu2rOulCiGEEDUhIakQQgjREKgBjlS2iaZMURTOnj2LyWTCbDaTl5eH\n1WolKipKC047dOjgtt8R14FQ6n9lIFTDo1YNq+GowWDA29u72j+30tJSPvzwQ/7nf/6HUaNG8fTT\nT5frSVqZkpIScnNzteA0KyuLN998k6eeeqpaaxBCCCGE20lIKoQQQjRkv1VxKkRjZ7FY2LJli3ZE\nv6CggNDQUC007dWrF15eXm75WtcaCKWGpjIQqv5QQ26LxYKiKBgMBry8vKr98zl//jzvvvsu69at\nY9y4cYwdOxaDwVCtz+V0OrHZbNX+eCGEEEK4nYSkQgghhBCi8XA6nRw6dEg7or9jxw58fHyIi4sj\nKSmJhIQEWrRo4bYg0zU0Vf+5DoNSq03FzaMoCjabDYvFAlDjcPT06dMsXbqUzMxMJk6cyKhRo8oN\nchJCCCFEoyAhqRBCCCGEaLwUReHChQtkZ2djMpnIycnhwoULREZGagOhwsPD3RZkqke7XYNTnU5X\nLjiVI/q1wzUc1el0GAyGGvWRPXbsGKmpqezYsYMpU6Zw//33S+AthBBCNF4SkgohhBBCiKbFbrez\nY8cOrdr04MGDBAcHk5iYiNFoJDo6ukYDfVxVNRDK9Xi+DISqGUVRsFqtWCwWPDw8MBgMNWp7cODA\nARYuXMiRI0eYPn06Q4cOlZ+PEEII0fhJSCqEEEIIIZo2RVE4duyYNhBq69at6PV6YmJiMBqNJCYm\n0qZNG7ce0VerTF0HQqnBqRqaSjB3bYqiYLFYsFqtWjhak2Pw+fn5pKSkUFJSQnJyMv3793fjaoUQ\nQghRz0lIKoQQQgghhCtFUbh06RJ5eXnaQKgzZ87QtWtXrdo0IiICDw8Pt329iqEpyECoqjidTqxW\nK1arFU9PT61ytLq2bNlCSkoKnp6ezJo1i9jYWDeuVgghhBANhISkQgghhBBC/Ban08nu3bu1I/q/\n/vorLVu2JCEhgaSkJPr27Yufn5/bjuirk9nV4NTpdF4Vmja1/phOpxOLxYLNZsPLywuDwVCj70Fm\nZiaLFi0iKCiIWbNm0aNHDzeuVgghhBANjISkQgghhBBC3ChFUThz5ox2RD8vLw+bzUZUVJRWbdqh\nQwe3VX+6hqZqcKrX68sFp411IJTD4cBisWC322scjiqKwvfff8/bb79Nt27dmDlzJuHh4W5esRBC\nCCEaIAlJhRBCCCGEcIeysjK2bNmC2WzGbDZz7NgxOnfujNFoJCkpiV69etWoZ6YrdSCUa3CqKEq5\nYVAN/Yi+azjq7e2Nt7d3tcNRp9PJ2rVrWbFiBXFxcUyfPp2QkBA3r1gIIYQQDZiEpEIIIYQQQtQG\np9PJwYMHtSP6O3fuxNfXl7i4OJKSkoiPj6dFixZuHwjlGpyqoakanDaEI/p2ux2LxYLD4cBgMODt\n7V3t75HNZiM9PZ20tDQGDx7M5MmTCQoKcvOKhRBCCNEISEgqhBBCCCGq9t133zFlyhScTifjxo1j\n5syZdb2kBktRFIqLi8nOzsZkMpGTk8PFixeJjIzUjuiHh4e7LcisaiCUWmWqhqb1odpUXWtZWRlO\np7PG4WhZWRmffPIJq1at4oEHHuBPf/oTzZs3d/OqhRBCCNGISEgqhBBCCCEq53Q66datG+vXryck\nJIT4+Hj++c9/0r1797peWqNht9v5+eefMZlMmEwmDh06RHBwMEajEaPRSHR0NAaDwS1fSz2i7xqa\nVhwI5enpeVNDU7XXqsViQVEUDAYDXl5e1V7DxYsXSUtLY82aNYwZM4bx48fj5+fn5lULIYQQohGS\nkFQIIYQQQlQuOzubOXPmsG7dOgDmzZuHTqeTatJapCgKBQUFmM1mTCYT27Ztw8PDg5iYGIxGI4mJ\niQQFBdXKQCj1vzdjIJSiKNhsNiwWCwA+Pj41CmiLiop45513+P777xk/fjyPP/443t7e7lyyEEII\nIRq3Kv8IcU9HeSGEEEII0WAdO3aMTp06af+/Y8eO5Obm1uGKGj+dTkenTp0YOXIkI0eORFEULl26\nRG5uLiaTiY8++oizZ8/StWtXLTTt3r17tY/o63Q6vLy88PLyAsoPhLLb7ZSVlQGUC01rMhDKNRzV\n6XQ1DkcLCwtZunQpWVlZ/OlPf+KVV15x23Asd9qzZw8jR45Ep9OhKAoHDhzgtdde47HHHmPkyJEc\nPnyYsLAw0tPTadGiRV0vVwghhBAupJJUCCGEEKKJ++yzz/j3v//Nu+++C8Ann3xCbm4uixcvruOV\nNW1Op5Ndu3ZpR/T37NlDq1atSEhIwGg00rdvX/z8/Nw6EMp1GJTrQCjXatNrURQFq9WKxWLBw8MD\ng8FQozCzoKCARYsWkZ+fz/PPP899993XIIZSwZXvZ8eOHcnJyWHJkiUEBgYyY8YM5s+fT1FREfPm\nzavrJQohhBBNkVSSCiGEEEKIynXo0IEjR45o/7+goIAOHTrU4YoEgF6vp2fPnvTs2ZOnn34aRVE4\nffo0ZrOZ9evXM3/+fOx2O1FRUdpAqJCQkGqHpnq9vtzRdXXIkt1ux2q1XnMglKIoWCwWrFYrHh4e\nNGvWDA8Pj2pf+759+1iwYAEnTpzghRdeYOnSpfVi8NSN+OGHH+jSpQudOnXiyy+/JCMjA4CxY8cy\ncOBACUmFEEKIekYqSYUQQgghmjiHw0FERATr16+nffv2JCQk8Omnn9KjR4+6Xpr4DWVlZWzevBmz\n2YzZbOb48eOEhoZqA6F69erltmPpVQ2EUkNSDw8P7Vh9de3cuZOUlBQuX77MrFmzSEpKcsva68K4\nceOIi4vj2WefpVWrVhQVFWlva926NefOnavD1QkhhBBNlgxuEkIIIYQQVfvuu++YPHkyTqeTcePG\nkZycXNdLEtXgdDo5cOCAdkR/586d+Pn5ER8fT1JSEvHx8TRv3rzGVZlOp1OrHFUrStUj+nq9vly1\nqU6n+82vl5eXx4IFCzAYDLz44otER0fXaH11zWazERISwq5duwgKCroqFA0MDOTs2bN1uEIhhBCi\nyZKQVAghhBBCiKZGURSKi4vJzs4mMzOTnJwcSktL6dmzp3ZEPyws7Lr7fJ45cwY/Pz/sdjteXl4Y\nDIZyH6se0XetNp04cSI2mw2j0UhSUhIxMTEYDAYUReGnn35i0aJFtG/fnlmzZhEREVFb34qbau3a\ntSxbtozvvvsOgB49erBhwwaCg4M5efIkgwYNYteuXXW8SiGEEKJJkpBUCCGEEEIIwf9r7/5Cs6D7\nPo5/VtMw6d+iHM1UBBNHLRVtpkEnamJkRLEMKYMy0KAiQakgiCKLDoSooIPEgsI8cgj90w6VljXm\nQRoG1tCZGqUh/qGcew4e7vF0l90+953uzu/rBdfBrt819r12+OZ7Xb+cPHky27dvH9w27e3tTXNz\nc2bMmJH29vZMnjw5F1100W9+p7u7O6+88kp6enqybdu2jBw58oy2UQcGBvLdd99ly5Yt6erqSldX\nV7799tu0trbmxx9/zKRJk/LCCy9kypQpZ+vtDon77rsv8+bNy+LFi5MkK1euTFNTU1auXOniJgAY\nWiIpAADwewMDA9m7d+9gNO3p6UljY2OmTp2aq666Kps2bcqOHTvy6KOPZsmSJbnkkkv+rb/T39+f\nDRs2ZM2aNZk0aVJGjhyZ7du3p6urK83NzZk1a1ZmzpyZWbNmZeLEiX+bW+z/2bFjxzJ27Njs3r17\n8H/1008/paOjI3v27MnYsWOzfv36XH755UM8KQCUJJICAAD/2qlTp7Jx48Y8++yz2bt3b2699dYc\nPHgw48ePH9w2/f9EzF9//TXr1q3L2rVrM3fu3Dz++ONpamoaPO/v789XX32VLVu2ZOvWrdmyZUsO\nHz6cm2++eTCczpw5M8OHDz9bbxkAqEMkBQAATu/UqVPp7OzMiy++mGPHjuWpp57KwoUL09jYmP7+\n/uzcuXNw23TXrl1pampKe3t72tvbM23atIwYMeI3H8E/fvx43nnnnaxbty533313li5desZbqN9/\n/322bt06+Pjggw9yxRVXnK23DgDUIZICAAC/949Nz1WrVmXkyJF55plnsmDBgj/dFB0YGMgPP/ww\nGE2/+OKL9Pf358Ybb0xbW1t6e3uzefPm3H///XnooYcyYsSIc/iOAABOSyQFAAB+a2BgINOmTcul\nl16ap59+OrNnzz6jC5n+yIkTJ/Lll1/m/fffz8UXX5znn38+w4YN+4snBgD4j4ikAADA7/X19aWl\npWWoxwAAOBdEUgAAAACgtNNG0jO7khIAAAAA4DwlkgIAAAAApYmkAAAAAEBpIikAAAAAUJpICgAA\nAACUJpICAAAAAKWJpAAAAABAaSIpAAAAAFCaSAoAAAAAlCaSAgAA/1VWr16d66+/Pm1tbVm0aFF+\n+eWXHDp0KHPnzs3EiRNz22235eeffx7qMQGA80jDwMDAn53/6SEAAMBfad++fbnlllvy9ddfZ/jw\n4bn33nszf/787NixI1deeWVWrFiRl19+OYcOHcpLL7001OMCAH8vDac7sEkKAAAFPPfccxk9enSm\nTp2aqVOn5qOPPho8W7VqVSZMmJBJkyblk08+GXy+u7s7bW1tue666/LEE0+cs1n7+/tz9OjRnDx5\nMsePH09LS0s6OzuzePHiJMnixYuzYcOGczYPAHD+E0kBAKCIJ598Mt3d3enu7s68efOSJDt37sz6\n9euzc+fOfPjhh1m2bFn+8WmzpUuX5q233squXbuya9eufPzxx2d9xmuuuSbLly/PmDFj0tLSkssu\nuyyzZ8/OgQMHMmrUqCRJc3NzDh48eNZnAQDqEEkBAKCIP/qqrc7Ozix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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc50864e950>"
>>>>>>> 2406bcc6d11cc474d572552fcdaec6c259b8e73d
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import sklearn\n",
"from sklearn.cluster import KMeans\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import cm\n",
"\n",
"max_number_of_clusters = 10\n",
"color_set = [\n",
"[0, 0, 255], #Blue\n",
"[255, 0, 0], #Red\n",
"[0, 255, 0], #Green\n",
"[255, 255, 0], #Yellow\n",
"[255, 0, 255], #Magenta\n",
"[255, 128, 128], #Pink\n",
"[128, 128, 128], #Gray\n",
"[128, 0, 0], #Brown\n",
"[255, 128, 0], #Orange\n",
"]\n",
"\n",
"def get_colors(N=5):\n",
" result = []\n",
" for x in range(N):\n",
" s = color_set[x % len(color_set)]\n",
" result.append([s[0]/255.0,s[1]/255.0,s[2]/255.0,1])\n",
" return result\n",
"palette = get_colors(max_number_of_clusters+1)\n",
"\n",
"\n",
"def get_best_clustering_model(X, max_number_of_clusters):\n",
" cost = []\n",
" KK = range(1,max_number_of_clusters+1)\n",
" kms = []\n",
" # calculate all the clustering and cost\n",
" for no_of_clusters in KK:\n",
" km = KMeans(n_clusters=no_of_clusters, precompute_distances = True, max_iter = 500, n_init = 30)\n",
" km.fit(X)\n",
" kms.append(km)\n",
" \n",
" sizes = [0]*no_of_clusters\n",
" for i in km.predict(X): \n",
" sizes[i] = sizes[i]+1\n",
" print sizes\n",
" \n",
" cost.append(km.inertia_)\n",
" \n",
" derivative1 = [cost[i+1]-cost[i] for i in range(len(cost)-1)]\n",
" derivative2 = [derivative1[i+1]-derivative1[i] for i in range(len(derivative1)-1)]\n",
" max2 = argrelextrema(np.argsort(derivative2), np.less) \n",
" num_clusters = 4 \n",
" if(len(max2[0]) > 0):\n",
" num_clusters = max2[0][0] + 3\n",
" else:\n",
" derivative3 = [derivative2[i+1]-derivative2[i] for i in range(len(derivative2)-1)]\n",
" max3 = argrelextrema(np.argsort(derivative3), np.greater) \n",
" if(len(max3[0]) > 0):\n",
" num_clusters = max3[0][0] + 4 \n",
" return kms[num_clusters-1], cost\n",
"\n",
"model,cost = get_best_clustering_model(X, max_number_of_clusters)\n",
"\n",
"labels = model.predict(X)\n",
"\n",
"num_clusters = model.n_clusters\n",
"print \"Num clusters:\", num_clusters\n",
"\n",
"KK = range(1,max_number_of_clusters+1)\n",
"\n",
"# elbow curve\n",
"kIdx = num_clusters \n",
"clr = cm.spectral( np.linspace(0,1,10) ).tolist()\n",
"mrk = 'os^p<dvh8>+x.'\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(KK, cost, 'b*-')\n",
"ax.plot(num_clusters, cost[num_clusters-1], marker='o', markersize=14, \n",
" markeredgewidth=2, markeredgecolor='r', markerfacecolor='None')\n",
"#ax.set_ylim((0,100))\n",
"plt.grid(True)\n",
"plt.xlabel('Number of clusters')\n",
"#plt.ylabel('Percentage of variance explained (%)')\n",
"plt.ylabel('Average within sum of squeres')\n",
"plt.title('Elbow for KMeans clustering')\n",
"\n",
"\n",
"#cluster histogramm\n",
"sizes = [0]*model.n_clusters\n",
"for i in model.predict(X): \n",
" sizes[i] = sizes[i]+1\n",
"print sizes\n",
"index_max = sizes.index(max(sizes))\n",
"\n",
"cluster_sizes = float(sizes[index_max])/ X.shape[0] * 100\n",
"\n",
"#print cluster_sizes\n",
"left = [] \n",
"for i in range(len(sizes)):\n",
" left.append(i)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(left,sizes, color = palette)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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XtNDDsKicBuDS3GWRVB9zUIEhEJICXIChtdADAEDE7gA1whxUYDiEpABnatvCJaEou5gB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\nfveNE1+/okTY29urVat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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa04a1d8490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"selected = 2\n",
"\n",
"if selected >= 0 :\n",
" X_plot = X[labels == selected,: ]\n",
" labels_plot = labels[labels == selected]\n",
"else:\n",
" X_plot = X\n",
" labels_plot = labels\n",
" \n",
"colors = []\n",
"g = 0\n",
"for i in labels_plot:\n",
" colors.append(palette[i])\n",
" \n",
"def plot3(indexes):\n",
" fig = figure(figsize=(24,24))\n",
" ax = fig.gca(projection='3d')\n",
" #fig = plt.figure(figsize=(14,14))\n",
" #ax = fig.add_subplot(111, projection='3d')\n",
"\n",
" ax.set_xlabel(features[indexes[0]])\n",
" ax.set_ylabel(features[indexes[1]])\n",
" ax.set_zlabel(features[indexes[2]])\n",
" azim = 125\n",
" elev = 15\n",
" #ax.set_autoscale_on(False)\n",
" #ax.axis([0.9, 1.5, -50, 50, 0, 400000])\n",
" ax.view_init(elev, azim) \n",
"\n",
" # plot points in 3D\n",
" class1 = 0.6 * random.standard_normal((200,3))\n",
" x = X_plot[:,indexes[0]]\n",
" y = X_plot[:,indexes[1]]\n",
" z = X_plot[:,indexes[2]]\n",
"\n",
" ax.scatter(x,y,z, s=5, edgecolors='none', c = colors, marker='o')\n",
"\n",
"plot3([0,1,2])"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ZgwoclJAUYCDaLfQ5HM200AMAcGm6qlDzOXA+933IHNR2C78QFdgmJAXoqXYL/fYMJyd3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\no5o1a+qrr77Sjz/+qFW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"text/plain": [
"<matplotlib.figure.Figure at 0x1f94750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot3([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kZT5CUlaME9mlm+RwZ1KPCwAAGH+33ZZccsm63H57kXa7Sqczez177rm9vOAF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\nJD/0Q1X+5E923OaTn2zmrW9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ZFMlP/VQ3+2ps5Qc+0ModdxR5znO6O81OHT3TaTS+lLJ8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\nXwWmaX6FELvwvDFUdqyhr1FK5vvRfbZVK5kQn7lj6TFJa6b9+/d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9QTQqOO88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XjdXK3y6TVJ6x7DMJg1axY33XQTvXr1onXr1vi+z+7du2nZsiWADkm1ui1R2+01rTYoOJne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\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fg2FcQq9e2+jevQuTJ3uY5mjeey/M+vWCnj0NbrvNqdVta8lr4kSfAQNktVsN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95CcunnoqnxkzdO66K0Dv3pmND2vfHu68M70OUpu2bUnNQXoY1bkzsn9/1DHHBG/YvRtt82bk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\nVQVblfUHlAOh/Md3S30wDUHxh+qqTWXFVt15r6GGrl6vT1VVm8pQvmZ811B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Q6Yc+fX0e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UJQXFxMdnY2FouFdevWUVhYSJcuXUhOTiY1NTVoKkjc1Vl2u91TUWM0Gmt8bHUdCCWFBncV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\nTSQFAAAAAApNJAUAAAAACu1/AZkKEXVrd0ssAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x30e11f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot3([1,3,4])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving Clusters"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db = MySQLdb.connect(host = db_host, user = db_user, passwd = db_password, db = db_name)\n",
"cur = db.cursor(MySQLdb.cursors.DictCursor)\n",
"for session, label in zip(sessions, labels):\n",
" cur.execute(\"update sessions set cluster_index={} WHERE id = {}\".format(label,session[\"id\"]))\n",
"db.commit()\n",
"db.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving Decision"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "OperationalError",
"evalue": "(1054, \"Unknown column 'selected_cluster' in 'field list'\")",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mOperationalError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-49-4c8fddf788bd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mdb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMySQLdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb_user\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpasswd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb_password\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mcur\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMySQLdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcursors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDictCursor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcur\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"update incidents set selected_cluster={} WHERE id = {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mselected_cluster\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mid_incident\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 5\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/dist-packages/MySQLdb/cursors.pyc\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, args)\u001b[0m\n\u001b[0;32m 172\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorhandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc\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 175\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 176\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_defer_warnings\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warning_check\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/dist-packages/MySQLdb/connections.pyc\u001b[0m in \u001b[0;36mdefaulterrorhandler\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mconnection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0merrorclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrorvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[0mre_numeric_part\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\"^(\\d+)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mOperationalError\u001b[0m: (1054, \"Unknown column 'selected_cluster' in 'field list'\")"
]
}
],
"source": [
"selected_cluster = 1\n",
"db = MySQLdb.connect(host = db_host, user = db_user, passwd = db_password, db = db_name)\n",
"cur = db.cursor(MySQLdb.cursors.DictCursor)\n",
"cur.execute(\"update incidents set selected_cluster={} WHERE id = {}\".format(selected_cluster,id_incident))\n",
"db.commit()\n",
"db.close()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"db.close()"
]
},
{
"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
}
| agpl-3.0 |
JustasB/MitralSuite | Davison2003.ipynb | 1 | 99015 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### Define Model Setup Steps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"modelPath = 'Models/Davison2003/bulbNet'\n",
"modelName = 'Davison2003'\n",
"\n",
"def getSomaScript(h):\n",
" \n",
" h.load_file('mosinit.hoc')\n",
" h.run_experiment('odour_baseline')\n",
" soma = h.Mit[0].soma\n",
" \n",
" return soma"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Model "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import helpers\n",
"\n",
"mod1 = helpers.createModel(name = modelName, \\\n",
" path = modelPath, \\\n",
" getSectionScript = getSomaScript)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Widget Javascript not detected. It may not be installed properly.\n"
]
}
],
"source": [
"# Interactively find stimulation currents\n",
"helpers.IClampWidget(mod1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from quantities import nA, s, ms\n",
"\n",
"##### Set currents\n",
"i_rest = {'amplitude': 0*nA, 'delay': 0.5*s, 'duration': 1*s}\n",
"i_passive = {'amplitude': -0.07*nA, 'delay': 0.5*s, 'duration': 1*s}\n",
"i_ap = {'amplitude': 7*nA, 'delay': 0.5*s, 'duration': 1*ms}\n",
"i_thresh = {'amplitude': 0.135*nA, 'delay': 0.5*s, 'duration': 1000*ms}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup Tests"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Resting potential test\n",
"Expected: -57.4345864662 mV +/- 24.630188347 mV SD\n",
"Actual: -65.6658765828 mV, Z: -0.334195175477 SDs\n"
]
},
{
"data": {
"image/png": 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z1VVw/DgcPlzvNZuZta6Kw0qS9gKdpU1AAFvT/u0RsUbSamAX0AUcARZGxJCklcBuScsi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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11073ad90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array(-65.66587658283359) * mV"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initialize the tests and get their expected values\n",
"tests = helpers.setupTests(i_rest, i_passive, i_ap, i_thresh, expectedSource = \"Pooled\")\n",
"\n",
"helpers.runOneTest(tests[0], mod1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Resting potential test\n",
"Expected: -57.4345864662 mV +/- 24.630188347 mV SD\n",
"Actual: -65.6658765828 mV, Z: -0.334195175477 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11073a8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Input resistance test\n",
"Expected: 145113133.641 ohm +/- 189410293.868 ohm SD\n",
"Actual: 78470665.1237 kg*m**2/(s**3*A**2), Z: -0.351841851655 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110ea4350>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Time constant test\n",
"Expected: 27.9651006711 ms +/- 66.3591805191 ms SD\n",
"Actual: 78.7822331466 ms, Z: 0.765789029913 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1110c9450>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Injected current AP width test\n",
"Expected: 1.513125 ms +/- 0.961592715477 ms SD\n",
"Actual: 0.425 ms, Z: -1.13158615127 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11188fa50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1118df150>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Injected current AP threshold test\n",
"Expected: -34.6053571429 mV +/- 56.7790487536 mV SD\n",
"Actual: -35.1232385971 mV, Z: -0.00912099560613 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1120e12d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x112117110>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Running Test: Injected current AP amplitude test\n",
"Expected: 68.776 mV +/- 50.4271404587 mV SD\n",
"Actual: 68.1004423041 mV, Z: -0.0133967083952 SDs\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1125ea090>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1125bb490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results = helpers.runAllTests(tests, mod1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Davison2003': {'Injected current AP amplitude test': array(68.1004423040704) * mV,\n",
" 'Injected current AP threshold test': array(-35.12323859705876) * mV,\n",
" 'Injected current AP width test': array(0.42500000000000004) * ms,\n",
" 'Input resistance test': array(78470665.12368964) * kg*m**2/(s**3*A**2),\n",
" 'Resting potential test': array(-65.66587658283359) * mV,\n",
" 'Time constant test': array(78.7822331466243) * ms}}\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Davison2003': {'Injected current AP amplitude test': array(68.1004423040704) * mV,\n",
" 'Injected current AP threshold test': array(-35.12323859705876) * mV,\n",
" 'Injected current AP width test': array(0.42500000000000004) * ms,\n",
" 'Input resistance test': array(78470665.12368964) * kg*m**2/(s**3*A**2),\n",
" 'Resting potential test': array(-65.66587658283359) * mV,\n",
" 'Time constant test': array(78.7822331466243) * ms}}\n",
"{'Davison2003': {'Injected current AP amplitude test': array(68.1004423040704) * mV,\n",
" 'Injected current AP threshold test': array(-35.12323859705876) * mV,\n",
" 'Injected current AP width test': array(0.42500000000000004) * ms,\n",
" 'Input resistance test': array(78470665.12368964) * kg*m**2/(s**3*A**2),\n",
" 'Resting potential test': array(-65.66587658283359) * mV,\n",
" 'Time constant test': array(78.7822331466243) * ms}}\n"
]
}
],
"source": [
"pp(results)\n",
"helpers.saveResults(results)\n",
"pp(helpers.loadResults())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mod1.h.quit()"
]
},
{
"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"
},
"widgets": {
"state": {
"204c8f5b73fd470d96e054d77a80abac": {
"views": []
},
"2f26bc2905654321a96f7c7938a49dd6": {
"views": []
},
"2f68d095542241fab3c9b9c055205396": {
"views": []
},
"405abd0a383b4e58b3dc4cf78fb0d102": {
"views": []
},
"4e8b673be24f4a27bdc226b3d9f9bc9d": {
"views": []
},
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| mit |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_megsim_data_single_trial.ipynb | 1 | 2192 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# MEGSIM single trial simulation dataset\n\n\nThe MEGSIM consists of experimental and simulated MEG data\nwhich can be useful for reproducing research results.\n\nThe MEGSIM files will be dowloaded automatically.\n\nThe datasets are documented in:\nAine CJ, Sanfratello L, Ranken D, Best E, MacArthur JA, Wallace T,\nGilliam K, Donahue CH, Montano R, Bryant JE, Scott A, Stephen JM\n(2012) MEG-SIM: A Web Portal for Testing MEG Analysis Methods using\nRealistic Simulated and Empirical Data. Neuroinformatics 10:141-158\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from mne import read_evokeds, combine_evoked\nfrom mne.datasets.megsim import load_data\n\nprint(__doc__)\n\ncondition = 'visual' # or 'auditory' or 'somatosensory'\n\n# Load experimental RAW files for the visual condition\nepochs_fnames = load_data(condition=condition, data_format='single-trial',\n data_type='simulation', verbose=True)\n\n# Take only 10 trials from the same simulation setup.\nepochs_fnames = [f for f in epochs_fnames if 'sim6_trial_' in f][:10]\n\nevokeds = [read_evokeds(f, verbose='error')[0] for f in epochs_fnames]\nmean_evoked = combine_evoked(evokeds, weights='nave')\n\n# Visualize the average\nmean_evoked.plot(time_unit='s')"
]
}
],
"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": 0
} | bsd-3-clause |
ML4DS/ML4all | U2.SpectralClustering/.ipynb_checkpoints/SpecClustering_student-checkpoint.ipynb | 1 | 914180 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "2b7256b9-a1c7-4314-a936-3f298d6d53f3"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Spectral Clustering Algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
" Notebook version: 1.1 (Nov 17, 2017)\n",
"\n",
" Author: Jesús Cid Sueiro ([email protected])\n",
" Jerónimo Arenas García ([email protected])\n",
"\n",
" Changes: v.1.0 - First complete version. \n",
" v.1.1 - Python 3 version"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "154f1592-4dc1-42cc-bc05-8a056a301385"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"\n",
"# use seaborn plotting defaults\n",
"import seaborn as sns; sns.set()\n",
"\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.datasets.samples_generator import make_blobs, make_circles\n",
"from sklearn.utils import shuffle\n",
"from sklearn.metrics.pairwise import rbf_kernel\n",
"from sklearn.cluster import SpectralClustering\n",
"\n",
"# For the graph representation\n",
"import networkx as nx"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "541253ea-29b0-4742-9293-e8d968fdb0d7"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 1. Introduction\n",
"\n",
"The key idea of spectral clustering algorithms is to search for groups of connected data. I.e, rather than pursuing compact clusters, spectral clustering allows for arbitrary shape clusters.\n",
"\n",
"This can be illustrated with two artifitial datasets that we will use along this notebook."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "08a40259-7b39-4628-bc49-dd2a2e0d3f45"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 1.1. Gaussian clusters:\n",
"\n",
"The first one consists of 4 compact clusters generated from a Gaussian distribution. This is the kind of dataset that are best suited to centroid-based clustering algorithms like $K$-means. If the goal of the clustering algorithm is to minimize the intra-cluster distances and find a representative prototype or centroid for each cluster, $K$-means may be a good option."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"nbpresent": {
"id": "6f43de04-0fd0-4f7b-a28f-b4d898420cda"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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c3mzQkePdCkeMlNZnVUVJvyHY0NyZdRjHhcJRvV0bkMdj/n2fV1pz0zzTjTDy\ncbnMZLYHcmNjo+644w698847Gj16tN2nBwBkIdvK5KqKEn3vS0tSWtKStGnnkUQIx6dHJT8mmVdw\nZyNqtgKJLnRFZ7srVb6xNZB7enr09NNPa9Qo89J5AMDwyHUt63jwmrWqJZm2tOPd3m/XBi56uc2a\nOZN1+zWz87oFPBDbiroMw9Ajjzyib33rWxo7dqxdpwUADIJZ0dZAlcmmreo9Ldq8O2A5JWnp/HLL\n7mczfp8n43i/31vwYSwNsoW8ceNGrV+/PuWx6dOn67bbbtPll1+e9XkmTRqnoiLfYC7BNmVlExx9\n/ULGvXcO9945w3Xvy8omaGXjSb3+/lH1haIa7ffqxk/P1NKFMyyfs3VfW2ar2mQP5FAoqpbTZ7V0\n4Qxt3deWURSWLr5hhc8rffqKck2aMDqn67KL2//uPYaRvnLp4Nx0000qLy+XJO3evVs1NTV68cUX\n+31Oe3u3HS89aGVlExy/hkLFvXcO9945Ttz7/pajNDv2h7+qTZ3v6/Moahgpoev3e3Xn9XPVey6s\ncaOL9NKbDVmPI/v9Xj24epEkDWvFtJv+7q0+GNg2hvz6668n/n399dfrX//1X+06NQBgkHKpTI6P\nCafvURzsOafahg5FjVigTi0Zmwhhr1eaXDxGwd4+hcJReTyxHaKiRmz6ktk62jv3t2nyxDF5O31p\nsJj2BABISJ/vu2N/m+qaTiUCds6lxWpq60m0iKNRqaPrY82rLFVN1ZTEGPXB5k7T1rPXI719fu5z\nvm4SMVhDEshvvfXWUJwWADAM4q3q9CKvaFRqaOnKaPVKsQCuKBuf8nxJam7vSVlcJBw1Es/P100i\nBosWMgDAlNnUKbMwlmLTit/6U0Bb9rRoVtKKXskt7pPBj/VWbSDlef1NxSo0rGUNADBlNXVq3oyJ\nls8JRww1tnTpqRd3acNr9ZIurKO97MrynKdiFRICGQBgympTh++sWaJPVV8ibz/zjyNRpcxX7u98\ntI5j6LIGAFiy2tTh61+oUWMgqB372rSlrkVhi/nK6d3RhbBJxGARyACAfllNnUo87pG27MkMZavu\n6HzfJGKw6LIGAFyUNSvn6Tt3LlZVxYU9lOmOzh0tZADARauqKNH37l6S08pgSEUgAwBsQ3f04NFl\nDQCACxDIAAC4AIEMAIALEMgAALgAgQwAgAsQyAAAuACBDACACxDIAAC4AIEMAIALEMgAALgAgQwA\ngAsQyAAKVoAWAAAR5ElEQVQAuACBDACACxDIAAC4AIEMAIALEMgAALgAgQwAgAsQyAAAuACBDACA\nCxDIAAC4AIEMAIALEMgAALhAkV0nikQievLJJ7Vv3z719fXpb//2b/W5z33OrtMDAJDXbAvk//qv\n/1I4HNavf/1rHT9+XJs2bbLr1AAA5D3bAnnbtm2aO3eu7rnnHhmGoUceecSuUwMAkPc8hmEYuT5p\n48aNWr9+fcpjkyZN0owZM7Ru3Tq9//77+ud//me9+OKL/Z4nHI6oqMiX68sDAJB3BhXIZu677z7d\ncsstuvnmmyVJ11xzjbZv397vc9rbu+146UErK5vg+DUUKu69c7j3zuHeO8dN976sbILp47ZVWX/q\nU5/S5s2bJUkHDhzQtGnT7Do1AAB5z7ZAvuOOO2QYhu644w498sgjevzxx+06NQAAec+2oq5Ro0bp\nySeftOt0AAAUFBYGAQDABQhkAABcgEAGAMAFCGQAAFyAQAYAwAUIZAAAXIBABgDABQhkAABcgEAG\nAMAFCGQAAFyAQAYAwAUIZAAAXIBABgDABQhkAABcgEAGAMAFCGQAAFyAQAYAwAUIZAAAXIBABgDA\nBQhkAABcgEAGAMAFCGQAAFyAQAYAwAUIZAAAXIBABgDABQhkAABcgEAGAMAFCGQAAFyAQAYAwAUI\nZADIQ42BoDbtPKLGQNDpS0GWiuw6UXd3t+677z719vZq1KhR+tGPfqSysjK7Tg8AyNKG1+q1ta5V\noXBU/iKvVtRM05qV85y+LAzAthbyK6+8ourqar300ku67bbb9Itf/MKuUwNAwcu2xXsoEEyEsSSF\nwlFt3duaVUuZVrWzbGshV1dXq6mpSZLU09OjoiLbTg0ABS29xVszZ7LmTC9RdWWpqipKUo5taO5M\nhHFcKBTVwebOjGP7ew1a1cPPYxiGkeuTNm7cqPXr16c8tnbtWj300EMaNWqUgsGgXnzxRV122WX9\nniccjqioyJfrywNAwThw+KS+99w76gtFM743yu/VTZ+eqb/58wUXjj9ySt97dnvK8aP9Xn3181fp\nzNmQrpgzRZfPmpzxGg89u12hsJHynCe+dk3GsRg6gwpkM9/4xje0fPlyrV69WgcOHNADDzyg3/3u\nd/0+p729246XHrSysgmOX0Oh4t47h3vvnGzufWMgqIPNnYnW76adR7Tx7UbL4/1+rx5cvSil9Zvc\n2vV6pckTRit4JmTZ+v3B/31fjS2Z17Xqs1W6demsQfyk7uOmv/uysgmmj9vWrzxx4kRNmBB7kSlT\npujMmTN2nRoACoJZt/Gy+eXyF3kzuqHjzLqj16ycp2DPOdU2dCgalTqC5y4cf35Medn8clVVlOhQ\nIKjDbZlB5fd5VF1Zav8PCUu2BfK9996rhx9+WC+99JLC4bC+//3v23VqAMh7m2sDent3QNHzuZsc\nnCtqpqUUaiXz+72J4Iy3rseNLlJd0ylFLfo/k0O8oblTEZOsn3nphH7HnGE/2wL50ksv1fPPP2/X\n6QCgIDQGgvr1mwdNu4zjwblm5Twtm1+ug82damoJqq7pVKwV7fdqxVXTVFVRop+9UhdrERuS1yPL\nMJYkn1caNzr29l9dWZrRAvf5pNU3zLX9Z0X/KIUGAIdseK1eW/a0KBwxT8/k1m9VRUmixZo+zvz0\nhj+q/lhX4nn9hbEkRaLSS282qLm9R2tWzktpgSeHPIYXgQwADojPF7YKY69XlsEYD+fGQFCP/J+d\nCnT0Wp4jGo0F+5xLi9XQ0mXaJZ7cAjebSoXhQSADgAPM5gvH+bzSmpvm6bpFFYnH0lvFG16r1+bd\nAdPx3+Rz9J4Lq7qyVDv2taW0oqXUseTkFjicQSADgAPMxm6l2PjtdQsqUsLYbGGQ+HixlYWfuCRx\njg2v1WtLXUvGMT6fqKR2ETaXAAAHVFWUaEXNNPmLYm/Dfp9HVdMn6rt3fipljrDZUpgDhfElE0fr\nlqtnpTzfrGvciEo79rfZ+FPhYtBCBgCHDDR22xgI6r+3f5TRiu4vjD2SOrrO6Ye/qtWKmmmaMnGM\nZdd41JC27m1VZVlxomubbmvnEMgA4CCrsdvkbup0fp9HIZMWr8+rxJhyvGjrxsUV8kiyyvBQKKoX\nXqtX1JCKfB5du2A6a1g7hC5rAHCZ9G7qZH6/VysWTNf1iyvkO/8O7vNJVdMnZBR4hUJR/f79Zssw\njou3uMMRQ2/XBjJ2e2IXqOFBCxkAXMaqArtmzmTdfs3sRIs6ubtbkp7csCujOztqUYVtJWpIO/e3\nJV6DXaCGDy1kAHCZeAV2Mr/fmxLGUqy7+9als1RVUSJD1t3SuYpvOXQxeysjdwQyALhMRgX2+dWz\nJFl2HTc0d8qevfukZVeWJ85ptbcy7EeXNQC4UHoF9o79bfrhr2otu46bWuxptXo8F/5tNlc6eTlP\n2IsWMgC4VLxL2pD67To+FAiqtqHDltc0DCVawFYtdaZGDQ1ayADgcv11HVdVlGjH/rYBN5TIVnoL\nmHWuhw+BDAAuV11ZmrGlotdzYdlLj01h7POZb2jBOtfDg0AGAJczq6BO/nrZleX6Q21gwCrr5IVD\nknk90uxpE7X6hrkXFbzpG2AgNwQyALicWQW1cX6+cDwAp5SMVkfwXMZz46t0+f1e1cyerLqmUynd\n32Y7Sw0G85UvHoEMAC5nVu3s9Uhvn99+0V/k1Ri/eY3u9Cnj9L+umpZotf7slbrE5hTxIq0ZU4u1\naeeRQbdsreYrL5tfTks5BwQyALhAf9298WrnRAvU51E4aiRW4QqFowpbbCBx45JKzZharIPNnXr1\n3SOqazqVGIuec2mxJPU7nSobAxWdITsEMgA47Gev1Kn2UIei51u7ZqG4ZuU8VZYVq7ahXf4ir3Yd\nTJ3mZChWlBWJXHis4pLx2vfRSb3wer3pEpr1x7p08FhXYux5sC1b5ivbg3nIAOCgn71Sp10HO1Ja\nu2bLU254rV4vvdmguqZT2tN40vRcyWEsSedC4ZRzm0kvBBvMSlzMV7YHLWQAcIjVgh7p3b3pY7Rh\nk60XzZgVeQ1ksC1b5itfPAIZABzS0NxpuqCH13thjnFjIKj/3v6R6e5P6XOTL9bFtmyZr3xxCGQA\ncIjZ2KskLfrEJaqqKEmZSpTO7/dqzqXFqj/WldNrejwy3YQifWvHZMwvHh4EMgA4JL162ueVFn7i\nEn39CzUZ3dTJ4i3ZKRPH5BzIZmFstrVjHPOLhw+BDAAOshp7NZtKJF1oyUrSpnePXPTr+3zSzLLx\npt/bXBvQ27sDGQVnzC8eGgQyADgsfey1MRDUya6PM5a6jLdkd+xv09u1gazGj+MrdZmZMmG0gr19\namzp1g9/VZvS+t3wWr3pazC/eOgQyADgIsldxF7PhTHfeDf1sRM9WYexZD1m7PNJwd6+RMV2cus3\nvt2j2Wswv3joEMgA4BLp48ZRQ/L7PFpRM13LrizXjv1tsUU+LMLYrOraKlRnlo1XY0t3yuPJc5BN\nq7q95rtBwR4EMgC4hOkSlBFDU0rGXGi1Wizy4fNJC6suSWweYbWzU/IYdHzJzLjk1m969bddm1DA\n2kWt1PX666/r/vvvT3y9e/durVq1SqtXr9ZPf/rTi744ACgk8WlQyeIhaVXkJcW6pa9bUKGvf6FG\nD35xkVZ9tkprbppneq7kauqZU4tV5PMkvhdv/ZqtvHXdwgrCeIgNuoX8xBNPaNu2bfrkJz+ZeOzR\nRx/VM888o8rKSt1zzz364IMPdMUVV9hyoQCQ7zI2kUhbqCO91erxSJ+ae4luvnqWqipKMuYLN7f3\npGxIMbMstplE8ji1zytVVUzU6utT90Jm5a3hN+hAXrx4sW688Ua9/PLLkqSenh719fVp5syZkqTl\ny5frnXfeIZABIAdWQWgV1slV0WbzhZfNL9ev32rQ4dYuNbZ06emX/qRI1EgUekWi0tETPabXwspb\nw2vAQN64caPWr1+f8ti6det022236d1330081tPTo+Li4sTX48ePV3Nzc7/nnjRpnIqKfLles63K\nyiY4+vqFjHvvHO69c7K592VlE7R04YyMx++7a4luXX5KHzSd1BVzpujyWZMlSQcOn9S2van7EW/f\n26pbl89RaelYNZ/oSYwnm62DHQpF1XL6rOlr5hO3/90PGMirVq3SqlWrBjxRcXGxzpw5k/j6zJkz\nmjhxYr/POX26N4tLHDplZRPU3t498IGwHffeOdx759hx76eM82vFleWSlDjXe3tb1RdKHV8+F4pq\n09YmdQTPZnwvnd/v1fRJY/P678JNf/dWHwxs236xuLhYfr9fR48elWEY2rZtm5YsWWLX6QEAFsyK\nwTweaUtdi+qaTmUc7/XEqqYltkp0E1unPT3++OP69re/rUgkouXLl2vBggV2nh4AYCJjTWyfZETN\nu6fjAUzBlvt4DMNsDZfh4XT3gZu6MAoN99453HvnDPW9j1dZn+z6WG/9KZDx/f52dMp3bvq7H/Iu\nawCAs6oqSnTr0llaNr98wDnIcB8CGQDyjNnCHowTux9LZwJAHmJhj5GHQAaAPMXCHiMLXdYAALgA\ngQwAgAsQyAAAuACBDAAu0hgIatPOI2oMBJ2+FAwziroAwCWsdmxCYaCFDAAucCgQTISxFNuxaeve\nVlrKBYRABgAXaGjuTIRxXCgU1cHmToeuCMONQAYAFzDbscnv96q6stShK8JwI5ABwAVY7hIUdQGA\nS7DcZWEjkAHARVjusnDRZQ0AgAsQyAAAuACBDACACxDIAAC4AIEMAIALEMgAALgAgQwAgAsQyAAA\nuACBDACAC3gMwzCcvggAAAodLWQAAFyAQAYAwAUIZAAAXIBABgDABQhkAABcgEAGAMAFipy+AKe8\n/vrrevXVV/XjH/9YkrR792794Ac/kM/n0/Lly/WNb3zD4SvMb4Zh6Nprr9Vll10mSVq4cKHuv/9+\nZy8qz0WjUT322GOqr6/XqFGj9MQTT2jWrFlOX1ZB+LM/+zMVFxdLkmbMmKEnn3zS4SvKf3v27NE/\n/uM/6oUXXtCRI0f03e9+Vx6PR3PnztWjjz4qr9d97dGCDOQnnnhC27Zt0yc/+cnEY48++qieeeYZ\nVVZW6p577tEHH3ygK664wsGrzG9Hjx7V/Pnz9dxzzzl9KQXjjTfeUF9fn15++WXt3r1bTz31lJ59\n9lmnLyvvnTt3ToZh6IUXXnD6UgrG888/r9/+9rcaO3asJOnJJ5/UN7/5TV199dVau3at3nzzTd10\n000OX2Um931EGAaLFy/WY489lvi6p6dHfX19mjlzpjwej5YvX6533nnHuQssAPv379fx48d19913\n66tf/aqampqcvqS8t2vXLq1YsUJSrEdi3759Dl9RYThw4IDOnj2rr3zlK/rSl76k3bt3O31JeW/m\nzJl65plnEl/v379fn/nMZyRJ1157rWvf3/O6hbxx40atX78+5bF169bptttu07vvvpt4rKenJ9Gd\nJEnjx49Xc3PzsF1nvjP7Paxdu1b33HOPbr31Vv3xj3/UAw88oN/85jcOXWFhSP879/l8CofDKirK\n67cBx40ZM0Z/9Vd/pVWrVunw4cP66le/qldffZX7PoRuvvlmHTt2LPG1YRjyeDySYu/v3d3dTl1a\nv/L6L2LVqlVatWrVgMcVFxfrzJkzia/PnDmjiRMnDuWlFRSz38PZs2fl8/kkSUuWLNGJEydS/tPA\nful/59FolFAYBrNnz9asWbPk8Xg0e/ZslZaWqr29XdOmTXP60gpG8nixm9/fC7LLOl1xcbH8fr+O\nHj0qwzC0bds2LVmyxOnLyms//elPE63mAwcOaNq0aYTxEFu8eLG2bNkiKVbEWF1d7fAVFYZ///d/\n11NPPSVJOn78uHp6elRWVubwVRWWK664ItErumXLFte+v/Px+LzHH39c3/72txWJRLR8+XItWLDA\n6UvKa/fcc48eeOABbd68WT6fj6rTYXDTTTdp+/btWr16tQzD0Lp165y+pILwF3/xF/r7v/97ffGL\nX5TH49G6devomRhm3/nOd/TII4/on/7pnzRnzhzdfPPNTl+SKXZ7AgDABeiyBgDABQhkAABcgEAG\nAMAFCGQAAFyAQAYAwAUIZAAAXIBABgDABQhkAABc4P8DwqfTljqKl+8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a8ccc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 300\n",
"nc = 4\n",
"Xs, ys = make_blobs(n_samples=N, centers=nc,\n",
" random_state=6, cluster_std=0.60, shuffle = False)\n",
"X, y = shuffle(Xs, ys, random_state=0)\n",
"\n",
"plt.scatter(X[:, 0], X[:, 1], s=30);\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "50e7375e-7cf9-467c-a698-dc46415bc32f"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Note that we have computed two data matrices: \n",
"\n",
"* ${\\bf X}$, which contains the data points in an arbitray ordering\n",
"* ${\\bf X}_s$, where samples are ordered by clusters, according to the cluster id array, ${\\bf y}$.\n",
"\n",
"Note that both matrices contain the same data (rows) but in different order. The sorted matrix will be useful later for illustration purposes, but keep in mind that, in a real clustering application, vector ${\\bf y}$ is unknown (learning is not supervised), and only a data matrix with an arbitrary ordering (like ${\\bf X}$) will be available. "
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "c363f2ed-7a6d-4dc4-a636-5da1a73c5a53"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 1.2. Concentric rings\n",
"\n",
"The second dataset contains two concentric rings. One could expect from a clustering algorithm to identify two different clusters, one per each ring of points. If this is the case, $K$-means or any other algorithm focused on minimizing distances to some cluster centroids is not a good choice."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"nbpresent": {
"id": "3d1726d2-7563-42bf-afcf-1ab07c6e6c36"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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RhLw8TyqHY7rKytJcH0LO8Nyt40TPEI53D+L62grMn1OO37x9GNsPncPEpIR8rxv33FyD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\n68ZJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d0776a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X2s, y2s = make_circles(n_samples=N, factor=.5, noise=.05, shuffle=False)\n",
"X2, y2 = shuffle(X2s, y2s, random_state=0)\n",
"plt.scatter(X2[:, 0], X2[:, 1], s=30)\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "289e6949-2aa0-4331-b0d8-0d361df8198b"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Note, again, that we have computed both the sorted (${\\bf X}_{2s}$) and the shuffled (${\\bf X}_2$) versions of the dataset in the code above."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "dd139c2e-cf0f-4619-a76f-e9f89d616a48"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 1:\n",
"\n",
"Using the code of the previous notebook, run the $K$-means algorithm with 4 centroids for the two datasets. In the light of your results, why do you think $K$-means does not work well for the second dataset?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"nbpresent": {
"id": "2986908d-e8b3-4a3a-9413-b2c3f49947b3"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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HYsSUgJFTQjJNcNeeFXjNPekaorPfqmHhdFSmZtodKcZPC1jylMmG+QbbHetT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\nAX+YHgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fc3d860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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KP+ngknxWpV17b6m0697ajUK3kvOECRN46aWXOPnkk1m4cCF77rln7rVEIsGp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"text/plain": [
"<matplotlib.figure.Figure at 0x11a5a3e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "981a130c-88a9-42cf-b102-f68dcd27dfc2"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Spectral clustering algorithms are focused on connectivity: clusters are determined by maximizing some measure of intra-cluster connectivity and maximizing some form of inter-cluster connectivity."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "bf938e8e-ef9a-4b43-b48e-fe56c550ec16"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 2. The affinity matrix\n",
"\n",
"### 2.1. Similarity function\n",
"\n",
"To implement a spectral clustering algorithm we must specify a similarity measure between data points. In this session, we will use the *rbf* kernel, that computes the similarity between ${\\bf x}$ and ${\\bf y}$ as:\n",
"\n",
"$$\\kappa({\\bf x},{\\bf y}) = \\exp(-\\gamma \\|{\\bf x}-{\\bf y}\\|^2)$$\n",
"\n",
"Other similarity functions can be used, like the kernel functions implemented in Scikit-learn (see the <a href=http://scikit-learn.org/stable/modules/metrics.html> metrics </a> module)."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "cf439cce-42e8-423d-a165-a95c7b860e2e"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 2.2. Affinity matrix\n",
"\n",
"For a dataset ${\\cal S} = \\{{\\bf x}^{(0)},\\ldots,{\\bf x}^{(N-1)}\\}$, the $N\\times N$ **affinity matrix** ${\\bf K}$ contains the similarity measure between each pair of samples. Thus, its components are\n",
"\n",
"$$K_{ij} = \\kappa\\left({\\bf x}^{(i)}, {\\bf x}^{(j)}\\right)$$\n",
"\n",
"The following fragment of code illustrates all pairs of distances between any two points in the dataset. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "0f9f293c-3137-4d17-aece-2045e8104ac8"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"gamma = 0.5\n",
"K = rbf_kernel(X, X, gamma=gamma)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "16417f57-b3c0-47e2-ab0d-98d5249ca768"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 2.3. Visualization\n",
"\n",
"We can visualize the affinity matrix as an image, by translating component values into pixel colors or intensities."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"nbpresent": {
"id": "eeffab07-357b-4096-b71c-7f5e21233434"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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1johKa9pMUhA/AMJOwo/vx9uJ+Onr9fsxXh2sbYkifI+DfGuyolQ5kqrqHVdD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ix8jny0hqhEwvpuyHfWoU799ThdH9VgwATVVG41sB327CeIgl5rawv/5Ccm02XO0guXTmPt+W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\nIlAqrAJFkZMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d94f9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(K, cmap='hot')\n",
"plt.colorbar()\n",
"plt.title('RBF Affinity Matrix for gamma = ' + str(gamma))\n",
"plt.grid('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "8497fea2-5fd3-4aa6-a6cc-2dc665e78823"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Despite the apparent randomness of the affinity matrix, it contains some hidden structure, that we can uncover by visualizing the affinity matrix computed with the sorted data matrix, ${\\bf X}_s$."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"nbpresent": {
"id": "f5d3f112-69d6-47ab-9ee3-63dd9181461a"
},
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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xcLzfttehABvqq4Zra+Hr63ABGU08+iSbk4z9+BBSUlIOHL8fYXFAfRaTkkTl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\nOzUu/wLcBYk0wJ2S8itl0/74aiNyaFcvVmdt3VtsrfoAqlB3L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"text/plain": [
"<matplotlib.figure.Figure at 0x11de18400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Ks = rbf_kernel(Xs, Xs, gamma=gamma)\n",
"\n",
"plt.imshow(Ks, cmap='hot')\n",
"plt.colorbar()\n",
"plt.title('RBF Affinity Matrix for gamma = ' + str(gamma))\n",
"plt.grid('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a37f7fe3-2aa3-4213-b574-734fe433ccf0"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Note that, despite their completely different appearance, both affinity matrices contain the same values, but with a different order of rows and columns.\n",
"\n",
"For this dataset, the sorted affinity matrix is almost block diagonal. Note, also, that the block-wise form of this matrix depends on parameter $\\gamma$."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "4b5f7ae5-bd92-481d-b2f9-f86e482a1a93"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 2:\n",
"\n",
"Modify the selection of $\\gamma$, and check the effect of this in the appearance of the *sorted* similarity matrix. Write down the values for which you consider that the structure of the matrix better resembles the number of clusters in the datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "40ef2f2d-9ac9-4a28-97c8-960219fe11d5"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a1c48591-1d25-4360-a144-0f362066607a"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Out from the diagonal block, similarities are close to zero. We can enforze a block diagonal structure be setting to zero the small similarity values. \n",
"\n",
"For instance, by thresholding ${\\bf K}_s$ with threshold $t$, we get the truncated (and sorted) affinity matrix\n",
"$$\n",
"\\overline{K}_{s,ij} = K_{s,ij} \\cdot \\text{u}(K_{s,ij} - t)\n",
"$$\n",
"\n",
"(where $\\text{u}()$ is the step function) which is block diagonal."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "57fc68ce-49bb-4418-bea2-d3b76a151fdc"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### Exercise 3:\n",
"\n",
"Compute the truncated and sorted affinity matrix with $t=0.001$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "856a2421-6e59-4d57-8968-7e59fbdb6c8d"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"t = 0.001\n",
"# Kt = <FILL IN> # Truncated affinity matrix\n",
"# Kst = <FILL IN> # Truncated and sorted affinity matrix\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "265d0881-369a-4047-a750-e5a7b6ba3ebe"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 3. Affinity matrix and data graph\n",
"\n",
"Any similarity matrix defines a weighted graph in such a way that the weight of the edge linking ${\\bf x}^{(i)}$ and ${\\bf x}^{(j)}$ is $K_{ij}$.\n",
"\n",
"If $K$ is a full matrix, the graph is fully connected (there is and edge connecting every pair of nodes). But we can get a more interesting sparse graph by setting to zero the edges with a small weights. \n",
"\n",
"For instance, let us visualize the graph for the truncated affinity matrix $\\overline{\\bf K}$ with threshold $t$. You can also check the effect of increasing or decreasing $t$."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"nbpresent": {
"id": "c0782247-c879-4b29-bc2d-6bbfb3d37865"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" b = plt.ishold()\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" plt.hold(b)\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
" warnings.warn(self.msg_depr_set % key)\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n",
" warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n"
]
},
{
"data": {
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MTAw33ngj+/fvr7Pg32q18ll8PFfn5ta9sEbMRERE6lAwkwbLyclhzpw55OXl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\nH6+MHKtAtNusAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e7b4320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G = nx.from_numpy_matrix(Kt)\n",
"graphplot = nx.draw(G, X, node_size=40, width=0.5,)\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "ccc022b6-0830-444f-a28f-d6e0269806bc"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Note that, for this dataset, the graph connects edges from the same cluster only. Therefore, the number of diagonal blocks in $\\overline{\\bf K}_s$ is equal to the number of connected components in the graph.\n",
"\n",
"Note, also, the graph does not depend on the sample ordering in the data matrix: the graphs for any matrix ${\\bf K}$ and its sorted version ${\\bf K}_s$ are the same."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "b069f8c4-6d49-4dfb-8762-ba94a5fc0063"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 4. The Laplacian matrix\n",
"\n",
"The <a href = https://en.wikipedia.org/wiki/Laplacian_matrix>Laplacian matrix</a> of a given affinity matrix ${\\bf K}$ is given by\n",
"$${\\bf L} = {\\bf D} - {\\bf K}$$\n",
"where ${\\bf D}$ is the diagonal **degree matrix** given by\n",
"$$D_{ii}=\\sum^{n}_{j} K_{ij}$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 4.1. Properties of the Laplacian matrix\n",
"\n",
"The Laplacian matrix of any symmetric matrix ${\\bf K}$ has several interesting properties:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### P1. \n",
"\n",
"> ${\\bf L}$ is symmetric and positive semidefinite. Therefore, all its eigenvalues $\\lambda_0,\\ldots, \\lambda_{N-1}$ are non-negative. Remind that each eigenvector ${\\bf v}$ with eigenvalue $\\lambda$ satisfies\n",
"\n",
"> $${\\bf L} \\cdot {\\bf v} = \\lambda {\\bf v}$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### P2. \n",
"\n",
"> ${\\bf L}$ has at least one eigenvector with zero eigenvalue: indeed, for ${\\bf v} = {\\bf 1}_N = (1, 1, \\ldots, 1)^\\intercal$ we get\n",
"> $${\\bf L} \\cdot {\\bf 1}_N = {\\bf 0}_N$$\n",
"\n",
"> where ${\\bf 0}_N$ is the $N$ dimensional all-zero vector."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### P3. \n",
"\n",
"> If ${\\bf K}$ is block diagonal, its Laplacian is block diagonal."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### P4. \n",
"\n",
"> If ${\\bf L}$ is a block diagonal with blocks ${\\bf L}_0, {\\bf L}_1, \\ldots, {\\bf L}_{c-1}$, then it has at least $c$ orthogonal eigenvectors with zero eigenvalue: indeed, each block ${\\bf L}_i$ is the Laplacian matrix of the graph containing the samples in the $i$ connected component, therefore, according to property P2,\n",
"\n",
"> $${\\bf L}_i \\cdot {\\bf 1}_{N_i} = {\\bf 0}_{N_i}$$\n",
"\n",
"> where $N_i$ is the number of samples in the $i$-th connected component.\n",
"\n",
"> Therefore, if $${\\bf v}_i = \\left(\\begin{array}{l} \n",
"{\\bf 0}_{N_0} \\\\\n",
"\\vdots \\\\\n",
"{\\bf 0}_{N_{i-1}} \\\\\n",
"{\\bf 1}_{N_i} \\\\\n",
"{\\bf 0}_{N_{i+1}} \\\\\n",
"\\vdots \\\\\n",
"{\\bf 0}_{N_{c-1}}\n",
"\\end{array}\n",
"\\right)\n",
"$$ \n",
"then\n",
"> $${\\bf L} \\cdot {\\bf v}_{i} = {\\bf 0}_{N}$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "28fce996-13b7-487a-977e-2022757453cb"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"We can compute the Laplacian matrix for the given dataset and visualize the eigenvalues:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"nbpresent": {
"id": "f4d08aab-9065-496e-811b-99d7c7b7d1a3"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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SB1GRarZ2iYgUjld4P9DXSch++0Cjtw4lxkWxpUtEFEYYvn7wt70XGwRvhAqw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\nREREfvb/AfjQiPKV56v2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d95d400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Dst = np.diag(np.sum(Kst, axis=1))\n",
"Lst = Dst - Kst\n",
"\n",
"# Next, we compute the eigenvalues of the matrix\n",
"w = np.linalg.eigvalsh(Lst)\n",
"plt.figure()\n",
"plt.plot(w, marker='.');\n",
"plt.title('Eigenvalues of the matrix')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "26820ff4-bafe-4d8e-b1ab-cde5945e1d7f"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 4: \n",
"Verify that ${\\bf 1}_N$ is an eigenvector with zero eigenvalues. To do so, compute ${\\bf L}_{st} \\cdot {\\bf 1}_N$ and verify that its <a href= https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html>euclidean norm</a> is close to zero (it may be not exactly zero due to finite precission errors).\n",
"\n",
"Verify that vectors ${\\bf v}_i$ defined above (that you can compute using `vi = (ys==i)`) also have zero eigenvalue. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"nbpresent": {
"id": "2c7f2660-a68a-462a-b9cb-8da9a5bd3615"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.6685939227e-13\n",
"7.23361208825e-14\n",
"9.18781831926e-14\n",
"6.66548169167e-14\n",
"9.86158062359e-14\n"
]
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "c6eee78c-18b7-47b5-a87d-6beedd90b40a"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 5: \n",
"\n",
"Verify that the spectral properties of the Laplacian matrix computed from ${\\bf K}_{st}$ still apply using the unsorted matrix, ${\\bf K}_t$: compute ${\\bf L}_{t} \\cdot {\\bf v}'_{i}$, where ${\\bf v}'_i$ is a binary vector with components equal to 1 at the positions corresponding to samples in cluster $i$ (that you can compute using `vi = (y==i)`)), and verify that its euclidean norm is close to zero."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"nbpresent": {
"id": "180d0716-e2b5-409e-966c-e88c881faee5"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.42122424262e-13\n",
"7.30253361464e-14\n",
"7.9896573425e-14\n",
"6.96696573109e-14\n",
"6.0239191263e-14\n"
]
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "513e8a60-8c97-4e42-a859-64bc0818f7b7"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Note that the position of 1's in eigenvectors ${\\bf v}_i$ points out the samples in the $i$-th connected component. This suggest the following tentative clustering algorithm:\n",
"\n",
"1. Compute the affinity matrix\n",
"2. Compute the laplacian matrix\n",
"3. Compute $c$ orthogonal eigenvectors with zero eigenvalue\n",
"4. If $v_{in}=1$, assign ${\\bf x}^{(n)}$ to cluster $i$. \n",
"\n",
"This is the grounding idea of some spectral clustering algorithms. In this precise form, this algorithm does not usually work, for several reasons that we will discuss next, but with some modifications it becomes a powerfull method."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a26295d1-cb15-4349-93c7-95c5894ae875"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 4.2. Computing eigenvectors of the Laplacian Matrix\n",
"\n",
"One of the reasons why the algorithm above may not work is that vectors ${\\bf v}'_0, \\ldots,{\\bf v}'_{c-1}$ are not the only zero eigenvectors or ${\\bf L}_t$: any linear combination of them is also a zero eigenvector. Eigenvector computation algorithms may return a different set of orthogonal eigenvectors.\n",
"\n",
"However, one can expect that eigenvector should have similar component in the positions corresponding to samples in the same connected component. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"nbpresent": {
"id": "86db57a2-6e1d-43d7-ba15-c0751fd41457"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11de25470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wst, vst = np.linalg.eigh(Lst)\n",
"\n",
"for n in range(nc):\n",
" plt.plot(vst[:,n], '.-')"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "1aa75837-0ae2-48a3-9dca-b8e0003a02e8"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 4.3. Non block diagonal matrices.\n",
"\n",
"Another reason to modify our tentative algorithm is that, in more realistic cases, the affinity matrix may have an imperfect block diagonal structure. In such cases, the smallest eigenvalues may be nonzero and eigenvectors may be not exactly piecewise constant."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "8bc8015c-514c-46f2-a175-31f15d1dd044"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 6\n",
"\n",
"Plot the eigenvector profile for the shuffled and not thresholded affinity matrix, ${\\bf K}$."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"nbpresent": {
"id": "3cdcb5ba-2e0e-412e-8cb3-db763608b1ce"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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jkfijaTCh0emy19eFkAr6PnkLroTuNM3dTNthK3KLa/08YuRxbl+b0kuEO0JJ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\nGK57fOrf/hzml7ex89GPAADOvuXfYXbhPIbTFv/g3ZfiRqyPoXENRrNO+jr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"text/plain": [
"<matplotlib.figure.Figure at 0x11de185f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "718f27af-91e4-475d-b7cb-fba201e6cd62"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Note that, despite the eigenvector components can not be used as a straighforward cluster indicator, they are strongly informative of the clustering structure. \n",
"\n",
"* All points in the same cluster have similar values of the corresponding eigenvector components $(v_{n0}, \\ldots, v_{n,c-1})$.\n",
"* Points from different clusters have different values of the corresponding eigenvector components $(v_{n0}, \\ldots, v_{n,c-1})$.\n",
"\n",
"Therfore we can define vectors ${\\bf z}^{(n)} = (v_{n0}, \\ldots, v_{n,c-1})$ and apply a centroid based algorithm (like $K$-means) to identify all points with similar eigenvector components. The corresponding samples in ${\\bf X}$ become the final clusters of the spectral clustering algorithm. \n",
"\n",
"\n",
"One possible way to identify the cluster structure is to apply a $K$-means algorithm over the eigenvector coordinates. The steps of the spectral clustering algorithm become the following"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "65685362-6064-4f02-8ef6-87ed5f060a98"
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"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 5. A spectral clustering (*graph cutting*) algorithm\n",
"\n",
"### 5.1. The steps of the spectral clustering algorithm.\n",
"\n",
"Summarizing, the steps of the spectral clustering algorithm for a data matrix ${\\bf X}$ are the following:\n",
"\n",
"1. Compute the affinity matrix, ${\\bf K}$. Optionally, truncate the smallest components to zero.\n",
"2. Compute the laplacian matrix, ${\\bf L}$\n",
"3. Compute the $c$ orthogonal eigenvectors with smallest eigenvalues, ${\\bf v}_0,\\ldots,{\\bf v}_{c-1}$\n",
"4. Construct the sample set ${\\bf Z}$ with rows ${\\bf z}^{(n)} = (v_{0n}, \\ldots, v_{c-1,n})$\n",
"5. Apply the $K$-means algorithms over ${\\bf Z}$ with $K=c$ centroids.\n",
"6. Assign samples in ${\\bf X}$ to clusters: if ${\\bf z}^{(n)}$ is assigned by $K$-means to cluster $i$, assign sample ${\\bf x}^{(n)}$ in ${\\bf X}$ to cluster $i$."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "981789e5-ccfc-474e-9d32-0fefcf9aa5ff"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Exercise 7:\n",
"\n",
"In this exercise we will apply the spectral clustering algorithm to the *two-rings* dataset ${\\bf X}_2$, using $\\gamma = 20$, $t=0.1$ and $c = 2$ clusters.\n",
"\n",
"* Complete step 1, and plot the graph induced by ${\\bf K}$"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"nbpresent": {
"id": "43c44088-9e78-4be4-a6ff-922681e09f6e"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" b = plt.ishold()\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" plt.hold(b)\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
" warnings.warn(self.msg_depr_set % key)\n",
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n",
" warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n"
]
},
{
"data": {
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0ADyio0lMTOSNN97Aw8OD+vXro1QqUalUHDp0iK5du5KRkcGAAQPo06cPRqOR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KOjI23btiU3N5ePPvrI9rwWiwVvb29atWpFSEgIeXl5nD59mr///e/Ex8czvlkzNul0//RPVKsp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\nhBBCCKGH/j9+RJsx8iFt4AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ef040b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "06201684-62f3-4ee6-a741-63c460a8c103"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Complete step 2, 3 and 4, and draw a scatter plot of the samples in ${\\bf Z}$"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"nbpresent": {
"id": "bc1e8136-120c-420b-b9ff-9e8b855cbff2"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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Yu0vxjW98Q3fccUcqj5cwog0AgCW4PQ4AgCWINgAAliDaAABYgmgDAGAJog0A\ngCWINgAAliDaAABYgmgDAGCJ/wc/m7t22s/xCgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ee5f5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# <SOL>\n",
"# </SOL>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "7efb7811-a289-4ded-927e-2a56b6f6be6b"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Complete step 5"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "1ddb1ab3-7456-48e3-8f86-3e638dce371f"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"est = KMeans(n_clusters=2)\n",
"clusters = est.fit_predict(Z2t)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "5a77d8f4-61bd-4031-84d9-e3da44a71218"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Finally, complete step 6 and show, in a scatter plot, the result of the clustering algorithm"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"nbpresent": {
"id": "f579c809-894b-41b9-81bd-be8f0ecd0be7"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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MF6/6gpYEZ4z30/WyIGldTRp2MelySZDTP6qeC1i3S3XSe0T6lKsJR85qXiCoLmLmLKjT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hwOB1VVVYwYMSIUv0qIiGAwwOPvNLL3VQsX97ZMxqHccPVwoNNM+S9l6DHwzXYT4xY5SRjQcis2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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f7f65c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X2[:, 0], X2[:, 1], c=clusters, s=50, cmap='rainbow')\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "ce6ff67b-20ed-491f-a63c-9ccd91218163"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### 5.2. Scikit-learn implementation.\n",
"\n",
"The <a href=http://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html> spectral clustering algorithm </a> in Scikit-learn requires the number of clusters to be specified. It works well for a small number of clusters but is not advised when using many clusters and/or data.\n",
"\n",
"Finally, we are going to run spectral clustering on both datasets. Spend a few minutes figuring out the meaning of parameters of the Spectral Clustering implementation of Scikit-learn:\n",
"\n",
"http://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html\n",
"\n",
"Note that there is not equivalent parameter to our threshold $t$, which has been useful for the graph representations. However, playing with $\\gamma$ should be enough to get a good clustering.\n",
"\n",
"The following piece of code executes the algorithm with an 'rbf' kernel. You can manually adjust the number of clusters and the parameter of the kernel to study the behavior of the algorithm. When you are done, you can also:\n",
"\n",
" - Modify the code to allow for kernels different than the 'rbf'\n",
" - Repeat the analysis for the second dataset (*two_rings*)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"nbpresent": {
"id": "bd5a6797-2d8a-4ccb-b4dc-69854465afbb"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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mMaLmU2WqaYdqu+793mUtIpU31YvzR3c09+TbWRyFvBLmea+fvnO/FYVcl0+z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\nAgQZAAoQZAAoQJABoIAPYkGlsHdD55kAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ed74780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_clusters = 4\n",
"gamma = .1 # Warning do not exceed gamma=100\n",
"SpClus = SpectralClustering(n_clusters=n_clusters,affinity='rbf',\n",
" gamma=gamma)\n",
"SpClus.fit(X)\n",
"\n",
"plt.scatter(X[:, 0], X[:, 1], c=SpClus.labels_.astype(np.int), s=50, \n",
" cmap='rainbow')\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"nbpresent": {
"id": "88eb9c1d-1e84-42ce-98e8-7dd254c25b62"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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Hf9BgtH4DADBr1iJw0cUAOF57JXds8umULRvLLb+Vjed49CrMnMNlM87VXhNk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"text/plain": [
"<matplotlib.figure.Figure at 0x11cf00940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nc = 2\n",
"gamma = 50 #Warning do not exceed gamma=300\n",
"\n",
"SpClus = SpectralClustering(n_clusters=nc, affinity='rbf', gamma=gamma)\n",
"SpClus.fit(X2)\n",
"\n",
"plt.scatter(X2[:, 0], X2[:, 1], c=SpClus.labels_.astype(np.int), s=50, \n",
" cmap='rainbow')\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"nbpresent": {
"id": "810003bf-2329-46f9-b051-9387386255db"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jcid/anaconda/envs/mypy36/lib/python3.6/site-packages/sklearn/manifold/spectral_embedding_.py:229: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n",
" warnings.warn(\"Graph is not fully connected, spectral embedding\"\n"
]
},
{
"data": {
"image/png": 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NR3NjG0GSpXsTmXEJKH37s/Vteh53eDkK2Ue1bNVDKrhLh9cKdBKVB7V5bAuB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\nMteXSlxYUphecw+ShFk8HbN4Olr3JvAQ5xySLfh3v0qyJtM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DgH4IgUpFlp9qK0/HrAKh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"<matplotlib.figure.Figure at 0x11cf007f0>"
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"source": [
"nc = 5\n",
"SpClus = SpectralClustering(n_clusters=nc, affinity='nearest_neighbors')\n",
"SpClus.fit(X2)\n",
"\n",
"plt.scatter(X2[:, 0], X2[:, 1], c=SpClus.labels_.astype(np.int), s=50, \n",
" cmap='rainbow')\n",
"plt.axis('equal')\n",
"plt.show()"
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"## 5.2. Other clustering algorithms.\n",
"\n",
"### 5.2.1. Agglomerative Clustering algorithms\n",
"\n",
"Bottom-up approach:\n",
"\n",
"* At the beginning, each data point is a different cluster\n",
"* At each step of the algorithm two clusters are merged, according to certain performance criterion\n",
"* At the end of the algorithm, all points belong to the root node\n",
"\n",
"In practice, this creates a hierarchical tree, that can be visualized with a dendogram. We can cut the tree at different levels, in each case obtaining a different number of clusters.\n",
"\n",
"<img src=https://www.mathworks.com/help/stats/dendrogram_partial.png> \n"
]
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"#### Criteria for merging clusters\n",
"\n",
"We merge the two closest clusters, where the distance between clusters is defined as:\n",
"\n",
"* Single: Distance between clusters is the minimum of the distances between any two points in the clusters\n",
"* Complete: Maximal distance between any two points in each cluster\n",
"* Average: Average distance between points in both clusters\n",
"* Centroid: Distance between the (Euclidean) centroids of both clusters\n",
"* Ward: We merge centroids so that the overall increment of {\\em within-cluster} variance is minimum. "
]
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"source": [
"#### Python implementations\n",
"\n",
"Hierarchical clustering may lead to clusters of very different sizes. Complete linkage is the worst strategy, while Ward gives the most regular sizes. However, the affinity (or distance used in clustering) cannot be varied with Ward, thus for non Euclidean metrics, average linkage is a good alternative. \n",
"\n",
"There are at least three different implementations of the algorithm:\n",
"\n",
"* Scikit-learn: Only implements `complete', `ward', and `average' linkage methods. Allows for the definition of connectivity constraints\n",
"* Scipy\n",
"* fastcluster: Similar to Scipy, but more efficient with respect to computation and memory."
]
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| mit |
idc9/law-net | vertex_metrics_experiment/procedural_v_substantive_scotus.ipynb | 1 | 379458 | {
"cells": [
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"repo_directory = '/Users/iaincarmichael/Dropbox/Research/law/law-net/'\n",
"\n",
"data_dir = '/Users/iaincarmichael/data/courtlistener/'\n",
"\n",
"import numpy as np\n",
"import sys\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# graph package\n",
"import igraph as ig\n",
"\n",
"# our code\n",
"sys.path.append(repo_directory + 'code/')\n",
"from setup_data_dir import setup_data_dir, make_subnetwork_directory\n",
"from pipeline.download_data import download_bulk_resource, download_master_edgelist, download_scdb\n",
"from helpful_functions import case_info\n",
"\n",
"from stats.viz import *\n",
"from stats.dim_reduction import *\n",
"from stats.linear_model import *\n",
"\n",
"sys.path.append(repo_directory + 'vertex_metrics_experiment/code/')\n",
"from rankscore_experiment_sort import *\n",
"from rankscore_experiment_LR import *\n",
"from make_tr_edge_df import *\n",
"\n",
"\n",
"# which network to download data for\n",
"network_name = 'scotus' # 'federal', 'ca1', etc\n",
"\n",
"\n",
"# some sub directories that get used\n",
"raw_dir = data_dir + 'raw/'\n",
"subnet_dir = data_dir + network_name + '/'\n",
"text_dir = subnet_dir + 'textfiles/'\n",
"\n",
"\n",
"# jupyter notebook settings\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"G = ig.Graph.Read_GraphML(subnet_dir + network_name +'_network.graphml')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# compute metrics"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%time d_pagerank = G.pagerank()\n",
"\n",
"%time u_pagerank = G.as_undirected().pagerank()"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%time d_betweenness = G.betweenness(directed=True)\n",
"\n",
"%time u_betweenness = G.as_undirected().betweenness(directed=False)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%time d_closeness = G.closeness(mode=\"IN\", normalized=True)\n",
"\n",
"%time u_closeness = G.as_undirected().closeness(normalized=True)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%time d_eigen = G.eigenvector_centrality()\n",
"\n",
"%time u_eigen = G.as_undirected().eigenvector_centrality()"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%time hubs = G.hub_score()\n",
"\n",
"%time authorities = G.authority_score()"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indegree = G.indegree()\n",
"\n",
"outdegree = G.outdegree()\n",
"\n",
"degree = G.degree()"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame(index=G.vs['name'])\n",
"\n",
"df['year'] = G.vs['year']\n",
"\n",
"df['indegree'] = indegree\n",
"df['outdegree'] = outdegree\n",
"df['degree'] = degree\n",
"df['d_pagerank'] = d_pagerank\n",
"df['u_pagerank'] = u_pagerank\n",
"df['d_betweenness'] = d_betweenness\n",
"df['u_betweenness'] = u_betweenness\n",
"df['d_closeness'] = d_closeness\n",
"df['u_closeness'] = u_closeness\n",
"df['d_eigen'] = d_eigen\n",
"df['u_eigen'] = u_eigen\n",
"df['hubs'] = hubs\n",
"df['authorities'] = authorities\n",
"\n",
"all_metrics = ['indegree', 'outdegree', 'degree',\n",
" 'd_pagerank', 'u_pagerank',\n",
" 'd_betweenness', 'u_betweenness',\n",
" 'd_closeness', 'u_closeness',\n",
" 'd_eigen', 'u_eigen',\n",
" 'hubs', 'authorities']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# issue area\n",
"\n",
"\n",
"Procedural\n",
"- 1\tCriminal Procedure\n",
"- 4\tDue Process\n",
"- 6\tAttorneys\n",
"- 9\tJudicial Power\n",
"\n",
"Substantive\n",
"- 2\tCivil Rights\n",
"- 3\tFirst Amendment\n",
"- 5\tPrivacy\n",
"- 7\tUnions\n",
"- 8\tEconomic Activity\n",
"- 12\tFederal Taxation\n",
"- 14\tPrivate Action\n",
"\n",
"Other\n",
"- 0 Missing\n",
"- 10\tFederalism\n",
"- 11\tInterstate Relations\n",
"- 13\tMiscellaneous\n",
"\n",
"\n",
"## hypothesis\n",
"- betweeness/closeness favor procedural cases\n",
"- eivenvector metrics (eigenvector centrality, hubs, authorities) favor substantive cases"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# map types to issues\n",
"type_to_issue = {'procedural': [1, 4, 6, 9],\n",
" 'substantive': [2, 3, 5, 7, 8, 12, 14],\n",
" 'other': [10, 11, 13, 0]}\n",
"\n",
"# map issues to type\n",
"issue_to_type = {i: '' for i in range(13 + 1)}\n",
"for t in type_to_issue.keys():\n",
" for i in type_to_issue[t]:\n",
" issue_to_type[i] = t"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create type\n",
"G.vs['issueArea'] = [int(i) for i in G.vs['issueArea']]\n",
"G.vs['type'] = [issue_to_type[i] for i in G.vs['issueArea']]\n",
"\n",
"# add to data frame\n",
"df['issueArea'] = G.vs['issueArea']\n",
"df['type'] = G.vs['type']"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"num substantive: 16891\n",
"num procedural: 9733\n",
"num other: 1261\n"
]
}
],
"source": [
"# get type subsets\n",
"df_sub = df[df['type'] == 'substantive']\n",
"df_pro = df[df['type'] == 'procedural']\n",
"df_oth = df[df['type'] == 'other']\n",
"\n",
"print 'num substantive: %d' % df_sub.shape[0]\n",
"print 'num procedural: %d' % df_pro.shape[0]\n",
"print 'num other: %d' % df_oth.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.to_csv(subnet_dir + 'issue_area/metrics.csv', index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# compare metric vs. issue type"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'year', u'indegree', u'outdegree', u'degree', u'd_pagerank',\n",
" u'u_pagerank', u'd_betweenness', u'u_betweenness', u'd_closeness',\n",
" u'u_closeness', u'd_eigen', u'u_eigen', u'hubs', u'authorities',\n",
" u'issueArea', u'type'],\n",
" dtype='object')"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x134dd9f50>"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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o2bMnpkyZgj59+pj1Pj4+2LFjBwCgdevWeO+99zBy5EiEhITg999/xzvvvONwW0888QT8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\n/MaGkAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x134dd9f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"metric = 'authorities'\n",
"\n",
"bins = np.linspace(min(df[metric]), max(df[metric]), 100)\n",
"\n",
"# substantive\n",
"plt.hist(df_sub[metric],\n",
" bins=bins,\n",
" color='red',\n",
" label='substantive (mean: %1.5f)' % np.mean(df_sub[metric]))\n",
"\n",
"# procedural\n",
"plt.hist(df_pro[metric],\n",
" bins=bins,\n",
" color='blue',\n",
" label='procedural (mean: %1.5f)' % np.mean(df_pro[metric]))\n",
"\n",
"# other\n",
"plt.hist(df_oth[metric],\n",
" bins=bins,\n",
" color='green',\n",
" label='other (mean: %1.5f)' % np.mean(df_oth[metric]))\n",
"\n",
"plt.xlim([0, .2])\n",
"plt.ylim([0, 2000])\n",
"\n",
"plt.xlabel(metric)\n",
"plt.legend(loc='upper right')"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"other NaN\n",
"procedural -0.169041\n",
"substantive 0.214262\n",
"Name: type, dtype: float64"
]
},
"execution_count": 220,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# look at propotion of top cases of each type\n",
"T = 100\n",
"\n",
"top_cases = df.sort_values(by=metric, ascending=False).iloc[0:T]['type']\n",
"top_breakdown = top_cases.value_counts(normalize=True)\n",
"\n",
"# compare to proportion of all cases\n",
"all_breakdown = df['type'].value_counts(normalize=True)\n",
"\n",
"diff = top_breakdown - all_breakdown\n",
"\n",
"diff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# permutation test\n",
"\n",
"Rank cases by _metric_ then look at the proportion of the top T (=100) cases that are substantive."
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"metric= 'indegree'\n",
"\n",
"df_pro_sub = df[df['type'] != 'other']\n",
"\n",
"T = 100\n",
"\n",
"# observed proportion of top cases that are substantive\n",
"obs_top_breakdown = df_pro_sub.\\\n",
" sort_values(by=metric, ascending=False).\\\n",
" iloc[0:T]['type'].\\\n",
" value_counts(normalize=True)\n",
" \n",
"obs_prop_sub = obs_top_breakdown['substantive']"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"R = 1000\n",
"\n",
"\n",
"perm_prop_sub = [0] * R\n",
"for r in range(R):\n",
" \n",
" # randomly select T cases\n",
" perm_indices = np.random.choice(range(df_pro_sub.shape[0]), replace=False, size=T)\n",
" \n",
" # compute the type breakdown of the T cases\n",
" perm_breakdown = df_pro_sub.\\\n",
" iloc[perm_indices]['type'].\\\n",
" value_counts(normalize=True)\n",
" \n",
" # proportion of T cases that are substantive\n",
" perm_prop_sub[r] = perm_breakdown['substantive']\n",
" \n",
"perm_prop_sub = np.array(perm_prop_sub)\n",
"pval = 1 - np.mean(perm_prop_sub < obs_prop_sub)"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x12f973bd0>"
]
},
"execution_count": 308,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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S/v2OYN0mk24p/3618XdL+b8P+BlARNwD3Afs3uC062rBRYuNWXvhYTzpwsP0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12fc15bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title('permutation test substantive vs. procedural (pval: %1.3f)' % pval)\n",
"plt.hist(perm_prop_sub,\n",
" color='blue',\n",
" label='permutation')\n",
"\n",
"plt.axvline(obs_prop_sub,\n",
" color='red',\n",
" label='obs')\n",
"\n",
"plt.xlabel(metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results\n",
"\n",
"hubs, authorities, u_eigen, d_eign, d_betweeness, u_betweeness are significant (confirming hypothesis)\n",
"\n",
"TODO: recompute u_closeness"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PC plot"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_pro_sub = df[df['type'] != 'other']\n",
"\n",
"U, D, V = get_PCA(df_pro_sub[all_metrics], scale=True)"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9Y90yaSaUgS2xM3mydYB17PBWrdqX1NQ8WrVaH6WGSTN0jPd+tXOuLTaAWuC9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\nUBq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2OfyaNTJMKCqS8LLjjvDcczKVaBFKiRht2zLmyM+X2BO3BMnKkl48Hg+88YbMxeLZ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QwZIiYYly3bxQdHIvmz4HbDPfeI6/nbb+Pu1pHPrUPwEiOOG1F3Q0XNFG21AA9x2rKBNmzG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2MB11Rc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\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PB3+JEtgi0va4LpCTAyQnA3Fx0R6NiAhZC+TmAvHxQEJCtEcjIrHOi0lxcUBiYrRHU1Xd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\nAABgJgmwAQAAAACYSQJsAAAAAABm0v8Pc9laylLMOG8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x130b02bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_2class_scores(U,\n",
" classes = df_pro_sub['type'],\n",
" start=6,\n",
" n_comp=5)"
]
}
],
"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": 0
}
| mit |
logmonster/blog | wordpress/move_around_mongo_n_es_1/import2mongo.ipynb | 2 | 10672 | {
"cells": [
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.4.0\n",
"something wrong with the data line > 5\n",
"something wrong with the data line > 11\n",
"something wrong with the data line > 14\n",
"something wrong with the data line > 21\n",
"something wrong with the data line > 28\n",
"something wrong with the data line > 35\n",
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"something wrong with the data line > 42\n",
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"something wrong with the data line > 82\n",
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"something wrong with the data line > 777\n",
"something wrong with the data line > 783\n",
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"something wrong with the data line > 814\n",
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"something wrong with the data line > 833\n",
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"something wrong with the data line > 890\n",
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"something wrong with the data line > 919\n",
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"something wrong with the data line > 933\n",
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"something wrong with the data line > 965\n",
"something wrong with the data line > 971\n",
"something wrong with the data line > 976\n",
"something wrong with the data line > 984\n",
"845 records inserted.\n"
]
}
],
"source": [
"# import the pymongo package\n",
"# if you could not reference this package =>\n",
"# remember jupyter is installed under anaconda; you would need to install this module under it\n",
"# \"conda install pymongo\" (might need sudo if folder access is required)\n",
"import pymongo\n",
"import json\n",
"\n",
"# print the pymongo version, check for api missing due to version...\n",
"print (pymongo.version)\n",
"\n",
"# setup a client; select the \"local\" database\n",
"_client = pymongo.MongoClient()\n",
"_db = _client.local\n",
"\n",
"# read the data file\n",
"_f = open(\"../../dataset/business_1000.json\", \"r\")\n",
"_lines = _f.readlines()\n",
"\n",
"# json list (for batch insert => insert_many)\n",
"_jList = []\n",
"\n",
"for _i in range(0, len(_lines)):\n",
" _line = _lines[_i]\n",
" _line = _line.replace(\"'\", \"\\\"\")\n",
" \n",
" try:\n",
" _json = json.loads(_line)\n",
" #print (_json['name']) # access a certain field under the json object\n",
" except:\n",
" print(\"something wrong with the data line > %i\" % _i)\n",
" _line = None\n",
" \n",
" # insert_one is not efficient comparing with bulk insert\n",
" #_result = _db.business.insert_one(_json)\n",
" #print(\"inserted~ id %s\" % _result.inserted_id) # the return response will have \"inserted_id\" field available\n",
" \n",
" if _line is not None:\n",
" _jList.append(_json)\n",
" \n",
"# insert_many\n",
"try:\n",
" _result = _db.business.insert_many( _jList )\n",
" print (\"%i records inserted.\" % len(_result.inserted_ids)) \n",
"except Exception as e:\n",
" print (e)\n",
" \n",
"_f.close()\n",
"_client.close()\n",
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
karlstroetmann/Formal-Languages | Python/FSM-2-Dot.ipynb | 2 | 6766 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.display import HTML\n",
"with open('../style.css', 'r') as file:\n",
" css = file.read()\n",
"HTML(css)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `dfa2string` converts the given deterministic <span style=\"font-variant:small-caps;\">Fsm</span> into a string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def dfa2string(Fsm):\n",
" states, sigma, delta, q0, final = Fsm\n",
" result = ''\n",
" n = 0\n",
" statesToNames = {}\n",
" for q in states:\n",
" statesToNames[q] = f'S{n}'\n",
" n += 1\n",
" result += 'states: {S0, ..., ' + f'S{n-1}' + '}\\n\\n' \n",
" result += f'start state: {statesToNames[q0]}' + '\\n\\n'\n",
" result += 'state encoding:\\n'\n",
" for q in states:\n",
" result += f'{statesToNames[q]} = {q}' + '\\n'\n",
" result += '\\ntransitions:\\n'\n",
" for q in states:\n",
" for c in sigma: \n",
" print(q, c, delta.get((q, c)))\n",
" if delta.get((q, c)) != None:\n",
" result += f'delta({statesToNames[q]}, {c}) = {statesToNames[delta[(q, c)]]}' + '\\n'\n",
" result += '\\nset of accepting states: {'\n",
" result += ', '.join({ statesToNames[q] for q in final })\n",
" result += '}\\n'\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import graphviz as gv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `dfa2dot` converts the given deterministic <span style=\"font-variant:small-caps;\">Fsm</span> into a graph in dot-format."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def dfa2dot(dfa):\n",
" states, sigma, delta, q0, final = dfa\n",
" dot = gv.Digraph('Deterministic FSM')\n",
" dot.graph_attr['rankdir'] = 'LR'\n",
" n = 0 # used to assign names to states\n",
" statesToNames = {} # assigns a name to every state\n",
" for q in states:\n",
" statesToNames[q] = f'S{n}'\n",
" n += 1\n",
" startName = statesToNames[q0]\n",
" dot.node('1', label='', width='0.1', height='0.1', style='filled', color='blue')\n",
" dot.edge('1', startName)\n",
" for q in states:\n",
" if q in final:\n",
" dot.node(statesToNames[q], peripheries='2')\n",
" else:\n",
" dot.node(statesToNames[q])\n",
" for q in states:\n",
" for c in sigma:\n",
" p = delta.get((q, c))\n",
" if p != None:\n",
" dot.edge(statesToNames[q], statesToNames[p], label = c)\n",
" return dot, statesToNames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `nfa2string` converts a non-deterministic finite state machine `nfa` into a string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def nfa2string(nfa):\n",
" states, sigma, delta, q0, final = nfa\n",
" n = 0\n",
" result = ''\n",
" result += f'states: {states}' + '\\n\\n' \n",
" result += f'start state: {q0}' + '\\n\\n'\n",
" result += 'transitions:\\n'\n",
" for q in states:\n",
" for c in sigma:\n",
" S = delta.get((q, c))\n",
" if S != None:\n",
" for p in S:\n",
" result += f'[{q}, {c}] |-> {p}' + '\\n'\n",
" S = delta.get((q, ''))\n",
" if S != None:\n",
" for p in S:\n",
" result += f'[{q}, \"\"] |-> {p}' + '\\n'\n",
" result += '\\n' + f'set of accepting states: {final}' + '\\n'\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `nfa2dot` takes a non-deterministic finite state machine and converts it \n",
"into a a dot graph."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def nfa2dot(nfa):\n",
" states, sigma, delta, q0, final = nfa\n",
" result = ''\n",
" n = 0\n",
" startName = str(q0)\n",
" dot = gv.Digraph('Non-Deterministic FSM')\n",
" dot.graph_attr['rankdir'] = 'LR'\n",
" dot.node('0', label='', width='0.1', height='0.1', style='filled', color='blue')\n",
" dot.edge('0', startName)\n",
" for q in states:\n",
" if q in final:\n",
" dot.node(str(q), peripheries='2')\n",
" else:\n",
" dot.node(str(q))\n",
" for q in states:\n",
" S = delta.get((q, ''))\n",
" if S != None:\n",
" for p in S:\n",
" dot.edge(str(q), str(p), label='𝜀', weight='0.1')\n",
" for q in states:\n",
" for c in sigma:\n",
" S = delta.get((q, c))\n",
" if S != None:\n",
" for p in S:\n",
" dot.edge(str(q), str(p), label=c, weight='10')\n",
" return dot"
]
},
{
"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": "3.9.0"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-2.0 |
crystalzhaizhai/cs207_yi_zhai | lectures/L4/Exercise_2_final.ipynb | 1 | 3668 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"This exercise will walk you through the creation of a dictionary.\n",
"1. First, load the data in the `names.txt` file. This file contains common names of some species. Load this into a list.\n",
"2. Do the same thing with the `species.txt` file. This file contains chemical formulae for some species.\n",
"3. Now create a dictionary where the data from `names.txt` are the values and the data from `species.txt` are the keys.\n",
" + You should automate the creation of the dictionary by using a `for` loop\n",
" + The `for` loop should iterate over the representation created by the `zip` function. You can refer to the example around line 32 in Lecture 4 for guidance.\n",
" + **HINT:** Initialize the dictionary by with `my_new_dict = {}` just before executing the `for` loop.\n",
"4. Access a value from the dictonary and print it to the screen.\n",
"\n",
"**Note:** The same dictionary can be created by executing the following one-liner: `species_dict_2 = dict(zip(species, names))`."
]
},
{
"cell_type": "code",
<<<<<<< HEAD:lectures/L4/Exercise_2_final.ipynb
"execution_count": 12,
=======
"execution_count": 27,
>>>>>>> 98b6e00cd2c5964c1e861a7a2ce90f14db43684e:lectures/L4/Exercise_2.ipynb
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
<<<<<<< HEAD:lectures/L4/Exercise_2_final.ipynb
"['Hydrogen', 'Oxygen', 'Hydroxyl', 'Water', 'Hydrogen', 'Peroxide']\n",
"['H2', 'O2', 'OH', 'H2O', 'H2O2']\n",
"{'Hydrogen': 'H2O2', 'Oxygen': 'O2', 'Hydroxyl': 'OH', 'Water': 'H2O'}\n"
=======
"['Hydrogen', 'Oxygen', 'Hydroxyl', 'Water', 'Hydrogen Peroxide', '']\n",
"['Hydrogen', 'Oxygen', 'Hydroxyl', 'Water', 'Hydrogen Peroxide']\n"
>>>>>>> 98b6e00cd2c5964c1e861a7a2ce90f14db43684e:lectures/L4/Exercise_2.ipynb
]
}
],
"source": [
<<<<<<< HEAD:lectures/L4/Exercise_2_final.ipynb
"\n",
"with open(\"names.txt\",\"r\") as f:\n",
" species=f.read().split();\n",
"print(species)\n",
"with open(\"species.txt\",'r') as g:\n",
" chemicals=g.read().split();\n",
"print(chemicals)\n",
"my_new_dict={};\n",
"for speci, chem in zip(species,chemicals):\n",
" my_new_dict[speci]=chem\n",
"print(my_new_dict)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Hydrogen': 'H2O2', 'Oxygen': 'O2', 'Hydroxyl': 'OH', 'Water': 'H2O'}\n"
]
}
],
"source": [
"my_second_dict=dict(zip(species, chemicals))\n",
"print(my_new_dict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
=======
"with open('names.txt') as fnames:\n",
" names = fnames.read()\n",
" names = names.split('\\n')\n",
" print(names)\n",
" names = filter(None, names)\n",
"print(list(names))"
]
>>>>>>> 98b6e00cd2c5964c1e861a7a2ce90f14db43684e:lectures/L4/Exercise_2.ipynb
}
],
"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 |
endJunction/CppDevTools | compile_time_analysis/compile_time_analysis.ipynb | 1 | 197452 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import matplotlib\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = open('timing.json')\n",
"data = json.load(f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(data)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f52e8f28c18>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jFvpo/+CybVu72KERNmCWSrt2FmG1b++ub9/eClfnztEdE7CKoKzMorgf/cgu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\nIlCwCSEkRfj/H3Z+x51frVUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f52e8eef438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(y='time')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f52e2933cc0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+zva1tr9he5UCuRvaPt/2I7Yftn2e7Q0K5C5t+6O2f2r7XNuH2l6qaW7Otu03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RQRC+E5xQ2zTpjJFPCilhCDHCtb7jx1ML+KNzTgWcJTVYjumo5RhPaU7ucD1aNSnqrTP1GmeFRE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\nKUmSJElaVBaakiRJkqRF9f8B3ZmrWfotY9MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f52e296b898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.sort('time').tail(40).plot(x='file', y='time', kind='bar', figsize=(16, 6), title='Compilation time in seconds')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
google/earthengine-community | guides/ipynb/image_math.ipynb | 1 | 9047 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Copyright 2021 The Earth Engine Community Authors { display-mode: \"form\" }\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mathematical Operations\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Image math can be performed using operators like `add()` and `subtract()`, but for complex computations with more than a couple of terms, the `expression()` function provides a good alternative. See the following sections for more information on [operators](https://developers.google.com#operators) and [expressions](https://developers.google.com#expressions)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Earth Engine setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"ee.Authenticate()\n",
"ee.Initialize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Folium setup (for interactive map display)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import folium\n",
"\n",
"\n",
"def add_ee_layer(self, ee_image_object, vis_params, name):\n",
" map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
" folium.raster_layers.TileLayer(\n",
" tiles=map_id_dict['tile_fetcher'].url_format,\n",
" attr='Map Data © <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
" name=name,\n",
" overlay=True,\n",
" control=True\n",
" ).add_to(self)\n",
"\n",
"folium.Map.add_ee_layer = add_ee_layer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operators\n",
"\n",
"Math operators perform basic arithmetic operations on image bands. They take two inputs: either two images or one image and a constant term, which is interpreted as a single-band constant image with no masked pixels. Operations are performed per pixel for each band.\n",
"\n",
"As a simple example, consider the task of calculating the Normalized Difference Vegetation Index (NDVI) using Landsat imagery, where `add()`, `subtract()`, and `divide()` operators are used:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load a 5-year Landsat 7 composite 1999-2003.\n",
"landsat_1999 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
"\n",
"# Compute NDVI.\n",
"ndvi_1999 = (landsat_1999.select('B4').subtract(landsat_1999.select('B3'))\n",
" .divide(landsat_1999.select('B4').add(landsat_1999.select('B3'))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** the normalized difference operation is available as a shortcut method: [`normalizedDifference()`](https://developers.google.com/earth-engine/apidocs/ee-image-normalizeddifference). \n",
"\n",
"Only the intersection of unmasked pixels between the two inputs are considered and returned as unmasked, all else are masked. In general, if either input has only one band, then it is used against all the bands in the other input. If the inputs have the same number of bands, but not the same names, they're used pairwise in the natural order. The output bands are named for the longer of the two inputs, or if they're equal in length, in the first input's order. The type of the output pixels is the union of the input types.\n",
"\n",
"The following example of multi-band image subtraction demonstrates how bands are matched automatically, resulting in a “change vector” for each pixel for each co-occurring band."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load a 5-year Landsat 7 composite 2008-2012.\n",
"landsat_2008 = ee.Image('LANDSAT/LE7_TOA_5YEAR/2008_2012')\n",
"\n",
"# Compute multi-band difference between the 2008-2012 composite and the\n",
"# previously loaded 1999-2003 composite.\n",
"diff = landsat_2008.subtract(landsat_1999)\n",
"\n",
"# Compute the squared difference in each band.\n",
"squared_difference = diff.pow(2)\n",
"\n",
"# Define a map centered on Australia.\n",
"map_diff = folium.Map(location=[-24.003, 133.565], zoom_start=5)\n",
"\n",
"# Add the image layers to the map and display it.\n",
"map_diff.add_ee_layer(diff,\n",
" {'bands': ['B4', 'B3', 'B2'], 'min': -32, 'max': 32},\n",
" 'diff.')\n",
"map_diff.add_ee_layer(squared_difference,\n",
" {'bands': ['B4', 'B3', 'B2'], 'max': 1000},\n",
" 'squared diff.')\n",
"display(map_diff.add_child(folium.LayerControl()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the second part of this example, the squared difference is computed using `image.pow(2)`. For the complete list of mathematical operators handling basic arithmetic, trigonometry, exponentiation, rounding, casting, bitwise operations and more, see the [API documentation](https://developers.google.com/earth-engine/apidocs)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressions\n",
"\n",
"To implement more complex mathematical expressions, consider using `image.expression()`, which parses a text representation of a math operation. The following example uses `expression()` to compute the Enhanced Vegetation Index (EVI):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load a Landsat 8 image.\n",
"image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')\n",
"\n",
"# Compute the EVI using an expression.\n",
"evi = image.expression(\n",
" '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {\n",
" 'NIR': image.select('B5'),\n",
" 'RED': image.select('B4'),\n",
" 'BLUE': image.select('B2')\n",
" })\n",
"\n",
"# Define a map centered on San Francisco Bay.\n",
"map_evi = folium.Map(location=[37.4675, -122.1363], zoom_start=9)\n",
"\n",
"# Add the image layer to the map and display it.\n",
"map_evi.add_ee_layer(evi, {\n",
" 'min': -1,\n",
" 'max': 1,\n",
" 'palette': ['a6611a', 'f5f5f5', '4dac26']\n",
"}, 'evi')\n",
"display(map_evi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observe that the first argument to `expression()` is the textual representation of the math operation, the second argument is a dictionary where the keys are variable names used in the expression and the values are the image bands to which the variables should be mapped. Bands in the image may be referred to as `b(\"band name\")` or `b(index)`, for example `b(0)`, instead of providing the dictionary. Bands can be defined from images other than the input when using the band map dictionary. Note that `expression()` uses \"floor division\", which discards the remainder and returns an integer when two integers are divided. For example `10 / 20 = 0`. To change this behavior, multiply one of the operands by `1.0`: `10 * 1.0 / 20 = 0.5`. Only the intersection of unmasked pixels are considered and returned as unmasked when bands from more than one source image are evaluated. Supported expression operators are listed in the following table.\n",
"\n",
"Operators for `expression()`:\n",
"\n",
"Type | Symbol | Name \n",
"---|---|--- \n",
"**Arithmetic** | \\+ - * / % ** | Add, Subtract, Multiply, Divide, Modulus, Exponent \n",
"**Relational** | == != < > <= >= | Equal, Not Equal, Less Than, Greater than, etc. \n",
"**Logical** | && || ! ^ | And, Or, Not, Xor \n",
"**Ternary** | ? : | If then else"
]
}
],
"metadata": {
"colab": {
"name": "Mathematical Operations"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
PAIR-code/facets | colab_facets.ipynb | 1 | 4658 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Facets Dive and Overview Colab Example",
"version": "0.3.2",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "M7JcESAhpKG-",
"colab_type": "code",
"colab": {}
},
"source": [
"#@title Install the facets_overview pip package.\n",
"!pip install facets-overview"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "blPpZw5R3Bb4",
"colab": {}
},
"source": [
"# Load UCI census train and test data into dataframes.\n",
"import pandas as pd\n",
"features = [\"Age\", \"Workclass\", \"fnlwgt\", \"Education\", \"Education-Num\", \"Marital Status\",\n",
" \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"Capital Gain\", \"Capital Loss\",\n",
" \"Hours per week\", \"Country\", \"Target\"]\n",
"train_data = pd.read_csv(\n",
" \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
" names=features,\n",
" sep=r'\\s*,\\s*',\n",
" engine='python',\n",
" na_values=\"?\")\n",
"test_data = pd.read_csv(\n",
" \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
" names=features,\n",
" sep=r'\\s*,\\s*',\n",
" skiprows=[0],\n",
" engine='python',\n",
" na_values=\"?\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "XtOzRy8Z3M36",
"colab": {}
},
"source": [
"\n",
"# Display the Dive visualization for the training data.\n",
"from IPython.core.display import display, HTML\n",
"\n",
"jsonstr = train_data.to_json(orient='records')\n",
"HTML_TEMPLATE = \"\"\"\n",
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\">\n",
" <facets-dive id=\"elem\" height=\"600\"></facets-dive>\n",
" <script>\n",
" var data = {jsonstr};\n",
" document.querySelector(\"#elem\").data = data;\n",
" </script>\"\"\"\n",
"html = HTML_TEMPLATE.format(jsonstr=jsonstr)\n",
"display(HTML(html))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "mjv5Kr1Mflq7",
"colab": {}
},
"source": [
"# Create the feature stats for the datasets and stringify it.\n",
"import base64\n",
"from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator\n",
"\n",
"gfsg = GenericFeatureStatisticsGenerator()\n",
"proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': train_data},\n",
" {'name': 'test', 'table': test_data}])\n",
"protostr = base64.b64encode(proto.SerializeToString()).decode(\"utf-8\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "b7zs2p2_goJa",
"colab": {}
},
"source": [
"# Display the facets overview visualization for this data\n",
"from IPython.core.display import display, HTML\n",
"\n",
"HTML_TEMPLATE = \"\"\"\n",
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\" >\n",
" <facets-overview id=\"elem\"></facets-overview>\n",
" <script>\n",
" document.querySelector(\"#elem\").protoInput = \"{protostr}\";\n",
" </script>\"\"\"\n",
"html = HTML_TEMPLATE.format(protostr=protostr)\n",
"display(HTML(html))"
],
"execution_count": 0,
"outputs": []
}
]
} | apache-2.0 |
Xero-Hige/Notebooks | Concurrentes/2017-1C/Ejercicio Seniales.ipynb | 1 | 7964 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#include <stdio.h>\n",
"#include <signal.h>\n",
"#include <unistd.h>\n",
"\n",
"int main (void){\n",
" printf(\"Proceso %d\\n\",getpid());\n",
"\n",
" sigset_t a;\n",
" sigemptyset(&a);\n",
"\n",
" sigaddset(&a,SIGINT);\n",
"\n",
" sigprocmask(SIG_BLOCK,&a,0);\n",
"\n",
" fork();\n",
"\n",
" printf(\"Proceso %d\\n\",getpid());\n",
"\n",
" while (1){sleep(1);}\n",
"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\t cgroups\tfilesystems kpagecgroup pagetypeinfo\t thread-self\r\n",
"116\t cmdline\tfs\t kpagecount partitions\t timer_list\r\n",
"12\t consoles\tinterrupts kpageflags sched_debug\t timer_stats\r\n",
"125\t cpuinfo\tiomem\t loadavg\t self\t\t tty\r\n",
"128\t crypto\tioports locks\t slabinfo\t uptime\r\n",
"23\t devices\tirq\t meminfo\t softirqs\t version\r\n",
"7\t diskstats\tkallsyms misc\t stat\t\t vmallocinfo\r\n",
"acpi\t dma\t\tkcore\t modules\t swaps\t\t vmstat\r\n",
"asound\t driver\tkey-users mounts\t sys\t\t zoneinfo\r\n",
"buddyinfo execdomains\tkeys\t mtrr\t sysrq-trigger\r\n",
"bus\t fb\t\tkmsg\t net\t sysvipc\r\n"
]
}
],
"source": [
"ls /proc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 129\r\n"
]
}
],
"source": [
"./example &"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\tbuddyinfo execdomains\tkeys\t mtrr\t sysrq-trigger\r\n",
"116\tbus\t fb\t\tkmsg\t net\t sysvipc\r\n",
"12\tcgroups filesystems\tkpagecgroup pagetypeinfo thread-self\r\n",
"125\tcmdline fs\t\tkpagecount partitions timer_list\r\n",
"129\tconsoles interrupts\tkpageflags sched_debug timer_stats\r\n",
"130\tcpuinfo iomem\tloadavg self\t tty\r\n",
"131\tcrypto\t ioports\tlocks\t slabinfo\t uptime\r\n",
"23\tdevices irq\t\tmeminfo softirqs\t version\r\n",
"7\tdiskstats kallsyms\tmisc\t stat\t vmallocinfo\r\n",
"acpi\tdma\t kcore\tmodules swaps\t vmstat\r\n",
"asound\tdriver\t key-users\tmounts\t sys\t zoneinfo\r\n"
]
}
],
"source": [
"ls /proc"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"attr\t\t cwd\t map_files oom_adj\t sessionid timers\r\n",
"autogroup\t environ maps oom_score setgroups timerslack_ns\r\n",
"auxv\t\t exe\t mem\t oom_score_adj smaps\t uid_map\r\n",
"cgroup\t\t fd\t mountinfo pagemap\t stack\t wchan\r\n",
"clear_refs\t fdinfo mounts personality stat\r\n",
"cmdline\t\t gid_map mountstats projid_map statm\r\n",
"comm\t\t io\t net\t root\t status\r\n",
"coredump_filter limits ns\t sched\t syscall\r\n",
"cpuset\t\t loginuid numa_maps schedstat task\r\n"
]
}
],
"source": [
"ls /proc/129"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name:\texample\r\n",
"State:\tS (sleeping)\r\n",
"Tgid:\t129\r\n",
"Ngid:\t0\r\n",
"Pid:\t129\r\n",
"PPid:\t125\r\n",
"TracerPid:\t0\r\n",
"Uid:\t0\t0\t0\t0\r\n",
"Gid:\t0\t0\t0\t0\r\n",
"FDSize:\t256\r\n",
"Groups:\t\r\n",
"NStgid:\t129\r\n",
"NSpid:\t129\r\n",
"NSpgid:\t129\r\n",
"NSsid:\t125\r\n",
"VmPeak:\t 4188 kB\r\n",
"VmSize:\t 4188 kB\r\n",
"VmLck:\t 0 kB\r\n",
"VmPin:\t 0 kB\r\n",
"VmHWM:\t 676 kB\r\n",
"VmRSS:\t 676 kB\r\n",
"RssAnon:\t 68 kB\r\n",
"RssFile:\t 608 kB\r\n",
"RssShmem:\t 0 kB\r\n",
"VmData:\t 184 kB\r\n",
"VmStk:\t 136 kB\r\n",
"VmExe:\t 4 kB\r\n",
"VmLib:\t 1780 kB\r\n",
"VmPTE:\t 32 kB\r\n",
"VmPMD:\t 12 kB\r\n",
"VmSwap:\t 0 kB\r\n",
"HugetlbPages:\t 0 kB\r\n",
"Threads:\t1\r\n",
"SigQ:\t1/31320\r\n",
"SigPnd:\t0000000000000000\r\n",
"ShdPnd:\t0000000000000000\r\n",
"SigBlk:\t0000000000000002\r\n",
"SigIgn:\t0000000001001000\r\n",
"SigCgt:\t0000000000000000\r\n",
"CapInh:\t00000000a80425fb\r\n",
"CapPrm:\t00000000a80425fb\r\n",
"CapEff:\t00000000a80425fb\r\n",
"CapBnd:\t00000000a80425fb\r\n",
"CapAmb:\t0000000000000000\r\n",
"Seccomp:\t0\r\n",
"Cpus_allowed:\tff\r\n",
"Cpus_allowed_list:\t0-7\r\n",
"Mems_allowed:\t00000000,00000001\r\n",
"Mems_allowed_list:\t0\r\n",
"voluntary_ctxt_switches:\t27\r\n",
"nonvoluntary_ctxt_switches:\t1\r\n"
]
}
],
"source": [
"cat /proc/129/status"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name:\texample\r\n",
"State:\tS (sleeping)\r\n",
"Tgid:\t130\r\n",
"Ngid:\t0\r\n",
"Pid:\t130\r\n",
"PPid:\t129\r\n",
"TracerPid:\t0\r\n",
"Uid:\t0\t0\t0\t0\r\n",
"Gid:\t0\t0\t0\t0\r\n",
"FDSize:\t64\r\n",
"Groups:\t\r\n",
"NStgid:\t130\r\n",
"NSpid:\t130\r\n",
"NSpgid:\t129\r\n",
"NSsid:\t125\r\n",
"VmPeak:\t 4188 kB\r\n",
"VmSize:\t 4188 kB\r\n",
"VmLck:\t 0 kB\r\n",
"VmPin:\t 0 kB\r\n",
"VmHWM:\t 80 kB\r\n",
"VmRSS:\t 80 kB\r\n",
"RssAnon:\t 80 kB\r\n",
"RssFile:\t 0 kB\r\n",
"RssShmem:\t 0 kB\r\n",
"VmData:\t 184 kB\r\n",
"VmStk:\t 136 kB\r\n",
"VmExe:\t 4 kB\r\n",
"VmLib:\t 1780 kB\r\n",
"VmPTE:\t 32 kB\r\n",
"VmPMD:\t 12 kB\r\n",
"VmSwap:\t 0 kB\r\n",
"HugetlbPages:\t 0 kB\r\n",
"Threads:\t1\r\n",
"SigQ:\t1/31320\r\n",
"SigPnd:\t0000000000000000\r\n",
"ShdPnd:\t0000000000000000\r\n",
"SigBlk:\t0000000000000002\r\n",
"SigIgn:\t0000000001001000\r\n",
"SigCgt:\t0000000000000000\r\n",
"CapInh:\t00000000a80425fb\r\n",
"CapPrm:\t00000000a80425fb\r\n",
"CapEff:\t00000000a80425fb\r\n",
"CapBnd:\t00000000a80425fb\r\n",
"CapAmb:\t0000000000000000\r\n",
"Seccomp:\t0\r\n",
"Cpus_allowed:\tff\r\n",
"Cpus_allowed_list:\t0-7\r\n",
"Mems_allowed:\t00000000,00000001\r\n",
"Mems_allowed_list:\t0\r\n",
"voluntary_ctxt_switches:\t30\r\n",
"nonvoluntary_ctxt_switches:\t0\r\n"
]
}
],
"source": [
"cat /proc/130/status"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "C",
"language": "c",
"name": "c_kernel"
},
"language_info": {
"file_extension": "c",
"mimetype": "text/plain",
"name": "c"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
rickiepark/tfk-notebooks | first-contact-with-tensorflow/chapter5_convolution_neural_network.ipynb | 1 | 11084 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"simple_neural_network 예제에서 MNIST 데이터를 이미 다운 받았으므로 다시 다운 받지 않습니다."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"x, y\\_ 플레이스홀더를 지정하고 x 를 28x28x1 크기로 차원을 변경합니다."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('x_image=', <tensorflow.python.framework.ops.Tensor object at 0x10e9dd990>)\n"
]
}
],
"source": [
"x = tf.placeholder(\"float\", shape=[None, 784])\n",
"y_ = tf.placeholder(\"float\", shape=[None, 10])\n",
"\n",
"x_image = tf.reshape(x, [-1,28,28,1])\n",
"print(\"x_image=\", x_image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"가중치를 표준편차 0.1을 갖는 난수로 초기화하는 함수와 바이어스를 0.1로 초기화하는 함수를 정의합니다."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"stride는 1로 하고 패딩은 0으로 하는 콘볼루션 레이어를 만드는 함수와 2x2 맥스 풀링 레이어를 위한 함수를 정의합니다."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"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], strides=[1, 2, 2, 1], padding='SAME')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"첫번째 콘볼루션 레이어를 만들기 위해 가중치와 바이어스 텐서를 만들고 활성화함수는 렐루 함수를 사용했습니다. 그리고 콘볼루션 레이어 뒤에 맥스 풀링 레이어를 추가했습니다."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"W_conv1 = weight_variable([5, 5, 1, 32])\n",
"b_conv1 = bias_variable([32])\n",
"\n",
"h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
"h_pool1 = max_pool_2x2(h_conv1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SAME 패딩이므로 콘볼루션으로는 차원이 변경되지 않고 풀링 단계에서 스트라이드에 따라 차원이 반으로 줄어든다."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(1)])\n",
"TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(32)])\n"
]
},
{
"data": {
"text/plain": [
"TensorShape([Dimension(None), Dimension(14), Dimension(14), Dimension(32)])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(x_image.get_shape())\n",
"print(h_conv1.get_shape())\n",
"h_pool1.get_shape()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"두번째 콘볼루션 레이어와 풀링 레이어를 만듭니다. 첫번째 콘볼루션의 필터가 32개라 두번째 콘볼루션의 컬러 채널이 32개가 되는 것과 같은 효과가 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"W_conv2 = weight_variable([5, 5, 32, 64])\n",
"b_conv2 = bias_variable([64])\n",
"\n",
"h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
"h_pool2 = max_pool_2x2(h_conv2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SAME 패딩이므로 콘볼루션으로는 차원이 변경되지 않고 풀링 단계에서 스트라이드에 따라 차원이 반으로 줄어든다."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorShape([Dimension(None), Dimension(14), Dimension(14), Dimension(64)])\n"
]
},
{
"data": {
"text/plain": [
"TensorShape([Dimension(None), Dimension(7), Dimension(7), Dimension(64)])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(h_conv2.get_shape())\n",
"h_pool2.get_shape()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"마지막 소프트맥스 레이어에 연결하기 위해 완전연결 레이어를 추가합니다. 이전 콘볼루션의 레이어의 결과 텐서를 다시 1차원 텐서로 변환하여 렐루 활성화 함수에 전달합니다."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"W_fc1 = weight_variable([7 * 7 * 64, 1024])\n",
"b_fc1 = bias_variable([1024])\n",
"\n",
"h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
"h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"드롭아웃되지 않을 확률 값을 저장할 플레이스홀더를 만들고 드롭아웃 레이어를 추가합니다."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"keep_prob = tf.placeholder(\"float\")\n",
"h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"마지막으로 소프트맥스 레이어를 추가합니다."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"W_fc2 = weight_variable([1024, 10])\n",
"b_fc2 = bias_variable([10])\n",
"\n",
"y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"크로스엔트로피와 최적화알고리즘, 평가를 위한 연산을 정의합니다."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
"train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
"correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"세션을 시작하고 변수를 초기화 합니다."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()\n",
"sess.run(tf.initialize_all_variables())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"20,000번 반복을 수행합니다."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 0, training accuracy 0.14\n",
"step 1000, training accuracy 0.96\n",
"step 2000, training accuracy 0.98\n",
"step 3000, training accuracy 1\n",
"step 4000, training accuracy 0.98\n",
"step 5000, training accuracy 0.98\n",
"step 6000, training accuracy 1\n",
"step 7000, training accuracy 1\n",
"step 8000, training accuracy 0.98\n",
"step 9000, training accuracy 0.98\n",
"step 10000, training accuracy 0.98\n",
"step 11000, training accuracy 1\n",
"step 12000, training accuracy 1\n",
"step 13000, training accuracy 1\n",
"step 14000, training accuracy 1\n",
"step 15000, training accuracy 1\n",
"step 16000, training accuracy 1\n",
"step 17000, training accuracy 1\n",
"step 18000, training accuracy 1\n",
"step 19000, training accuracy 1\n"
]
}
],
"source": [
"for i in range(20000):\n",
" batch = mnist.train.next_batch(50)\n",
" if i % 1000 == 0:\n",
" train_accuracy = sess.run(accuracy, feed_dict={\n",
" x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
" print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
" sess.run(train_step,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"최종 정확도를 출력합니다."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test accuracy 0.9915\n"
]
}
],
"source": [
"print(\"test accuracy %g\"% sess.run(\n",
" accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tritemio/FRETBursts | notebooks/Example - Customize the us-ALEX histogram.ipynb | 1 | 13387 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example - Customize the μs-ALEX histogram\n",
"\n",
"*This notebook is part of smFRET burst analysis software [FRETBursts](http://opensmfs.github.io/FRETBursts/).*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> In this notebook shows how to plot different styles of μs-ALEX histograms and $E$ and $S$ marginal distributions.\n",
"> For a complete tutorial on burst analysis see \n",
"> [FRETBursts - us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fretbursts import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns = init_notebook(apionly=True)\n",
"print('seaborn version: ', sns.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Tweak here matplotlib style\n",
"import matplotlib as mpl\n",
"mpl.rcParams['font.sans-serif'].insert(0, 'Arial')\n",
"mpl.rcParams['font.size'] = 12\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get and process data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
"download_file(url, save_dir='./data')\n",
"full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
"\n",
"d = loader.photon_hdf5(full_fname)\n",
"loader.alex_apply_period(d)\n",
"d.calc_bg(bg.exp_fit, time_s=1000, tail_min_us=(800, 4000, 1500, 1000, 3000))\n",
"d.burst_search(L=10, m=10, F=6)\n",
"ds = d.select_bursts(select_bursts.size, add_naa=True, th1=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ALEX joint plot\n",
"\n",
"The `alex_jointplot` function allows plotting an ALEX histogram with marginals.\n",
"This is how it looks by default:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The inner plot in an hexbin plot, basically a 2D histogram with hexagonal bins.\n",
"This kind of histograms resembles a scatter plot when sample size is small,\n",
"and is immune from grid artifacts typical of rectangular grids.\n",
"For more info for hexbin see [this document](doi.org/10.1371/journal.pone.0160716.s004).\n",
"\n",
"The marginal plots are histograms with an overlay KDE plot. \n",
"The same FRETBursts function that plots standalone *E* and *S* histograms \n",
"is used here to plot the marginals in the joint plot.\n",
"\n",
"Below I show how to customize appearance and type of this plot."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Changing colors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default the colormap range is computed on the range S=[0.2, 0.8],\n",
"so that the FRET populations (S ~ 0.5) have more contrast.\n",
"\n",
"To normalize the colormap to the whole data use the `vmax` argument:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, vmax_fret=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, vmax_fret=False, marginal_color=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, vmax_fret=False, marginal_color=7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, kind='kde')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or you can manually choose the max value mapped by the colormap (`vmax`):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, vmax=40)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Changing the colormap will affect both inner and marginal plots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, cmap='plasma')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To pick a different color from the colormap for the marginal histograms use `histcolor_id`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, cmap='plasma', marginal_color=83)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kinds of joint-plots\n",
"\n",
"The inner plot can be changed to a scatter plot or a [KDE plot](https://seaborn.pydata.org/generated/seaborn.kdeplot.html):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, kind='scatter')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, kind='kde')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsf = ds.select_bursts(select_bursts.naa, th1=40)\n",
"alex_jointplot(dsf, kind='kde',\n",
" joint_kws={'shade': False, 'n_levels': 12, 'bw': 0.04})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## No marginals\n",
"\n",
"Finally, we can plot only the hexbin 2D histogram without marginals:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5,5))\n",
"hexbin_alex(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figure layout"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can get an handle of the different axes in the figure for layout customization:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = alex_jointplot(ds)\n",
"g.ax_marg_x.grid(False)\n",
"g.ax_marg_y.grid(False)\n",
"g.ax_joint.set_xlim(-0.1, 1.1)\n",
"g.ax_joint.set_ylim(-0.1, 1.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`alex_jointplot` returns `g` which contains the axis handles (`g.ax_join`, `g.ax_marg_x`, `g.ax_marg_y)`.\n",
"The object `g` is a [`seaborn.JointGrid`](https://seaborn.pydata.org/generated/seaborn.JointGrid.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = alex_jointplot(ds)\n",
"g.ax_marg_x.grid(False)\n",
"g.ax_marg_y.grid(False)\n",
"g.ax_joint.set_xlim(-0.19, 1.19)\n",
"g.ax_joint.set_ylim(-0.19, 1.19)\n",
"plt.subplots_adjust(wspace=0, hspace=0)\n",
"g.ax_marg_y.spines['bottom'].set_visible(True)\n",
"g.ax_marg_x.spines['left'].set_visible(True)\n",
"g.ax_marg_y.tick_params(reset=True, bottom=True, top=False, right=False, labelleft=False)\n",
"g.ax_marg_x.tick_params(reset=True, left=True, top=False, right=False, labelbottom=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = alex_jointplot(ds)\n",
"g.ax_marg_x.grid(False)\n",
"g.ax_marg_y.grid(False)\n",
"g.ax_joint.set_xlim(-0.19, 1.19)\n",
"g.ax_joint.set_ylim(-0.19, 1.19)\n",
"plt.subplots_adjust(wspace=0, hspace=0)\n",
"g.ax_marg_y.tick_params(reset=True, bottom=True, top=False, right=False, labelleft=False)\n",
"g.ax_marg_x.tick_params(reset=True, left=True, top=False, right=False, labelbottom=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = alex_jointplot(ds)\n",
"g.ax_marg_x.grid(False, axis='x')\n",
"g.ax_marg_y.grid(False, axis='y')\n",
"g.ax_joint.set_xlim(-0.19, 1.19)\n",
"g.ax_joint.set_ylim(-0.19, 1.19)\n",
"plt.subplots_adjust(wspace=0, hspace=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Arguments of inner plots\n",
"\n",
"Additional arguments can be passed to the inner or marginal plots passing \n",
"a dictionary to `joint_kws` and `marginal_kws` respectively.\n",
"\n",
"The marginal plots are created by [`hist_burst_data`](http://fretbursts.readthedocs.io/en/latest/plots.html?highlight=hist_burst_data#fretbursts.burst_plot.hist_burst_data) \n",
"which is the same function used to plot standalone *E* and *S* histograms\n",
"in FRETBursts. \n",
"\n",
"For example, we can remove the KDE overlay like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alex_jointplot(ds, marginal_kws={'show_kde': False})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interactive plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import widgets, interact, interactive, fixed\n",
"from IPython.display import display, display_png, display_svg, clear_output\n",
"from IPython.core.pylabtools import print_figure"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmaps = ['viridis', 'plasma', 'inferno', 'magma',\n",
" 'afmhot', 'Blues', 'BuGn', 'BuPu', 'GnBu', 'YlGnBu',\n",
" 'coolwarm', 'RdYlBu', 'RdYlGn', 'Spectral',]# 'icefire'] uncomment if using seaborn 0.8"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@interact(overlay = widgets.RadioButtons(options=['fit model', 'KDE'], value='KDE'),\n",
" binwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
" bandwidth = widgets.FloatText(value=0.03, min=0.01, max=1),\n",
" gridsize = (10, 100),\n",
" min_size=(10, 500, 5),\n",
" cmap=widgets.Dropdown(value='Spectral', options=cmaps),\n",
" reverse_cmap = True,\n",
" vmax_fret = True,\n",
" )\n",
"def plot_(min_size=50, overlay='KDE', binwidth=0.03, bandwidth=0.03, \n",
" gridsize=50, cmap='Spectral', reverse_cmap=False, \n",
" vmax_fret=True):\n",
" dx = d.select_bursts(select_bursts.size, add_naa=True, th1=min_size)\n",
" bext.bursts_fitter(dx, 'E', binwidth=binwidth, bandwidth=bandwidth, \n",
" model=mfit.factory_three_gaussians())\n",
" bext.bursts_fitter(dx, 'S', binwidth=binwidth, bandwidth=bandwidth, \n",
" model=mfit.factory_two_gaussians()) \n",
" \n",
" if reverse_cmap: cmap += '_r'\n",
"\n",
" if binwidth < 0.01: binwidth = 0.01\n",
" if bandwidth < 0.01: bandwidth = 0.01\n",
" if overlay == 'fit model':\n",
" marginal_kws = dict(binwidth=binwidth, show_model=True, pdf=True, \n",
" show_kde=False)\n",
" else:\n",
" marginal_kws = dict(binwidth=binwidth, show_kde=True, \n",
" bandwidth=bandwidth)\n",
" alex_jointplot(dx, cmap=cmap, gridsize=gridsize, vmax_fret=vmax_fret, \n",
" marginal_kws=marginal_kws,)\n",
" \n",
" fig = gcf()\n",
" plt.close()\n",
" display(fig)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
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"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "264px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": false,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_position": {
"height": "673px",
"left": "0px",
"right": "1139.11px",
"top": "107px",
"width": "212px"
},
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"toc_window_display": true,
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}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
StingraySoftware/notebooks | Simulator/Concepts/Inverse Transform Sampling.ipynb | 1 | 26164 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inverse Transform Sampling\n",
"\n",
"This notebook will conceptualize how inverse transform sampling works"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import numpy.random as ra\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is a spectrum which follows an `almost` bell-curve type distribution (anyway, the specific type of distribution is not important here). "
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 2, 3, 4, 5, 6], [2000, 4040, 6500, 6000, 4020, 2070]]"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spectrum = [[1, 2, 3, 4, 5, 6],[2000, 4040, 6500, 6000, 4020, 2070]]\n",
"energies = np.array(spectrum[0])\n",
"fluxes = np.array(spectrum[1])\n",
"spectrum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, first we compute probabilities of flux. Afterwards, we compute the cumulative probability."
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.08120179, 0.2452294 , 0.5091352 , 0.75274056, 0.91595615, 1. ])"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob = fluxes/float(sum(fluxes))\n",
"cum_prob = np.cumsum(prob)\n",
"cum_prob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We draw ten thousand numbers from uniform random distribution."
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.49834338, 0.31993222, 0.35882619, 0.15837646, 0.22595417,\n",
" 0.85575223, 0.85203039, 0.78380252, 0.04170078])"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 10000\n",
"R = ra.uniform(0, 1, N)\n",
"R[1:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We assign energies to events corresponding to the random number drawn.\n",
"\n",
"_Note: The command below finds bin interval using a single command. I am not sure though that it's very readble. Would\n",
"we want to split that in multiple lines and maybe use explicit loops to make it more readable? Or is it fine as it is?\n",
"Comments?_"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3, 3, 3, 2, 2, 5, 5, 5, 1]"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen_energies = [int(energies[np.argwhere(cum_prob == min(cum_prob[(cum_prob - r) > 0]))]) for r in R]\n",
"gen_energies[1:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram energies to get shape approximation."
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 825, 1652, 2626, 2466, 1589, 842], dtype=int64)"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen_energies = ((np.array(gen_energies) - 1) / 1).astype(int)\n",
"times = np.arange(1, 6, 1)\n",
"lc = np.bincount(gen_energies, minlength=len(times))\n",
"lc"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
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vx7O8jYhscd1WiEg5T7c1zlBVOo1pTOOclXmy+ftOxzHJ1F21m7Kw7Gd8uWIA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xba38d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot1, = plt.plot(lc/float(sum(lc)), 'r--', label='Assigned energies')\n",
"plot2, = plt.plot(prob,'g',label='Original Spectrum')\n",
"plt.xlabel('Energies')\n",
"plt.ylabel('Probability')\n",
"plt.legend(handles=[plot1,plot2])\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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
gvanderheide/discreteMarkovChain | docs/multirandomwalk.ipynb | 1 | 5105 | {
"metadata": {
"name": "",
"signature": "sha256:505ba4c2550b28b16b276cf231b060ef5e3f50cdf4a960b88f87e937c819b046"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First lets assign the variables for our random walk."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 2\n",
"m = 0\n",
"M = 4\n",
"uprate = 2.\n",
"downrate = 1."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's look at the transitions for a specific state, namely:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"state = np.array([0,3])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The possible transitions are going up in either of the two random walks, or going down in the 2nd random walk. Below we define the possible events, which are simply two stacked identity matrices."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"events = np.vstack((np.eye(n,dtype=int),-np.eye(n,dtype=int)))\n",
"events\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"array([[ 1, 0],\n",
" [ 0, 1],\n",
" [-1, 0],\n",
" [ 0, -1]])"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"state + events"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"array([[ 1, 3],\n",
" [ 0, 4],\n",
" [-1, 3],\n",
" [ 0, 2]])"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the states after the transitions, but clearly there is a mistake since [-1,3] is not allowed. We are going to fix that now, by looking only at the possible events in our state."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"up = state < M \n",
"down = state > m\n",
"possibleEvents = np.concatenate((up,down))\n",
"possibleEvents"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"array([ True, True, False, True], dtype=bool)"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"state + events[possibleEvents]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"array([[1, 3],\n",
" [0, 4],\n",
" [0, 2]])"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is correct now. We can also calculate the corresponding transition rates:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"eventRates = np.concatenate((uprate*np.ones(n),downrate*np.ones(n)))\n",
"eventRates\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"array([ 2., 2., 1., 1.])"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ones that correspond to our state are simply"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eventRates[possibleEvents]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([ 2., 2., 1.])"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
}
| mit |
spacedrabbit/PythonBootcamp | Advanced Python Objects - Test.ipynb | 1 | 5568 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advanced Python Objects Test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Numbers\n",
"\n",
"**Problem 1: Convert 1024 to binary and hexadecimal representation:**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0b10000000000\n",
"0x400\n"
]
}
],
"source": [
"print bin(1024)\n",
"print hex(1024)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 2: Round 5.23222 to two decimal places**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.23\n"
]
}
],
"source": [
"print round(5.2322, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Strings\n",
"\n",
"**Problem 3: Check if every letter in the string s is lower case**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nope\n"
]
}
],
"source": [
"s = 'hello how are you Mary, are you feeling okay?'\n",
"print 'Yup' if s.islower() else 'Nope'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 4: How many times does the letter 'w' show up in the string below?**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12\n"
]
}
],
"source": [
"s = 'twywywtwywbwhsjhwuwshshwuwwwjdjdid'\n",
"print s.count('w')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Sets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 5: Find the elements in set1 that are not in set2:**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set([2])\n",
"set([7])\n"
]
}
],
"source": [
"set1 = {2,3,1,5,6,8}\n",
"set2 = {3,1,7,5,6,8}\n",
"\n",
"print set1.difference(set2) # in set 1 but not set 2\n",
"print set2.difference(set1) # in set 2 but not set 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 6: Find all elements that are in either set:**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set([1, 2, 3, 5, 6, 7, 8])\n",
"set([8, 1, 3, 5, 6])\n"
]
}
],
"source": [
"print set1.union(set2) # all unique elements in either set\n",
"print set1.intersection(set2) # all elements in both sets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Dictionaries\n",
"\n",
"**Problem 7: Create this dictionary:\n",
"{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}\n",
" using dictionary comprehension.**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{x:x**3 for x in range(5)}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Lists\n",
"\n",
"**Problem 8: Reverse the list below:**"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[4, 3, 2, 1]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1,2,3,4]\n",
"l.reverse() # reverses in place, call the list again to check\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 9: Sort the list below**"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [3,4,2,5,1]\n",
"l.sort()\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Great Job!"
]
}
],
"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": 0
}
| mit |
gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | 6_ARTVIEW-dev-interface.ipynb | 2 | 994 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## DEVELOPMENT OF ARTVIEW, A MODULAR GRAPHICAL USER INTERFACE FOR RADAR DATA\n",
"##### 1:30 PM"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Stuff"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/sysmon_ghostpack_safetykatz.ipynb | 1 | 2658 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Detection of SafetyKatz\n",
"Detects possible SafetyKatz Behaviour"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Detection of SafetyKatz\n",
" id: e074832a-eada-4fd7-94a1-10642b130e16\n",
" status: experimental\n",
" description: Detects possible SafetyKatz Behaviour\n",
" references:\n",
" - https://github.com/GhostPack/SafetyKatz\n",
" tags:\n",
" - attack.credential_access\n",
" - attack.t1003\n",
" author: Markus Neis\n",
" date: 2018/07/24\n",
" logsource:\n",
" product: windows\n",
" service: sysmon\n",
" category: null\n",
" detection:\n",
" selection:\n",
" EventID: 11\n",
" TargetFilename: '*\\Temp\\debug.bin'\n",
" condition: selection\n",
" falsepositives:\n",
" - Unknown\n",
" level: high\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-endpoint-winevent-sysmon-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='(event_id:\"11\" AND file_name.keyword:*\\\\Temp\\\\debug.bin)')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
pramitchoudhary/Experiments | notebook_gallery/other_experiments/explore-models/modelinterpretation/titanic_sbrl_evaluation.ipynb | 1 | 22385 | {
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"code_folding": []
},
"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>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>"
],
"text/plain": [
" 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 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Reading the csv\n",
"titanic_df = pd.read_csv(\"/home/deploy/pramit/data/titanic/train.csv\")\n",
"titanic_df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"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",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Encoded \\\n",
"0 0 A/5 21171 7.2500 NaN S 1 \n",
"1 0 PC 17599 71.2833 C85 C 0 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 \n",
"3 0 113803 53.1000 C123 S 0 \n",
"4 0 373450 8.0500 NaN S 1 \n",
"\n",
" Embarked_Encoded \n",
"0 2 \n",
"1 0 \n",
"2 2 \n",
"3 2 \n",
"4 2 \n",
" PassengerId Survived Pclass Age SibSp Parch Fare Sex_Encoded \\\n",
"0 1 0 3 22.0 1 0 7.2500 1 \n",
"1 2 1 1 38.0 1 0 71.2833 0 \n",
"2 3 1 3 26.0 0 0 7.9250 0 \n",
"3 4 1 1 35.0 1 0 53.1000 0 \n",
"4 5 0 3 35.0 0 0 8.0500 1 \n",
"\n",
" Embarked_Encoded \n",
"0 2 \n",
"1 0 \n",
"2 2 \n",
"3 2 \n",
"4 2 \n"
]
}
],
"source": [
"# Quick data transformation and cleaning ...\n",
"titanic_df[\"Sex\"] = titanic_df[\"Sex\"].astype('category')\n",
"titanic_df[\"Sex_Encoded\"] = titanic_df[\"Sex\"].cat.codes\n",
"\n",
"titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].astype('category')\n",
"titanic_df[\"Embarked_Encoded\"] = titanic_df[\"Embarked\"].cat.codes\n",
"print(titanic_df.head(5))\n",
"titanic_df_clean = titanic_df.drop(['Ticket','Cabin', 'Name', 'Sex', 'Embarked'], axis=1)\n",
"# # Remove NaN values\n",
"titanic_df_clean = titanic_df_clean.dropna() \n",
"print(titanic_df_clean.head(5))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"y = titanic_df_clean['Survived']"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" PassengerId Pclass Age SibSp Parch Fare Sex_Encoded \\\n",
"0 1 3 22.0 1 0 7.2500 1 \n",
"1 2 1 38.0 1 0 71.2833 0 \n",
"2 3 3 26.0 0 0 7.9250 0 \n",
"3 4 1 35.0 1 0 53.1000 0 \n",
"4 5 3 35.0 0 0 8.0500 1 \n",
"\n",
" Embarked_Encoded label \n",
"0 2 0 \n",
"1 0 1 \n",
"2 2 1 \n",
"3 2 1 \n",
"4 2 0 \n",
"<type 'str'>\n",
"['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_Encoded', 'Embarked_Encoded', 'label']\n"
]
}
],
"source": [
"data = titanic_df_clean.drop(['Survived'], axis=1)\n",
"data['label'] = y\n",
"print(data.head())\n",
"# Lets trying building an Interpretable Model\n",
"feature_labels = list(data.columns)\n",
"print(type(feature_labels[0]))\n",
"print(feature_labels)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'rpy2.robjects.vectors.DataFrame'>\n"
]
}
],
"source": [
"from rpy2.robjects import r, pandas2ri\n",
"pandas2ri.activate()\n",
"r_data = pandas2ri.py2ri(data)\n",
"\n",
"as_factor = ro.r['as.factor']\n",
"s_apply = ro.r['sapply']\n",
"frame = ro.r['data.frame']\n",
"t = frame(s_apply(r_data, as_factor))\n",
"print(type(t))"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'rpy2.robjects.vectors.DataFrame'>\n"
]
}
],
"source": [
"print(type(r_data))"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"sbrl = importr('sbrl')"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'__rpackagename__': 'sbrl', '__rname__': 'sbrl', '_local_env': <rpy2.rinterface.SexpEnvironment - Python:0x7fc45eaebf60 / R:0xa8ba0c8>, '_prm_translate': OrderedDict([('neg_sign', 'neg_sign'), ('minsupport_pos', 'minsupport_pos'), ('pos_sign', 'pos_sign'), ('iters', 'iters'), ('tdata', 'tdata'), ('nchain', 'nchain'), ('rule_minlen', 'rule_minlen'), ('eta', 'eta'), ('rule_maxlen', 'rule_maxlen'), ('minsupport_neg', 'minsupport_neg'), ('alpha', 'alpha'), ('lambda', 'lambda')])}\n"
]
}
],
"source": [
"print(sbrl.sbrl.__dict__)"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Eclat\n",
"\n",
"parameter specification:\n",
" tidLists support minlen maxlen target ext\n",
" FALSE 0.1 1 3 frequent itemsets FALSE\n",
"\n",
"algorithmic control:\n",
" sparse sort verbose\n",
" 7 -2 TRUE\n",
"\n",
"Absolute minimum support count: 29 \n",
"\n",
"create itemset ... \n",
"set transactions ...[514 item(s), 290 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [12 item(s)] done [0.00s].\n",
"creating bit matrix ... [12 row(s), 290 column(s)] done [0.00s].\n",
"writing ... [89 set(s)] done [0.00s].\n",
"Creating S4 object ... done [0.00s].\n",
"Eclat\n",
"\n",
"parameter specification:\n",
" tidLists support minlen maxlen target ext\n",
" FALSE 0.1 1 3 frequent itemsets FALSE\n",
"\n",
"algorithmic control:\n",
" sparse sort verbose\n",
" 7 -2 TRUE\n",
"\n",
"Absolute minimum support count: 42 \n",
"\n",
"create itemset ... \n",
"set transactions ...[673 item(s), 424 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [11 item(s)] done [0.00s].\n",
"creating bit matrix ... [11 row(s), 424 column(s)] done [0.00s].\n",
"writing ... [55 set(s)] done [0.00s].\n",
"Creating S4 object ... done [0.00s].\n",
"The rules list is : \n",
"If {Pclass=3,Sex_Encoded=0} (rule[60]) then positive probability = 0.46153846\n",
"else if {Sex_Encoded=0} (rule[72]) then positive probability = 0.93788820\n",
"else if {Pclass=1} (rule[38]) then positive probability = 0.39805825\n",
"else if {Parch=0} (rule[10]) then positive probability = 0.12328767\n",
"else (default rule) then positive probability = 0.29687500\n",
"\n"
]
}
],
"source": [
"%timeit\n",
"model = sbrl.sbrl(t, iters=50000, \n",
" pos_sign=1, neg_sign=0, rule_minlen=1, \n",
" rule_maxlen=3, minsupport_pos=0.10, minsupport_neg=0.10, eta=1.0, nchain=40)\n",
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"result_r_frame = ro.r.predict(model, t)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'rpy2.robjects.vectors.ListVector'>\n"
]
}
],
"source": [
"print(type(result_r_frame))"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
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" <td>0.123288</td>\n",
" <td>0.937888</td>\n",
" <td>0.461538</td>\n",
" <td>0.937888</td>\n",
" <td>0.123288</td>\n",
" <td>0.398058</td>\n",
" <td>0.296875</td>\n",
" <td>0.461538</td>\n",
" <td>0.937888</td>\n",
" <td>0.461538</td>\n",
" <td>...</td>\n",
" <td>0.937888</td>\n",
" <td>0.123288</td>\n",
" <td>0.461538</td>\n",
" <td>0.123288</td>\n",
" <td>0.123288</td>\n",
" <td>0.461538</td>\n",
" <td>0.123288</td>\n",
" <td>0.937888</td>\n",
" <td>0.398058</td>\n",
" <td>0.123288</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 714 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"0 0.876712 0.062112 0.538462 0.062112 0.876712 0.601942 0.703125 \n",
"1 0.123288 0.937888 0.461538 0.937888 0.123288 0.398058 0.296875 \n",
"\n",
" 7 8 9 ... 704 705 706 \\\n",
"0 0.538462 0.062112 0.538462 ... 0.062112 0.876712 0.538462 \n",
"1 0.461538 0.937888 0.461538 ... 0.937888 0.123288 0.461538 \n",
"\n",
" 707 708 709 710 711 712 713 \n",
"0 0.876712 0.876712 0.538462 0.876712 0.062112 0.601942 0.876712 \n",
"1 0.123288 0.123288 0.461538 0.123288 0.937888 0.398058 0.123288 \n",
"\n",
"[2 rows x 714 columns]"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas_df = pandas2ri.ri2py_dataframe(result_r_frame)\n",
"pandas_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n"
]
}
],
"source": [
"predicted_scores_prob = pd.DataFrame(pandas_df.values.T)\n",
"print(type(predicted_scores_prob))"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(714,)\n",
"(714, 2)\n"
]
}
],
"source": [
"print(y.shape)\n",
"print(predicted_scores_prob.shape)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(714,)"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted_scores_prob[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"#from sklearn.metrics import roc_auc_score\n",
"from sklearn import metrics\n",
"fpr, tpr, thresholds = metrics.roc_curve(y ,predicted_scores_prob[1], pos_label=1)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.843900455433\n"
]
}
],
"source": [
"roc_auc = metrics.auc(fpr, tpr)\n",
"print(roc_auc)"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1\n",
"0 0.8 0.2\n",
"1 0.2 0.8\n",
"2 0.1 0.9\n",
"3 0.2 0.8\n",
"4 1.0 0.0\n",
"0.999113532856\n"
]
}
],
"source": [
"ytrain = data['label']\n",
"Xtrain = data.drop(['label'], axis=1)\n",
"rf_model = RandomForestClassifier().fit(Xtrain, ytrain)\n",
"rf_predict_score = pd.DataFrame(rf_model.predict_proba(Xtrain))\n",
"print(rf_predict_score).head()\n",
"\n",
"rf_predict_score[0].head()\n",
"rf_fpr, rf_tpr, rf_thresholds = metrics.roc_curve(ytrain, rf_predict_score[1], pos_label=1)\n",
"rf_roc_auc = metrics.auc(rf_fpr, rf_tpr)\n",
"print(rf_roc_auc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": false,
"sideBar": false,
"threshold": 4,
"toc_cell": true,
"toc_position": {
"height": "40px",
"left": "1240px",
"right": "20px",
"top": "154px",
"width": "189px"
},
"toc_section_display": "none",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Seq2Seq_solutions.ipynb | 1 | 258483 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.3.0'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.13.1'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.__version__"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"author = \"kyubyong. https://github.com/Kyubyong/tensorflow-exercises\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q1. Let's practice the seq2seq framework with a simple example. In this example, we will take the last state of the encoder as the initial state of the decoder. Complete the code."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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EO3Dbmj1Y6/azZ0PfvobuWgghXEKhHBsnPRkrRwhR0BX6C7QA69ZZh1Bo29bctjib1EWN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\nEEIIIYQQQgghhBBCCCGEEEII4/w/yR2FYkFFcGAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b198b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Inputs and outputs: ten digits\n",
"x = tf.placeholder(tf.int32, shape=(32, 10))\n",
"y = tf.placeholder(tf.int32, shape=(32, 10))\n",
"\n",
"# One-hot encoding\n",
"enc_inputs = tf.one_hot(x, 10)\n",
"dec_inputs = tf.concat((tf.zeros_like(y[:, :1]), y[:, :-1]), -1)\n",
"dec_inputs = tf.one_hot(dec_inputs, 10)\n",
"\n",
"# encoder\n",
"encoder_cell = tf.contrib.rnn.GRUCell(128)\n",
"memory, last_state = tf.nn.dynamic_rnn(encoder_cell, enc_inputs, dtype=tf.float32, scope=\"encoder\")\n",
"\n",
"# decoder\n",
"decoder_cell = tf.contrib.rnn.GRUCell(128)\n",
"outputs, _ = tf.nn.dynamic_rnn(decoder_cell, dec_inputs, initial_state=last_state, scope=\"decoder\")\n",
"\n",
"# Readout\n",
"logits = tf.layers.dense(outputs, 10)\n",
"preds = tf.argmax(logits, -1, output_type=tf.int32)\n",
"\n",
"# Evaluation\n",
"hits = tf.reduce_sum(tf.to_float(tf.equal(preds, y)))\n",
"acc = hits / tf.to_float(tf.size(x))\n",
"\n",
"# Loss and train\n",
"loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)\n",
"mean_loss = tf.reduce_mean(loss)\n",
"opt = tf.train.AdamOptimizer(0.001)\n",
"train_op = opt.minimize(mean_loss)\n",
"\n",
"# Session\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" losses, accs = [], []\n",
" for step in range(2000):\n",
" # Data design\n",
" # We feed sequences of random digits in the `x`,\n",
" # and take its reverse as the target.\n",
" _x = np.random.randint(0, 10, size=(32, 10), dtype=np.int32)\n",
" _y = _x[:, ::-1] # Reverse\n",
" _, _loss, _acc = sess.run([train_op, mean_loss, acc], {x:_x, y:_y})\n",
" losses.append(_loss)\n",
" accs.append(_acc)\n",
" \n",
" # Plot\n",
" plt.plot(losses, label=\"loss\")\n",
" plt.plot(accs, label=\"accuracy\")\n",
" plt.legend()\n",
" plt.grid()\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q2. At this time, we will use the Bahdanau attention mechanism. Complete the code."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == 'face':\n"
]
},
{
"data": {
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JuAa7cr8mRwzMFxw7AvfkOUZEEipky3E4sApLgrUDmAGMzjjmHSzNSmbeqlxlzwamueVp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\nPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bdd4b10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 100\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11bb4c390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 200\n"
]
},
{
"data": {
"image/png": 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a0TAU8ajpKlnVUy1UREREREREREREREREREREREQE4P8BKEKftOwHhgIAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b98c390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 300\n"
]
},
{
"data": {
"image/png": 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jx1Yr83iKTdd/pVi7lQFGNbrGMwx70D4Ya35F17W7GfvDnYvVUN4Ejsb+2PcE\nZmO1ov8BLsISr/wC+8NfhAVD3M9bsBrOwxRrOuWu/3mKmdYruQl77jcHC6jRYyudF73mGcB1WGdC\nDuvA+DbW2XAQVvtbCDzkztmAdaTcClxTocxgwfFarCn7KI2/AKqISE3yaBiKOGq6Slb1VgsVERER\nEREREREREREREREREREB+H+Adtzx1SBaZAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb6a890>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 400\n"
]
},
{
"data": {
"image/png": 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RdzwEOBF4DzYB5avu/B5s3OeRWE0MbFzlzyPnF7rzP8QCZaH8T8BqbE8Am2KU\neSnwgttfD8xz+08DZ7v9c+g+UcNg4HAseN+EzXo8GwuUubWynsq80Z3/KRbcfxHjZ5CEU6Crfcdj\nDRTb3fGl2He7qHPp+bWs0PXc8/nyb8Pme4vjtcj+wcjxQbK/f32AvwVez7n3BuzV831Y0Du3wDMK\nlTk6l2Efkj23oZQhCfPR1bM3Ygv43uqO5wFfIBsgTsJe6X4DXIJN/wPwBmxa8d1kX1EvxhYsfgmr\n8WVeFaMTPxbKvyftlPc/zYfpvoBQZr2N0dhMHzdiNcO3YtORD46kLVbmsWRfXf+BbO1W6oxqdLXn\nMOxDeyP2+hWd1+4O7B/ucqyGsh34IPaP/XRgGVYr+j/gamzhle9j//A3YMEQ9+edWA3nYbI1nXz5\nf4jsSuuF3I5993sSC6jRtIXui+Z5OfBdrDGhH9aA8QWsseFsrPb3NDDX3XMAa0i5C/hOgTKDBccZ\n2Kvs76j9CVBFRErShrqhiKNXV0mrnmqhIiIiIiIiIiIiIiIiIiIiIiIiAP8PgjE+5+OZ8JsAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d033c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 500\n"
]
},
{
"data": {
"image/png": 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0z+nAr7HOhL5YB8b3sM6GQ7Ha3/PAPHfPVqwj5Sbg6jxlBguOM7Gm7MNU/wKo\nIiJFaULDUMRR01XSqqdaqIiIiIiIiIiIiIiIiIiIiIiICMD/A4lTss9IWKpaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb4c390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 600\n"
]
},
{
"data": {
"image/png": 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fA+eXulGpruui4NUX26v9z0GlDsHG6kSkzlU5vaRQysJhJW73deDB0PHVwIVAR7l6lmrR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b9b4c10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 700\n"
]
},
{
"data": {
"image/png": 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YOrKfF1FJEUmgQi9KL1++nOXL82YgBP8E1l8E/og9o/unO+eVqiHNJ9Bd777gYXe8CGj2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\nChUREREREREREREREREREREREQH4fyfIx9kBln6iAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb34210>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 800\n"
]
},
{
"data": {
"image/png": 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OwB6jzXXnJpBaf7qdjgJdNXA3cHoBfwgRiblOeGF4utuCJgf2r3FbuhfIo0XaUaBrAQYD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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b7118d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 900\n"
]
},
{
"data": {
"image/png": 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pPsnOCYSS+Tb2w1b6uAarGR6ILUfeI3BtrjIPI9V0/S9StVupMqrRVZ6u2Iv2\nTljzK7iu3VTsH+5irIbSBJyM/WM/HFiI1Yr+B/gFlnjlD9g//DVYMMT9vB2r4TxJqqaT6flfJ5Vp\nPZvbsPd+i7CAGrw2233BZ04AbsY6EzpiHRg/xjobjsFqfy8DM909O7COlOnATVnKDBYcJ2NN2b9R\n+QugiojkJYGGoYijpqvEVVu1UBEREREREREREREREREREREREYD/B+lEqAI1oKerAAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb6a890>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1000\n"
]
},
{
"data": {
"image/png": 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tnT4JuI6uS8edjg1/G1zqRqUC3e+Cn6cAnwTagv1rsClhItLLVSGv66Ohz48DO4f2dwYO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\nRDDEgQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b98c390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1100\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11be20dd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1200\n"
]
},
{
"data": {
"image/png": 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uxUqGh2PTke/juTddnocQr7r+O/HSrZQZlehKTxfsQ3tHrPrlndduOvYPdylW\nQtkKnIH9Yx8ILMZKRc8Av8AWXrkH+4e/HguGuJ+/x0o4zxMv6SRL/9vEV1pP5V7su98SLKB67031\nnDfNS4EpWGNCFdaA8VOssWEkVvpbBcx2z+zBGlJmAnelyDNYcJyMVWX/l9KfAFVEJCt1qBuKOKq6\nSlRlKoWKiIiIiIiIiIiIiIiIiIiIiIgA/D/8QczizkLxNgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ccd5d50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1300\n"
]
},
{
"data": {
"image/png": 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RsmTQMBTxqOsqaVWqFSoiIiIiIiIiIiIiIiIiIiIiIgLw/4aZH+SoBtXkAAAA\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cba1610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1400\n"
]
},
{
"data": {
"image/png": 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yl5HbjUugOw9bbXggFoGfBc53uE5EEi5gZ8QO4MfAM1gP7J1YJepc7/tpwDew7IGtWNP0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b997d10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1500\n"
]
},
{
"data": {
"image/png": 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pfBsDsJU+JmI1w/2x5ch7Ba7NV+ZhtDZd/4PW2q10MKrRVZ7u2Iv2rljzK7iu\n3U3YX9yFWA1lA3Ai9pf9UGA+Vit6CPg9lnjlBuwv/mosGOJ+3ozVcB6ltaaT7fnfoTXTei43Yu/9\nFmABNXhtrvuCzzwbmIp1JnTBOjB+iXU2jMJqf8uA2e6ez7COlBnAtTnKDBYcp2BN2Seo/AVQRUQK\nkkLDUMRR01WSqq1aqIiIiIiIiIiIiIiIiIiIiIiICMD/A9XUsQ39Qd62AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c095b10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1600\n"
]
},
{
"data": {
"image/png": 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tPO/Il+foXIbdiPfchlKGOMxHV8u2xBbw/YM7vh/4EW0BYgesSvcAcAI2/Q/A\npti04u/RVkU9Dluw+AOsxJepKkYnfsyXfkfWUN5/mjNov4BQZr2N7bCZPi7BSoY7YtORbxS5t1Ce\nh9NWdf1P2kq3UmNUoqs+G2If2ntg1a/ovHZXYf9wn8VKKG8Dh2P/2PcE5mClonuAn2MLr/wJ+4f/\nKhYMcT+vwUo4M2gr6eRK/9u0rbSez5XYd79nsIAavTffc9E0JwFTsMaE7lgDxo+wxob9sdLf88B0\n98xarCHlWuD3efIMFhwnY1XZh6j+CVBFRIqSRt1QxFHVVZKqo1KoiIiIiIiIiIiIiIiIiIiIiIgI\nwP8DM0Pel7UobGcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c0951d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1700\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11bb34210>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1800\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11bb27310>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step= 1900\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11bfd3f50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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+kEbP4fu4fentXMy4CMDpZ08TWC+wyOcKIVyTZG+g7duha1c13bw5HD9e+udq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\n2wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11afb0a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tf.reset_default_graph()\n",
"# Inputs and outputs: ten digits\n",
"x = tf.placeholder(tf.int32, shape=(32, 10))\n",
"y = tf.placeholder(tf.int32, shape=(32, 10))\n",
"\n",
"# One-hot encoding\n",
"enc_inputs = tf.one_hot(x, 10)\n",
"dec_inputs = tf.concat((tf.zeros_like(y[:, :1]), y[:, :-1]), -1)\n",
"dec_inputs = tf.one_hot(dec_inputs, 10)\n",
"\n",
"# encoder\n",
"encoder_cell = tf.contrib.rnn.GRUCell(128)\n",
"memory, last_state = tf.nn.dynamic_rnn(encoder_cell, enc_inputs, dtype=tf.float32, scope=\"encoder\")\n",
"\n",
"# decoder\n",
"attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(128, memory) \n",
"decoder_cell = tf.contrib.rnn.GRUCell(128)\n",
"cell_with_attention = tf.contrib.seq2seq.AttentionWrapper(decoder_cell, \n",
" attention_mechanism, \n",
" attention_layer_size=256,\n",
" alignment_history=True,\n",
" output_attention=False)\n",
"outputs, state = tf.nn.dynamic_rnn(cell_with_attention, dec_inputs, dtype=tf.float32)\n",
"alignments = tf.transpose(state.alignment_history.stack(),[1,2,0])\n",
"\n",
"# Readout\n",
"logits = tf.layers.dense(outputs, 10)\n",
"preds = tf.argmax(logits, -1, output_type=tf.int32)\n",
"\n",
"# Evaluation\n",
"hits = tf.reduce_sum(tf.to_float(tf.equal(preds, y)))\n",
"acc = hits / tf.to_float(tf.size(x))\n",
"\n",
"# Loss and train\n",
"loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)\n",
"mean_loss = tf.reduce_mean(loss)\n",
"opt = tf.train.AdamOptimizer(0.001)\n",
"train_op = opt.minimize(mean_loss)\n",
"\n",
"# Session\n",
"def plot_alignment(alignment):\n",
" fig, ax = plt.subplots()\n",
" im=ax.imshow(alignment, cmap='Greys', interpolation='none')\n",
" fig.colorbar(im, ax=ax)\n",
" plt.xlabel('Decoder timestep')\n",
" plt.ylabel('Encoder timestep')\n",
" plt.show()\n",
" \n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" losses, accs = [], []\n",
" for step in range(2000):\n",
" # Data design\n",
" # We feed sequences of random digits in the `x`,\n",
" # and take its reverse as the target.\n",
" _x = np.random.randint(0, 10, size=(32, 10), dtype=np.int32)\n",
" _y = _x[:, ::-1] # Reverse\n",
" _, _loss, _acc = sess.run([train_op, mean_loss, acc], {x:_x, y:_y})\n",
" losses.append(_loss)\n",
" accs.append(_acc)\n",
" \n",
" if step % 100 == 0:\n",
" print(\"step=\", step)\n",
" _alignments = sess.run(alignments, {x: _x, y: _y})\n",
" plot_alignment(_alignments[0])\n",
" \n",
" # Plot\n",
" plt.plot(losses, label=\"loss\")\n",
" plt.plot(accs, label=\"accuracy\")\n",
" plt.legend()\n",
" plt.grid()\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
ssdoz2sk/hour_of_code_python_crawler_2016_5 | notebook2/Simple2 - Elearning.ipynb | 1 | 13383 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample2 - Elearning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirement"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied (use --upgrade to upgrade): requests in /Users/csie/Desktop/hour_of_code_python_crawler_2016_5/venv/notebook/lib/python3.5/site-packages\r\n",
"\u001b[33mYou are using pip version 7.1.2, however version 8.1.2 is available.\r\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n"
]
}
],
"source": [
"!pip3 install requests"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied (use --upgrade to upgrade): beautifulsoup4 in /Users/csie/Desktop/hour_of_code_python_crawler_2016_5/venv/notebook/lib/python3.5/site-packages\n",
"\u001b[33mYou are using pip version 7.1.2, however version 8.1.2 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip3 install beautifulsoup4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick Start"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import necessary librarys"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"\n",
"#import lxml\n",
"import requests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make a request"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<RequestsCookieJar[<Cookie MOODLEID_1042elearning=%25ED%25C3%251CC%25B7d for elearning.ncyu.edu.tw/>]>\n"
]
}
],
"source": [
"url = 'https://elearning.ncyu.edu.tw/1042'"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session = requests.session()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s1023008\n"
]
}
],
"source": [
"username=input()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ThisIsTest\n"
]
}
],
"source": [
"password=input()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d = {\n",
" 'username':username,\n",
" 'password':password,\n",
" 'testcookies':'1'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session.get(url)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"response = session.post('https://elearning.ncyu.edu.tw/1042/login/index.php', data = d)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"response = session.get('http://elearning.ncyu.edu.tw/1042/my/index.php?action=mycourse')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soup = BeautifulSoup(response.text, \"html.parser\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<a href=\"index.php\" title=\"最新公告\"><span>最新公告</span></a>,\n",
" <a class=\"nolink\"><span>我的課程</span></a>,\n",
" <a href=\"index.php?action=officehour\" title=\"雲端OfficeHour\"><span>雲端OfficeHour</span></a>,\n",
" <a href=\"index.php?action=history\" title=\"歷年課程\"><span>歷年課程</span></a>,\n",
" <a href=\"index.php?action=resource\" title=\"線上資源\"><span>線上資源</span></a>,\n",
" <a href=\"index.php?action=contact\" title=\"聯絡管理者\"><span>聯絡管理者</span></a>,\n",
" <a href=\"index.php?action=moocs\" title=\"MOOCs專區\"><span>MOOCs專區</span></a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=2948\\\" title=\"點按進入課程\">導師生交流平台 </a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1235\\\" title=\"點按進入課程\">行動裝置應用程式設計 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470055\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470055</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1230\\\" title=\"點按進入課程\">資料探勘導論 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470050\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470050</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1229\\\" title=\"點按進入課程\">安全程式設計 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470048\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470048</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1227\\\" title=\"點按進入課程\">計算機專題（I） 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470036\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470036</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1224\\\" title=\"點按進入課程\">微積分（II） 大學部資工系一年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470026\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470026</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1219\\\" title=\"點按進入課程\">組合語言與實習 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470013\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470013</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1218\\\" title=\"點按進入課程\">系統程式 大學部資工系三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470012\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470012</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/mycc/remoteauth-client2.php?tagid=10423470007\" target=\"_blank\" title=\"點按進入課程\">機率學 大學部資工系二年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470007\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10423470007</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=89\\\" title=\"點按進入課程\">創業講座(跨) 大學部三年級甲班</a>,\n",
" <a href=\"https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10420150030\" target=\"_blank\">https://web085003.adm.ncyu.edu.tw/pub_tagoutline.aspx?tagid=10420150030</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/user/view.php?id=20161&course=1\">張弘霖 s1023008</a>,\n",
" <a href=\"http://elearning.ncyu.edu.tw/1042/login/logout.php?sesskey=B39kjGgcEq\">登出</a>]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.find_all('a')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=2948\\\" title=\"點按進入課程\">導師生交流平台 </a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1235\\\" title=\"點按進入課程\">行動裝置應用程式設計 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1230\\\" title=\"點按進入課程\">資料探勘導論 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1229\\\" title=\"點按進入課程\">安全程式設計 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1227\\\" title=\"點按進入課程\">計算機專題（I） 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1224\\\" title=\"點按進入課程\">微積分（II） 大學部資工系一年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1219\\\" title=\"點按進入課程\">組合語言與實習 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1218\\\" title=\"點按進入課程\">系統程式 大學部資工系三年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/mycc/remoteauth-client2.php?tagid=10423470007\" target=\"_blank\" title=\"點按進入課程\">機率學 大學部資工系二年級甲班</a>\n",
"<a href=\"http://elearning.ncyu.edu.tw/1042/course/view.php?id=89\\\" title=\"點按進入課程\">創業講座(跨) 大學部三年級甲班</a>\n"
]
}
],
"source": [
"all_course_tag_a = soup.find_all('a', {'title':'點按進入課程'})\n",
"for course in all_course_tag_a:\n",
" print(course)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=2948\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1235\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1230\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1229\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1227\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1224\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1219\\\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=1218\\\n",
"http://elearning.ncyu.edu.tw/1042/mycc/remoteauth-client2.php?tagid=10423470007\n",
"http://elearning.ncyu.edu.tw/1042/course/view.php?id=89\\\n"
]
}
],
"source": [
"for course in all_course_tag_a:\n",
" print(course['href'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
aboSamoor/compsocial | freebase/Professions.ipynb | 1 | 21658 | {
"metadata": {
"name": "",
"signature": "sha256:4a78c085b0025c5fdae2f162d4c72617ad2188fbe15cdaaeedb6c7388dc0822c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from time import time,sleep\n",
"from glob import glob\n",
"import json\n",
"import urllib\n",
"import string\n",
"import cPickle as pickle\n",
"from os import path\n",
"from collections import defaultdict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Freebase"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"API_KEY = \"AIzaSyAnupT8pNVHf2WFPidvMcmdrfXgt6RoM0w\"\n",
"SERVICE_URL = 'https://www.googleapis.com/freebase/v1/mqlread'\n",
"cursor = \"\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_iterator(q):\n",
" params = {\n",
" 'query': json.dumps(q),\n",
" 'key': API_KEY,\n",
" 'cursor': cursor\n",
" }\n",
" progress = True\n",
" while progress:\n",
" url = SERVICE_URL + '?' + urllib.urlencode(params)\n",
" try:\n",
" response = json.loads(urllib.urlopen(url).read())\n",
" except:\n",
" sleep(30)\n",
" continue\n",
" if not 'cursor' in response:\n",
" sleep(30)\n",
" continue\n",
" #raise BadResponse(\"Response does not contain cursor.\")\n",
" params['cursor'] = response['cursor']\n",
" if response['cursor'] == False:\n",
" progress = False\n",
" yield response['cursor'], response['result']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Specialization-of attribute"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"null = None\n",
"query = [{\n",
" \"id\": null,\n",
" \"name\": null,\n",
" \"type\": \"/people/profession\",\n",
" \"/people/profession/specialization_of\": []\n",
"}]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"response = []\n",
"for cursor, partial_results in get_iterator(query):\n",
" response.extend(partial_results)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(response)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"4152"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"graph = defaultdict(lambda: [])\n",
"for link in response:\n",
" graph[link[\"name\"]].extend(link[\"/people/profession/specialization_of\"])\n",
"graph = dict(graph)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"graph[\"Manager\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"[u'Baseball Coach', u'Ironmaster', u'Coach', u'Business executive']"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Specializations attribute"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"null = None\n",
"query2 = [{\n",
" \"id\": null,\n",
" \"name\": null,\n",
" \"type\": \"/people/profession\",\n",
" \"/people/profession/specializations\": []\n",
"}]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 252
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"specializations = []\n",
"for cursor, partial_results in get_iterator(query2):\n",
" specializations.extend(partial_results)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-255-cc059087b587>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mspecializations\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----> 2\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpartial_results\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mget_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery2\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 3\u001b[0m \u001b[0mspecializations\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartial_results\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-3-b6e262e691f9>\u001b[0m in \u001b[0;36mget_iterator\u001b[1;34m(q)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;34m'cursor'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;31m#raise BadResponse(\"Response does not contain cursor.\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 255
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(specializations)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"special_graph = defaultdict(lambda: [])\n",
"for link in response:\n",
" special_graph[link[\"name\"]].extend(link[\"/people/profession/specializations\"])\n",
"special_graph = dict(special_graph)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'/people/profession/specializations'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-251-0ac2466f2bbd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mspecial_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mlink\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mspecial_graph\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlink\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlink\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"/people/profession/specializations\"\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 4\u001b[0m \u001b[0mspecial_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: '/people/profession/specializations'"
]
}
],
"prompt_number": 251
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Eliminating double edges"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for k in graph:\n",
" graph[k] = set(graph[k])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating Depth and All Parents"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"parents_cache = graph\n",
"all_parents_cache = {}\n",
"depth_cache = {}\n",
"grandparent_cache = {}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 187
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_parents(k):\n",
" if k in parents_cache:\n",
" return parents_cache[k]\n",
" else:\n",
" return []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_depth(k, deep=0):\n",
" if deep >= 6:\n",
" return 0\n",
" if k not in depth_cache:\n",
" depths = [get_depth(x, deep+1) for x in get_parents(k)]\n",
" depths.append(0)\n",
" depth_cache[k] = max(depths) + 1\n",
" return depth_cache[k]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_all_parents(k, deep=0):\n",
" if deep >= 6: return []\n",
" if k not in all_parents_cache:\n",
" tmp = list(get_parents(k))\n",
" all_parents = list(tmp)\n",
" for parent in tmp:\n",
" all_parents.extend(get_all_parents(parent, deep+1))\n",
" all_parents_cache[k] = list(set(all_parents).difference([k]))\n",
" return all_parents_cache[k]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_grandparent(k, deep=0):\n",
" if deep >= 6: return k\n",
" if not get_parents(k): return k\n",
" if k not in grandparent_cache:\n",
" grandparents = [get_grandparent(x, deep+1) for x in get_parents(k)]\n",
" grandparents = [x for x in grandparents if x]\n",
" grandparent_cache[k] = grandparents[0]\n",
" return grandparent_cache[k]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for k in graph:\n",
" get_depth(k)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for k in graph:\n",
" get_all_parents(k)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for k in graph:\n",
" grandparent_cache[k] = get_grandparent(k)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 193
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the Frequency"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle as pickle\n",
"from collections import Counter"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"people_db = pickle.load(open(\"/data/csc/fb_persons/100percentpeople.pkl\", \"rb\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"freqs = []\n",
"for x in people_db[[\"profession\"]].dropna().values.flatten():\n",
" if isinstance(x, tuple):\n",
" freqs.extend(x)\n",
" else:\n",
" freqs.append(x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"people_db = None"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 130
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"prof_freq = Counter(freqs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 131
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"total = float(sum(prof_freq.values()))\n",
"prof_prob = {k:c/total for k, c in prof_freq.iteritems()}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 135
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"grandparent_cache[\"Lawyer\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 250,
"text": [
"u'Criminal defense lawyer'"
]
}
],
"prompt_number": 250
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Strategy #1\n",
"\n",
"Merge only the infrequent labels"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_freqs = []\n",
"threshold = prof_freq.most_common()[200][1]/total\n",
"for f in freqs:\n",
" if prof_prob[f] < threshold:\n",
" if (f not in grandparent_cache or\n",
" f == grandparent_cache[f] or \n",
" grandparent_cache[f] not in prof_prob or \n",
" prof_prob[grandparent_cache[f]] < threshold):\n",
" new_freqs.append(\"Other\")\n",
" continue\n",
" else:\n",
" new_freqs.append(grandparent_cache[f])\n",
"\n",
" else:\n",
" new_freqs.append(f)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 259
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mapping = defaultdict(lambda: set([]))\n",
"threshold = prof_freq.most_common()[200][1]/total\n",
"for f in freqs:\n",
" if not f: continue\n",
" if prof_prob[f] < threshold:\n",
" if (f not in grandparent_cache or\n",
" f == grandparent_cache[f] or \n",
" grandparent_cache[f] not in prof_prob or \n",
" prof_prob[grandparent_cache[f]] < threshold):\n",
" mapping[\"Other\"].update([f])\n",
" continue\n",
" else:\n",
" mapping[grandparent_cache[f]].update([f])\n",
" else:\n",
" mapping[f].update([f])\n",
"mapping = dict(mapping)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 260
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tmp = Counter(new_freqs)\n",
"selected_profs = tmp.keys()\n",
"print len(selected_profs)\n",
"lines = u\"\\n\".join([u\"{},{},{}\".format(k,v, \"|\".join(mapping.get(k, []))) for k, v in tmp.most_common()])\n",
"fh = open(\"professions.csv\", \"w\")\n",
"fh.write(lines.encode(\"utf8\"))\n",
"fh.close()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"203\n"
]
}
],
"prompt_number": 261
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Startegy #2\n",
"\n",
"Merge All"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_freqs2 = []\n",
"threshold = 2e-5\n",
"for f in freqs:\n",
" if f in grandparent_cache:\n",
" g = grandparent_cache[f]\n",
" if (g not in prof_prob or \n",
" prof_prob[g] < threshold):\n",
" new_freqs2.append(\"Other\")\n",
" else:\n",
" new_freqs2.append(g)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 247
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tmp = Counter(new_freqs2)\n",
"selected_profs = tmp.keys()\n",
"print len(selected_profs)\n",
"print \"\\n\".join([\"{},{}\".format(k,v) for k, v in tmp.most_common()])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"99\n",
"Actor,453366\n",
"Artist,298794\n",
"Writer,202413\n",
"Athlete,134553\n",
"Other,106602\n",
"Film Producer,98519\n",
"Film director,76402\n",
"Politician,51540\n",
"Editor,49424\n",
"Television producer,32785\n",
"Businessperson,28053\n",
"Scientist,21400\n",
"Educator,20931\n",
"Baseball Coach,19645\n",
"Mathematician,19309\n",
"Performer,17952\n",
"Engineer,15234\n",
"Broadcaster,13957\n",
"Television Director,13518\n",
"Referee,10993\n",
"Entertainer,10526\n",
"Business executive,10173\n",
"Set Decorator,7914\n",
"Entrepreneur,5331\n",
"Casting Director,5307\n",
"Historian,4319\n",
"Soldier,4128\n",
"Navigator,4094\n",
"Philosopher,3946\n",
"Economist,3437\n",
"Producer,2719\n",
"Critic,1981\n",
"Activist,1898\n",
"Military Officer,1875\n",
"Peace officer,1700\n",
"Technician,1473\n",
"Publisher,908\n",
"Scholar,872\n",
"Political Scientist,807\n",
"Anthropologist,795\n",
"Accountant,705\n",
"Television presenter,645\n",
"Filmmaker,631\n",
"Salesperson,561\n",
"Office Worker,380\n",
"Philanthropist,363\n",
"Investor,348\n",
"Social Worker,336\n",
"Civil servant,321\n",
"Special effects supervisor,296\n",
"Stunt Coordinator,287\n",
"Boom Operator,272\n",
"Tarento,265\n",
"Sex worker,261\n",
"Explorer,259\n",
"Animation Director,232\n",
"Warlord,213\n",
"Foley Artist,198\n",
"Business magnate,171\n",
"Set Dresser,151\n",
"Manufacturer,125\n",
"Socialite,122\n",
"Sound Mixer,121\n",
"Public Servant,116\n",
"Lighting Director,108\n",
"Truck driver,108\n",
"ADR Recordist,106\n",
"Art collector,100\n",
"TV Art Director,93\n",
"ADR Editor,86\n",
"Sound Department,79\n",
"Cinematography,76\n",
"ADR Mixer,75\n",
"Key Makeup Artist,70\n",
"Foley Editor,70\n",
"Bodyguard,70\n",
"Veteran,70\n",
"Visual Effects Coordinator,67\n",
"Foley Mixer,65\n",
"Advertising Executive,65\n",
"Nobleman,63\n",
"Visual Effects,58\n",
"Script supervisor,55\n",
"Taxi driver,50\n",
"Media proprietor,46\n",
"Mail carrier,46\n",
"Rector,45\n",
"ADR Director,45\n",
"Humanitarian,45\n",
"Bus driver,44\n",
"Postal worker,43\n",
"Special Effects Foreman,43\n",
"CG Supervisor,43\n",
"Sound Supervisor,43\n",
"Bookkeeper,42\n",
"On-set Dresser,42\n",
"Foley Recordist,39\n",
"Ophthalmology,39\n",
"Special Effects,38\n"
]
}
],
"prompt_number": 248
}
],
"metadata": {}
}
]
} | gpl-3.0 |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/tutorials/04-Intro_pyspark_sparkSQL.ipynb | 1 | 17982 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **Introduction to Spark SQL via PySpark**\n",
"----------------------------------------------------------------------------\n",
"## Goals:\n",
"* Get familiarized with the basics of Spark SQL and PySpark\n",
"* Learn to create a SparkSession\n",
"* Verify if Jupyter can talk to Spark Master"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"References:\n",
"* https://spark.apache.org/docs/latest/api/python/pyspark.html\n",
"* https://spark.apache.org/docs/latest/sql-getting-started.html\n",
"* https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession\n",
"* https://jaceklaskowski.gitbooks.io/mastering-spark-sql/\n",
"* http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is Spark SQL?\n",
"* It is a Spark module that leverages Sparks functional programming APIs to allow SQL relational processing tasks on (semi)structured data.\n",
"* Spark SQL provides Spark with more information about the structure of both the data and the computation being performed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is PySpark?\n",
"PySpark is the Python API for Spark.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How do I start using Pyspark and SparkSQL?\n",
"* Start by importing the PySpark SQL SparkSession Class and creating a SparkSession instance .\n",
"* A SparkSession class is considered the entry point to programming Spark with the Dataset and DataFrame API.\n",
"* A SparkSession can be used create DataFrames, register DataFrames as tables, execute SQL over tables, cache tables, and read parquet files."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import SparkSession Class"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pyspark.sql import SparkSession"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is a SparkSession?\n",
"* It is the driver process that controls a spark application\n",
"* A SparkSession instance is responsible for executing the driver program’s commands (code) across executors (in a cluster) to complete a given task.\n",
"* You can have as many SparkSessions as you want in a single Spark application."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How do I create a SparkSession?\n",
"* You can use the SparkSession class attribute called **[Builder](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession.builder)**.\n",
"* The class attribute builder allows you to run some of the following functions:\n",
" * **appName**: Sets a name for the application\n",
" * **master**: URL for the Spark master (Local or Spark standalone cluster)\n",
" * **enableHiveSupport**: Enables Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions.\n",
" * **getOrCreate**:Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a SparkSession instance\n",
"* Define a **spark** variable\n",
"* Pass values to the **appName** and **master** functions\n",
" * For the master function, we are going to use the HELK's Spark Master container (helk-spark-master)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"spark = SparkSession.builder \\\n",
" .appName(\"Python Spark SQL basic example\") \\\n",
" .master(\"spark://helk-spark-master:7077\") \\\n",
" .enableHiveSupport() \\\n",
" .getOrCreate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the SparkSession variable"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <p><b>SparkSession - hive</b></p>\n",
" \n",
" <div>\n",
" <p><b>SparkContext</b></p>\n",
"\n",
" <p><a href=\"http://403892d82956:4040\">Spark UI</a></p>\n",
"\n",
" <dl>\n",
" <dt>Version</dt>\n",
" <dd><code>v2.4.0</code></dd>\n",
" <dt>Master</dt>\n",
" <dd><code>spark://helk-spark-master:7077</code></dd>\n",
" <dt>AppName</dt>\n",
" <dd><code>Python Spark SQL basic example</code></dd>\n",
" </dl>\n",
" </div>\n",
" \n",
" </div>\n",
" "
],
"text/plain": [
"<pyspark.sql.session.SparkSession at 0x7fa015e94f28>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spark"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is a Dataframe?\n",
"* In Spark, a dataframe is the most common Structured API, and it is used to represent data in a table format with rows and columns.\n",
"* Think of a dataframe as a spreadsheet with headers. The difference is that one Spark Dataframe can be distributed across several computers due to its large size or high computation requirements for faster analysis.\n",
"* The list of column names from a dataframe with its respective data types is called the **schema**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Is a Spark Dataframe the same as a Python Pandas Dataframe?\n",
"* A Python dataframe sits on one computer whereas a Spark Dataframe, once again, can be distributed across several computers.\n",
"* PySpark allows the conversion from Python Pandas dataframes to Spark dataframes. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create your first Dataframe\n",
"Let's create our first dataframe by using **range** and **toDF** functions.\n",
"* One column named **numbers**\n",
"* 10 rows containing numbers from 0-9\n",
"\n",
"**[range(start, end=None, step=1, numPartitions=None)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession.range)**\n",
"* Create a DataFrame with single pyspark.sql.types.LongType column named id, containing elements in a range from start to end (exclusive) with step value step.\n",
"\n",
"**[toDF(*cols)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.toDF)**\n",
"* Returns a new class:DataFrame that with new specified column names"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"first_df = spark.range(10).toDF(\"numbers\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-------+\n",
"|numbers|\n",
"+-------+\n",
"| 0|\n",
"| 1|\n",
"| 2|\n",
"| 3|\n",
"| 4|\n",
"| 5|\n",
"| 6|\n",
"| 7|\n",
"| 8|\n",
"| 9|\n",
"+-------+\n",
"\n"
]
}
],
"source": [
"first_df.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create another Dataframe\n",
"**[createDataFrame(data, schema=None, samplingRatio=None, verifySchema=True)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession.createDataFrame)**\n",
"\n",
"* Creates a DataFrame from an RDD, a list or a pandas.DataFrame.\n",
"* When schema is a list of column names, the type of each column will be inferred from data.\n",
"* When schema is None, it will try to infer the schema (column names and types) from data, which should be an RDD of Row, or namedtuple, or dict."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dog_data=[['Pedro','Doberman',3],['Clementine','Golden Retriever',8],['Norah','Great Dane',6]\\\n",
" ,['Mabel','Austrailian Shepherd',1],['Bear','Maltese',4],['Bill','Great Dane',10]]\n",
"dog_df=spark.createDataFrame(dog_data, ['name','breed','age'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+--------------------+---+\n",
"| name| breed|age|\n",
"+----------+--------------------+---+\n",
"| Pedro| Doberman| 3|\n",
"|Clementine| Golden Retriever| 8|\n",
"| Norah| Great Dane| 6|\n",
"| Mabel|Austrailian Shepherd| 1|\n",
"| Bear| Maltese| 4|\n",
"| Bill| Great Dane| 10|\n",
"+----------+--------------------+---+\n",
"\n"
]
}
],
"source": [
"dog_df.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the Dataframe schema\n",
"* We are going to do apply a concept called schema inference which lets spark takes its best guess at figuring out the schema.\n",
"* Spark reads part of the dataframe and then tries to parse the types of data in each row. \n",
"* You can also define a strict schema when you read in data which does not let Spark guess. This is recommended for production use cases. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[schema](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.schema)**\n",
"* Returns the schema of this DataFrame as a pyspark.sql.types.StructType."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StructType(List(StructField(name,StringType,true),StructField(breed,StringType,true),StructField(age,LongType,true)))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dog_df.schema"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[printSchema()](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.printSchema)**\n",
"* Prints out the schema in the tree format"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- name: string (nullable = true)\n",
" |-- breed: string (nullable = true)\n",
" |-- age: long (nullable = true)\n",
"\n"
]
}
],
"source": [
"dog_df.printSchema()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Access Dataframe Columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[select(*cols)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.select)**\n",
"* Projects a set of expressions and returns a new DataFrame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Access Dataframes's columns by attribute (df.name):"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+\n",
"| name|\n",
"+----------+\n",
"| Pedro|\n",
"|Clementine|\n",
"| Norah|\n",
"| Mabel|\n",
"| Bear|\n",
"| Bill|\n",
"+----------+\n",
"\n"
]
}
],
"source": [
"dog_df.select(\"name\").show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Access Dataframe's columns by indexing (df['name']). \n",
"* According to Sparks documentation, the indexing form is the recommended one because it is future proof and won’t break with column names that are also attributes on the DataFrame class."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+\n",
"| name|\n",
"+----------+\n",
"| Pedro|\n",
"|Clementine|\n",
"| Norah|\n",
"| Mabel|\n",
"| Bear|\n",
"| Bill|\n",
"+----------+\n",
"\n"
]
}
],
"source": [
"dog_df.select(dog_df[\"name\"]).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filter Dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[filter(condition)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.filter)**\n",
"* Filters rows using the given condition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select dogs that are older than 4 years"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+----------------+---+\n",
"| name| breed|age|\n",
"+----------+----------------+---+\n",
"|Clementine|Golden Retriever| 8|\n",
"| Norah| Great Dane| 6|\n",
"| Bill| Great Dane| 10|\n",
"+----------+----------------+---+\n",
"\n"
]
}
],
"source": [
"dog_df.filter(dog_df[\"age\"] > 4).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Group Dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[groupBy(*cols)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.groupBy)**\n",
"* Groups the DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"group dogs and count them by their age"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+-----+\n",
"|age|count|\n",
"+---+-----+\n",
"| 6| 1|\n",
"| 1| 1|\n",
"| 10| 1|\n",
"| 3| 1|\n",
"| 8| 1|\n",
"| 4| 1|\n",
"+---+-----+\n",
"\n"
]
}
],
"source": [
"dog_df.groupBy(dog_df[\"age\"]).count().show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run SQL queries on your Dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[createOrReplaceTempView(name)](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.createOrReplaceTempView)**\n",
"* Creates or replaces a local temporary view with this DataFrame.\n",
"* The lifetime of this temporary table is tied to the SparkSession that was used to create this DataFrame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Register the current Dataframe as a SQL temporary view"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+--------------------+---+\n",
"| name| breed|age|\n",
"+----------+--------------------+---+\n",
"| Pedro| Doberman| 3|\n",
"|Clementine| Golden Retriever| 8|\n",
"| Norah| Great Dane| 6|\n",
"| Mabel|Austrailian Shepherd| 1|\n",
"| Bear| Maltese| 4|\n",
"| Bill| Great Dane| 10|\n",
"+----------+--------------------+---+\n",
"\n"
]
}
],
"source": [
"dog_df.createOrReplaceTempView(\"dogs\")\n",
"\n",
"sql_dog_df = spark.sql(\"SELECT * FROM dogs\")\n",
"sql_dog_df.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----+--------+---+\n",
"| name| breed|age|\n",
"+-----+--------+---+\n",
"|Pedro|Doberman| 3|\n",
"+-----+--------+---+\n",
"\n"
]
}
],
"source": [
"sql_dog_df = spark.sql(\"SELECT * FROM dogs WHERE name='Pedro'\")\n",
"sql_dog_df.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "PySpark_Python3",
"language": "python",
"name": "pyspark3"
},
"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
}
| gpl-3.0 |
opengeostat/pygslib | pygslib/Ipython_templates/broken/vtk_tools.ipynb | 2 | 161065 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# VTK tools\n",
"\n",
"Pygslib use VTK:\n",
"\n",
" - as data format and data converting tool\n",
" - to plot in 3D\n",
" - as a library with some basic computational geometry functions, for example to know if a point is inside a surface\n",
"\n",
"\n",
"Some of the functions in VTK were obtained or modified from Adamos Kyriakou at https://pyscience.wordpress.com/\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<script type=\"text/Javascript\"> \n",
"var x = document.getElementById(\"ipython_notebook\")\n",
"x.innerHTML = '<img title=\"Opengeostat\" alt=\"Opengeostat\" 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\" />'\n",
"</script>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pygslib \n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions in vtktools"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on module pygslib.vtktools in pygslib:\n",
"\n",
"NAME\n",
" pygslib.vtktools - PyGSLIB vtktools, Module with tools to work with VTK.\n",
"\n",
"FILE\n",
" c:\\og_python\\pygslib\\pygslib\\vtktools.pyd\n",
"\n",
"DESCRIPTION\n",
" Copyright (C) 2015 Adrian Martinez Vargas \n",
" \n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" any later version.\n",
" \n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
" \n",
" You should have received a copy of the GNU General Public License\n",
" along with this program. If not, see <http://www.gnu.org/licenses/>\n",
"\n",
"FUNCTIONS\n",
" GetPointsInPolydata(...)\n",
" p= GetPointsInPolydata(polydata)\n",
" \n",
" Returns the points coordinates in a polydata (e.j. wireframe) \n",
" as a numpy array. \n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" polydata : VTK polydata\n",
" \n",
" \n",
" Returns\n",
" -------\n",
" p : 2D array of float\n",
" Points coordinates in polydata object \n",
" \n",
" See also\n",
" --------\n",
" SetPointsInPolydata, SavePolydata\n",
" \n",
" SavePolydata(...)\n",
" SavePolydata(polydata, path)\n",
" \n",
" Save polydata in a VTK XML polydata file (ext vtp) \n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" polydata : VTK polydata\n",
" path : str \n",
" Extension (*.vtp) will be added if not provided\n",
" \n",
" SetPointsInPolydata(...)\n",
" SetPointsInPolydata(polydata)\n",
" \n",
" Set the points coordinates in a polydata (e.j. wireframe). \n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" polydata : VTK polydata\n",
" points : 2D array \n",
" New points coordinates to be set in the polydata\n",
" points shape may be equal to [polydata.GetNumberOfPoints(),3] \n",
" \n",
" \n",
" See also\n",
" --------\n",
" GetPointsInPolydata, SavePolydata\n",
" \n",
" addLine(...)\n",
" addLine(renderer, \n",
" p1,\n",
" p2, \n",
" color=(0.0, 0.0, 1.0))\n",
" \n",
" Adds a line into an existing VTK renderer \n",
" \n",
" Parameters\n",
" ----------\n",
" renderer : VTK renderer\n",
" renderer with vtk objects and properties \n",
" p1,p2: tuple with 3 float\n",
" point location\n",
" radius: radius of the point \n",
" Default 1.\n",
" color: tuple with 3 float\n",
" Default (0.0, 0.0, 0.0)\n",
" \n",
" Returns\n",
" -------\n",
" VtkRenderer : an Vtk renderer containing VTK polydata \n",
" \n",
" See Also\n",
" --------\n",
" vtk_show, loadSTL\n",
" \n",
" Notes\n",
" -----\n",
" This Code was modified fron the original code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
" \n",
" addPoint(...)\n",
" addPoint(renderer, \n",
" p, \n",
" radius=1.0, \n",
" color=(0.0, 0.0, 0.0))\n",
" \n",
" Adds a point into an existing VTK renderer \n",
" \n",
" Parameters\n",
" ----------\n",
" renderer : VTK renderer\n",
" renderer with vtk objects and properties \n",
" p: tuple with 3 float\n",
" point location\n",
" radius: radius of the point \n",
" Default 1.\n",
" color: tuple with 3 float\n",
" Default (0.0, 0.0, 0.0)\n",
" \n",
" Returns\n",
" -------\n",
" VtkRenderer : an Vtk renderer containing VTK polydata \n",
" \n",
" See Also\n",
" --------\n",
" vtk_show, loadSTL\n",
" \n",
" Notes\n",
" -----\n",
" This Code was modified fron the original code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
" \n",
" dmtable2wireframe(...)\n",
" mywireframe = dmtable2wireframe(x, y, z, pid1,pid2,pid3) \n",
" \n",
" Takes a wireframe defined by two table: \n",
" \n",
" - x, y, z : This is the points table\n",
" - pid1,pid2,pid3: This is another table defining triangles with \n",
" three point IDs. \n",
" \n",
" Parameters\n",
" ---------- \n",
" x,y,z : 1D array of floats\n",
" coordinates of the points to be tested \n",
" pid1,pid2,pid3 : 1D array of integers\n",
" triangles defined by three existing point IDs\n",
" str filename : Str (Default None)\n",
" file name and path. If provided the file wireframe is saved to \n",
" this file. \n",
" \n",
" Returns\n",
" -------\n",
" surface : VTK polydata\n",
" wireframe imported\n",
" \n",
" \n",
" Notes\n",
" -----\n",
" ``pid`` is the row number in the point table.\n",
" \n",
" ``pid`` indices start at zero\n",
" \n",
" For example: \n",
" \n",
" a point table\n",
" \n",
" | x | y | z |\n",
" -------------\n",
" | 0 | 0 | 0 |\n",
" | 1 | 0 | 0 | \n",
" | 0 | 1 | 0 |\n",
" \n",
" a triangle table\n",
" \n",
" |pid1|pid2|pid3|\n",
" -------------\n",
" | 0 | 1 | 2 |\n",
" \n",
" getbounds(...)\n",
" boundingbox(polydata)\n",
" \n",
" Returns the bounding box limits x,y,z minimum and maximum\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" polydata : VTK polydata\n",
" \n",
" \n",
" Returns\n",
" -------\n",
" xmin,xmax, ymin,ymax, zmin,zmax : float\n",
" The geometry bounding box\n",
" \n",
" grid2vtkfile(...)\n",
" grid2vtkfile(path, x,y,z, data)\n",
" \n",
" save grid in vtk file if x,y,z are 1D it saves VtkRectilinearGrid,\n",
" if are 3D it saves VtkStructuredGrid\n",
" \n",
" \n",
" Parameters \n",
" ----------\n",
" \n",
" \n",
" \n",
" Returns\n",
" -------\n",
" \n",
" loadSTL(...)\n",
" loadSTL(filenameSTL)\n",
" \n",
" Load a STL wireframe file\n",
" \n",
" Parameters\n",
" ----------\n",
" filenameSTL : file path\n",
" \n",
" Returns\n",
" -------\n",
" polydata : VTK Polydata object \n",
" \n",
" \n",
" Notes\n",
" -----\n",
" Code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
" \n",
" partialgrid2vtkfile(...)\n",
" partialgrid2vtkfile(path, x,y,z, dx,dy,dz,var, varname)\n",
" \n",
" save unstructurw vtk grid of parent blocks into a file\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" \n",
" \n",
" \n",
" Returns\n",
" -------\n",
" \n",
" pointinsolid(...)\n",
" inside = pygslib.vtktools.pointinsolid(surface, x, y, z)\n",
" \n",
" Find points inside a closed surface using vtkSelectEnclosedPoints\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" surface : VTK polydata\n",
" this may work with any 3D object..., no necessarily triangles \n",
" x,y,z : 1D array of floats\n",
" coordinates of the points to be tested\n",
" \n",
" \n",
" Returns\n",
" -------\n",
" inside : 1D array of integers \n",
" Indicator of point inclusion with values [0,1] \n",
" 0 means that the point is not inside\n",
" 1 means that the point is inside\n",
" \n",
" See Also\n",
" --------\n",
" vtk_raycasting, pointquering\n",
" \n",
" Notes\n",
" -----\n",
" It is assumed that the surface is closed and manifold. \n",
" Note that if this check is not performed and the surface is not \n",
" closed, the results are undefined.\n",
" \n",
" pointquering(...)\n",
" query = pygslib.vtktools.pointquering(surface, azm, dip, x, y, z, test)\n",
" \n",
" Find points inside, over and below surface/solid\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" surface : VTK polydata\n",
" this may work with any 3D object..., no necessarily triangles \n",
" azm, dip: float\n",
" rotation defining the direction we will use to test the points\n",
" azm 0 will point north and dip positive meas downward direction\n",
" (like surface drillholes)\n",
" x,y,z : 1D array of floats\n",
" coordinates of the points to be tested\n",
" test : integer\n",
" 1 test inside closed surface. Here we use \n",
" vtkOBBTree::InsideOrOutside. Closed surface are required\n",
" 2 test 'above' surface \n",
" 3 test 'below' surface \n",
" 4 test 'inside' surface (the surface can be open)\n",
" \n",
" Returns\n",
" -------\n",
" inside : 1D array of integers \n",
" Indicator of point inclusion with values [0,1] \n",
" 0 means that the point is not inside, above or below surface\n",
" 1 means that the point is inside, above or below surface\n",
" p1 : 1D array size 3\n",
" rays to show where are pointing in the output\n",
" \n",
" \n",
" See Also\n",
" --------\n",
" vtk_raycasting\n",
" \n",
" Notes\n",
" -----\n",
" The test 1 requires the surface to be close, to find points between\n",
" two surfaces you can use test=4\n",
" The test, 2,3 and 4 use raycasting and rays orientation are defined\n",
" by azm and dip values. The results will depend on this direction,\n",
" for example, to find the points between two open surfaces you may \n",
" use ray direction perpendicular to surfaces \n",
" If a surface is closed the points over and below a surface will \n",
" include points inside surface\n",
" \n",
" points2vtkfile(...)\n",
" bpoints2vtkfile(path, x,y,z, data)\n",
" \n",
" Save points in vtk file\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" \n",
" \n",
" \n",
" Returns\n",
" -------\n",
" \n",
" polydata2renderer(...)\n",
" polydata2renderer(polydata, \n",
" color=None, \n",
" opacity=None, \n",
" background=None)\n",
" \n",
" Creates vtk renderer from vtk polydata\n",
" \n",
" Parameters\n",
" ----------\n",
" polydata : VTK polydata\n",
" \n",
" color: tuple with 3 floats (RGB)\n",
" default color=(1.,0.,0.)\n",
" opacity: float \n",
" default 1 (opaque)\n",
" background: tuple with 3 floats (RGB)\n",
" default background=(1.,1.,1.)\n",
" \n",
" \n",
" Returns\n",
" -------\n",
" VtkRenderer : an Vtk renderer containing VTK polydata \n",
" \n",
" See Also\n",
" --------\n",
" vtk_show, loadSTL\n",
" \n",
" Notes\n",
" -----\n",
" This Code was modified fron the original code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
" \n",
" vtk_raycasting(...)\n",
" vtk_raycasting(surface, pSource, pTarget)\n",
" \n",
" Intersects a line defined by two points with a polydata vtk object,\n",
" for example a closed surface. \n",
" \n",
" This function is to find intersection and inclusion of points \n",
" within, below or over a surface. This is handy to select points \n",
" or define blocks inside solids. \n",
" \n",
" Parameters\n",
" ----------\n",
" surface : VTK polydata\n",
" this may work with any 3D object..., no necessarily triangles \n",
" pSource, pTarget: tuples with 3 float\n",
" point location defining a ray\n",
" \n",
" Returns\n",
" -------\n",
" intersect : integer, with values -1, 0, 1\n",
" 0 means that no intersection points were found\n",
" 1 means that some intersection points were found\n",
" -1 means the ray source lies within the surface\n",
" pointsIntersection : tuple of tuples with 3 float\n",
" this is a tuple with coordinates tuples defining \n",
" intersection points\n",
" \n",
" pointsVTKIntersectionData : an Vtk polydata object \n",
" similar to pointsIntersection but in Vtk polydata format\n",
" \n",
" \n",
" See Also\n",
" --------\n",
" vtk_show, loadSTL\n",
" \n",
" Notes\n",
" -----\n",
" This Code was modified fron the original code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
" \n",
" vtk_show(...)\n",
" vtk_show(renderer, \n",
" width=400, \n",
" height=300,\n",
" camera_position=None,\n",
" camera_focalpoint=None)\n",
" \n",
" Display a vtk renderer in Ipython Image\n",
" \n",
" Parameters\n",
" ----------\n",
" renderer : VTK renderer\n",
" renderer with vtk objects and properties \n",
" width, height: float\n",
" Size of the image, default (400,300)\n",
" camera_position, camera_focalpoint: tuples with 3 float \n",
" camera position, camera focal point (ex. center of mass)\n",
" default None. If not None the the camera will be overwrite\n",
" Returns\n",
" -------\n",
" Image : an IPython display Image object\n",
" \n",
" See Also\n",
" --------\n",
" polydata2renderer, loadSTL\n",
" \n",
" Notes\n",
" -----\n",
" This Code was modified fron the original code by Adamos Kyriakou\n",
" published in https://pyscience.wordpress.com/\n",
"\n",
"DATA\n",
" __test__ = {}\n",
"\n",
"\n"
]
}
],
"source": [
"help(pygslib.vtktools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a cube defined in an stl file and plot it\n",
"\n",
"STL is a popular mesh format included an many non-commercial and commercial software, example: Paraview, Datamine Studio, etc. \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vtkPolyData (00000000114BF7D0)\n",
" Debug: Off\n",
" Modified Time: 185\n",
" Reference Count: 1\n",
" Registered Events: (none)\n",
" Information: 0000000007BD7FC0\n",
" Data Released: False\n",
" Global Release Data: Off\n",
" UpdateTime: 186\n",
" Field Data:\n",
" Debug: Off\n",
" Modified Time: 146\n",
" Reference Count: 1\n",
" Registered Events: (none)\n",
" Number Of Arrays: 0\n",
" Number Of Components: 0\n",
" Number Of Tuples: 0\n",
" Number Of Points: 8\n",
" Number Of Cells: 12\n",
" Cell Data:\n",
" Debug: Off\n",
" Modified Time: 154\n",
" Reference Count: 1\n",
" Registered Events: \n",
" Registered Observers:\n",
" vtkObserver (0000000007FC9AE0)\n",
" Event: 33\n",
" EventName: ModifiedEvent\n",
" Command: 0000000002BA42A0\n",
" Priority: 0\n",
" Tag: 1\n",
" Number Of Arrays: 0\n",
" Number Of Components: 0\n",
" Number Of Tuples: 0\n",
" Copy Tuple Flags: ( 1 1 1 1 1 0 1 1 )\n",
" Interpolate Flags: ( 1 1 1 1 1 0 0 1 )\n",
" Pass Through Flags: ( 1 1 1 1 1 1 1 1 )\n",
" Scalars: (none)\n",
" Vectors: (none)\n",
" Normals: (none)\n",
" TCoords: (none)\n",
" Tensors: (none)\n",
" GlobalIds: (none)\n",
" PedigreeIds: (none)\n",
" EdgeFlag: (none)\n",
" Point Data:\n",
" Debug: Off\n",
" Modified Time: 156\n",
" Reference Count: 1\n",
" Registered Events: \n",
" Registered Observers:\n",
" vtkObserver (0000000007FC98D0)\n",
" Event: 33\n",
" EventName: ModifiedEvent\n",
" Command: 0000000002BA42A0\n",
" Priority: 0\n",
" Tag: 1\n",
" Number Of Arrays: 0\n",
" Number Of Components: 0\n",
" Number Of Tuples: 0\n",
" Copy Tuple Flags: ( 1 1 1 1 1 0 1 1 )\n",
" Interpolate Flags: ( 1 1 1 1 1 0 0 1 )\n",
" Pass Through Flags: ( 1 1 1 1 1 1 1 1 )\n",
" Scalars: (none)\n",
" Vectors: (none)\n",
" Normals: (none)\n",
" TCoords: (none)\n",
" Tensors: (none)\n",
" GlobalIds: (none)\n",
" PedigreeIds: (none)\n",
" EdgeFlag: (none)\n",
" Bounds: \n",
" Xmin,Xmax: (-5, 5)\n",
" Ymin,Ymax: (-5, 5)\n",
" Zmin,Zmax: (-5, 5)\n",
" Compute Time: 200\n",
" Number Of Points: 8\n",
" Point Coordinates: 0000000010779F10\n",
" Locator: 0000000000000000\n",
" Number Of Vertices: 0\n",
" Number Of Lines: 0\n",
" Number Of Polygons: 12\n",
" Number Of Triangle Strips: 0\n",
" Number Of Pieces: 1\n",
" Piece: 0\n",
" Ghost Level: 0\n",
"\n",
"\n"
]
}
],
"source": [
"#load the cube \n",
"mycube=pygslib.vtktools.loadSTL('../datasets/stl/cube.stl')\n",
"\n",
"# see the information about this data... Note that it is an vtkPolyData\n",
"print mycube"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEsCAIAAABi1XKVAAAHwklEQVR4Xu3Z23XbOhRFUTmVuYVb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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a VTK render containing a surface (mycube)\n",
"renderer = pygslib.vtktools.polydata2renderer(mycube, color=(1,0,0), opacity=0.50, background=(1,1,1))\n",
"# Now we plot the render\n",
"pygslib.vtktools.vtk_show(renderer, camera_position=(-20,20,20), camera_focalpoint=(0,0,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ray casting to find intersections of a lines with the cube \n",
"\n",
"This is basically how we plan to find points inside solid and to define blocks inside solid"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEsCAIAAABi1XKVAAAK+klEQVR4Xu3b3XUbRxKA0YbPprJx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\nu9i2nd8AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we have a line, for example a block model row \n",
"# defined by two points or an infinite line passing trough a dillhole sample \n",
"pSource = [-50.0, 0.0, 0.0]\n",
"pTarget = [50.0, 0.0, 0.0]\n",
"\n",
"# now we want to see how this looks like\n",
"pygslib.vtktools.addLine(renderer,pSource, pTarget, color=(0, 1, 0))\n",
"pygslib.vtktools.vtk_show(renderer) # the camera position was already defined \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the line intersects? True\n",
"the line is over the surface? False\n",
"[(-5.0, 0.0, 0.0), (5.0, 0.0, 0.0)]\n"
]
}
],
"source": [
"# now we find the point coordinates of the intersections \n",
"intersect, points, pointsVTK= pygslib.vtktools.vtk_raycasting(mycube, pSource, pTarget)\n",
"\n",
"print \"the line intersects? \", intersect==1\n",
"print \"the line is over the surface?\", intersect==-1\n",
"\n",
"# list of coordinates of the points intersecting \n",
"print points\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEsCAIAAABi1XKVAAAOx0lEQVR4Xu3dS29cZx2A8WfsOInr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\nP4BmVzaGKlIeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Now we plot the intersecting points\n",
"\n",
"# To do this we add the points to the renderer \n",
"for p in points: \n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.5, color=(0.0, 0.0, 1.0))\n",
"\n",
"pygslib.vtktools.vtk_show(renderer) \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test line on surface"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the line intersects? True\n",
"the line is over the surface? False\n",
"[(-5.0, 5.010000228881836, 0.0), (5.0, 5.010000228881836, 0.0)]\n"
]
},
{
"data": {
"image/png": 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hMqzCIgBHN/naNt+ERejAY3jR5Sq1lsEKxA14Fp6Ga7AJq7AAwBEcwFpWq2M44JXr2Cu1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\nRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we have a line, for example a block model row \n",
"# defined by two points or an infinite line passing trough a dillhole sample \n",
"pSource = [-50.0, 5.01, 0]\n",
"pTarget = [50.0, 5.01, 0]\n",
"\n",
"# now we find the point coordinates of the intersections \n",
"intersect, points, pointsVTK= pygslib.vtktools.vtk_raycasting(mycube, pSource, pTarget)\n",
"\n",
"print \"the line intersects? \", intersect==1\n",
"print \"the line is over the surface?\", intersect==-1\n",
"\n",
"# list of coordinates of the points intersecting \n",
"print points\n",
"\n",
"# now we want to see how this looks like\n",
"pygslib.vtktools.addLine(renderer,pSource, pTarget, color=(0, 1, 0))\n",
"\n",
"for p in points: \n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.5, color=(0.0, 0.0, 1.0))\n",
"\n",
"pygslib.vtktools.vtk_show(renderer) # the camera position was already defined \n",
"\n",
"# note that there is a tolerance of about 0.01"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Finding points"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#using same cube but generation arbitrary random points\n",
"x = np.random.uniform(-10,10,150)\n",
"y = np.random.uniform(-10,10,150)\n",
"z = np.random.uniform(-10,10,150)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Find points inside a solid"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[]\n",
"[]\n"
]
}
],
"source": [
"# selecting all inside the solid\n",
"# This two methods are equivelent but test=4 also works with open surfaces \n",
"inside,p=pygslib.vtktools.pointquering(mycube, azm=0, dip=0, x=x, y=y, z=z, test=1)\n",
"inside1,p=pygslib.vtktools.pointquering(mycube, azm=0, dip=0, x=x, y=y, z=z, test=4)\n",
"err=inside==inside1\n",
"#print inside, tuple(p)\n",
"print x[~err]\n",
"print y[~err]\n",
"print z[~err]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(vtkOpenGLRenderer)00000000114F7780"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here we prepare to plot the solid, the x,y,z indicator and we also \n",
"# plot the line (direction) used to ray trace\n",
"\n",
"# convert the data in the STL file into a renderer and then we plot it\n",
"renderer = pygslib.vtktools.polydata2renderer(mycube, color=(1,0,0), opacity=0.70, background=(1,1,1))\n",
"# add indicator (r->x, g->y, b->z)\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-7,-10,-10], color=(1, 0, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-7,-10], color=(0, 1, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-10,-7], color=(0, 0, 1))\n",
"\n",
"# add ray to see where we are pointing\n",
"pygslib.vtktools.addLine(renderer, (0.,0.,0.), tuple(p), color=(0, 0, 0))\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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X1D/zkLoKKoHe3tA+ZeViYQ24cItfaZWTBlaNck07tHtbQCdh3DVDYGJZ9nB3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\nMHQHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here we plot the points selected and non-selected in different color and size\n",
"# add the points selected\n",
"for i in range(len(inside)):\n",
" p=[x[i],y[i],z[i]]\n",
" \n",
" if inside[i]!=0:\n",
" #inside\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.5, color=(0.0, 0.0, 1.0))\n",
" else:\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.2, color=(0.0, 1.0, 0.0))\n",
"\n",
" \n",
"#lets rotate a bit this\n",
"pygslib.vtktools.vtk_show(renderer, camera_position=(0,0,50), camera_focalpoint=(0,0,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find points over a surface"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(vtkOpenGLRenderer)000000001150E728"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# selecting all over a solid (test = 2) \n",
"inside,p=pygslib.vtktools.pointquering(mycube, azm=0, dip=0, x=x, y=y, z=z, test=2)\n",
"\n",
"# here we prepare to plot the solid, the x,y,z indicator and we also \n",
"# plot the line (direction) used to ray trace\n",
"\n",
"# convert the data in the STL file into a renderer and then we plot it\n",
"renderer = pygslib.vtktools.polydata2renderer(mycube, color=(1,0,0), opacity=0.70, background=(1,1,1))\n",
"# add indicator (r->x, g->y, b->z)\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-7,-10,-10], color=(1, 0, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-7,-10], color=(0, 1, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-10,-7], color=(0, 0, 1))\n",
"\n",
"# add ray to see where we are pointing\n",
"pygslib.vtktools.addLine(renderer, (0.,0.,0.), tuple(-p), color=(0, 0, 0))\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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c0YJXQJ7I2wtW0EguINnupHd4QHL5rhqi6zVCK9SDwihsrngv/uR2k68MK2fC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\nDQssY0xsWGAZY2LDAssYExv/H6yjl+D1bfZJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here we plot the points selected and non-selected in different color and size\n",
"# add the points selected\n",
"for i in range(len(inside)):\n",
" p=[x[i],y[i],z[i]]\n",
" \n",
" if inside[i]!=0:\n",
" #inside\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.5, color=(0.0, 0.0, 1.0))\n",
" else:\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.2, color=(0.0, 1.0, 0.0))\n",
"\n",
" \n",
"#lets rotate a bit this\n",
"pygslib.vtktools.vtk_show(renderer, camera_position=(0,0,50), camera_focalpoint=(0,0,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find points below a surface"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(vtkOpenGLRenderer)0000000011DDB6D0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# selecting all over a solid (test = 2) \n",
"inside,p=pygslib.vtktools.pointquering(mycube, azm=0, dip=0, x=x, y=y, z=z, test=3)\n",
"\n",
"# here we prepare to plot the solid, the x,y,z indicator and we also \n",
"# plot the line (direction) used to ray trace\n",
"\n",
"# convert the data in the STL file into a renderer and then we plot it\n",
"renderer = pygslib.vtktools.polydata2renderer(mycube, color=(1,0,0), opacity=0.70, background=(1,1,1))\n",
"# add indicator (r->x, g->y, b->z)\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-7,-10,-10], color=(1, 0, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-7,-10], color=(0, 1, 0))\n",
"pygslib.vtktools.addLine(renderer,[-10,-10,-10], [-10,-10,-7], color=(0, 0, 1))\n",
"\n",
"# add ray to see where we are pointing\n",
"pygslib.vtktools.addLine(renderer, (0.,0.,0.), tuple(p), color=(0, 0, 0))\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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/UvpvkXIGXa/JbwpNB9bogkt61o7gwlgERoRNN4cLNTX0KpXThB3o/5T+l2IW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\nhgWWMSY2LLCMMbFhgWWMiY3/DzUMPbZvflFgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here we plot the points selected and non-selected in different color and size\n",
"# add the points selected\n",
"for i in range(len(inside)):\n",
" p=[x[i],y[i],z[i]]\n",
" \n",
" if inside[i]!=0:\n",
" #inside\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.5, color=(0.0, 0.0, 1.0))\n",
" else:\n",
" pygslib.vtktools.addPoint(renderer, p, radius=0.2, color=(0.0, 1.0, 0.0))\n",
"\n",
" \n",
"#lets rotate a bit this\n",
"pygslib.vtktools.vtk_show(renderer, camera_position=(0,0,50), camera_focalpoint=(0,0,0))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Export points to a VTK file"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"data = {'inside': inside}\n",
"\n",
"pygslib.vtktools.points2vtkfile('points', x,y,z, data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results can be ploted in an external viewer, for example mayavi or paraview:\n",
"\n",
"<img src=\"figures/Fig_paraview.png\">\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bmanubay/open-forcefield-tools | examples/substructure_linking.ipynb | 1 | 47783 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linking molecule fragments via reaction SMIRKS\n",
"\n",
"To test/develop SMIRFF force fields, it is extremely helpful to be able to generate molecules containing assorted combinations of various molecular fragments or substructures (i.e. substructures containing particular SMIRKS). Here, I experiment with using reaction SMIRKS to link substructures to generate libraries of molecules.\n",
"\n",
"Library generation can be done by legitimate chemical reactions in order to expand libraries. However, here, I just want to link together sets of fragments with pre-specified attachment points. This can be done more simply by involving dummy elements which are present only to react. Here I'll make a `B` and `F` as caps of groups I want to link together in a library."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import OpenEye stuff\n",
"import openeye.oechem as oechem\n",
"import openeye.oedepict as oedepict\n",
"from IPython.display import display\n",
"import openeye.oeomega as oeomega\n",
"\n",
"# Add utility function for depiction\n",
"def depict(mol, width=500, height=200):\n",
" from IPython.display import Image\n",
" dopt = oedepict.OEPrepareDepictionOptions()\n",
" dopt.SetDepictOrientation( oedepict.OEDepictOrientation_Horizontal)\n",
" oedepict.OEPrepareDepiction(mol, dopt)\n",
" opts = oedepict.OE2DMolDisplayOptions(width, height, oedepict.OEScale_AutoScale)\n",
" disp = oedepict.OE2DMolDisplay(mol, opts)\n",
" ofs = oechem.oeosstream()\n",
" oedepict.OERenderMolecule(ofs, 'png', disp)\n",
" ofs.flush()\n",
" return Image(data = \"\".join(ofs.str()))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Try out a simple reaction to link two SMILES strings"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"product smiles= COCCCOCOCOC\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test by linking two molecules - anything with a C or O followed by a B can react with a C or O followed by an F to form \n",
"# a bond betwen the two C or O atoms, dropping the B or F. \n",
"libgen = oechem.OELibraryGen(\"[C,O:1][B:2].[C,O:3][F:4]>>[C,O:1][C,O:3]\") \n",
"mol = oechem.OEGraphMol()\n",
"oechem.OESmilesToMol(mol, 'COCCB')\n",
"libgen.SetStartingMaterial(mol, 0)\n",
"mol.Clear()\n",
"oechem.OESmilesToMol(mol, 'COCOCOCF')\n",
"libgen.SetStartingMaterial(mol, 1)\n",
"\n",
"mols = []\n",
"for product in libgen.GetProducts():\n",
" print(\"product smiles= %s\" %oechem.OEMolToSmiles(product))\n",
" mols.append(oechem.OEMol(product))\n",
" \n",
"# Depict result\n",
"depict(mols[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Proceed to library generation\n",
"\n",
"### First, build some sets of molecules to link, capped by our \"reactant\" groups"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Build two small libraries of molecules for linking\n",
"\n",
"# Build a first set of molecules\n",
"import itertools\n",
"smileslist1 = []\n",
"#Take all two-item combinations of entries in the list\n",
"for item in itertools.permutations(['C','O','c1ccccc1','CC', 'COC', 'CCOC', 'CCCOC', 'C1CC1', 'C1CCC1', 'C1CCCC1', 'C1CCCCC1','C1OCOCC1'], 2): \n",
" smileslist1.append( ''.join(item))\n",
"#Now cap all of them terminally with a reaction site\n",
"smileslist1_rxn = [ smi+'B' for smi in smileslist1]\n",
"\n",
"# Build a second set of molecules in the same manner\n",
"smileslist2 = []\n",
"for item in itertools.permutations(['c1ccccc1OC','c1ccccc1COC','c1ccccc1O(CO)C','C(O)C','C(OCO)', 'C1OOC1','C1OCOC1', 'C1CCCCCCOC1','CO(COCO)C', 'COCO(O)OC'],2):\n",
" smileslist2.append( ''.join(item))\n",
"# Cap all with reaction site\n",
"smileslist2_rxn = [smi + 'F' for smi in smileslist2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now, generate our library"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10890\n"
]
},
{
"data": {
"image/png": 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PP+ZChQqZ/PoAIPvRWY59DZY90dcWLlzIsuXNL+wtWrTgffv2Jbp8qIwHZPTd\npk0bf0Ce7JfnjxN4UAlVD66DAAi4jwAE3X19BouTSWD58uVmRKxCrj8NGjTg7du3h6xNToVjORXO\nL7r6+8aNG0PmT+qNS5cusQThcdasWU0bmTNn5sGDB/PFixeTWpUpM2TIEAlMz2LqypQpEw8YMIDP\nnz+f5LpQAARAwJ0EIOju7DdYnQQC+/fvZx0B+4S8ePHivGDBgpA1yKlwZhTum47X0bmO0kNNx4es\nKJE3Dh48KEvbrf32yRnxPHfu3ESWZp45cybfcccd/vJ/+ctfwjLaT7QByAgCIOAIAhB0R3QDjLCC\ngI50ddSqI18Vcx0JjxgxgnVkHCxpgNqECRNY18c1v66X67r5uXPngmUP+7WVK1eybGnzC3PdunVZ\nDqsJ2Y5sh4szg1BV9pb/oHvMkUAABFIlAQh6qux27zstL0zhYsWK+cXxmWeeYR0Jh0pyKhyXK1fO\nn18j2TWi3e50/fp1njJlCsuWNmNLunTpuGvXrnzq1Cm/KbK1jmVbnX+dXLbWmYA+LYsEAiCQeglA\n0FNv33vScx3R1qtXzy/MlStXTnDUumfPHm7WrJk/f6lSpXjRokURZ6MCLofScJRsOdPZAhV4DeYb\nNmyYf809Y8aMLOfKs0bgI4EACIAABB3fAc8Q0Ol13zq5CuB7773HoUatciocDxw4kDV4TMvIqXA8\nZswYlhPiHMUj/gOKz7+nnnqK9WEECQRAAAR8BKLkDwQSCHiCgEyRGz/kcBjzClPZmhbUrzly9Km+\n6lSm4M2pcG3btpWD1MaQBL8FzR/Ji7JPnWSfupzQOtWcMKfvX//222+pTp06kTQLbYMACDiQAN62\n5sBOgUnJI1C6dGlTUNa/KZiYb9261Qjh008/bcRcTnqjVatWkUSwO1LMY1OQZQHzMW/evBDz2GDw\nOwiAgJ8ARuh+FPjF6wQeeeQRkjehUf78+c1LWDp06EB6tjoSCIAACHiBAATdC70IHxJF4JVXXqHj\nx4/LC8f6ykvE8BaxREFDJhAAAdcQgKC7pqtgaEoJ9OjRI6VVWF5eX+HapEkTeaNpQ5IIdsvbQwMg\nAALeIYD5Ru/0JTzxAIHo6GhasWKFvLL8z3eWe8AnuAACIGAPAQi6PZzRCgiAAAiAAAhYSgCCbile\nVA4CIAACIAAC9hCAoNvDGa2AAAiAAAiAgKUEIOiW4kXlIAACIAACIGAPAQi6PZzRCgiAAAiAAAhY\nSgCCbileVA4CIAACIAAC9hCAoNvDGa2AAAiAAAiAgKUEIOiW4kXlIAACIAACIGAPAQi6PZzRCgiA\nAAiAAAhYSgCCbileVA4CIAACIAAC9hCAoNvDGa2AAAiAAAiAgKUEIOiW4kXlIAACIAACIGAPAQi6\nPZzRCgiAAAiAAAhYSgCCbileVA4CIAACIAAC9hCAoNvDGa2AAAiAAAiAgKUEIOiW4kXlIAACIAAC\nIGAPAQi6PZzRCgiAAAiAAAhYSgCCbileVA4CIAACIAAC9hCAoNvDGa2AAAiAAAiAgKUEIOiW4kXl\nIAACIAACIGAPAQi6PZzRCgiAAAiAAAhYSgCCbileVA4CSSNw1113UcmSJalu3bpJK4jcIAACqZ5A\nVKonAACphsCECRPo0qVL1L17d8qSJYsj/c6ZMydFR0c70jYYBQIg4GwCGKE7u39gXRgJDBo0iPr3\n709lypShmTNnEjOHsXZUBQIgAAKRJQBBjyx/tG4jgSVLllDVqlXp4MGD1KZNG6pZsyatX7/eRgvQ\nFAiAAAhYRwCCbh1b1Gwzgf3795sWV65cSZcvXw5ovUaNGrRhwwaaNm0aFSxYkNasWUN6rV27dnT4\n8OGA/E66sG7dOmPO+fPnnWQWbAEBEHAQAQi6gzoDpqSMQLZs2UwFy5Yto3LlytH8+fMDKkyTJg11\n6NDBrFP369ePMmTIQNOnT6fSpUvT8OHDzRp7QKEIXvj111+pdevW1KRJE2NF+vTpI2gNmgYBEHAy\nAQi6k3sHtiWJwMSJE2nhwoVUsWJF2rt3Lz311FMmWvznn38OqEfFf9SoUbR9+3aT78KFC/Taa69R\n2bJlac6cOQH57b5w8eJFGjJkiLFn1qxZlDlzZtIHkAMHDthtCtoDARBwCwEJDEICAU8RuHbtGk+e\nPJnz5cunUW+cNm1a7tSpEx89ejSknytWrODKlSub/FqmVq1avHHjxpD5rbpx48YNnjFjBhcpUsRv\nS6tWrVhG6lY1iXpBAAQ8QkAjfZFAwJMETp8+zT169GCZpjbimCNHDh47dizL+npQf69fv87/+te/\nOH/+/Ca/TM+zTM/zkSNHguYP98W1a9eyrOn7hbxatWq8atWqcDeD+kAABDxKAILu0Y6FWzcJ/PLL\nL/zYY4/5hVIObmGZmr+ZId5vZ8+e5T59+rCsr5syMj3PMj3Psoc9Xs7wfJSoe27bti3rA4TODhQq\nVIg/+OAD1tE6EgiAAAgklgAEPbGkkM/1BBYvXszly5f3C/vDDz/MW7ZsCenXrl27uFmzZv78coob\nz5s3L2T+pN6QdXIeNmwYyyE3po1MmTKx7JPnc+fOJbUq5AcBEAABhqDjS5CqCFy9epUleI7z5Mlj\nRDRdunT80ksv8fHjx0NykKh5lkA7v7DXqVOHN23aFDJ/Ym7Mnj2bixYt6q+zRYsWLIF8iSmKPCAA\nAiAQlAAEPSgWXPQ6gVOnTnG3bt04KirKiGquXLlYjoblK1euBHVdA+3efffdOIF2I0eODJo3oYuy\nD57lQBu/kFepUoW///77hIrgHgiAAAgkigC2rbllOwLsDCuB3Llzk25z27x5MzVq1IjOnDlDL7/8\nstnytmjRooC2ZCRPL774Isk0PPXs2ZP0s++wl4DMQS5IYJ3Z/169enWSQDcqUKAATZ06lSSSnmrX\nrh2kBC6BAAiAQNIIpFHZT1oR5AYB7xH48ssvjVBLAJ1xrmHDhjR+/HiSNfegzp48eZKyZ89uDqYJ\nmuHPi/oyGK1H97zrKW8ZM2Y0L4cZOHCgKZ9QWdwDARAAgaQQgKAnhRbyepqArK/TO++8Q0OHDjUj\ndpmOJ1lfNwe8yJp7kn2XADqSaHnat2+fKdu8eXOSbXNUvHjxJNeFAiAAAiBwKwIQ9FsRwv1UR0BH\n3/pmtilTppDsTScVcz21TcVdRf5WSQLmSPa/k6yNm6z33HMP6atb8Y7zW5HDfRAAgZQQgKCnhB7K\neprA1q1bzbr60qVLjZ96PrwKs07HB0tyEh0NGDCAZA85yR5yuu2228z58H/9619JTqsLVgTXQAAE\nQCBsBCDoYUOJirxK4LPPPqPevXubF7qoj40bNzbr4vpedU36Zre33nrLiLfsITfr6hJBb86Gz5kz\np8mD/4AACICA1QQg6FYTRv2eICDb2WjSpEn0+uuvk5wkR/rWsy5dupj3qw8ePJj27Nlj/GzatKlZ\nJy9VqpQn/IYTIAAC7iEAQXdPX8FSBxCQA2hII9R1y5lOq/tShQoVzHR8/fr1fZfwLwiAAAjYSgCC\nbituNOYVArp/Xd9RfujQIbMlrVevXmZvulf8gx8gAALuIwBBd1+fwWIQAAEQAAEQCCCA0NsAJLgA\nAiAAAiAAAu4jAEF3X5/BYhAAARAAARAIIABBD0CCCyAAAiAAAiDgPgIQdPf1GSwGARAAARAAgQAC\nEPQAJLgAAiAAAiAAAu4jAEF3X5/BYhAAARAAARAIIABBD0CCCyAAAiAAAiDgPgIQdPf1GSwGARAA\nARAAgQAC/w+kBKs8AjAkuQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Build overall set of reactants\n",
"libgen = oechem.OELibraryGen(\"[C,O:1][B:2].[C,O:3][F:4]>>[C,O:1][C,O:3]\") \n",
"mol = oechem.OEGraphMol()\n",
"for idx, smi in enumerate(smileslist1_rxn):\n",
" oechem.OESmilesToMol(mol, smi)\n",
" libgen.AddStartingMaterial(mol, 0)\n",
" mol.Clear()\n",
"for idx, smi in enumerate(smileslist2_rxn):\n",
" oechem.OESmilesToMol(mol, smi)\n",
" libgen.AddStartingMaterial(mol, 1)\n",
" mol.Clear()\n",
" \n",
"products = [ oechem.OEMol(product) for product in libgen.GetProducts() ]\n",
"print len(products)\n",
"depict(products[0])\n"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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EF0D/YNZlw4YkVCDGLbE0oEGHQs6JSpZ0nB0F0TFODBSZdpqCnoce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\nxrwDCZAACZAACfhO4P8BdUYytEPmVdMAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"depict(products[4])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate a conformer for each and write out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"omega = oeomega.OEOmega()\n",
"omega.SetMaxConfs(1)\n",
"omega.SetStrictStereo(False)\n",
"# First do just the first 10 molecules\n",
"#products = products[0:10]\n",
"ofs = oechem.oemolostream('linked_substructures.oeb')\n",
"for oemol in products:\n",
" omega(oemol)\n",
" oechem.OETriposAtomNames(mol)\n",
" oechem.OEWriteMolecule(ofs, oemol)\n",
"ofs.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Make sure I can read and write to mol2\n",
"ifs = oechem.oemolistream('linked_substructures.oeb')\n",
"ofs = oechem.oemolostream('linked_substructures_sample.mol2')\n",
"ct = 0\n",
"mol = oechem.OEMol()\n",
"while oechem.OEReadMolecule(ifs, mol):\n",
" oechem.OEWriteMolecule(ofs, mol)\n",
" ct+=1\n",
" mol=oechem.OEMol()\n",
" if ct > 10: break #Don't eat up tons of space, just test\n",
"ifs.close()\n",
"ofs.close()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
predictscan3/scan3 | analysis_nbs/Normalise Binary Fields.ipynb | 1 | 5382 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from os.path import join, basename, dirname\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sys import path\n",
"from os import getcwd\n",
"\n",
"# Sort out paths so that this can use functions from the main codebase\n",
"path.insert(0, dirname(getcwd()))\n",
"\n",
"import environment\n",
"from scan3 import settings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded 63788 rows of data\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lukelatimer/.conda/envs/scan3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (50) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"# Load up some data to work with\n",
"data_fname = join(settings.DATA_IN_ROOT, \"data_staging\", \"all_by_baby_enriched_v3.csv\")\n",
"df = pd.read_csv(data_fname)\n",
"print(\"Loaded {} rows of data\".format(len(df)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dem_alcohol_norm\n",
"\tnan 46% 29602\n",
"\t0.0 51% 32831\n",
"\t1.0 2% 1355\n",
"dem_cigarettes_norm\n",
"\tnan 22% 14010\n",
"\t0.0 74% 47210\n",
"\t1.0 4% 2568\n"
]
}
],
"source": [
"# Test that we can generate some reports, though this should be an actual test, this is really just so that I\n",
"# can easily run some code to generate tables to send to the guys\n",
"\n",
"from scan3.server.data_import import binary_norm\n",
"\n",
"df_n = binary_norm.apply_binary_norm(df)\n",
"report = binary_norm.generate_report(df_n)\n",
"\n",
"for k, sub_report in dict(report).items():\n",
" print(k)\n",
" for k2, v in sub_report.items():\n",
" print(\"\\t{}\".format(v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overview\n",
"\n",
"Define some functions that tidy up and normalise the values for each binary field.\n",
"\n",
"Generate some lookup tables that can be incorporated in the main pipeline"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dem_alcohol_norm\n",
"\tnan 46% 29602\n",
"\t0.0 51% 32831\n",
"\t1.0 2% 1355\n",
"dem_cigarettes_norm\n",
"\tnan 22% 14010\n",
"\t0.0 74% 47210\n",
"\t1.0 4% 2568\n"
]
}
],
"source": [
"\n",
"BINARY_FIELD_MAPS = dict(\n",
" dem_alcohol={\n",
" \"n/k\": None,\n",
" \"no alcohol\": 0,\n",
" \"alcohol\": 1\n",
" },\n",
" dem_cigarettes={\n",
" \"no\": 0,\n",
" \"smoker\": 1\n",
" }\n",
")\n",
"\n",
"def binary_norm(fname, fval):\n",
" mapper = BINARY_FIELD_MAPS[fname]\n",
" if isinstance(fval, float) and np.isnan(fval):\n",
" return None\n",
" try:\n",
" return mapper[fval.lower()]\n",
" except KeyError:\n",
" raise KeyError(\"No {} mapping for {}\".format(fname, fval))\n",
"\n",
"def get_binary_counts(df, fname):\n",
" vals = (\"nan\", 0., 1.)\n",
" counts = []\n",
" pcts = []\n",
" for val in vals:\n",
" if val == \"nan\":\n",
" counts.append(len(df[df[fname].map(np.isnan) == True]))\n",
" else:\n",
" counts.append(len(df[(df[fname] == val) == True]))\n",
" pcts.append(counts[-1] / float(len(df)))\n",
" return zip(vals, pcts, counts)\n",
" \n",
"cat_field_test = [\"dem_alcohol\", \"dem_cigarettes\"]\n",
"\n",
"for fname in cat_field_test:\n",
" normed_name = \"{}_norm\".format(fname)\n",
" df[normed_name] = df[fname].map(lambda x: binary_norm(fname, x))\n",
" \n",
" print(normed_name)\n",
" \n",
" for v in get_binary_counts(df, normed_name):\n",
" print(\"\\t{} {:.0%} {:.0f}\".format(*v))\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{nan, 'No', 'Smoker'}\n"
]
}
],
"source": [
"print(set(df[\"dem_cigarettes\"]))"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
fs714/ipyexample | chan/01_Centre_Expand_Handler_Alpha_14.ipynb | 2 | 7764 | {
"cells": [
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from IPython.core.debugger import Pdb; pdb = Pdb()\n",
"\n",
"\n",
"def get_down_centre_last_low(p_list):\n",
" zn_num = len(p_list) - 1\n",
" available_num = min(9, (zn_num - 6))\n",
" \n",
" index = len(p_list) - 4\n",
" for i in range(0, available_num // 2):\n",
" if p_list[index - 2] < p_list[index]:\n",
" index = index -2\n",
" else:\n",
" return index\n",
" return index + 2\n",
"\n",
"def get_down_centre_first_high(p_list):\n",
" s = max(enumerate(p_list[3:]), key=lambda x: x[1])[0]\n",
" return s + 3\n",
"\n",
"def down_centre_expand_spliter(p_list):\n",
" lr0 = get_down_centre_last_low(p_list)\n",
" hl0 = get_down_centre_first_high(p_list[: lr0 - 2])\n",
" \n",
" hr0 = lr0 -1\n",
" while hr0 < len(p_list) - 6:\n",
" if p_list[hr0] > p_list[hl0] and (len(p_list) - hr0) > 5:\n",
" hl0 = hr0\n",
" lr0 = (len(p_list) - 1 + hr0) // 2\n",
" if lr0 % 2 == 1:\n",
" lr0 = lr0 -1\n",
" # lr0 = hr0 + 3\n",
" break\n",
" hr0 = hr0 + 2\n",
" return [0, hl0, lr0, len(p_list) - 1], [p_list[0], p_list[hl0], p_list[lr0], p_list[-1]]\n",
"\n",
"\n",
"# y = [0, 100, 60, 130, 70, 120, 40, 90, 50, 140, 85, 105]\n",
"# y = [0, 100, 60, 110, 70, 72, 61, 143, 77, 91, 82, 100, 83, 124, 89, 99]\n",
"# y = [0, 100, 60, 110, 70, 115, 75, 120, 80, 125, 85, 130, 90, 135]\n",
"# y = [0, 100, 60, 110, 70, 78, 77, 121, 60, 93, 82, 141, 78, 134]\n",
"\n",
"# x = list(range(0, len(y)))\n",
"# gg = [min(y[1], y[3])] * len(y)\n",
"# dd = [max(y[2], y[4])] * len(y)\n",
"\n",
"# plt.figure(figsize=(len(y),4))\n",
"# plt.grid()\n",
"# plt.plot(x, y)\n",
"# plt.plot(x, gg, '--')\n",
"# plt.plot(x, dd, '--')\n",
"# sx, sy = down_centre_expand_spliter(y)\n",
"# plt.plot(sx, sy)\n",
"# plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Centre Expand Prototype\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"y_base = [0, 100, 60, 130, 70, 120, 40, 90, 50, 140, 85, 105, 55, 80]\n",
"\n",
"for i in range(10, len(y_base)):\n",
" y = y_base[:(i + 1)]\n",
" x = list(range(0, len(y)))\n",
" gg = [min(y[1], y[3])] * len(y)\n",
" dd = [max(y[2], y[4])] * len(y)\n",
"\n",
" plt.figure(figsize=(i,4))\n",
" plt.grid()\n",
" plt.plot(x, y)\n",
" plt.plot(x, gg, '--')\n",
" plt.plot(x, dd, '--')\n",
" if i % 2 == 1:\n",
" sx, sy = down_centre_expand_spliter(y)\n",
" plt.plot(sx, sy)\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Random Centre Generator\n",
"%matplotlib inline\n",
"\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"\n",
"y_max = 150\n",
"y_min = 50\n",
"num_max = 14\n",
"\n",
"def generate_next(y_list, direction):\n",
" if direction == 1:\n",
" y_list.append(random.randint(max(y_list[2], y_list[4], y_list[-1]) + 1, y_max))\n",
" elif direction == -1:\n",
" y_list.append(random.randint(y_min, min(y_list[1], y_list[3], y_list[-1]) - 1))\n",
"\n",
"y_base = [0, 100, 60, 110, 70]\n",
"# y_base = [0, 110, 70, 100, 60]\n",
"# y_base = [0, 100, 60, 90, 70]\n",
"# y_base = [0, 90, 70, 100, 60]\n",
"\n",
"direction = 1\n",
"for i in range(5, num_max):\n",
" generate_next(y_base, direction)\n",
" direction = 0 - direction\n",
"\n",
"print(y_base)\n",
"for i in range(11, len(y_base), 2):\n",
" y = y_base[:(i + 1)]\n",
" x = list(range(0, len(y)))\n",
" gg = [min(y[1], y[3])] * len(y)\n",
" dd = [max(y[2], y[4])] * len(y)\n",
"\n",
" plt.figure(figsize=(i,4))\n",
" plt.title(y)\n",
" plt.grid()\n",
" plt.plot(x, y)\n",
" plt.plot(x, gg, '--')\n",
" plt.plot(x, dd, '--')\n",
" sx, sy = down_centre_expand_spliter(y)\n",
" plt.plot(sx, sy)\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Group 1\n",
"# y_base = [0, 100, 60, 110, 70, 99, 66, 121, 91, 141, 57, 111, 69, 111]\n",
"# y_base = [0, 100, 60, 110, 70, 105, 58, 102, 74, 137, 87, 142, 55, 128]\n",
"y_base = [0, 100, 60, 110, 70, 115, 75, 120, 80, 125, 85, 130, 90, 135]\n",
"# y_base = [0, 100, 60, 110, 70, 120, 80, 130, 90, 140, 50, 75]\n",
"# y_base = [0, 100, 60, 110, 70, 114, 52, 75, 54, 77, 65, 100, 66, 87, 70, 116]\n",
"# y_base = [0, 100, 60, 110, 70, 72, 61, 143, 77, 91, 82, 100, 83, 124, 89, 99, 89, 105]\n",
"\n",
"# Group 2\n",
"# y_base = [0, 110, 70, 100, 60, 142, 51, 93, 78, 109, 60, 116, 50, 106]\n",
"# y_base = [0, 110, 70, 100, 60, 88, 70, 128, 82, 125, 72, 80, 63, 119]\n",
"# y_base = [0, 110, 70, 100, 60, 74, 66, 86, 57, 143, 50, 95, 70, 91]\n",
"# y_base = [0, 110, 70, 100, 60, 77, 73, 122, 96, 116, 82, 124, 69, 129]\n",
"# y_base = [0, 110, 70, 100, 60, 147, 53, 120, 77, 103, 56, 76, 74, 92]\n",
"# y_base = [0, 110, 70, 100, 60, 95, 55, 90, 50, 85, 45, 80, 40, 75]\n",
"\n",
"# Group 3\n",
"# y_base = [0, 100, 60, 90, 70, 107, 55, 123, 79, 112, 64, 85, 74, 110]\n",
"# y_base = [0, 100, 60, 90, 70, 77, 55, 107, 76, 141, 87, 91, 60, 83]\n",
"# y_base = [0, 100, 60, 90, 70, 114, 67, 93, 58, 134, 53, 138, 64, 107]\n",
"# y_base = [0, 100, 60, 90, 70, 77, 66, 84, 79, 108, 87, 107, 72, 89]\n",
"# y_base = [0, 100, 60, 90, 70, 88, 72, 86, 74, 84, 76, 82, 74, 80]\n",
"\n",
"# Group 4\n",
"# y_base = [0, 90, 70, 100, 60, 131, 57, 144, 85, 109, 82, 124, 87, 101]\n",
"# y_base = [0, 90, 70, 100, 60, 150, 56, 112, 63, 95, 84, 118, 58, 110]\n",
"# y_base = [0, 90, 70, 100, 60, 145, 64, 112, 69, 86, 71, 119, 54, 95]\n",
"# y_base = [0, 90, 70, 100, 60, 105, 55, 110, 50, 115, 45, 120, 40, 125]\n",
"\n",
"for i in range(11, len(y_base), 2):\n",
" y = y_base[:(i + 1)]\n",
" x = list(range(0, len(y)))\n",
" gg = [min(y[1], y[3])] * len(y)\n",
" dd = [max(y[2], y[4])] * len(y)\n",
"\n",
" plt.figure(figsize=(i,4))\n",
" plt.title(y)\n",
" plt.grid()\n",
" plt.plot(x, y)\n",
" plt.plot(x, gg, '--')\n",
" plt.plot(x, dd, '--')\n",
" sx, sy = down_centre_expand_spliter(y)\n",
" plt.plot(sx, sy)\n",
" plt.show()\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
kubeflow/kfp-tekton | samples/katib/early-stopping.ipynb | 1 | 12750 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kubeflow Pipelines with Katib component\n",
"\n",
"In this notebook you will:\n",
"- Create Katib Experiment using random algorithm.\n",
"- Use median stopping rule as an early stopping algorithm.\n",
"- Use Kubernetes Job with mxnet mnist training container as a Trial template.\n",
"- Create Pipeline to get the optimal hyperparameters.\n",
"\n",
"Reference documentation:\n",
"- https://kubeflow.org/docs/components/katib/experiment/#random-search\n",
"- https://kubeflow.org/docs/components/katib/early-stopping/\n",
"- https://kubeflow.org/docs/pipelines/overview/concepts/component/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Install required package\n",
"\n",
"Kubeflow Pipelines SDK and Kubeflow Katib SDK."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Update the PIP version.\n",
"!python -m pip install --upgrade pip\n",
"!pip install kfp==1.7.2\n",
"!pip install kubeflow-katib==0.11.1\n",
"!pip install kfp-tekton==1.0.0\n",
"!pip install kubernetes==12.0.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Restart the Notebook kernel to use the SDK packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display_html\n",
"display_html(\"<script>Jupyter.notebook.kernel.restart()</script>\",raw=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import kfp\n",
"import kfp.dsl as dsl\n",
"from kfp import components\n",
"\n",
"from kubeflow.katib import ApiClient\n",
"from kubeflow.katib import V1beta1ExperimentSpec\n",
"from kubeflow.katib import V1beta1AlgorithmSpec\n",
"from kubeflow.katib import V1beta1EarlyStoppingSpec\n",
"from kubeflow.katib import V1beta1EarlyStoppingSetting\n",
"from kubeflow.katib import V1beta1ObjectiveSpec\n",
"from kubeflow.katib import V1beta1ParameterSpec\n",
"from kubeflow.katib import V1beta1FeasibleSpace\n",
"from kubeflow.katib import V1beta1TrialTemplate\n",
"from kubeflow.katib import V1beta1TrialParameterSpec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define an Experiment\n",
"\n",
"You have to create an Experiment object before deploying it. This Experiment is similar to [this](https://github.com/kubeflow/katib/blob/master/examples/v1beta1/early-stopping/median-stop.yaml) YAML."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Experiment name and namespace.\n",
"experiment_name = \"median-stop\"\n",
"# for multi user deployment, please specify your own namespace instead of \"anonymous\"\n",
"experiment_namespace = \"anonymous\"\n",
"\n",
"# Trial count specification.\n",
"max_trial_count = 18\n",
"max_failed_trial_count = 3\n",
"parallel_trial_count = 2\n",
"\n",
"# Objective specification.\n",
"objective=V1beta1ObjectiveSpec(\n",
" type=\"maximize\",\n",
" goal= 0.99,\n",
" objective_metric_name=\"Validation-accuracy\",\n",
" additional_metric_names=[\n",
" \"Train-accuracy\"\n",
" ]\n",
")\n",
"\n",
"# Algorithm specification.\n",
"algorithm=V1beta1AlgorithmSpec(\n",
" algorithm_name=\"random\",\n",
")\n",
"\n",
"# Early Stopping specification.\n",
"early_stopping=V1beta1EarlyStoppingSpec(\n",
" algorithm_name=\"medianstop\",\n",
" algorithm_settings=[\n",
" V1beta1EarlyStoppingSetting(\n",
" name=\"min_trials_required\",\n",
" value=\"2\"\n",
" )\n",
" ]\n",
")\n",
"\n",
"\n",
"# Experiment search space.\n",
"# In this example we tune learning rate, number of layer and optimizer.\n",
"# Learning rate has bad feasible space to show more early stopped Trials.\n",
"parameters=[\n",
" V1beta1ParameterSpec(\n",
" name=\"lr\",\n",
" parameter_type=\"double\",\n",
" feasible_space=V1beta1FeasibleSpace(\n",
" min=\"0.01\",\n",
" max=\"0.3\"\n",
" ),\n",
" ),\n",
" V1beta1ParameterSpec(\n",
" name=\"num-layers\",\n",
" parameter_type=\"int\",\n",
" feasible_space=V1beta1FeasibleSpace(\n",
" min=\"2\",\n",
" max=\"5\"\n",
" ),\n",
" ),\n",
" V1beta1ParameterSpec(\n",
" name=\"optimizer\",\n",
" parameter_type=\"categorical\",\n",
" feasible_space=V1beta1FeasibleSpace(\n",
" list=[\n",
" \"sgd\", \n",
" \"adam\",\n",
" \"ftrl\"\n",
" ]\n",
" ),\n",
" ),\n",
"]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a Trial template\n",
"\n",
"In this example, the Trial's Worker is the Kubernetes Job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# JSON template specification for the Trial's Worker Kubernetes Job.\n",
"trial_spec={\n",
" \"apiVersion\": \"batch/v1\",\n",
" \"kind\": \"Job\",\n",
" \"spec\": {\n",
" \"template\": {\n",
" \"metadata\": {\n",
" \"annotations\": {\n",
" \"sidecar.istio.io/inject\": \"false\"\n",
" }\n",
" },\n",
" \"spec\": {\n",
" \"containers\": [\n",
" {\n",
" \"name\": \"training-container\",\n",
" \"image\": \"docker.io/kubeflowkatib/mxnet-mnist:v1beta1-45c5727\",\n",
" \"command\": [\n",
" \"python3\",\n",
" \"/opt/mxnet-mnist/mnist.py\",\n",
" \"--batch-size=64\",\n",
" \"--lr=${trialParameters.learningRate}\",\n",
" \"--num-layers=${trialParameters.numberLayers}\",\n",
" \"--optimizer=${trialParameters.optimizer}\"\n",
" ]\n",
" }\n",
" ],\n",
" \"restartPolicy\": \"Never\"\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"# Configure parameters for the Trial template.\n",
"# We set the retain parameter to \"True\" to not clean-up the Trial Job's Kubernetes Pods.\n",
"trial_template=V1beta1TrialTemplate(\n",
" retain=True,\n",
" primary_container_name=\"training-container\",\n",
" trial_parameters=[\n",
" V1beta1TrialParameterSpec(\n",
" name=\"learningRate\",\n",
" description=\"Learning rate for the training model\",\n",
" reference=\"lr\"\n",
" ),\n",
" V1beta1TrialParameterSpec(\n",
" name=\"numberLayers\",\n",
" description=\"Number of training model layers\",\n",
" reference=\"num-layers\"\n",
" ),\n",
" V1beta1TrialParameterSpec(\n",
" name=\"optimizer\",\n",
" description=\"Training model optimizer (sdg, adam or ftrl)\",\n",
" reference=\"optimizer\"\n",
" ),\n",
" ],\n",
" trial_spec=trial_spec\n",
")\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define an Experiment specification\n",
"\n",
"Create an Experiment specification from the above parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_spec=V1beta1ExperimentSpec(\n",
" max_trial_count=max_trial_count,\n",
" max_failed_trial_count=max_failed_trial_count,\n",
" parallel_trial_count=parallel_trial_count,\n",
" objective=objective,\n",
" algorithm=algorithm,\n",
" early_stopping=early_stopping,\n",
" parameters=parameters,\n",
" trial_template=trial_template\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a Pipeline using Katib component\n",
"\n",
"The best hyperparameters are printed after Experiment is finished.\n",
"The Experiment is not deleted after the Pipeline is finished."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get the Katib launcher.\n",
"katib_experiment_launcher_op = components.load_component_from_url(\n",
" \"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/katib-launcher/component.yaml\")\n",
"\n",
"PRINT_STR = \"\"\"\n",
"name: print\n",
"description: print msg\n",
"inputs:\n",
" - {name: message, type: JsonObject}\n",
"implementation:\n",
" container:\n",
" image: library/bash:4.4.23\n",
" command:\n",
" - sh\n",
" - -c\n",
" args:\n",
" - |\n",
" echo \"Best HyperParameters: $0\"\n",
" - {inputValue: message}\n",
"\"\"\"\n",
"\n",
"print_op = components.load_component_from_text(PRINT_STR)\n",
"\n",
"@dsl.pipeline(\n",
" name=\"launch-katib-early-stopping-experiment\",\n",
" description=\"An example to launch Katib Experiment with early stopping\"\n",
")\n",
"def median_stop():\n",
" \n",
" # Katib launcher component.\n",
" # Experiment Spec should be serialized to a valid Kubernetes object.\n",
" op = katib_experiment_launcher_op(\n",
" experiment_name=experiment_name,\n",
" experiment_namespace=experiment_namespace,\n",
" experiment_spec=ApiClient().sanitize_for_serialization(experiment_spec),\n",
" experiment_timeout_minutes=60,\n",
" delete_finished_experiment=False)\n",
" \n",
" # Output container to print the results.\n",
" print_op(op.output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run the Pipeline\n",
"\n",
"You can check the Katib Experiment info in the Katib UI.\n",
"\n",
"If you run this in a multi-user deployment, you need to follow the instructions\n",
"here: https://github.com/kubeflow/kfp-tekton/tree/master/guides/kfp-user-guide#2-upload-pipelines-using-the-kfp_tektontektonclient-in-python\n",
"Check the `multi tenant` section and create TektonClient with `host` and `cookies` arguments.\n",
"For example:\n",
"```\n",
" TektonClient(\n",
" host='http://<Kubeflow_public_endpoint_URL>/pipeline',\n",
" cookies='authservice_session=xxxxxxx'\n",
" )\n",
"```\n",
"You also need to specify `namespace` argument when calling `create_run_from_pipeline_func` function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from kfp_tekton._client import TektonClient \n",
"# Example code for multi user deployment:\n",
"# TektonClient(\n",
"# host='http://<Kubeflow_public_endpoint_URL>/pipeline',\n",
"# cookies='authservice_session=xxxxxxx'\n",
"# ).create_run_from_pipeline_func(median_stop, arguments={}, namespace='user namespace')\n",
"TektonClient().create_run_from_pipeline_func(median_stop, arguments={})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
fionapigott/Data-Science-45min-Intros | python-decorators-101/python-decorators-101.ipynb | 6 | 42484 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python decorators\n",
"\n",
"Josh Montague, 2015-12\n",
"\n",
"*Built on OS X, with IPython 3.0 on Python 2.7*\n",
"\n",
"In this session, we'll dig into some details of Python functions. The end goal will be to understand how and why you might want to create decorators with Python functions.\n",
"\n",
">*Note:* there is an Appendix at the end of this notebook that dives deeper into scope and Python namespaces. I wrote out the content because they're quite relevant to talking about decorators. But, ultimately, we only 45 minutes, and it couldn't all fit. If you're curious, take a few extra minutes to review that material, as well. \n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"A lot of this RST has to do with understanding the subtleties of Python functions. So, we're going to spend some time exploring them.\n",
"\n",
"**In Python, functions are objects**\n",
"\n",
"> [T]his means the language supports passing functions as arguments to other functions, returning them as the values from other functions, and assigning them to variables or storing them in data structures. ([wiki](https://en.wikipedia.org/wiki/First-class_function))\n",
"\n",
"This is not true (or at least not easy) in all programming languages. I don't have a ton of experience to back this up. But, many moons ago, I remember that Java functions only lived inside objects and classes. \n",
"\n",
"Let's take a moment to **look at a relatively simple function and appreciate what it does** and what we can do with it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def duplicator(str_arg):\n",
" \"\"\"Create a string that is a doubling of the passed-in arg.\"\"\" \n",
" # use the passed arg to create a larger string (double it, with a space between)\n",
" internal_variable = ' '.join( [str_arg, str_arg] )\n",
" \n",
" return internal_variable"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<function duplicator at 0x103c330c8>\n"
]
}
],
"source": [
"# print (don't call) the function \n",
"print( duplicator )\n",
"\n",
"# equivalently (in IPython):\n",
"#duplicator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that IPython and Jupyter will automatically (and conveniently!) call the ``__repr__`` of an object if it is the last thing in the cell. But I'll use the ``print()`` function explicitly just to be clear. \n",
"\n",
"This displays the string representation of the object. It usually includes: \n",
"\n",
"- an object type (class)\n",
"- an object name\n",
"- a memory location\n",
"\n",
"Now, let's actually call the function (which we do with use of the parentheses), and assign the return value (a string) to a new variable. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# now *call* the function by using parens\n",
"output = duplicator('yo')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"yo yo\n"
]
}
],
"source": [
"# verify the expected behavior\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because functions are objects, **they have attributes** just like any other Python object."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__call__',\n",
" '__class__',\n",
" '__closure__',\n",
" '__code__',\n",
" '__defaults__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__doc__',\n",
" '__format__',\n",
" '__get__',\n",
" '__getattribute__',\n",
" '__globals__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__module__',\n",
" '__name__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" 'func_closure',\n",
" 'func_code',\n",
" 'func_defaults',\n",
" 'func_dict',\n",
" 'func_doc',\n",
" 'func_globals',\n",
" 'func_name']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the dir() built-in function displays the argument's attributes\n",
"dir(duplicator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because functions are objects, **we can pass them around** like any other data type. For example, **we can assign them to other variables**! If you occasionally still have dreams about the Enumerator, this will look familiar."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ring ring'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first, recall the normal behavior of useful_function() \n",
"duplicator('ring')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# now create a new variable and assign our function to it\n",
"another_duplicator = duplicator"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'giggity giggity'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# now, we can use the *call* notation because the new variable is \n",
"# assigned the original function\n",
"another_duplicator('giggity')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"original function: <function duplicator at 0x103c330c8>\n",
"\n",
"new function: <function duplicator at 0x103c330c8>\n"
]
}
],
"source": [
"# and we can verify that this is actually a reference to the \n",
"# original function\n",
"print( \"original function: %s\" % duplicator )\n",
"print\n",
"print( \"new function: %s\" % another_duplicator )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By looking at the memory location, we can see that the second function is just a pointer to the first function! Cool!\n",
"\n",
"\n",
"## Functions inside functions\n",
"\n",
"With an understanding of what's inside a function and what we can do with it, **consider the case were we define a new function *within* another function.** \n",
"\n",
"*This may seem overly complicated for a little while, but stick with me.*\n",
"\n",
"\n",
"In the example below, we'll define an outer function which includes a local variable, then a local function definition. The inner function returns a string. The outer function calls the inner function, and returns the resulting value (a string)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def speaker():\n",
" \"\"\"\n",
" Simply return a word (a string).\n",
" \n",
" Other than possibly asking 'why are you writing this simple \n",
" function in such a complicated fashion?' this should \n",
" hopefuly should be pretty clear.\n",
" \"\"\"\n",
" \n",
" # define a local variable\n",
" word='hello'\n",
" \n",
" def shout():\n",
" \"\"\"Return a capitalized version of word.\"\"\"\n",
" \n",
" # though not in the innermost scope, this is in the namespace one \n",
" # level out from here\n",
" return word.upper()\n",
" \n",
" # call shout and then return the result of calling it (a string)\n",
" return shout() "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HELLO\n"
]
}
],
"source": [
"# remember that the result is a string, now print it. the sequence:\n",
"# - put word and shout in local namespace\n",
"# - define shout()\n",
"# - call shout()\n",
"# - look for 'word', return it\n",
"# - return the return value of shout()\n",
"print( speaker() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, this may be intuitive, but it's important to note that the inner function is *not* accessible outside of the outer function. The interpreter can always step out into larger (or \"more outer\") namespaces, but we can't dig deeper into smaller ones."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name 'shout' is not defined\n"
]
}
],
"source": [
"try:\n",
" # this function only exists in the local scope of the outer function\n",
" shout()\n",
"except NameError, e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions out of functions\n",
"\n",
"What if we'd like our outer function to **return a function**? For example, return the inner function instead of the *return value* of the inner function."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def speaker_func():\n",
" \"\"\"Similar to speaker(), but this time return the actual inner function!\"\"\"\n",
" \n",
" word = 'hello'\n",
" \n",
" def shout():\n",
" \"\"\"Return an all-caps version of the passed word.\"\"\"\n",
" return word.upper()\n",
" \n",
" # don't *call* shout(), just return it\n",
" return shout"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<function shout at 0x103c33320>\n"
]
}
],
"source": [
"# remember: our function returns another function \n",
"print( speaker_func() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that the return value of the outer function is another function. And just like we saw earlier, we can ``print`` the function to see the name and memory location.\n",
"\n",
"Note that the name is that of the inner function. Makes sense, since that's what we returned.\n",
"\n",
"Like we said before, since this is an object, **we can pass this function around and assign it to other variables**. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this will assign to the variable new_shout, a value that is the shout function\n",
"new_shout = speaker_func()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which means **we can also call it** with parens, as usual."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'HELLO'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# which means we can *call* it\n",
"new_shout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions into functions\n",
"\n",
"If functions are objects, **we can just as easily pass a function *into* another function.** You've probably seen this before in the context of sorting, or maybe using ``map``:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(9, 2), (5, 4), (1, 5)]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from operator import itemgetter\n",
"\n",
"# we might want to sort this by the first or second item\n",
"tuple_list = [(1,5),(9,2),(5,4)]\n",
"\n",
"# itemgetter is a callable (like a function) that we pass in as an argument to sorted()\n",
"sorted(tuple_list, key=itemgetter(1))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[6, 11, 9]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def tuple_add(tup):\n",
" \"\"\"Sum the items in a tuple.\"\"\"\n",
" return sum(tup)\n",
"\n",
"# now we can map the tuple_add() function across the tuple_list iterable.\n",
"# note that we're passing a function as an argument!\n",
"map(tuple_add, tuple_list) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we can pass functions into and out of other functions, then I propose that **we can *extend* or *modify the behavior of* a function without actually editing the original function!**\n",
"\n",
"\n",
"## Decorators \n",
"\n",
"🎉💥🎉💥🎉💥🎉💥🎉💥\n",
"\n",
"For example, say there's some previously-defined function in and you'd like it to be more verbose. For now, let's just assume that printing a bunch of information to stdout is our goal. \n",
"\n",
"Below, we define a function ``verbose()`` that takes another function as an argument. It does other tasks both before and after actually calling the passed-in function."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def verbose(func):\n",
" \"\"\"Add some marginally annoying verbosity to the passed func.\"\"\"\n",
" \n",
" def inner():\n",
" print(\"heeeeey everyone, I'm about to do a thing!\")\n",
" print(\"hey hey hey, I'm about to call a function called: {}\".format(func.__name__))\n",
" print\n",
" # now call (and print) the passed function\n",
" print func()\n",
" print\n",
" print(\"whoa, did everyone see that thing I just did?! SICK!!\")\n",
" \n",
" return inner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, imagine we have a function that we wish had more of this type of \"logging.\" But, we don't want to jump in and add a bunch of code to the original function."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# here's our original function (that we don't want to modify) \n",
"def say_hi():\n",
" \"\"\"Return 'hi'.\"\"\"\n",
" return '--> hi. <--'"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'--> hi. <--'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# understand the original behavior of the function\n",
"say_hi()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead, we pass the original function as an arg to our ``verbose`` function. Remember that this returns the inner function, so we can assign it and then call it."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this is now a function...\n",
"verbose_say_hi = verbose(say_hi)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"heeeeey everyone, I'm about to do a thing!\n",
"hey hey hey, I'm about to call a function called: say_hi\n",
"\n",
"--> hi. <--\n",
"\n",
"whoa, did everyone see that thing I just did?! SICK!!\n"
]
}
],
"source": [
"# which we can call...\n",
"verbose_say_hi()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the output, we can see that when we called ``verbose_say_hi()``, all of the code in it ran:\n",
"\n",
"- two print statements\n",
"- then the passed function ``say_hi()`` was called \n",
"- it's return value was printed \n",
"- finally, there was some other printing defined in the inner function\n",
"\n",
"We'd now say that **``verbose_say_hi()`` is a *decorated version* of ``say_hi()``.** And, correspondingly, **that ``verbose()`` is our decorator.**\n",
"\n",
">A decorator is a callable that takes a function as an argument and returns a function (probably a modified version of the original function).\n",
"\n",
"\n",
"Now, you may also decide that the modified version of the function is *the only* version you want around. And, further, you don't want to change any other code that may depend on this. In that case, **you want to overwrite the namespace value for the original function**!"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"heeeeey everyone, I'm about to do a thing!\n",
"hey hey hey, I'm about to call a function called: say_hi\n",
"\n",
"--> hi. <--\n",
"\n",
"whoa, did everyone see that thing I just did?! SICK!!\n"
]
}
],
"source": [
"# this will clobber the existing namespace value (the original function def). \n",
"# in it's place we have the verbose version!\n",
"say_hi = verbose(say_hi)\n",
"\n",
"say_hi()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Uneditable source code\n",
"\n",
"One use-case where this technique can be useful is when you need to use an existing base of code that you can't edit. There's an existing library that defines classes and methods that are aligned with your needs, but you need a slight variation on them.\n",
"\n",
"Imagine there is a library called (creatively) ``uneditable_lib`` that implements a ``Coordinate`` class (a point in two-dimensional space), and an ``add()`` method. The ``add()`` method allows you to add the vectors of two ``Coordinates`` together and returns a new ``Coordinate`` object. It has great documentation and you know the source Python source code looks like this:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#!/usr/bin/env python\r\n",
"# -*- coding: UTF-8 -*-\r\n",
"\r\n",
"\"\"\"\r\n",
"A simple module representing a Coordinate class and related method. \r\n",
"\r\n",
"Modified from Simeon Franklin's blog:\r\n",
"- http://simeonfranklin.com/blog/2012/jul/1/python-decorators-in-12-steps/ \r\n",
"\"\"\"\r\n",
"\r\n",
"class Coordinate(object):\r\n",
" \"\"\"Represents a point in two-dimensional space.\"\"\"\r\n",
" def __init__(self, x, y):\r\n",
" self.x = x\r\n",
" self.y = y\r\n",
"\r\n",
" def __repr__(self):\r\n",
" return \"Coord: \" + str(self.__dict__)\r\n",
"\r\n",
"def add(a, b):\r\n",
" \"\"\"Combine two Coordinates by addition.\"\"\"\r\n",
" return Coordinate(a.x + b.x, a.y + b.y)\r\n",
"\r\n"
]
}
],
"source": [
"! cat _uneditable_lib.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BUT\n",
"\n",
"Imagine you don't actually have the Python source, you have the compiled binary. Try opening this file in vi and see how it looks."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"uneditable_lib.pyc\r\n"
]
}
],
"source": [
"! ls | grep .pyc"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# you can still *use* the compiled code\n",
"from uneditable_lib import Coordinate, add"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coord: {'y': 200, 'x': 100}\n"
]
}
],
"source": [
"# make a couple of coordinates using the existing library\n",
"coord_1 = Coordinate(x=100, y=200)\n",
"coord_2 = Coordinate(x=-500, y=400)\n",
"\n",
"print( coord_1 )"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coord: {'y': 600, 'x': -400}\n"
]
}
],
"source": [
"print( add(coord_1, coord_2) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But, imagine that **for our particular use-case, we need to confine the resulting coordinates to the first quadrant** (that is, ``x > 0`` and ``y > 0``). We want any negative component in the coordinates to just be truncated to zero.\n",
"\n",
"We can't edit the source code, but we can decorate (and modify) it!"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def coordinate_decorator(func): \n",
" \"\"\"Decorates the pre-built source code for Coordinates.\n",
" \n",
" We need the resulting coordinates to only exist in the \n",
" first quadrant, so we'll truncate negative values to zero.\n",
" \"\"\"\n",
" def checker(a, b): \n",
" \"\"\"Enforces first-quadrant coordinates.\"\"\"\n",
" ret = func(a, b)\n",
" \n",
" # check the result and make sure we're still in the \n",
" # first quadrant at the end [ that is, x and y > 0 ]\n",
" if ret.x < 0 or ret.y < 0:\n",
" ret = Coordinate(ret.x if ret.x > 0 else 0, \n",
" ret.y if ret.y > 0 else 0\n",
" ) \n",
" return ret\n",
" \n",
" return checker"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can decorate the preexisting ``add()`` function with our new wrapper. And since we may be using other code from ``uneditable_lib`` with an API that expects the function to still be called ``add()``, we can just overwrite that namespace variable."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# first we decorate the existing function\n",
"add = coordinate_decorator(add)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coord: {'y': 600, 'x': 0}\n"
]
}
],
"source": [
"# then we can call it as before\n",
"print( add(coord_1, coord_2) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And, we now have a truncated ``Coordinate`` that lives in the first quadrant."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://i.giphy.com/8VrtCswiLDNnO.gif\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(url='http://i.giphy.com/8VrtCswiLDNnO.gif')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we are running out of time, this is an ok place to wrap up.\n",
"\n",
"## Examples \n",
"\n",
"Here are some real examples you might run across in the wild:\n",
"\n",
"- Flask (web framework) uses decorators really well\n",
" - [``@app.route``](http://flask.pocoo.org/docs/0.10/api/#flask.Flask.add_url_rule) is a decorator that lets you decorate an arbitrary Python function and turn it into a URL path.\n",
" - [``@login_required``](http://flask.pocoo.org/docs/0.10/patterns/viewdecorators/#login-required-decorator) is a decorator that lets your function define the appropriate authentication.\n",
"- Fabric (ops / remote server tasks) [includes a number of \"helper decorators\"](http://docs.fabfile.org/en/1.10/api/core/decorators.html) for task and hostname management.\n",
"\n",
"\n",
"## Here are some things we didn't cover\n",
"\n",
"If you go home tonight and can't possibly wait to learn more about decorators, here are the next things to look up:\n",
"\n",
"- passing arguments to a decorator \n",
"- ``@functools.wraps``\n",
"- implementing a decorator as a class\n",
"\n",
"If there is sufficient interest in a **Decorators, Part Deux,** those would be good starters.\n",
"\n",
"\n",
"# THE END\n",
"\n",
"----\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Is there still time?!\n",
"\n",
"If so, here are a couple of other useful things worth saying, quickly...\n",
"\n",
"\n",
"## Decorating a function at definition (with ``@``)\n",
"\n",
"You might still want to use a decorator to **modify a function that you wrote in your own code.**\n",
"\n",
"You might ask *\"But if we're already writing the code, why not just make the function do what we want in the first place?\"* Valid question. \n",
"\n",
"One place where this comes up is in practicing [DRY](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself) (Don't Repeat Yourself) software engineering practices. **If an identical block of logic is to be used in many places, that code should ideally be written in only one place.** \n",
"\n",
"In our case, we could imagine making a bunch of different functions more verbose. Instead of adding the verbosity (print statements) to each of the functions, we should define that once and then decorate the other functions.\n",
"\n",
"Another nice example is making your code easier to understand by **separating necessary operational logic from the business logic.**\n",
"\n",
"There's a nice shorthand - some syntactic sugar - for this kind of statement. To illustrate it, let's just use a variation on a method from earlier. First, see how the original function behaves:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'--> bye. <--'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def say_bye():\n",
" \"\"\"Return 'bye'.\"\"\"\n",
" return '--> bye. <--'\n",
"\n",
"say_bye()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember the ``verbose()`` decorator that we already created? If this function (and perhaps others) should be made verbose at the time they're defined, we can apply the decorator right then and there using the ``@`` shorthand:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"heeeeey everyone, I'm about to do a thing!\n",
"hey hey hey, I'm about to call a function called: say_bye\n",
"\n",
"--> bye. <--\n",
"\n",
"whoa, did everyone see that thing I just did?! SICK!!\n"
]
}
],
"source": [
"@verbose\n",
"def say_bye():\n",
" \"\"\"Return 'bye'.\"\"\"\n",
" return '--> bye. <--'\n",
"\n",
"say_bye()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Image(url='http://i.giphy.com/piupi6AXoUgTe.gif')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But that shouldn't actually blow your mind. Based on our discussion before, you can probably guess that the decorator notation is just shorthand for: \n",
"\n",
"``say_bye = verbose( say_bye )``\n",
"\n",
"One place where this shorthand can come in particularly handy is when you need to stack a bunch of decorators. In place of nested decorators like this:\n",
"\n",
"```python\n",
"my_func = do_thing_a( add_numbers( subtract( verify( my_func ))))\n",
"```\n",
"\n",
"We can write this as:\n",
"\n",
"```python\n",
"@do_thing_a\n",
"@add_numbers\n",
"@subtract\n",
"@verify\n",
"def my_func():\n",
" # something useful happens here\n",
"```\n",
"\n",
"Note that the order matters!\n",
"\n",
"----\n",
"\n",
"\n",
"Ok, thank you, please come again.\n",
"\n",
"\n",
"# THE END AGAIN\n",
"\n",
"----"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, final round, I promise. \n",
"\n",
"\n",
"# Appendix\n",
"\n",
"\n",
"This is material that I originally intended to include in this RST (because it's relevant), but ultimately cut for clarity. You can come back and revisit it any time.\n",
"\n",
"## Scopes and namespaces\n",
"\n",
"Roughly speaking, scope and namespace are the reason you can type some code (like ``variable_1 = 'dog'``) and then transparently use ``variable_1`` later in your code. Sounds obvious, but easy to take for granted!\n",
"\n",
"The concepts of scope and namespace in Python are pretty interesting and complex. Some time when you're bored and want to learn some details, have a read of [this nice explainer](http://sebastianraschka.com/Articles/2014_python_scope_and_namespaces.html) or the official docs on [the Python Execution Model](https://docs.python.org/2/reference/executionmodel.html). \n",
"\n",
"A short way to think about them is the following:\n",
"\n",
"- A **namespace** is **a mapping from (variable) names to values**; think about it like a Python dictionary, where our code can look up the keys (the variable names) and then use the corresponding values. You will generally have many namespaces in your code, they are usually nested (sort of like an onion), and they can even have identical keys (variable names). \n",
"- The **scope** (at a particular location in the code) **defines in which namespace we look for variables** (dictionary keys) when our code is executing.\n",
"\n",
"While this RST isn't explicitly about scope, understanding these concepts will make it easier to read the code later on. Let's look at some examples.\n",
"\n",
"There are two built-in functions that can aid in exploring the namespace at various points in your code: `globals()` and `locals()` return a dictionary of the names and values in their respective scope. \n",
"\n",
"Since the namespaces in IPython are often huge, let's use IPython's bash magic to call out to a normal Python session to test how ``globals()`` works:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# -c option starts a new interpreter session in a subshell and evaluates the code in quotes. \n",
"# here, we just assign the value 3 to the variable x and print the global namespace\n",
"! python -c 'x=3; print( globals() )'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that there are a bunch of other dunder names that are in the global namespace. In particular, note that `'__name__' = '__main__'` because we ran this code from the command line (a comparison that you've made many times in the past!). And you can see the variable x that we assigned the value of 3. \n",
"\n",
"We can also look at the namespace in a more local scope with the `locals()` function. Inside the body of a function, the local namespace is limited to those variables defined within the function. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this var is defined at the \"outermost\" level of this code block\n",
"z = 10\n",
"\n",
"def printer(x):\n",
" \"\"\"Print some things to stdout.\"\"\"\n",
" \n",
" # create a new var within the scope of this function\n",
" animal = 'baboon'\n",
" \n",
" # ask about the namespace of the inner-most scope, \"local\" scope\n",
" print('local namespace: {}\\n'.format(locals()))\n",
" \n",
" # now, what about this var, which is defined *outside* the function?\n",
" print('variable defined *outside* the function: {}'.format(z))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"printer(17)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, you can see that when our scope is 'inside the function', **the namespace is very small**. It's the local variables defined within the function, including the arg we passed the function. \n",
"\n",
"But, you can also see that we can still \"see\" the variable `z`, which was defined outside the function. This is because even though `z` doesn't exist in the local namespace, **this is just the \"innermost\" of a series of nested namespaces**. When we failed to find `z` in `locals()`, the interpreter steps \"out\" a layer, and looks for a namespace key (variable name) that's defined outside of the function. If we look through this (and any larger) namespace and still fail to find a key (variable name) for ``z``, **the interpreter will raise a ``NameError``**.\n",
"\n",
"While the interpreter will always continue looking in *larger* or *more outer* scopes, it can't do the opposite. Since `y` is created and assigned within the scope of our function, it goes \"out of scope\" as soon as the function returns. Local variables defined within the scope of a function are only accessible from that same scope - inside the function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"try:\n",
" # remember that this var was created and assigned only within the function\n",
" animal\n",
"except NameError, e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Closures\n",
"\n",
"This is all relevant, because part of the mechanism behind a decorator is the concept of a function closure. **A function closure captures the enclosing state (namespace) at the time a non-global function is defined.**\n",
"\n",
"To see an example, consider the following code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def outer(x):\n",
" def inner():\n",
" print x\n",
" return inner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We saw earlier, that the variable ``x`` isn't directly accessible outside of the function ``outer()`` because it's created within the scope of that function. But, Python's function closures mean that because ``inner()`` is not defined in the global scope, it keeps track of the surrounding namespace wherein it was defined. We can verify this by inspecting an example object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"o = outer(7)\n",
"\n",
"o()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"try: \n",
" x\n",
"except NameError, e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print( dir(o) ) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print( o.func_closure ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And, there in the ``repr`` of the object's ``func_closure`` attribute, we can see there is an ``int`` still stored! This is the value that we passed in during the creation of the function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Links\n",
"\n",
"- [Decorators PEP](https://www.python.org/dev/peps/pep-0318/)\n",
"- [a big list of decorators (only marginally useful)](https://wiki.python.org/moin/PythonDecorators)\n",
"- [Simeon Franklin's article](http://simeonfranklin.com/blog/2012/jul/1/python-decorators-in-12-steps/)\n",
"- [The Code Ship](http://thecodeship.com/patterns/guide-to-python-function-decorators/)\n",
"- [incredible SO explanation](http://stackoverflow.com/questions/739654/how-can-i-make-a-chain-of-function-decorators-in-python/1594484#1594484)\n",
"- [another SO response](http://stackoverflow.com/questions/5929107/python-decorators-with-parameters)\n",
"\n",
"*fin*"
]
},
{
"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
}
| unlicense |
herruzojm/udacity-deep-learning | language-translation/dlnd_language_translation.ipynb | 1 | 607501 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Language Translation\n",
"In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n",
"## Get the Data\n",
"Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"source_path = 'data/small_vocab_en'\n",
"target_path = 'data/small_vocab_fr'\n",
"source_text = helper.load_data(source_path)\n",
"target_text = helper.load_data(target_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data\n",
"Play around with view_sentence_range to view different parts of the data."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset Stats\n",
"Roughly the number of unique words: 227\n",
"Number of sentences: 137861\n",
"Average number of words in a sentence: 13.225277634719028\n",
"\n",
"English sentences 0 to 10:\n",
"new jersey is sometimes quiet during autumn , and it is snowy in april .\n",
"the united states is usually chilly during july , and it is usually freezing in november .\n",
"california is usually quiet during march , and it is usually hot in june .\n",
"the united states is sometimes mild during june , and it is cold in september .\n",
"your least liked fruit is the grape , but my least liked is the apple .\n",
"his favorite fruit is the orange , but my favorite is the grape .\n",
"paris is relaxing during december , but it is usually chilly in july .\n",
"new jersey is busy during spring , and it is never hot in march .\n",
"our least liked fruit is the lemon , but my least liked is the grape .\n",
"the united states is sometimes busy during january , and it is sometimes warm in november .\n",
"\n",
"French sentences 0 to 10:\n",
"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n",
"les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n",
"california est généralement calme en mars , et il est généralement chaud en juin .\n",
"les états-unis est parfois légère en juin , et il fait froid en septembre .\n",
"votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n",
"son fruit préféré est l'orange , mais mon préféré est le raisin .\n",
"paris est relaxant en décembre , mais il est généralement froid en juillet .\n",
"new jersey est occupé au printemps , et il est jamais chaude en mars .\n",
"notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n",
"les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"view_sentence_range = (0, 10)\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"\n",
"print('Dataset Stats')\n",
"print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n",
"\n",
"sentences = source_text.split('\\n')\n",
"word_counts = [len(sentence.split()) for sentence in sentences]\n",
"print('Number of sentences: {}'.format(len(sentences)))\n",
"print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n",
"\n",
"print()\n",
"print('English sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n",
"print()\n",
"print('French sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement Preprocessing Function\n",
"### Text to Word Ids\n",
"As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\n",
"\n",
"You can get the `<EOS>` word id by doing:\n",
"```python\n",
"target_vocab_to_int['<EOS>']\n",
"```\n",
"You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n",
" \"\"\"\n",
" Convert source and target text to proper word ids\n",
" :param source_text: String that contains all the source text.\n",
" :param target_text: String that contains all the target text.\n",
" :param source_vocab_to_int: Dictionary to go from the source words to an id\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: A tuple of lists (source_id_text, target_id_text)\n",
" \"\"\"\n",
<<<<<<< HEAD
" source_ids = [[source_vocab_to_int[word] for word in (line).split()]\n",
" for line in source_text.split('\\n')]\n",
"\n",
" target_ids = [[target_vocab_to_int[word] for word in line.split()] + [target_vocab_to_int[ '<EOS>']] \n",
" for line in target_text.split('\\n')]\n",
"\n",
" return source_ids, target_ids\n",
" \n",
" \n",
" print(source_id_text)\n",
" print()\n",
" print(target_id_text)\n",
" return (source_id_text, target_id_text)\n",
=======
" # TODO: Implement Function\n",
" \n",
" return None, None\n",
>>>>>>> refs/remotes/udacity/master
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_text_to_ids(text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocess all the data and save it\n",
"Running the code cell below will preprocess all the data and save it to file."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"helper.preprocess_and_save_data(source_path, target_path, text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Point\n",
"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"import helper\n",
"\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the Version of TensorFlow and Access to GPU\n",
"This will check to make sure you have the correct version of TensorFlow and access to a GPU"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorFlow Version: 1.0.0\n",
"Default GPU Device: /gpu:0\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"from distutils.version import LooseVersion\n",
"import warnings\n",
"import tensorflow as tf\n",
"from tensorflow.python.layers.core import Dense\n",
"\n",
"# Check TensorFlow Version\n",
"assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n",
"print('TensorFlow Version: {}'.format(tf.__version__))\n",
"\n",
"# Check for a GPU\n",
"if not tf.test.gpu_device_name():\n",
" warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
"else:\n",
" print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Neural Network\n",
"You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n",
"- `model_inputs`\n",
"- `process_decoder_input`\n",
"- `encoding_layer`\n",
"- `decoding_layer_train`\n",
"- `decoding_layer_infer`\n",
"- `decoding_layer`\n",
"- `seq2seq_model`\n",
"\n",
"### Input\n",
"Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
"\n",
"- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n",
"- Targets placeholder with rank 2.\n",
"- Learning rate placeholder with rank 0.\n",
"- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n",
"- Target sequence length placeholder named \"target_sequence_length\" with rank 1\n",
"- Max target sequence length tensor named \"max_target_len\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\n",
"- Source sequence length placeholder named \"source_sequence_length\" with rank 1\n",
"\n",
"Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def model_inputs():\n",
" \"\"\"\n",
" Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\n",
" :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\n",
" max target sequence length, source sequence length)\n",
" \"\"\"\n",
<<<<<<< HEAD
" inputs = tf.placeholder(tf.int32, [None, None], name='input')\n",
" targets = tf.placeholder(tf.int32, [None, None], name='targets')\n",
" learning_rate = tf.placeholder(tf.float32)\n",
" keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
" \n",
" return inputs, targets, learning_rate, keep_prob\n",
=======
" # TODO: Implement Function\n",
" return None, None, None, None, None, None, None\n",
"\n",
>>>>>>> refs/remotes/udacity/master
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_model_inputs(model_inputs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process Decoder Input\n",
"Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n",
" \"\"\"\n",
" Preprocess target data for encoding\n",
" :param target_data: Target Placehoder\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param batch_size: Batch Size\n",
" :return: Preprocessed target data\n",
" \"\"\"\n",
" ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n",
" dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n",
" return dec_input\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_process_encoding_input(process_decoder_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Encoding\n",
"Implement `encoding_layer()` to create a Encoder RNN layer:\n",
" * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n",
" * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\n",
" * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"from imp import reload\n",
"reload(tests)\n",
"\n",
"def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \n",
" source_sequence_length, source_vocab_size, \n",
" encoding_embedding_size):\n",
" \"\"\"\n",
" Create encoding layer\n",
" :param rnn_inputs: Inputs for the RNN\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param keep_prob: Dropout keep probability\n",
<<<<<<< HEAD
" :return: RNN state\n",
" \"\"\" \n",
" cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
" cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)\n",
" multi_rnn_cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)\n",
" \n",
" rnn_state = tf.nn.dynamic_rnn(multi_rnn_cell, rnn_inputs, dtype=tf.float32)[1]\n",
" return rnn_state\n",
=======
" :param source_sequence_length: a list of the lengths of each sequence in the batch\n",
" :param source_vocab_size: vocabulary size of source data\n",
" :param encoding_embedding_size: embedding size of source data\n",
" :return: tuple (RNN output, RNN state)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return None, None\n",
>>>>>>> refs/remotes/udacity/master
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_encoding_layer(encoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Training\n",
"Create a training decoding layer:\n",
"* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"\n",
"def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n",
" target_sequence_length, max_summary_length, \n",
" output_layer, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for training\n",
" :param encoder_state: Encoder State\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embed_input: Decoder embedded input\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_summary_length: The length of the longest sequence in the batch\n",
" :param output_layer: Function to apply the output layer\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing training logits and sample_id\n",
" \"\"\"\n",
" train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n",
" train_pred = tf.contrib.seq2seq.dynamic_rnn_decoder(\n",
" dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)[0]\n",
" \n",
" train_pred = tf.nn.dropout(train_pred, keep_prob=keep_prob)\n",
" train_logits = output_fn(train_pred)\n",
" return train_logits\n",
" \n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_train(decoding_layer_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Inference\n",
"Create inference decoder:\n",
"* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\n",
" end_of_sequence_id, max_target_sequence_length,\n",
" vocab_size, output_layer, batch_size, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for inference\n",
" :param encoder_state: Encoder state\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embeddings: Decoder embeddings\n",
" :param start_of_sequence_id: GO ID\n",
" :param end_of_sequence_id: EOS Id\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param vocab_size: Size of decoder/target vocabulary\n",
" :param decoding_scope: TenorFlow Variable Scope for decoding\n",
" :param output_layer: Function to apply the output layer\n",
" :param batch_size: Batch size\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing inference logits and sample_id\n",
" \"\"\"\n",
" train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(output_fn, encoder_state, dec_embeddings,\n",
" start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, dtype=tf.int32)\n",
" \n",
" outputs_inference = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, \n",
" decoder_fn=train_decoder_fn, scope=decoding_scope)[0]\n",
"\n",
" inference_logits = tf.contrib.layers.dropout(outputs_inference, keep_prob)\n",
"\n",
" return outputs_inference #inference_logits\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_infer(decoding_layer_infer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Decoding Layer\n",
"Implement `decoding_layer()` to create a Decoder RNN layer.\n",
"\n",
"* Embed the target sequences\n",
"* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\n",
"* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\n",
"* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\n",
"* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\n",
"\n",
"Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def decoding_layer(dec_input, encoder_state,\n",
" target_sequence_length, max_target_sequence_length,\n",
" rnn_size,\n",
" num_layers, target_vocab_to_int, target_vocab_size,\n",
" batch_size, keep_prob, decoding_embedding_size):\n",
" \"\"\"\n",
" Create decoding layer\n",
" :param dec_input: Decoder input\n",
" :param encoder_state: Encoder state\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param target_vocab_size: Size of target vocabulary\n",
" :param batch_size: The size of the batch\n",
" :param keep_prob: Dropout keep probability\n",
" :param decoding_embedding_size: Decoding embedding size\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
" cell = tf.contrib.rnn.BasicLSTMCell(rnn_size) \n",
" cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)\n",
" multi_rnn_cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers) \n",
" \n",
" with tf.variable_scope('decoding') as decoding_scope:\n",
" output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)\n",
" \n",
" train_logits = decoding_layer_train(encoder_state, multi_rnn_cell, dec_embed_input, sequence_length, \n",
" decoding_scope, output_fn, keep_prob) \n",
" \n",
" decoding_scope.reuse_variables() \n",
" \n",
" infer_logits = decoding_layer_infer(encoder_state, multi_rnn_cell, dec_embeddings, target_vocab_to_int['<GO>'],\n",
" target_vocab_to_int['<EOS>'], sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)\n",
" \n",
" return train_logits, infer_logits\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer(decoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Neural Network\n",
"Apply the functions you implemented above to:\n",
"\n",
"- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\n",
"- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\n",
"- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def seq2seq_model(input_data, target_data, keep_prob, batch_size,\n",
" source_sequence_length, target_sequence_length,\n",
" max_target_sentence_length,\n",
" source_vocab_size, target_vocab_size,\n",
" enc_embedding_size, dec_embedding_size,\n",
" rnn_size, num_layers, target_vocab_to_int):\n",
" \"\"\"\n",
" Build the Sequence-to-Sequence part of the neural network\n",
" :param input_data: Input placeholder\n",
" :param target_data: Target placeholder\n",
" :param keep_prob: Dropout keep probability placeholder\n",
" :param batch_size: Batch Size\n",
" :param source_sequence_length: Sequence Lengths of source sequences in the batch\n",
" :param target_sequence_length: Sequence Lengths of target sequences in the batch\n",
" :param source_vocab_size: Source vocabulary size\n",
" :param target_vocab_size: Target vocabulary size\n",
" :param enc_embedding_size: Decoder embedding size\n",
" :param dec_embedding_size: Encoder embedding size\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
<<<<<<< HEAD
" \n",
" embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\n",
" encoder_state = encoding_layer(embed_input, rnn_size, num_layers, keep_prob)\n",
"\n",
" decoded_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n",
" decoded_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size])) \n",
" decoded_embed_input = tf.nn.embedding_lookup(decoded_embeddings, decoded_input)\n",
" \n",
" train_logits, infer_logits = decoding_layer(decoded_embed_input, decoded_embeddings, encoder_state, target_vocab_size, \n",
" sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)\n",
"\n",
" return train_logits, infer_logits\n",
=======
" # TODO: Implement Function\n",
" return None, None\n",
>>>>>>> refs/remotes/udacity/master
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_seq2seq_model(seq2seq_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neural Network Training\n",
"### Hyperparameters\n",
"Tune the following parameters:\n",
"\n",
"- Set `epochs` to the number of epochs.\n",
"- Set `batch_size` to the batch size.\n",
"- Set `rnn_size` to the size of the RNNs.\n",
"- Set `num_layers` to the number of layers.\n",
"- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n",
"- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n",
"- Set `learning_rate` to the learning rate.\n",
"- Set `keep_probability` to the Dropout keep probability\n",
"- Set `display_step` to state how many steps between each debug output statement"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Number of Epochs\n",
"epochs = 10\n",
"# Batch Size\n",
"batch_size = 256\n",
"# RNN Size\n",
"rnn_size = 256\n",
"# Number of Layers\n",
"num_layers = 2\n",
"# Embedding Size\n",
"encoding_embedding_size = 128\n",
"decoding_embedding_size = 128\n",
"# Learning Rate\n",
"learning_rate = 0.001\n",
"# Dropout Keep Probability\n",
<<<<<<< HEAD
"keep_probability = 0.6"
=======
"keep_probability = None\n",
"display_step = None"
>>>>>>> refs/remotes/udacity/master
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Graph\n",
"Build the graph using the neural network you implemented."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"save_path = 'checkpoints/dev'\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n",
"max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\n",
"\n",
"train_graph = tf.Graph()\n",
"with train_graph.as_default():\n",
" input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\n",
"\n",
" #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\n",
" input_shape = tf.shape(input_data)\n",
"\n",
" train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\n",
" targets,\n",
" keep_prob,\n",
" batch_size,\n",
" source_sequence_length,\n",
" target_sequence_length,\n",
" max_target_sequence_length,\n",
" len(source_vocab_to_int),\n",
" len(target_vocab_to_int),\n",
" encoding_embedding_size,\n",
" decoding_embedding_size,\n",
" rnn_size,\n",
" num_layers,\n",
" target_vocab_to_int)\n",
"\n",
"\n",
" training_logits = tf.identity(train_logits.rnn_output, name='logits')\n",
" inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\n",
"\n",
" masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n",
"\n",
" with tf.name_scope(\"optimization\"):\n",
" # Loss function\n",
" cost = tf.contrib.seq2seq.sequence_loss(\n",
" training_logits,\n",
" targets,\n",
" masks)\n",
"\n",
" # Optimizer\n",
" optimizer = tf.train.AdamOptimizer(lr)\n",
"\n",
" # Gradient Clipping\n",
" gradients = optimizer.compute_gradients(cost)\n",
" capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
" train_op = optimizer.apply_gradients(capped_gradients)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Batch and pad the source and target sequences"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def pad_sentence_batch(sentence_batch, pad_int):\n",
" \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n",
" max_sentence = max([len(sentence) for sentence in sentence_batch])\n",
" return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\n",
"\n",
"\n",
"def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\n",
" \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n",
" for batch_i in range(0, len(sources)//batch_size):\n",
" start_i = batch_i * batch_size\n",
"\n",
" # Slice the right amount for the batch\n",
" sources_batch = sources[start_i:start_i + batch_size]\n",
" targets_batch = targets[start_i:start_i + batch_size]\n",
"\n",
" # Pad\n",
" pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n",
" pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n",
"\n",
" # Need the lengths for the _lengths parameters\n",
" pad_targets_lengths = []\n",
" for target in pad_targets_batch:\n",
" pad_targets_lengths.append(len(target))\n",
"\n",
" pad_source_lengths = []\n",
" for source in pad_sources_batch:\n",
" pad_source_lengths.append(len(source))\n",
"\n",
" yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train\n",
"Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0 Batch 0/538 - Train Accuracy: 0.234, Validation Accuracy: 0.316, Loss: 5.860\n",
"Epoch 0 Batch 1/538 - Train Accuracy: 0.231, Validation Accuracy: 0.316, Loss: 5.520\n",
"Epoch 0 Batch 2/538 - Train Accuracy: 0.252, Validation Accuracy: 0.316, Loss: 5.067\n",
"Epoch 0 Batch 3/538 - Train Accuracy: 0.229, Validation Accuracy: 0.316, Loss: 4.761\n",
"Epoch 0 Batch 4/538 - Train Accuracy: 0.237, Validation Accuracy: 0.316, Loss: 4.614\n",
"Epoch 0 Batch 5/538 - Train Accuracy: 0.275, Validation Accuracy: 0.328, Loss: 4.414\n",
"Epoch 0 Batch 6/538 - Train Accuracy: 0.300, Validation Accuracy: 0.345, Loss: 4.206\n",
"Epoch 0 Batch 7/538 - Train Accuracy: 0.279, Validation Accuracy: 0.346, Loss: 4.170\n",
"Epoch 0 Batch 8/538 - Train Accuracy: 0.280, Validation Accuracy: 0.347, Loss: 4.059\n",
"Epoch 0 Batch 9/538 - Train Accuracy: 0.279, Validation Accuracy: 0.347, Loss: 3.994\n",
"Epoch 0 Batch 10/538 - Train Accuracy: 0.260, Validation Accuracy: 0.347, Loss: 3.981\n",
"Epoch 0 Batch 11/538 - Train Accuracy: 0.273, Validation Accuracy: 0.347, Loss: 3.830\n",
"Epoch 0 Batch 12/538 - Train Accuracy: 0.267, Validation Accuracy: 0.347, Loss: 3.821\n",
"Epoch 0 Batch 13/538 - Train Accuracy: 0.329, Validation Accuracy: 0.356, Loss: 3.558\n",
"Epoch 0 Batch 14/538 - Train Accuracy: 0.308, Validation Accuracy: 0.375, Loss: 3.666\n",
"Epoch 0 Batch 15/538 - Train Accuracy: 0.355, Validation Accuracy: 0.383, Loss: 3.452\n",
"Epoch 0 Batch 16/538 - Train Accuracy: 0.349, Validation Accuracy: 0.392, Loss: 3.419\n",
"Epoch 0 Batch 17/538 - Train Accuracy: 0.331, Validation Accuracy: 0.393, Loss: 3.467\n",
"Epoch 0 Batch 18/538 - Train Accuracy: 0.318, Validation Accuracy: 0.388, Loss: 3.485\n",
"Epoch 0 Batch 19/538 - Train Accuracy: 0.315, Validation Accuracy: 0.388, Loss: 3.441\n",
"Epoch 0 Batch 20/538 - Train Accuracy: 0.342, Validation Accuracy: 0.388, Loss: 3.245\n",
"Epoch 0 Batch 21/538 - Train Accuracy: 0.273, Validation Accuracy: 0.388, Loss: 3.480\n",
"Epoch 0 Batch 22/538 - Train Accuracy: 0.322, Validation Accuracy: 0.388, Loss: 3.262\n",
"Epoch 0 Batch 23/538 - Train Accuracy: 0.334, Validation Accuracy: 0.393, Loss: 3.217\n",
"Epoch 0 Batch 24/538 - Train Accuracy: 0.351, Validation Accuracy: 0.401, Loss: 3.174\n",
"Epoch 0 Batch 25/538 - Train Accuracy: 0.338, Validation Accuracy: 0.402, Loss: 3.181\n",
"Epoch 0 Batch 26/538 - Train Accuracy: 0.351, Validation Accuracy: 0.417, Loss: 3.172\n",
"Epoch 0 Batch 27/538 - Train Accuracy: 0.358, Validation Accuracy: 0.419, Loss: 3.127\n",
"Epoch 0 Batch 28/538 - Train Accuracy: 0.417, Validation Accuracy: 0.419, Loss: 2.830\n",
"Epoch 0 Batch 29/538 - Train Accuracy: 0.388, Validation Accuracy: 0.429, Loss: 2.993\n",
"Epoch 0 Batch 30/538 - Train Accuracy: 0.363, Validation Accuracy: 0.428, Loss: 3.072\n",
"Epoch 0 Batch 31/538 - Train Accuracy: 0.402, Validation Accuracy: 0.435, Loss: 2.906\n",
"Epoch 0 Batch 32/538 - Train Accuracy: 0.380, Validation Accuracy: 0.431, Loss: 2.995\n",
"Epoch 0 Batch 33/538 - Train Accuracy: 0.399, Validation Accuracy: 0.434, Loss: 2.944\n",
"Epoch 0 Batch 34/538 - Train Accuracy: 0.382, Validation Accuracy: 0.433, Loss: 2.990\n",
"Epoch 0 Batch 35/538 - Train Accuracy: 0.384, Validation Accuracy: 0.446, Loss: 2.976\n",
"Epoch 0 Batch 36/538 - Train Accuracy: 0.411, Validation Accuracy: 0.451, Loss: 2.851\n",
"Epoch 0 Batch 37/538 - Train Accuracy: 0.382, Validation Accuracy: 0.441, Loss: 2.912\n",
"Epoch 0 Batch 38/538 - Train Accuracy: 0.378, Validation Accuracy: 0.448, Loss: 2.978\n",
"Epoch 0 Batch 39/538 - Train Accuracy: 0.390, Validation Accuracy: 0.452, Loss: 2.910\n",
"Epoch 0 Batch 40/538 - Train Accuracy: 0.457, Validation Accuracy: 0.459, Loss: 2.644\n",
"Epoch 0 Batch 41/538 - Train Accuracy: 0.392, Validation Accuracy: 0.455, Loss: 2.855\n",
"Epoch 0 Batch 42/538 - Train Accuracy: 0.410, Validation Accuracy: 0.463, Loss: 2.820\n",
"Epoch 0 Batch 43/538 - Train Accuracy: 0.414, Validation Accuracy: 0.467, Loss: 2.825\n",
"Epoch 0 Batch 44/538 - Train Accuracy: 0.401, Validation Accuracy: 0.465, Loss: 2.866\n",
"Epoch 0 Batch 45/538 - Train Accuracy: 0.430, Validation Accuracy: 0.458, Loss: 2.660\n",
"Epoch 0 Batch 46/538 - Train Accuracy: 0.415, Validation Accuracy: 0.469, Loss: 2.781\n",
"Epoch 0 Batch 47/538 - Train Accuracy: 0.445, Validation Accuracy: 0.477, Loss: 2.633\n",
"Epoch 0 Batch 48/538 - Train Accuracy: 0.441, Validation Accuracy: 0.464, Loss: 2.603\n",
"Epoch 0 Batch 49/538 - Train Accuracy: 0.409, Validation Accuracy: 0.480, Loss: 2.803\n",
"Epoch 0 Batch 50/538 - Train Accuracy: 0.431, Validation Accuracy: 0.479, Loss: 2.675\n",
"Epoch 0 Batch 51/538 - Train Accuracy: 0.378, Validation Accuracy: 0.483, Loss: 2.904\n",
"Epoch 0 Batch 52/538 - Train Accuracy: 0.438, Validation Accuracy: 0.482, Loss: 2.667\n",
"Epoch 0 Batch 53/538 - Train Accuracy: 0.464, Validation Accuracy: 0.468, Loss: 2.483\n",
"Epoch 0 Batch 54/538 - Train Accuracy: 0.439, Validation Accuracy: 0.485, Loss: 2.642\n",
"Epoch 0 Batch 55/538 - Train Accuracy: 0.424, Validation Accuracy: 0.489, Loss: 2.686\n",
"Epoch 0 Batch 56/538 - Train Accuracy: 0.445, Validation Accuracy: 0.483, Loss: 2.553\n",
"Epoch 0 Batch 57/538 - Train Accuracy: 0.403, Validation Accuracy: 0.479, Loss: 2.704\n",
"Epoch 0 Batch 58/538 - Train Accuracy: 0.410, Validation Accuracy: 0.489, Loss: 2.686\n",
"Epoch 0 Batch 59/538 - Train Accuracy: 0.419, Validation Accuracy: 0.487, Loss: 2.620\n",
"Epoch 0 Batch 60/538 - Train Accuracy: 0.421, Validation Accuracy: 0.478, Loss: 2.616\n",
"Epoch 0 Batch 61/538 - Train Accuracy: 0.431, Validation Accuracy: 0.489, Loss: 2.570\n",
"Epoch 0 Batch 62/538 - Train Accuracy: 0.446, Validation Accuracy: 0.486, Loss: 2.504\n",
"Epoch 0 Batch 63/538 - Train Accuracy: 0.441, Validation Accuracy: 0.473, Loss: 2.456\n",
"Epoch 0 Batch 64/538 - Train Accuracy: 0.455, Validation Accuracy: 0.479, Loss: 2.460\n",
"Epoch 0 Batch 65/538 - Train Accuracy: 0.405, Validation Accuracy: 0.468, Loss: 2.596\n",
"Epoch 0 Batch 66/538 - Train Accuracy: 0.438, Validation Accuracy: 0.482, Loss: 2.424\n",
"Epoch 0 Batch 67/538 - Train Accuracy: 0.433, Validation Accuracy: 0.488, Loss: 2.487\n",
"Epoch 0 Batch 68/538 - Train Accuracy: 0.457, Validation Accuracy: 0.490, Loss: 2.354\n",
"Epoch 0 Batch 69/538 - Train Accuracy: 0.420, Validation Accuracy: 0.484, Loss: 2.539\n",
"Epoch 0 Batch 70/538 - Train Accuracy: 0.455, Validation Accuracy: 0.494, Loss: 2.393\n",
"Epoch 0 Batch 71/538 - Train Accuracy: 0.430, Validation Accuracy: 0.481, Loss: 2.447\n",
"Epoch 0 Batch 72/538 - Train Accuracy: 0.464, Validation Accuracy: 0.490, Loss: 2.347\n",
"Epoch 0 Batch 73/538 - Train Accuracy: 0.429, Validation Accuracy: 0.492, Loss: 2.442\n",
"Epoch 0 Batch 74/538 - Train Accuracy: 0.470, Validation Accuracy: 0.501, Loss: 2.345\n",
"Epoch 0 Batch 75/538 - Train Accuracy: 0.466, Validation Accuracy: 0.499, Loss: 2.332\n",
"Epoch 0 Batch 76/538 - Train Accuracy: 0.392, Validation Accuracy: 0.468, Loss: 2.449\n",
"Epoch 0 Batch 77/538 - Train Accuracy: 0.413, Validation Accuracy: 0.484, Loss: 2.395\n",
"Epoch 0 Batch 78/538 - Train Accuracy: 0.457, Validation Accuracy: 0.492, Loss: 2.327\n",
"Epoch 0 Batch 79/538 - Train Accuracy: 0.423, Validation Accuracy: 0.461, Loss: 2.252\n",
"Epoch 0 Batch 80/538 - Train Accuracy: 0.438, Validation Accuracy: 0.501, Loss: 2.442\n",
"Epoch 0 Batch 81/538 - Train Accuracy: 0.432, Validation Accuracy: 0.497, Loss: 2.397\n",
"Epoch 0 Batch 82/538 - Train Accuracy: 0.408, Validation Accuracy: 0.462, Loss: 2.309\n",
"Epoch 0 Batch 83/538 - Train Accuracy: 0.412, Validation Accuracy: 0.480, Loss: 2.369\n",
"Epoch 0 Batch 84/538 - Train Accuracy: 0.450, Validation Accuracy: 0.487, Loss: 2.217\n",
"Epoch 0 Batch 85/538 - Train Accuracy: 0.458, Validation Accuracy: 0.483, Loss: 2.161\n",
"Epoch 0 Batch 86/538 - Train Accuracy: 0.395, Validation Accuracy: 0.459, Loss: 2.332\n",
"Epoch 0 Batch 87/538 - Train Accuracy: 0.424, Validation Accuracy: 0.482, Loss: 2.305\n",
"Epoch 0 Batch 88/538 - Train Accuracy: 0.442, Validation Accuracy: 0.481, Loss: 2.264\n",
"Epoch 0 Batch 89/538 - Train Accuracy: 0.415, Validation Accuracy: 0.463, Loss: 2.247\n",
"Epoch 0 Batch 90/538 - Train Accuracy: 0.437, Validation Accuracy: 0.473, Loss: 2.179\n",
"Epoch 0 Batch 91/538 - Train Accuracy: 0.441, Validation Accuracy: 0.503, Loss: 2.274\n",
"Epoch 0 Batch 92/538 - Train Accuracy: 0.427, Validation Accuracy: 0.479, Loss: 2.212\n",
"Epoch 0 Batch 93/538 - Train Accuracy: 0.385, Validation Accuracy: 0.458, Loss: 2.225\n",
"Epoch 0 Batch 94/538 - Train Accuracy: 0.409, Validation Accuracy: 0.482, Loss: 2.309\n",
"Epoch 0 Batch 95/538 - Train Accuracy: 0.479, Validation Accuracy: 0.497, Loss: 2.026\n",
"Epoch 0 Batch 96/538 - Train Accuracy: 0.448, Validation Accuracy: 0.470, Loss: 2.059\n",
"Epoch 0 Batch 97/538 - Train Accuracy: 0.399, Validation Accuracy: 0.473, Loss: 2.186\n",
"Epoch 0 Batch 98/538 - Train Accuracy: 0.459, Validation Accuracy: 0.488, Loss: 2.014\n",
"Epoch 0 Batch 99/538 - Train Accuracy: 0.404, Validation Accuracy: 0.465, Loss: 2.200\n",
"Epoch 0 Batch 100/538 - Train Accuracy: 0.412, Validation Accuracy: 0.463, Loss: 2.123\n",
"Epoch 0 Batch 101/538 - Train Accuracy: 0.430, Validation Accuracy: 0.487, Loss: 2.136\n",
"Epoch 0 Batch 102/538 - Train Accuracy: 0.400, Validation Accuracy: 0.472, Loss: 2.174\n",
"Epoch 0 Batch 103/538 - Train Accuracy: 0.422, Validation Accuracy: 0.462, Loss: 2.095\n",
"Epoch 0 Batch 104/538 - Train Accuracy: 0.430, Validation Accuracy: 0.469, Loss: 2.083\n",
"Epoch 0 Batch 105/538 - Train Accuracy: 0.435, Validation Accuracy: 0.471, Loss: 2.018\n",
"Epoch 0 Batch 106/538 - Train Accuracy: 0.405, Validation Accuracy: 0.471, Loss: 2.095\n",
"Epoch 0 Batch 107/538 - Train Accuracy: 0.399, Validation Accuracy: 0.474, Loss: 2.120\n",
"Epoch 0 Batch 108/538 - Train Accuracy: 0.424, Validation Accuracy: 0.469, Loss: 2.071\n",
"Epoch 0 Batch 109/538 - Train Accuracy: 0.417, Validation Accuracy: 0.469, Loss: 2.076\n",
"Epoch 0 Batch 110/538 - Train Accuracy: 0.414, Validation Accuracy: 0.471, Loss: 2.120\n",
"Epoch 0 Batch 111/538 - Train Accuracy: 0.442, Validation Accuracy: 0.470, Loss: 1.982\n",
"Epoch 0 Batch 112/538 - Train Accuracy: 0.395, Validation Accuracy: 0.464, Loss: 2.062\n",
"Epoch 0 Batch 113/538 - Train Accuracy: 0.423, Validation Accuracy: 0.486, Loss: 2.077\n",
"Epoch 0 Batch 114/538 - Train Accuracy: 0.454, Validation Accuracy: 0.473, Loss: 1.936\n",
"Epoch 0 Batch 115/538 - Train Accuracy: 0.425, Validation Accuracy: 0.475, Loss: 2.025\n",
"Epoch 0 Batch 116/538 - Train Accuracy: 0.456, Validation Accuracy: 0.483, Loss: 1.972\n",
"Epoch 0 Batch 117/538 - Train Accuracy: 0.449, Validation Accuracy: 0.477, Loss: 1.923\n",
"Epoch 0 Batch 118/538 - Train Accuracy: 0.448, Validation Accuracy: 0.483, Loss: 1.940\n",
"Epoch 0 Batch 119/538 - Train Accuracy: 0.456, Validation Accuracy: 0.487, Loss: 1.887\n",
"Epoch 0 Batch 120/538 - Train Accuracy: 0.412, Validation Accuracy: 0.468, Loss: 1.983\n",
"Epoch 0 Batch 121/538 - Train Accuracy: 0.449, Validation Accuracy: 0.489, Loss: 1.903\n",
"Epoch 0 Batch 122/538 - Train Accuracy: 0.420, Validation Accuracy: 0.461, Loss: 1.907\n",
"Epoch 0 Batch 123/538 - Train Accuracy: 0.448, Validation Accuracy: 0.470, Loss: 1.859\n",
"Epoch 0 Batch 124/538 - Train Accuracy: 0.463, Validation Accuracy: 0.478, Loss: 1.825\n",
"Epoch 0 Batch 125/538 - Train Accuracy: 0.431, Validation Accuracy: 0.469, Loss: 1.906\n",
"Epoch 0 Batch 126/538 - Train Accuracy: 0.465, Validation Accuracy: 0.482, Loss: 1.822\n",
"Epoch 0 Batch 127/538 - Train Accuracy: 0.429, Validation Accuracy: 0.501, Loss: 1.948\n",
"Epoch 0 Batch 128/538 - Train Accuracy: 0.430, Validation Accuracy: 0.461, Loss: 1.833\n",
"Epoch 0 Batch 129/538 - Train Accuracy: 0.448, Validation Accuracy: 0.487, Loss: 1.854\n",
"Epoch 0 Batch 130/538 - Train Accuracy: 0.458, Validation Accuracy: 0.502, Loss: 1.843\n",
"Epoch 0 Batch 131/538 - Train Accuracy: 0.398, Validation Accuracy: 0.475, Loss: 1.945\n",
"Epoch 0 Batch 132/538 - Train Accuracy: 0.464, Validation Accuracy: 0.488, Loss: 1.827\n",
"Epoch 0 Batch 133/538 - Train Accuracy: 0.484, Validation Accuracy: 0.510, Loss: 1.742\n",
"Epoch 0 Batch 134/538 - Train Accuracy: 0.387, Validation Accuracy: 0.468, Loss: 1.957\n",
"Epoch 0 Batch 135/538 - Train Accuracy: 0.442, Validation Accuracy: 0.480, Loss: 1.811\n",
"Epoch 0 Batch 136/538 - Train Accuracy: 0.452, Validation Accuracy: 0.496, Loss: 1.802\n",
"Epoch 0 Batch 137/538 - Train Accuracy: 0.445, Validation Accuracy: 0.480, Loss: 1.781\n",
"Epoch 0 Batch 138/538 - Train Accuracy: 0.441, Validation Accuracy: 0.480, Loss: 1.785\n",
"Epoch 0 Batch 139/538 - Train Accuracy: 0.412, Validation Accuracy: 0.482, Loss: 1.884\n",
"Epoch 0 Batch 140/538 - Train Accuracy: 0.430, Validation Accuracy: 0.487, Loss: 1.872\n",
"Epoch 0 Batch 141/538 - Train Accuracy: 0.424, Validation Accuracy: 0.479, Loss: 1.852\n",
"Epoch 0 Batch 142/538 - Train Accuracy: 0.467, Validation Accuracy: 0.470, Loss: 1.665\n",
"Epoch 0 Batch 143/538 - Train Accuracy: 0.438, Validation Accuracy: 0.500, Loss: 1.809\n",
"Epoch 0 Batch 144/538 - Train Accuracy: 0.439, Validation Accuracy: 0.483, Loss: 1.788\n",
"Epoch 0 Batch 145/538 - Train Accuracy: 0.456, Validation Accuracy: 0.490, Loss: 1.717\n",
"Epoch 0 Batch 146/538 - Train Accuracy: 0.474, Validation Accuracy: 0.496, Loss: 1.622\n",
"Epoch 0 Batch 147/538 - Train Accuracy: 0.456, Validation Accuracy: 0.473, Loss: 1.668\n",
"Epoch 0 Batch 148/538 - Train Accuracy: 0.425, Validation Accuracy: 0.492, Loss: 1.786\n",
"Epoch 0 Batch 149/538 - Train Accuracy: 0.430, Validation Accuracy: 0.485, Loss: 1.707\n",
"Epoch 0 Batch 150/538 - Train Accuracy: 0.456, Validation Accuracy: 0.490, Loss: 1.686\n",
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"Epoch 0 Batch 375/538 - Train Accuracy: 0.549, Validation Accuracy: 0.558, Loss: 0.886\n",
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"Epoch 0 Batch 382/538 - Train Accuracy: 0.538, Validation Accuracy: 0.553, Loss: 0.912\n",
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"Epoch 0 Batch 398/538 - Train Accuracy: 0.540, Validation Accuracy: 0.566, Loss: 0.882\n",
"Epoch 0 Batch 399/538 - Train Accuracy: 0.510, Validation Accuracy: 0.554, Loss: 0.906\n",
"Epoch 0 Batch 400/538 - Train Accuracy: 0.538, Validation Accuracy: 0.556, Loss: 0.846\n",
"Epoch 0 Batch 401/538 - Train Accuracy: 0.547, Validation Accuracy: 0.562, Loss: 0.886\n",
"Epoch 0 Batch 402/538 - Train Accuracy: 0.534, Validation Accuracy: 0.562, Loss: 0.855\n",
"Epoch 0 Batch 403/538 - Train Accuracy: 0.531, Validation Accuracy: 0.562, Loss: 0.881\n",
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"Epoch 0 Batch 405/538 - Train Accuracy: 0.559, Validation Accuracy: 0.559, Loss: 0.840\n",
"Epoch 0 Batch 406/538 - Train Accuracy: 0.546, Validation Accuracy: 0.555, Loss: 0.841\n",
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"Epoch 0 Batch 409/538 - Train Accuracy: 0.519, Validation Accuracy: 0.561, Loss: 0.868\n",
"Epoch 0 Batch 410/538 - Train Accuracy: 0.536, Validation Accuracy: 0.558, Loss: 0.858\n",
"Epoch 0 Batch 411/538 - Train Accuracy: 0.523, Validation Accuracy: 0.559, Loss: 0.834\n",
"Epoch 0 Batch 412/538 - Train Accuracy: 0.541, Validation Accuracy: 0.555, Loss: 0.789\n",
"Epoch 0 Batch 413/538 - Train Accuracy: 0.521, Validation Accuracy: 0.556, Loss: 0.860\n",
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"Epoch 0 Batch 416/538 - Train Accuracy: 0.585, Validation Accuracy: 0.559, Loss: 0.807\n",
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"Epoch 0 Batch 420/538 - Train Accuracy: 0.552, Validation Accuracy: 0.563, Loss: 0.832\n",
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"Epoch 0 Batch 422/538 - Train Accuracy: 0.557, Validation Accuracy: 0.560, Loss: 0.833\n",
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"Epoch 0 Batch 427/538 - Train Accuracy: 0.538, Validation Accuracy: 0.557, Loss: 0.842\n",
"Epoch 0 Batch 428/538 - Train Accuracy: 0.581, Validation Accuracy: 0.561, Loss: 0.808\n",
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"Epoch 0 Batch 432/538 - Train Accuracy: 0.579, Validation Accuracy: 0.560, Loss: 0.762\n",
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"Epoch 0 Batch 442/538 - Train Accuracy: 0.583, Validation Accuracy: 0.570, Loss: 0.740\n",
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"Epoch 0 Batch 446/538 - Train Accuracy: 0.570, Validation Accuracy: 0.563, Loss: 0.774\n",
"Epoch 0 Batch 447/538 - Train Accuracy: 0.547, Validation Accuracy: 0.565, Loss: 0.808\n",
"Epoch 0 Batch 448/538 - Train Accuracy: 0.576, Validation Accuracy: 0.568, Loss: 0.762\n",
"Epoch 0 Batch 449/538 - Train Accuracy: 0.564, Validation Accuracy: 0.570, Loss: 0.841\n",
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"Epoch 0 Batch 451/538 - Train Accuracy: 0.557, Validation Accuracy: 0.576, Loss: 0.818\n",
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"Epoch 0 Batch 453/538 - Train Accuracy: 0.554, Validation Accuracy: 0.566, Loss: 0.822\n",
"Epoch 0 Batch 454/538 - Train Accuracy: 0.549, Validation Accuracy: 0.561, Loss: 0.794\n",
"Epoch 0 Batch 455/538 - Train Accuracy: 0.583, Validation Accuracy: 0.562, Loss: 0.742\n",
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"Epoch 0 Batch 463/538 - Train Accuracy: 0.537, Validation Accuracy: 0.570, Loss: 0.812\n",
"Epoch 0 Batch 464/538 - Train Accuracy: 0.563, Validation Accuracy: 0.574, Loss: 0.813\n",
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"Epoch 0 Batch 472/538 - Train Accuracy: 0.584, Validation Accuracy: 0.581, Loss: 0.766\n",
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"Epoch 0 Batch 479/538 - Train Accuracy: 0.606, Validation Accuracy: 0.577, Loss: 0.751\n",
"Epoch 0 Batch 480/538 - Train Accuracy: 0.596, Validation Accuracy: 0.581, Loss: 0.751\n",
"Epoch 0 Batch 481/538 - Train Accuracy: 0.584, Validation Accuracy: 0.581, Loss: 0.763\n",
"Epoch 0 Batch 482/538 - Train Accuracy: 0.597, Validation Accuracy: 0.585, Loss: 0.704\n",
"Epoch 0 Batch 483/538 - Train Accuracy: 0.555, Validation Accuracy: 0.591, Loss: 0.796\n",
"Epoch 0 Batch 484/538 - Train Accuracy: 0.612, Validation Accuracy: 0.590, Loss: 0.780\n",
"Epoch 0 Batch 485/538 - Train Accuracy: 0.581, Validation Accuracy: 0.590, Loss: 0.763\n",
"Epoch 0 Batch 486/538 - Train Accuracy: 0.574, Validation Accuracy: 0.586, Loss: 0.750\n",
"Epoch 0 Batch 487/538 - Train Accuracy: 0.587, Validation Accuracy: 0.578, Loss: 0.729\n",
"Epoch 0 Batch 488/538 - Train Accuracy: 0.603, Validation Accuracy: 0.580, Loss: 0.767\n",
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"Epoch 0 Batch 490/538 - Train Accuracy: 0.583, Validation Accuracy: 0.582, Loss: 0.755\n",
"Epoch 0 Batch 491/538 - Train Accuracy: 0.560, Validation Accuracy: 0.581, Loss: 0.795\n",
"Epoch 0 Batch 492/538 - Train Accuracy: 0.571, Validation Accuracy: 0.578, Loss: 0.785\n",
"Epoch 0 Batch 493/538 - Train Accuracy: 0.559, Validation Accuracy: 0.584, Loss: 0.759\n",
"Epoch 0 Batch 494/538 - Train Accuracy: 0.562, Validation Accuracy: 0.583, Loss: 0.800\n",
"Epoch 0 Batch 495/538 - Train Accuracy: 0.581, Validation Accuracy: 0.587, Loss: 0.782\n",
"Epoch 0 Batch 496/538 - Train Accuracy: 0.591, Validation Accuracy: 0.588, Loss: 0.769\n",
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"Epoch 0 Batch 499/538 - Train Accuracy: 0.593, Validation Accuracy: 0.570, Loss: 0.738\n",
"Epoch 0 Batch 500/538 - Train Accuracy: 0.591, Validation Accuracy: 0.568, Loss: 0.709\n",
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"Epoch 0 Batch 503/538 - Train Accuracy: 0.608, Validation Accuracy: 0.580, Loss: 0.737\n",
"Epoch 0 Batch 504/538 - Train Accuracy: 0.567, Validation Accuracy: 0.595, Loss: 0.755\n",
"Epoch 0 Batch 505/538 - Train Accuracy: 0.601, Validation Accuracy: 0.605, Loss: 0.742\n",
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"Epoch 0 Batch 511/538 - Train Accuracy: 0.611, Validation Accuracy: 0.586, Loss: 0.709\n",
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"Epoch 0 Batch 514/538 - Train Accuracy: 0.561, Validation Accuracy: 0.592, Loss: 0.761\n",
"Epoch 0 Batch 515/538 - Train Accuracy: 0.584, Validation Accuracy: 0.594, Loss: 0.733\n",
"Epoch 0 Batch 516/538 - Train Accuracy: 0.552, Validation Accuracy: 0.598, Loss: 0.752\n",
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"Epoch 0 Batch 522/538 - Train Accuracy: 0.569, Validation Accuracy: 0.604, Loss: 0.745\n",
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"Epoch 0 Batch 524/538 - Train Accuracy: 0.556, Validation Accuracy: 0.600, Loss: 0.774\n",
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"Epoch 0 Batch 530/538 - Train Accuracy: 0.575, Validation Accuracy: 0.596, Loss: 0.771\n",
"Epoch 0 Batch 531/538 - Train Accuracy: 0.599, Validation Accuracy: 0.597, Loss: 0.751\n",
"Epoch 0 Batch 532/538 - Train Accuracy: 0.563, Validation Accuracy: 0.595, Loss: 0.728\n",
"Epoch 0 Batch 533/538 - Train Accuracy: 0.604, Validation Accuracy: 0.598, Loss: 0.741\n",
"Epoch 0 Batch 534/538 - Train Accuracy: 0.586, Validation Accuracy: 0.600, Loss: 0.706\n",
"Epoch 0 Batch 535/538 - Train Accuracy: 0.610, Validation Accuracy: 0.609, Loss: 0.718\n",
"Epoch 0 Batch 536/538 - Train Accuracy: 0.602, Validation Accuracy: 0.612, Loss: 0.731\n",
"Epoch 1 Batch 0/538 - Train Accuracy: 0.576, Validation Accuracy: 0.605, Loss: 0.728\n",
"Epoch 1 Batch 1/538 - Train Accuracy: 0.594, Validation Accuracy: 0.609, Loss: 0.728\n",
"Epoch 1 Batch 2/538 - Train Accuracy: 0.578, Validation Accuracy: 0.603, Loss: 0.768\n",
"Epoch 1 Batch 3/538 - Train Accuracy: 0.558, Validation Accuracy: 0.608, Loss: 0.728\n",
"Epoch 1 Batch 4/538 - Train Accuracy: 0.603, Validation Accuracy: 0.607, Loss: 0.733\n",
"Epoch 1 Batch 5/538 - Train Accuracy: 0.566, Validation Accuracy: 0.610, Loss: 0.741\n",
"Epoch 1 Batch 6/538 - Train Accuracy: 0.595, Validation Accuracy: 0.602, Loss: 0.703\n",
"Epoch 1 Batch 7/538 - Train Accuracy: 0.609, Validation Accuracy: 0.601, Loss: 0.731\n",
"Epoch 1 Batch 8/538 - Train Accuracy: 0.578, Validation Accuracy: 0.605, Loss: 0.741\n",
"Epoch 1 Batch 9/538 - Train Accuracy: 0.586, Validation Accuracy: 0.602, Loss: 0.721\n",
"Epoch 1 Batch 10/538 - Train Accuracy: 0.556, Validation Accuracy: 0.601, Loss: 0.751\n",
"Epoch 1 Batch 11/538 - Train Accuracy: 0.587, Validation Accuracy: 0.606, Loss: 0.733\n",
"Epoch 1 Batch 12/538 - Train Accuracy: 0.572, Validation Accuracy: 0.602, Loss: 0.732\n",
"Epoch 1 Batch 13/538 - Train Accuracy: 0.601, Validation Accuracy: 0.603, Loss: 0.683\n",
"Epoch 1 Batch 14/538 - Train Accuracy: 0.571, Validation Accuracy: 0.604, Loss: 0.711\n",
"Epoch 1 Batch 15/538 - Train Accuracy: 0.600, Validation Accuracy: 0.607, Loss: 0.696\n",
"Epoch 1 Batch 16/538 - Train Accuracy: 0.587, Validation Accuracy: 0.605, Loss: 0.691\n",
"Epoch 1 Batch 17/538 - Train Accuracy: 0.594, Validation Accuracy: 0.611, Loss: 0.737\n",
"Epoch 1 Batch 18/538 - Train Accuracy: 0.577, Validation Accuracy: 0.616, Loss: 0.761\n",
"Epoch 1 Batch 19/538 - Train Accuracy: 0.579, Validation Accuracy: 0.613, Loss: 0.757\n",
"Epoch 1 Batch 20/538 - Train Accuracy: 0.598, Validation Accuracy: 0.611, Loss: 0.707\n",
"Epoch 1 Batch 21/538 - Train Accuracy: 0.582, Validation Accuracy: 0.607, Loss: 0.737\n",
"Epoch 1 Batch 22/538 - Train Accuracy: 0.583, Validation Accuracy: 0.602, Loss: 0.725\n",
"Epoch 1 Batch 23/538 - Train Accuracy: 0.584, Validation Accuracy: 0.604, Loss: 0.740\n",
"Epoch 1 Batch 24/538 - Train Accuracy: 0.576, Validation Accuracy: 0.610, Loss: 0.727\n",
"Epoch 1 Batch 25/538 - Train Accuracy: 0.578, Validation Accuracy: 0.612, Loss: 0.723\n",
"Epoch 1 Batch 26/538 - Train Accuracy: 0.568, Validation Accuracy: 0.610, Loss: 0.739\n",
"Epoch 1 Batch 27/538 - Train Accuracy: 0.600, Validation Accuracy: 0.610, Loss: 0.716\n",
"Epoch 1 Batch 28/538 - Train Accuracy: 0.615, Validation Accuracy: 0.610, Loss: 0.659\n",
"Epoch 1 Batch 29/538 - Train Accuracy: 0.596, Validation Accuracy: 0.608, Loss: 0.694\n",
"Epoch 1 Batch 30/538 - Train Accuracy: 0.588, Validation Accuracy: 0.607, Loss: 0.737\n",
"Epoch 1 Batch 31/538 - Train Accuracy: 0.599, Validation Accuracy: 0.607, Loss: 0.681\n",
"Epoch 1 Batch 32/538 - Train Accuracy: 0.595, Validation Accuracy: 0.610, Loss: 0.682\n",
"Epoch 1 Batch 33/538 - Train Accuracy: 0.611, Validation Accuracy: 0.613, Loss: 0.696\n",
"Epoch 1 Batch 34/538 - Train Accuracy: 0.596, Validation Accuracy: 0.611, Loss: 0.725\n",
"Epoch 1 Batch 35/538 - Train Accuracy: 0.593, Validation Accuracy: 0.606, Loss: 0.706\n",
"Epoch 1 Batch 36/538 - Train Accuracy: 0.587, Validation Accuracy: 0.607, Loss: 0.685\n",
"Epoch 1 Batch 37/538 - Train Accuracy: 0.588, Validation Accuracy: 0.606, Loss: 0.702\n",
"Epoch 1 Batch 38/538 - Train Accuracy: 0.573, Validation Accuracy: 0.616, Loss: 0.715\n",
"Epoch 1 Batch 39/538 - Train Accuracy: 0.584, Validation Accuracy: 0.615, Loss: 0.725\n",
"Epoch 1 Batch 40/538 - Train Accuracy: 0.641, Validation Accuracy: 0.615, Loss: 0.646\n",
"Epoch 1 Batch 41/538 - Train Accuracy: 0.589, Validation Accuracy: 0.616, Loss: 0.719\n",
"Epoch 1 Batch 42/538 - Train Accuracy: 0.615, Validation Accuracy: 0.619, Loss: 0.707\n",
"Epoch 1 Batch 43/538 - Train Accuracy: 0.604, Validation Accuracy: 0.616, Loss: 0.732\n",
"Epoch 1 Batch 44/538 - Train Accuracy: 0.589, Validation Accuracy: 0.616, Loss: 0.726\n",
"Epoch 1 Batch 45/538 - Train Accuracy: 0.605, Validation Accuracy: 0.615, Loss: 0.676\n",
"Epoch 1 Batch 46/538 - Train Accuracy: 0.600, Validation Accuracy: 0.616, Loss: 0.688\n",
"Epoch 1 Batch 47/538 - Train Accuracy: 0.605, Validation Accuracy: 0.618, Loss: 0.701\n",
"Epoch 1 Batch 48/538 - Train Accuracy: 0.633, Validation Accuracy: 0.615, Loss: 0.665\n",
"Epoch 1 Batch 49/538 - Train Accuracy: 0.590, Validation Accuracy: 0.612, Loss: 0.715\n",
"Epoch 1 Batch 50/538 - Train Accuracy: 0.597, Validation Accuracy: 0.608, Loss: 0.699\n",
"Epoch 1 Batch 51/538 - Train Accuracy: 0.554, Validation Accuracy: 0.611, Loss: 0.760\n",
"Epoch 1 Batch 52/538 - Train Accuracy: 0.598, Validation Accuracy: 0.614, Loss: 0.725\n",
"Epoch 1 Batch 53/538 - Train Accuracy: 0.618, Validation Accuracy: 0.610, Loss: 0.647\n",
"Epoch 1 Batch 54/538 - Train Accuracy: 0.612, Validation Accuracy: 0.612, Loss: 0.690\n",
"Epoch 1 Batch 55/538 - Train Accuracy: 0.589, Validation Accuracy: 0.613, Loss: 0.708\n",
"Epoch 1 Batch 56/538 - Train Accuracy: 0.601, Validation Accuracy: 0.618, Loss: 0.673\n",
"Epoch 1 Batch 57/538 - Train Accuracy: 0.566, Validation Accuracy: 0.616, Loss: 0.727\n",
"Epoch 1 Batch 58/538 - Train Accuracy: 0.563, Validation Accuracy: 0.615, Loss: 0.721\n",
"Epoch 1 Batch 59/538 - Train Accuracy: 0.594, Validation Accuracy: 0.621, Loss: 0.713\n",
"Epoch 1 Batch 60/538 - Train Accuracy: 0.611, Validation Accuracy: 0.622, Loss: 0.694\n",
"Epoch 1 Batch 61/538 - Train Accuracy: 0.589, Validation Accuracy: 0.621, Loss: 0.688\n",
"Epoch 1 Batch 62/538 - Train Accuracy: 0.609, Validation Accuracy: 0.619, Loss: 0.687\n",
"Epoch 1 Batch 63/538 - Train Accuracy: 0.623, Validation Accuracy: 0.619, Loss: 0.661\n",
"Epoch 1 Batch 64/538 - Train Accuracy: 0.604, Validation Accuracy: 0.619, Loss: 0.656\n",
"Epoch 1 Batch 65/538 - Train Accuracy: 0.575, Validation Accuracy: 0.615, Loss: 0.710\n",
"Epoch 1 Batch 66/538 - Train Accuracy: 0.612, Validation Accuracy: 0.610, Loss: 0.646\n",
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"Epoch 1 Batch 70/538 - Train Accuracy: 0.610, Validation Accuracy: 0.613, Loss: 0.659\n",
"Epoch 1 Batch 71/538 - Train Accuracy: 0.585, Validation Accuracy: 0.617, Loss: 0.707\n",
"Epoch 1 Batch 72/538 - Train Accuracy: 0.633, Validation Accuracy: 0.610, Loss: 0.682\n",
"Epoch 1 Batch 73/538 - Train Accuracy: 0.582, Validation Accuracy: 0.610, Loss: 0.706\n",
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"Epoch 1 Batch 75/538 - Train Accuracy: 0.622, Validation Accuracy: 0.617, Loss: 0.652\n",
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"Epoch 1 Batch 77/538 - Train Accuracy: 0.584, Validation Accuracy: 0.617, Loss: 0.701\n",
"Epoch 1 Batch 78/538 - Train Accuracy: 0.623, Validation Accuracy: 0.617, Loss: 0.673\n",
"Epoch 1 Batch 79/538 - Train Accuracy: 0.629, Validation Accuracy: 0.618, Loss: 0.638\n",
"Epoch 1 Batch 80/538 - Train Accuracy: 0.589, Validation Accuracy: 0.617, Loss: 0.700\n",
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"Epoch 1 Batch 82/538 - Train Accuracy: 0.592, Validation Accuracy: 0.623, Loss: 0.688\n",
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"Epoch 1 Batch 85/538 - Train Accuracy: 0.626, Validation Accuracy: 0.620, Loss: 0.629\n",
"Epoch 1 Batch 86/538 - Train Accuracy: 0.616, Validation Accuracy: 0.622, Loss: 0.692\n",
"Epoch 1 Batch 87/538 - Train Accuracy: 0.601, Validation Accuracy: 0.618, Loss: 0.674\n",
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"Epoch 1 Batch 90/538 - Train Accuracy: 0.622, Validation Accuracy: 0.630, Loss: 0.673\n",
"Epoch 1 Batch 91/538 - Train Accuracy: 0.602, Validation Accuracy: 0.629, Loss: 0.682\n",
"Epoch 1 Batch 92/538 - Train Accuracy: 0.584, Validation Accuracy: 0.618, Loss: 0.691\n",
"Epoch 1 Batch 93/538 - Train Accuracy: 0.587, Validation Accuracy: 0.617, Loss: 0.687\n",
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"Epoch 1 Batch 95/538 - Train Accuracy: 0.636, Validation Accuracy: 0.616, Loss: 0.620\n",
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"Epoch 1 Batch 99/538 - Train Accuracy: 0.603, Validation Accuracy: 0.619, Loss: 0.675\n",
"Epoch 1 Batch 100/538 - Train Accuracy: 0.590, Validation Accuracy: 0.626, Loss: 0.666\n",
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"Epoch 1 Batch 104/538 - Train Accuracy: 0.628, Validation Accuracy: 0.618, Loss: 0.642\n",
"Epoch 1 Batch 105/538 - Train Accuracy: 0.598, Validation Accuracy: 0.619, Loss: 0.635\n",
"Epoch 1 Batch 106/538 - Train Accuracy: 0.599, Validation Accuracy: 0.622, Loss: 0.649\n",
"Epoch 1 Batch 107/538 - Train Accuracy: 0.574, Validation Accuracy: 0.617, Loss: 0.685\n",
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"Epoch 1 Batch 110/538 - Train Accuracy: 0.604, Validation Accuracy: 0.615, Loss: 0.674\n",
"Epoch 1 Batch 111/538 - Train Accuracy: 0.623, Validation Accuracy: 0.625, Loss: 0.642\n",
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"Epoch 1 Batch 115/538 - Train Accuracy: 0.614, Validation Accuracy: 0.624, Loss: 0.680\n",
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"Epoch 1 Batch 117/538 - Train Accuracy: 0.629, Validation Accuracy: 0.627, Loss: 0.629\n",
"Epoch 1 Batch 118/538 - Train Accuracy: 0.634, Validation Accuracy: 0.631, Loss: 0.640\n",
"Epoch 1 Batch 119/538 - Train Accuracy: 0.635, Validation Accuracy: 0.629, Loss: 0.619\n",
"Epoch 1 Batch 120/538 - Train Accuracy: 0.616, Validation Accuracy: 0.628, Loss: 0.647\n",
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"Epoch 1 Batch 123/538 - Train Accuracy: 0.640, Validation Accuracy: 0.626, Loss: 0.618\n",
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"Epoch 1 Batch 127/538 - Train Accuracy: 0.582, Validation Accuracy: 0.621, Loss: 0.688\n",
"Epoch 1 Batch 128/538 - Train Accuracy: 0.617, Validation Accuracy: 0.618, Loss: 0.639\n",
"Epoch 1 Batch 129/538 - Train Accuracy: 0.602, Validation Accuracy: 0.619, Loss: 0.624\n",
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"Epoch 1 Batch 131/538 - Train Accuracy: 0.604, Validation Accuracy: 0.622, Loss: 0.654\n",
"Epoch 1 Batch 132/538 - Train Accuracy: 0.608, Validation Accuracy: 0.616, Loss: 0.640\n",
"Epoch 1 Batch 133/538 - Train Accuracy: 0.623, Validation Accuracy: 0.615, Loss: 0.614\n",
"Epoch 1 Batch 134/538 - Train Accuracy: 0.597, Validation Accuracy: 0.618, Loss: 0.680\n",
"Epoch 1 Batch 135/538 - Train Accuracy: 0.618, Validation Accuracy: 0.622, Loss: 0.660\n",
"Epoch 1 Batch 136/538 - Train Accuracy: 0.607, Validation Accuracy: 0.622, Loss: 0.645\n",
"Epoch 1 Batch 137/538 - Train Accuracy: 0.596, Validation Accuracy: 0.622, Loss: 0.645\n",
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"Epoch 1 Batch 140/538 - Train Accuracy: 0.581, Validation Accuracy: 0.623, Loss: 0.698\n",
"Epoch 1 Batch 141/538 - Train Accuracy: 0.618, Validation Accuracy: 0.624, Loss: 0.679\n",
"Epoch 1 Batch 142/538 - Train Accuracy: 0.657, Validation Accuracy: 0.627, Loss: 0.623\n",
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"Epoch 1 Batch 144/538 - Train Accuracy: 0.614, Validation Accuracy: 0.623, Loss: 0.671\n",
"Epoch 1 Batch 145/538 - Train Accuracy: 0.612, Validation Accuracy: 0.621, Loss: 0.652\n",
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"Epoch 1 Batch 147/538 - Train Accuracy: 0.634, Validation Accuracy: 0.629, Loss: 0.627\n",
"Epoch 1 Batch 148/538 - Train Accuracy: 0.591, Validation Accuracy: 0.629, Loss: 0.688\n",
"Epoch 1 Batch 149/538 - Train Accuracy: 0.617, Validation Accuracy: 0.622, Loss: 0.640\n",
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"Epoch 1 Batch 152/538 - Train Accuracy: 0.634, Validation Accuracy: 0.622, Loss: 0.615\n",
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"Epoch 1 Batch 154/538 - Train Accuracy: 0.621, Validation Accuracy: 0.619, Loss: 0.632\n",
"Epoch 1 Batch 155/538 - Train Accuracy: 0.654, Validation Accuracy: 0.623, Loss: 0.637\n",
"Epoch 1 Batch 156/538 - Train Accuracy: 0.611, Validation Accuracy: 0.628, Loss: 0.636\n",
"Epoch 1 Batch 157/538 - Train Accuracy: 0.621, Validation Accuracy: 0.624, Loss: 0.607\n",
"Epoch 1 Batch 158/538 - Train Accuracy: 0.620, Validation Accuracy: 0.629, Loss: 0.657\n",
"Epoch 1 Batch 159/538 - Train Accuracy: 0.613, Validation Accuracy: 0.632, Loss: 0.654\n",
"Epoch 1 Batch 160/538 - Train Accuracy: 0.617, Validation Accuracy: 0.630, Loss: 0.617\n",
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"Epoch 1 Batch 162/538 - Train Accuracy: 0.652, Validation Accuracy: 0.630, Loss: 0.616\n",
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"Epoch 1 Batch 164/538 - Train Accuracy: 0.609, Validation Accuracy: 0.633, Loss: 0.660\n",
"Epoch 1 Batch 165/538 - Train Accuracy: 0.626, Validation Accuracy: 0.634, Loss: 0.590\n",
"Epoch 1 Batch 166/538 - Train Accuracy: 0.640, Validation Accuracy: 0.639, Loss: 0.633\n",
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"Epoch 1 Batch 170/538 - Train Accuracy: 0.638, Validation Accuracy: 0.633, Loss: 0.623\n",
"Epoch 1 Batch 171/538 - Train Accuracy: 0.606, Validation Accuracy: 0.632, Loss: 0.649\n",
"Epoch 1 Batch 172/538 - Train Accuracy: 0.610, Validation Accuracy: 0.630, Loss: 0.624\n",
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"Epoch 1 Batch 174/538 - Train Accuracy: 0.588, Validation Accuracy: 0.626, Loss: 0.648\n",
"Epoch 1 Batch 175/538 - Train Accuracy: 0.603, Validation Accuracy: 0.634, Loss: 0.640\n",
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"Epoch 1 Batch 177/538 - Train Accuracy: 0.610, Validation Accuracy: 0.629, Loss: 0.624\n",
"Epoch 1 Batch 178/538 - Train Accuracy: 0.632, Validation Accuracy: 0.626, Loss: 0.605\n",
"Epoch 1 Batch 179/538 - Train Accuracy: 0.613, Validation Accuracy: 0.627, Loss: 0.649\n",
"Epoch 1 Batch 180/538 - Train Accuracy: 0.653, Validation Accuracy: 0.626, Loss: 0.619\n",
"Epoch 1 Batch 181/538 - Train Accuracy: 0.588, Validation Accuracy: 0.625, Loss: 0.660\n",
"Epoch 1 Batch 182/538 - Train Accuracy: 0.584, Validation Accuracy: 0.626, Loss: 0.645\n",
"Epoch 1 Batch 183/538 - Train Accuracy: 0.643, Validation Accuracy: 0.631, Loss: 0.586\n",
"Epoch 1 Batch 184/538 - Train Accuracy: 0.621, Validation Accuracy: 0.628, Loss: 0.602\n",
"Epoch 1 Batch 185/538 - Train Accuracy: 0.617, Validation Accuracy: 0.632, Loss: 0.623\n",
"Epoch 1 Batch 186/538 - Train Accuracy: 0.622, Validation Accuracy: 0.632, Loss: 0.614\n",
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"Epoch 1 Batch 188/538 - Train Accuracy: 0.604, Validation Accuracy: 0.629, Loss: 0.632\n",
"Epoch 1 Batch 189/538 - Train Accuracy: 0.613, Validation Accuracy: 0.634, Loss: 0.636\n",
"Epoch 1 Batch 190/538 - Train Accuracy: 0.622, Validation Accuracy: 0.638, Loss: 0.636\n",
"Epoch 1 Batch 191/538 - Train Accuracy: 0.641, Validation Accuracy: 0.637, Loss: 0.601\n",
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"Epoch 1 Batch 194/538 - Train Accuracy: 0.601, Validation Accuracy: 0.632, Loss: 0.647\n",
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"Epoch 1 Batch 196/538 - Train Accuracy: 0.613, Validation Accuracy: 0.636, Loss: 0.610\n",
"Epoch 1 Batch 197/538 - Train Accuracy: 0.616, Validation Accuracy: 0.635, Loss: 0.605\n",
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"Epoch 1 Batch 200/538 - Train Accuracy: 0.626, Validation Accuracy: 0.627, Loss: 0.616\n",
"Epoch 1 Batch 201/538 - Train Accuracy: 0.626, Validation Accuracy: 0.630, Loss: 0.614\n",
"Epoch 1 Batch 202/538 - Train Accuracy: 0.624, Validation Accuracy: 0.625, Loss: 0.637\n",
"Epoch 1 Batch 203/538 - Train Accuracy: 0.597, Validation Accuracy: 0.632, Loss: 0.646\n",
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"Epoch 1 Batch 205/538 - Train Accuracy: 0.628, Validation Accuracy: 0.628, Loss: 0.600\n",
"Epoch 1 Batch 206/538 - Train Accuracy: 0.603, Validation Accuracy: 0.636, Loss: 0.645\n",
"Epoch 1 Batch 207/538 - Train Accuracy: 0.632, Validation Accuracy: 0.640, Loss: 0.595\n",
"Epoch 1 Batch 208/538 - Train Accuracy: 0.610, Validation Accuracy: 0.637, Loss: 0.620\n",
"Epoch 1 Batch 209/538 - Train Accuracy: 0.599, Validation Accuracy: 0.637, Loss: 0.611\n",
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"Epoch 1 Batch 211/538 - Train Accuracy: 0.596, Validation Accuracy: 0.636, Loss: 0.648\n",
"Epoch 1 Batch 212/538 - Train Accuracy: 0.629, Validation Accuracy: 0.633, Loss: 0.607\n",
"Epoch 1 Batch 213/538 - Train Accuracy: 0.621, Validation Accuracy: 0.630, Loss: 0.604\n",
"Epoch 1 Batch 214/538 - Train Accuracy: 0.607, Validation Accuracy: 0.629, Loss: 0.609\n",
"Epoch 1 Batch 215/538 - Train Accuracy: 0.607, Validation Accuracy: 0.639, Loss: 0.626\n",
"Epoch 1 Batch 216/538 - Train Accuracy: 0.592, Validation Accuracy: 0.636, Loss: 0.636\n",
"Epoch 1 Batch 217/538 - Train Accuracy: 0.643, Validation Accuracy: 0.633, Loss: 0.598\n",
"Epoch 1 Batch 218/538 - Train Accuracy: 0.591, Validation Accuracy: 0.631, Loss: 0.625\n",
"Epoch 1 Batch 219/538 - Train Accuracy: 0.600, Validation Accuracy: 0.637, Loss: 0.648\n",
"Epoch 1 Batch 220/538 - Train Accuracy: 0.595, Validation Accuracy: 0.636, Loss: 0.589\n",
"Epoch 1 Batch 221/538 - Train Accuracy: 0.635, Validation Accuracy: 0.638, Loss: 0.596\n",
"Epoch 1 Batch 222/538 - Train Accuracy: 0.616, Validation Accuracy: 0.642, Loss: 0.578\n",
"Epoch 1 Batch 223/538 - Train Accuracy: 0.613, Validation Accuracy: 0.637, Loss: 0.624\n",
"Epoch 1 Batch 224/538 - Train Accuracy: 0.587, Validation Accuracy: 0.636, Loss: 0.633\n",
"Epoch 1 Batch 225/538 - Train Accuracy: 0.634, Validation Accuracy: 0.638, Loss: 0.604\n",
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"Epoch 1 Batch 228/538 - Train Accuracy: 0.608, Validation Accuracy: 0.638, Loss: 0.596\n",
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"Epoch 1 Batch 232/538 - Train Accuracy: 0.628, Validation Accuracy: 0.640, Loss: 0.625\n",
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"Epoch 1 Batch 234/538 - Train Accuracy: 0.579, Validation Accuracy: 0.640, Loss: 0.623\n",
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"Epoch 1 Batch 236/538 - Train Accuracy: 0.598, Validation Accuracy: 0.637, Loss: 0.630\n",
"Epoch 1 Batch 237/538 - Train Accuracy: 0.620, Validation Accuracy: 0.635, Loss: 0.597\n",
"Epoch 1 Batch 238/538 - Train Accuracy: 0.654, Validation Accuracy: 0.639, Loss: 0.587\n",
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"Epoch 1 Batch 254/538 - Train Accuracy: 0.614, Validation Accuracy: 0.632, Loss: 0.608\n",
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"Epoch 5 Batch 132/538 - Train Accuracy: 0.881, Validation Accuracy: 0.913, Loss: 0.117\n",
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"Epoch 5 Batch 134/538 - Train Accuracy: 0.887, Validation Accuracy: 0.906, Loss: 0.127\n",
"Epoch 5 Batch 135/538 - Train Accuracy: 0.914, Validation Accuracy: 0.907, Loss: 0.127\n",
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"Epoch 5 Batch 140/538 - Train Accuracy: 0.899, Validation Accuracy: 0.894, Loss: 0.132\n",
"Epoch 5 Batch 141/538 - Train Accuracy: 0.914, Validation Accuracy: 0.892, Loss: 0.122\n",
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"Epoch 5 Batch 143/538 - Train Accuracy: 0.900, Validation Accuracy: 0.897, Loss: 0.121\n",
"Epoch 5 Batch 144/538 - Train Accuracy: 0.914, Validation Accuracy: 0.901, Loss: 0.125\n",
"Epoch 5 Batch 145/538 - Train Accuracy: 0.894, Validation Accuracy: 0.900, Loss: 0.127\n",
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"Epoch 5 Batch 147/538 - Train Accuracy: 0.924, Validation Accuracy: 0.898, Loss: 0.111\n",
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"Epoch 5 Batch 149/538 - Train Accuracy: 0.929, Validation Accuracy: 0.905, Loss: 0.111\n",
"Epoch 5 Batch 150/538 - Train Accuracy: 0.914, Validation Accuracy: 0.903, Loss: 0.103\n",
"Epoch 5 Batch 151/538 - Train Accuracy: 0.922, Validation Accuracy: 0.904, Loss: 0.111\n",
"Epoch 5 Batch 152/538 - Train Accuracy: 0.917, Validation Accuracy: 0.909, Loss: 0.117\n",
"Epoch 5 Batch 153/538 - Train Accuracy: 0.902, Validation Accuracy: 0.919, Loss: 0.109\n",
"Epoch 5 Batch 154/538 - Train Accuracy: 0.908, Validation Accuracy: 0.920, Loss: 0.099\n",
"Epoch 5 Batch 155/538 - Train Accuracy: 0.900, Validation Accuracy: 0.912, Loss: 0.120\n",
"Epoch 5 Batch 156/538 - Train Accuracy: 0.921, Validation Accuracy: 0.915, Loss: 0.100\n",
"Epoch 5 Batch 157/538 - Train Accuracy: 0.924, Validation Accuracy: 0.911, Loss: 0.102\n",
"Epoch 5 Batch 158/538 - Train Accuracy: 0.914, Validation Accuracy: 0.903, Loss: 0.120\n",
"Epoch 5 Batch 159/538 - Train Accuracy: 0.929, Validation Accuracy: 0.915, Loss: 0.124\n",
"Epoch 5 Batch 160/538 - Train Accuracy: 0.901, Validation Accuracy: 0.909, Loss: 0.101\n",
"Epoch 5 Batch 161/538 - Train Accuracy: 0.924, Validation Accuracy: 0.910, Loss: 0.110\n",
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"Epoch 5 Batch 163/538 - Train Accuracy: 0.896, Validation Accuracy: 0.906, Loss: 0.124\n",
"Epoch 5 Batch 164/538 - Train Accuracy: 0.899, Validation Accuracy: 0.910, Loss: 0.120\n",
"Epoch 5 Batch 165/538 - Train Accuracy: 0.908, Validation Accuracy: 0.906, Loss: 0.100\n",
"Epoch 5 Batch 166/538 - Train Accuracy: 0.936, Validation Accuracy: 0.903, Loss: 0.097\n",
"Epoch 5 Batch 167/538 - Train Accuracy: 0.910, Validation Accuracy: 0.895, Loss: 0.124\n",
"Epoch 5 Batch 168/538 - Train Accuracy: 0.882, Validation Accuracy: 0.893, Loss: 0.130\n",
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"Epoch 5 Batch 170/538 - Train Accuracy: 0.910, Validation Accuracy: 0.895, Loss: 0.120\n",
"Epoch 5 Batch 171/538 - Train Accuracy: 0.916, Validation Accuracy: 0.898, Loss: 0.109\n",
"Epoch 5 Batch 172/538 - Train Accuracy: 0.906, Validation Accuracy: 0.904, Loss: 0.110\n",
"Epoch 5 Batch 173/538 - Train Accuracy: 0.925, Validation Accuracy: 0.904, Loss: 0.098\n",
"Epoch 5 Batch 174/538 - Train Accuracy: 0.913, Validation Accuracy: 0.900, Loss: 0.106\n",
"Epoch 5 Batch 175/538 - Train Accuracy: 0.913, Validation Accuracy: 0.898, Loss: 0.102\n",
"Epoch 5 Batch 176/538 - Train Accuracy: 0.899, Validation Accuracy: 0.909, Loss: 0.126\n",
"Epoch 5 Batch 177/538 - Train Accuracy: 0.921, Validation Accuracy: 0.907, Loss: 0.105\n",
"Epoch 5 Batch 178/538 - Train Accuracy: 0.879, Validation Accuracy: 0.902, Loss: 0.110\n",
"Epoch 5 Batch 179/538 - Train Accuracy: 0.921, Validation Accuracy: 0.900, Loss: 0.111\n",
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"Epoch 5 Batch 181/538 - Train Accuracy: 0.899, Validation Accuracy: 0.908, Loss: 0.123\n",
"Epoch 5 Batch 182/538 - Train Accuracy: 0.933, Validation Accuracy: 0.900, Loss: 0.096\n",
"Epoch 5 Batch 183/538 - Train Accuracy: 0.934, Validation Accuracy: 0.904, Loss: 0.101\n",
"Epoch 5 Batch 184/538 - Train Accuracy: 0.938, Validation Accuracy: 0.903, Loss: 0.107\n",
"Epoch 5 Batch 185/538 - Train Accuracy: 0.934, Validation Accuracy: 0.911, Loss: 0.100\n",
"Epoch 5 Batch 186/538 - Train Accuracy: 0.898, Validation Accuracy: 0.912, Loss: 0.103\n",
"Epoch 5 Batch 187/538 - Train Accuracy: 0.914, Validation Accuracy: 0.912, Loss: 0.110\n",
"Epoch 5 Batch 188/538 - Train Accuracy: 0.916, Validation Accuracy: 0.910, Loss: 0.098\n",
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"Epoch 5 Batch 191/538 - Train Accuracy: 0.915, Validation Accuracy: 0.898, Loss: 0.108\n",
"Epoch 5 Batch 192/538 - Train Accuracy: 0.922, Validation Accuracy: 0.905, Loss: 0.100\n",
"Epoch 5 Batch 193/538 - Train Accuracy: 0.905, Validation Accuracy: 0.903, Loss: 0.111\n",
"Epoch 5 Batch 194/538 - Train Accuracy: 0.892, Validation Accuracy: 0.901, Loss: 0.115\n",
"Epoch 5 Batch 195/538 - Train Accuracy: 0.938, Validation Accuracy: 0.908, Loss: 0.109\n",
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"Epoch 5 Batch 197/538 - Train Accuracy: 0.917, Validation Accuracy: 0.907, Loss: 0.104\n",
"Epoch 5 Batch 198/538 - Train Accuracy: 0.927, Validation Accuracy: 0.916, Loss: 0.099\n",
"Epoch 5 Batch 199/538 - Train Accuracy: 0.906, Validation Accuracy: 0.918, Loss: 0.109\n",
"Epoch 5 Batch 200/538 - Train Accuracy: 0.924, Validation Accuracy: 0.915, Loss: 0.094\n",
"Epoch 5 Batch 201/538 - Train Accuracy: 0.907, Validation Accuracy: 0.910, Loss: 0.110\n",
"Epoch 5 Batch 202/538 - Train Accuracy: 0.934, Validation Accuracy: 0.919, Loss: 0.102\n",
"Epoch 5 Batch 203/538 - Train Accuracy: 0.919, Validation Accuracy: 0.923, Loss: 0.120\n",
"Epoch 5 Batch 204/538 - Train Accuracy: 0.900, Validation Accuracy: 0.915, Loss: 0.122\n",
"Epoch 5 Batch 205/538 - Train Accuracy: 0.930, Validation Accuracy: 0.928, Loss: 0.091\n",
"Epoch 5 Batch 206/538 - Train Accuracy: 0.904, Validation Accuracy: 0.909, Loss: 0.111\n",
"Epoch 5 Batch 207/538 - Train Accuracy: 0.908, Validation Accuracy: 0.910, Loss: 0.106\n",
"Epoch 5 Batch 208/538 - Train Accuracy: 0.923, Validation Accuracy: 0.912, Loss: 0.120\n",
"Epoch 5 Batch 209/538 - Train Accuracy: 0.931, Validation Accuracy: 0.906, Loss: 0.094\n",
"Epoch 5 Batch 210/538 - Train Accuracy: 0.886, Validation Accuracy: 0.902, Loss: 0.107\n",
"Epoch 5 Batch 211/538 - Train Accuracy: 0.919, Validation Accuracy: 0.914, Loss: 0.113\n",
"Epoch 5 Batch 212/538 - Train Accuracy: 0.919, Validation Accuracy: 0.917, Loss: 0.100\n",
"Epoch 5 Batch 213/538 - Train Accuracy: 0.922, Validation Accuracy: 0.902, Loss: 0.103\n",
"Epoch 5 Batch 214/538 - Train Accuracy: 0.913, Validation Accuracy: 0.899, Loss: 0.094\n",
"Epoch 5 Batch 215/538 - Train Accuracy: 0.920, Validation Accuracy: 0.912, Loss: 0.102\n",
"Epoch 5 Batch 216/538 - Train Accuracy: 0.936, Validation Accuracy: 0.920, Loss: 0.101\n",
"Epoch 5 Batch 217/538 - Train Accuracy: 0.934, Validation Accuracy: 0.918, Loss: 0.108\n",
"Epoch 5 Batch 218/538 - Train Accuracy: 0.886, Validation Accuracy: 0.914, Loss: 0.102\n",
"Epoch 5 Batch 219/538 - Train Accuracy: 0.895, Validation Accuracy: 0.904, Loss: 0.127\n",
"Epoch 5 Batch 220/538 - Train Accuracy: 0.903, Validation Accuracy: 0.908, Loss: 0.101\n",
"Epoch 5 Batch 221/538 - Train Accuracy: 0.929, Validation Accuracy: 0.904, Loss: 0.096\n",
"Epoch 5 Batch 222/538 - Train Accuracy: 0.883, Validation Accuracy: 0.881, Loss: 0.095\n",
"Epoch 5 Batch 223/538 - Train Accuracy: 0.901, Validation Accuracy: 0.882, Loss: 0.119\n",
"Epoch 5 Batch 224/538 - Train Accuracy: 0.910, Validation Accuracy: 0.892, Loss: 0.108\n",
"Epoch 5 Batch 225/538 - Train Accuracy: 0.916, Validation Accuracy: 0.898, Loss: 0.102\n",
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"Epoch 5 Batch 227/538 - Train Accuracy: 0.917, Validation Accuracy: 0.904, Loss: 0.101\n",
"Epoch 5 Batch 228/538 - Train Accuracy: 0.896, Validation Accuracy: 0.899, Loss: 0.098\n",
"Epoch 5 Batch 229/538 - Train Accuracy: 0.917, Validation Accuracy: 0.908, Loss: 0.110\n",
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"Epoch 5 Batch 234/538 - Train Accuracy: 0.922, Validation Accuracy: 0.914, Loss: 0.111\n",
"Epoch 5 Batch 235/538 - Train Accuracy: 0.924, Validation Accuracy: 0.914, Loss: 0.085\n",
"Epoch 5 Batch 236/538 - Train Accuracy: 0.911, Validation Accuracy: 0.910, Loss: 0.113\n",
"Epoch 5 Batch 237/538 - Train Accuracy: 0.917, Validation Accuracy: 0.906, Loss: 0.091\n",
"Epoch 5 Batch 238/538 - Train Accuracy: 0.932, Validation Accuracy: 0.906, Loss: 0.104\n",
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"Epoch 5 Batch 241/538 - Train Accuracy: 0.905, Validation Accuracy: 0.904, Loss: 0.107\n",
"Epoch 5 Batch 242/538 - Train Accuracy: 0.931, Validation Accuracy: 0.905, Loss: 0.097\n",
"Epoch 5 Batch 243/538 - Train Accuracy: 0.919, Validation Accuracy: 0.905, Loss: 0.103\n",
"Epoch 5 Batch 244/538 - Train Accuracy: 0.899, Validation Accuracy: 0.913, Loss: 0.093\n",
"Epoch 5 Batch 245/538 - Train Accuracy: 0.918, Validation Accuracy: 0.912, Loss: 0.103\n",
"Epoch 5 Batch 246/538 - Train Accuracy: 0.931, Validation Accuracy: 0.916, Loss: 0.086\n",
"Epoch 5 Batch 247/538 - Train Accuracy: 0.910, Validation Accuracy: 0.920, Loss: 0.100\n",
"Epoch 5 Batch 248/538 - Train Accuracy: 0.915, Validation Accuracy: 0.922, Loss: 0.094\n",
"Epoch 5 Batch 249/538 - Train Accuracy: 0.913, Validation Accuracy: 0.916, Loss: 0.087\n",
"Epoch 5 Batch 250/538 - Train Accuracy: 0.916, Validation Accuracy: 0.924, Loss: 0.094\n",
"Epoch 5 Batch 251/538 - Train Accuracy: 0.919, Validation Accuracy: 0.920, Loss: 0.104\n",
"Epoch 5 Batch 252/538 - Train Accuracy: 0.928, Validation Accuracy: 0.919, Loss: 0.088\n",
"Epoch 5 Batch 253/538 - Train Accuracy: 0.905, Validation Accuracy: 0.920, Loss: 0.099\n",
"Epoch 5 Batch 254/538 - Train Accuracy: 0.901, Validation Accuracy: 0.925, Loss: 0.113\n",
"Epoch 5 Batch 255/538 - Train Accuracy: 0.945, Validation Accuracy: 0.927, Loss: 0.097\n",
"Epoch 5 Batch 256/538 - Train Accuracy: 0.910, Validation Accuracy: 0.929, Loss: 0.102\n",
"Epoch 5 Batch 257/538 - Train Accuracy: 0.919, Validation Accuracy: 0.920, Loss: 0.108\n",
"Epoch 5 Batch 258/538 - Train Accuracy: 0.913, Validation Accuracy: 0.920, Loss: 0.103\n",
"Epoch 5 Batch 259/538 - Train Accuracy: 0.927, Validation Accuracy: 0.919, Loss: 0.090\n",
"Epoch 5 Batch 260/538 - Train Accuracy: 0.900, Validation Accuracy: 0.918, Loss: 0.098\n",
"Epoch 5 Batch 261/538 - Train Accuracy: 0.908, Validation Accuracy: 0.913, Loss: 0.108\n",
"Epoch 5 Batch 262/538 - Train Accuracy: 0.933, Validation Accuracy: 0.913, Loss: 0.094\n",
"Epoch 5 Batch 263/538 - Train Accuracy: 0.893, Validation Accuracy: 0.919, Loss: 0.092\n",
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"Epoch 5 Batch 265/538 - Train Accuracy: 0.905, Validation Accuracy: 0.913, Loss: 0.105\n",
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"Epoch 5 Batch 267/538 - Train Accuracy: 0.917, Validation Accuracy: 0.920, Loss: 0.098\n",
"Epoch 5 Batch 268/538 - Train Accuracy: 0.926, Validation Accuracy: 0.919, Loss: 0.088\n",
"Epoch 5 Batch 269/538 - Train Accuracy: 0.930, Validation Accuracy: 0.912, Loss: 0.103\n",
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"Epoch 5 Batch 271/538 - Train Accuracy: 0.936, Validation Accuracy: 0.913, Loss: 0.094\n",
"Epoch 5 Batch 272/538 - Train Accuracy: 0.918, Validation Accuracy: 0.922, Loss: 0.108\n",
"Epoch 5 Batch 273/538 - Train Accuracy: 0.907, Validation Accuracy: 0.917, Loss: 0.099\n",
"Epoch 5 Batch 274/538 - Train Accuracy: 0.883, Validation Accuracy: 0.913, Loss: 0.109\n",
"Epoch 5 Batch 275/538 - Train Accuracy: 0.905, Validation Accuracy: 0.918, Loss: 0.115\n",
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"Epoch 5 Batch 278/538 - Train Accuracy: 0.914, Validation Accuracy: 0.909, Loss: 0.092\n",
"Epoch 5 Batch 279/538 - Train Accuracy: 0.898, Validation Accuracy: 0.893, Loss: 0.097\n",
"Epoch 5 Batch 280/538 - Train Accuracy: 0.927, Validation Accuracy: 0.908, Loss: 0.102\n",
"Epoch 5 Batch 281/538 - Train Accuracy: 0.911, Validation Accuracy: 0.921, Loss: 0.095\n",
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"Epoch 5 Batch 285/538 - Train Accuracy: 0.917, Validation Accuracy: 0.913, Loss: 0.084\n",
"Epoch 5 Batch 286/538 - Train Accuracy: 0.922, Validation Accuracy: 0.918, Loss: 0.100\n",
"Epoch 5 Batch 287/538 - Train Accuracy: 0.935, Validation Accuracy: 0.920, Loss: 0.089\n",
"Epoch 5 Batch 288/538 - Train Accuracy: 0.915, Validation Accuracy: 0.911, Loss: 0.096\n",
"Epoch 5 Batch 289/538 - Train Accuracy: 0.914, Validation Accuracy: 0.904, Loss: 0.090\n",
"Epoch 5 Batch 290/538 - Train Accuracy: 0.955, Validation Accuracy: 0.914, Loss: 0.083\n",
"Epoch 5 Batch 291/538 - Train Accuracy: 0.916, Validation Accuracy: 0.917, Loss: 0.098\n",
"Epoch 5 Batch 292/538 - Train Accuracy: 0.928, Validation Accuracy: 0.918, Loss: 0.081\n",
"Epoch 5 Batch 293/538 - Train Accuracy: 0.923, Validation Accuracy: 0.911, Loss: 0.089\n",
"Epoch 5 Batch 294/538 - Train Accuracy: 0.917, Validation Accuracy: 0.910, Loss: 0.101\n",
"Epoch 5 Batch 295/538 - Train Accuracy: 0.933, Validation Accuracy: 0.922, Loss: 0.096\n",
"Epoch 5 Batch 296/538 - Train Accuracy: 0.919, Validation Accuracy: 0.921, Loss: 0.107\n",
"Epoch 5 Batch 297/538 - Train Accuracy: 0.929, Validation Accuracy: 0.902, Loss: 0.106\n",
"Epoch 5 Batch 298/538 - Train Accuracy: 0.906, Validation Accuracy: 0.889, Loss: 0.094\n",
"Epoch 5 Batch 299/538 - Train Accuracy: 0.906, Validation Accuracy: 0.901, Loss: 0.113\n",
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"Epoch 5 Batch 301/538 - Train Accuracy: 0.897, Validation Accuracy: 0.910, Loss: 0.097\n",
"Epoch 5 Batch 302/538 - Train Accuracy: 0.938, Validation Accuracy: 0.909, Loss: 0.099\n",
"Epoch 5 Batch 303/538 - Train Accuracy: 0.931, Validation Accuracy: 0.910, Loss: 0.100\n",
"Epoch 5 Batch 304/538 - Train Accuracy: 0.930, Validation Accuracy: 0.905, Loss: 0.100\n",
"Epoch 5 Batch 305/538 - Train Accuracy: 0.929, Validation Accuracy: 0.906, Loss: 0.087\n",
"Epoch 5 Batch 306/538 - Train Accuracy: 0.903, Validation Accuracy: 0.914, Loss: 0.101\n",
"Epoch 5 Batch 307/538 - Train Accuracy: 0.921, Validation Accuracy: 0.920, Loss: 0.091\n",
"Epoch 5 Batch 308/538 - Train Accuracy: 0.916, Validation Accuracy: 0.906, Loss: 0.091\n",
"Epoch 5 Batch 309/538 - Train Accuracy: 0.929, Validation Accuracy: 0.918, Loss: 0.091\n",
"Epoch 5 Batch 310/538 - Train Accuracy: 0.945, Validation Accuracy: 0.919, Loss: 0.096\n",
"Epoch 5 Batch 311/538 - Train Accuracy: 0.917, Validation Accuracy: 0.919, Loss: 0.103\n",
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"Epoch 5 Batch 314/538 - Train Accuracy: 0.917, Validation Accuracy: 0.910, Loss: 0.089\n",
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"Epoch 5 Batch 317/538 - Train Accuracy: 0.933, Validation Accuracy: 0.916, Loss: 0.099\n",
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"Epoch 5 Batch 320/538 - Train Accuracy: 0.923, Validation Accuracy: 0.909, Loss: 0.086\n",
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"Epoch 5 Batch 326/538 - Train Accuracy: 0.932, Validation Accuracy: 0.921, Loss: 0.084\n",
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"Epoch 5 Batch 328/538 - Train Accuracy: 0.923, Validation Accuracy: 0.914, Loss: 0.081\n",
"Epoch 5 Batch 329/538 - Train Accuracy: 0.934, Validation Accuracy: 0.916, Loss: 0.093\n",
"Epoch 5 Batch 330/538 - Train Accuracy: 0.921, Validation Accuracy: 0.912, Loss: 0.082\n",
"Epoch 5 Batch 331/538 - Train Accuracy: 0.927, Validation Accuracy: 0.912, Loss: 0.089\n",
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"Epoch 5 Batch 336/538 - Train Accuracy: 0.933, Validation Accuracy: 0.917, Loss: 0.088\n",
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"Epoch 5 Batch 347/538 - Train Accuracy: 0.931, Validation Accuracy: 0.915, Loss: 0.093\n",
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"Epoch 5 Batch 350/538 - Train Accuracy: 0.929, Validation Accuracy: 0.905, Loss: 0.094\n",
"Epoch 5 Batch 351/538 - Train Accuracy: 0.912, Validation Accuracy: 0.903, Loss: 0.104\n",
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"Epoch 5 Batch 353/538 - Train Accuracy: 0.908, Validation Accuracy: 0.909, Loss: 0.105\n",
"Epoch 5 Batch 354/538 - Train Accuracy: 0.890, Validation Accuracy: 0.914, Loss: 0.104\n",
"Epoch 5 Batch 355/538 - Train Accuracy: 0.923, Validation Accuracy: 0.916, Loss: 0.099\n",
"Epoch 5 Batch 356/538 - Train Accuracy: 0.904, Validation Accuracy: 0.917, Loss: 0.087\n",
"Epoch 5 Batch 357/538 - Train Accuracy: 0.922, Validation Accuracy: 0.914, Loss: 0.089\n",
"Epoch 5 Batch 358/538 - Train Accuracy: 0.929, Validation Accuracy: 0.909, Loss: 0.080\n",
"Epoch 5 Batch 359/538 - Train Accuracy: 0.912, Validation Accuracy: 0.914, Loss: 0.097\n",
"Epoch 5 Batch 360/538 - Train Accuracy: 0.915, Validation Accuracy: 0.912, Loss: 0.086\n",
"Epoch 5 Batch 361/538 - Train Accuracy: 0.924, Validation Accuracy: 0.921, Loss: 0.093\n",
"Epoch 5 Batch 362/538 - Train Accuracy: 0.920, Validation Accuracy: 0.928, Loss: 0.084\n",
"Epoch 5 Batch 363/538 - Train Accuracy: 0.906, Validation Accuracy: 0.926, Loss: 0.092\n",
"Epoch 5 Batch 364/538 - Train Accuracy: 0.913, Validation Accuracy: 0.923, Loss: 0.107\n",
"Epoch 5 Batch 365/538 - Train Accuracy: 0.905, Validation Accuracy: 0.914, Loss: 0.092\n",
"Epoch 5 Batch 366/538 - Train Accuracy: 0.919, Validation Accuracy: 0.918, Loss: 0.102\n",
"Epoch 5 Batch 367/538 - Train Accuracy: 0.922, Validation Accuracy: 0.918, Loss: 0.075\n",
"Epoch 5 Batch 368/538 - Train Accuracy: 0.933, Validation Accuracy: 0.918, Loss: 0.085\n",
"Epoch 5 Batch 369/538 - Train Accuracy: 0.924, Validation Accuracy: 0.912, Loss: 0.081\n",
"Epoch 5 Batch 370/538 - Train Accuracy: 0.912, Validation Accuracy: 0.912, Loss: 0.089\n",
"Epoch 5 Batch 371/538 - Train Accuracy: 0.918, Validation Accuracy: 0.925, Loss: 0.096\n",
"Epoch 5 Batch 372/538 - Train Accuracy: 0.945, Validation Accuracy: 0.919, Loss: 0.090\n",
"Epoch 5 Batch 373/538 - Train Accuracy: 0.915, Validation Accuracy: 0.919, Loss: 0.074\n",
"Epoch 5 Batch 374/538 - Train Accuracy: 0.910, Validation Accuracy: 0.911, Loss: 0.092\n",
"Epoch 5 Batch 375/538 - Train Accuracy: 0.927, Validation Accuracy: 0.908, Loss: 0.086\n",
"Epoch 5 Batch 376/538 - Train Accuracy: 0.919, Validation Accuracy: 0.903, Loss: 0.086\n",
"Epoch 5 Batch 377/538 - Train Accuracy: 0.934, Validation Accuracy: 0.913, Loss: 0.093\n",
"Epoch 5 Batch 378/538 - Train Accuracy: 0.922, Validation Accuracy: 0.913, Loss: 0.079\n",
"Epoch 5 Batch 379/538 - Train Accuracy: 0.923, Validation Accuracy: 0.909, Loss: 0.089\n",
"Epoch 5 Batch 380/538 - Train Accuracy: 0.915, Validation Accuracy: 0.911, Loss: 0.081\n",
"Epoch 5 Batch 381/538 - Train Accuracy: 0.932, Validation Accuracy: 0.913, Loss: 0.084\n",
"Epoch 5 Batch 382/538 - Train Accuracy: 0.900, Validation Accuracy: 0.917, Loss: 0.087\n",
"Epoch 5 Batch 383/538 - Train Accuracy: 0.912, Validation Accuracy: 0.921, Loss: 0.081\n",
"Epoch 5 Batch 384/538 - Train Accuracy: 0.914, Validation Accuracy: 0.914, Loss: 0.083\n",
"Epoch 5 Batch 385/538 - Train Accuracy: 0.930, Validation Accuracy: 0.922, Loss: 0.082\n",
"Epoch 5 Batch 386/538 - Train Accuracy: 0.921, Validation Accuracy: 0.921, Loss: 0.100\n",
"Epoch 5 Batch 387/538 - Train Accuracy: 0.921, Validation Accuracy: 0.921, Loss: 0.088\n",
"Epoch 5 Batch 388/538 - Train Accuracy: 0.921, Validation Accuracy: 0.919, Loss: 0.085\n",
"Epoch 5 Batch 389/538 - Train Accuracy: 0.888, Validation Accuracy: 0.920, Loss: 0.106\n",
"Epoch 5 Batch 390/538 - Train Accuracy: 0.918, Validation Accuracy: 0.915, Loss: 0.082\n",
"Epoch 5 Batch 391/538 - Train Accuracy: 0.924, Validation Accuracy: 0.920, Loss: 0.082\n",
"Epoch 5 Batch 392/538 - Train Accuracy: 0.906, Validation Accuracy: 0.921, Loss: 0.087\n",
"Epoch 5 Batch 393/538 - Train Accuracy: 0.936, Validation Accuracy: 0.917, Loss: 0.082\n",
"Epoch 5 Batch 394/538 - Train Accuracy: 0.897, Validation Accuracy: 0.922, Loss: 0.094\n",
"Epoch 5 Batch 395/538 - Train Accuracy: 0.916, Validation Accuracy: 0.920, Loss: 0.096\n",
"Epoch 5 Batch 396/538 - Train Accuracy: 0.915, Validation Accuracy: 0.920, Loss: 0.081\n",
"Epoch 5 Batch 397/538 - Train Accuracy: 0.919, Validation Accuracy: 0.926, Loss: 0.095\n",
"Epoch 5 Batch 398/538 - Train Accuracy: 0.923, Validation Accuracy: 0.937, Loss: 0.082\n",
"Epoch 5 Batch 399/538 - Train Accuracy: 0.909, Validation Accuracy: 0.931, Loss: 0.090\n",
"Epoch 5 Batch 400/538 - Train Accuracy: 0.925, Validation Accuracy: 0.920, Loss: 0.094\n",
"Epoch 5 Batch 401/538 - Train Accuracy: 0.936, Validation Accuracy: 0.908, Loss: 0.090\n",
"Epoch 5 Batch 402/538 - Train Accuracy: 0.927, Validation Accuracy: 0.918, Loss: 0.085\n",
"Epoch 5 Batch 403/538 - Train Accuracy: 0.916, Validation Accuracy: 0.921, Loss: 0.096\n",
"Epoch 5 Batch 404/538 - Train Accuracy: 0.925, Validation Accuracy: 0.920, Loss: 0.093\n",
"Epoch 5 Batch 405/538 - Train Accuracy: 0.930, Validation Accuracy: 0.917, Loss: 0.081\n",
"Epoch 5 Batch 406/538 - Train Accuracy: 0.928, Validation Accuracy: 0.902, Loss: 0.093\n",
"Epoch 5 Batch 407/538 - Train Accuracy: 0.922, Validation Accuracy: 0.909, Loss: 0.086\n",
"Epoch 5 Batch 408/538 - Train Accuracy: 0.921, Validation Accuracy: 0.908, Loss: 0.094\n",
"Epoch 5 Batch 409/538 - Train Accuracy: 0.911, Validation Accuracy: 0.916, Loss: 0.086\n",
"Epoch 5 Batch 410/538 - Train Accuracy: 0.934, Validation Accuracy: 0.910, Loss: 0.087\n",
"Epoch 5 Batch 411/538 - Train Accuracy: 0.948, Validation Accuracy: 0.914, Loss: 0.086\n",
"Epoch 5 Batch 412/538 - Train Accuracy: 0.911, Validation Accuracy: 0.915, Loss: 0.084\n",
"Epoch 5 Batch 413/538 - Train Accuracy: 0.933, Validation Accuracy: 0.916, Loss: 0.087\n",
"Epoch 5 Batch 414/538 - Train Accuracy: 0.884, Validation Accuracy: 0.913, Loss: 0.102\n",
"Epoch 5 Batch 415/538 - Train Accuracy: 0.910, Validation Accuracy: 0.912, Loss: 0.086\n",
"Epoch 5 Batch 416/538 - Train Accuracy: 0.922, Validation Accuracy: 0.912, Loss: 0.093\n",
"Epoch 5 Batch 417/538 - Train Accuracy: 0.929, Validation Accuracy: 0.912, Loss: 0.089\n",
"Epoch 5 Batch 418/538 - Train Accuracy: 0.916, Validation Accuracy: 0.921, Loss: 0.097\n",
"Epoch 5 Batch 419/538 - Train Accuracy: 0.921, Validation Accuracy: 0.919, Loss: 0.079\n",
"Epoch 5 Batch 420/538 - Train Accuracy: 0.935, Validation Accuracy: 0.919, Loss: 0.088\n",
"Epoch 5 Batch 421/538 - Train Accuracy: 0.915, Validation Accuracy: 0.923, Loss: 0.084\n",
"Epoch 5 Batch 422/538 - Train Accuracy: 0.928, Validation Accuracy: 0.927, Loss: 0.091\n",
"Epoch 5 Batch 423/538 - Train Accuracy: 0.926, Validation Accuracy: 0.925, Loss: 0.092\n",
"Epoch 5 Batch 424/538 - Train Accuracy: 0.925, Validation Accuracy: 0.917, Loss: 0.094\n",
"Epoch 5 Batch 425/538 - Train Accuracy: 0.906, Validation Accuracy: 0.911, Loss: 0.100\n",
"Epoch 5 Batch 426/538 - Train Accuracy: 0.934, Validation Accuracy: 0.914, Loss: 0.087\n",
"Epoch 5 Batch 427/538 - Train Accuracy: 0.900, Validation Accuracy: 0.911, Loss: 0.092\n",
"Epoch 5 Batch 428/538 - Train Accuracy: 0.928, Validation Accuracy: 0.915, Loss: 0.084\n",
"Epoch 5 Batch 429/538 - Train Accuracy: 0.915, Validation Accuracy: 0.918, Loss: 0.092\n",
"Epoch 5 Batch 430/538 - Train Accuracy: 0.910, Validation Accuracy: 0.914, Loss: 0.084\n",
"Epoch 5 Batch 431/538 - Train Accuracy: 0.929, Validation Accuracy: 0.916, Loss: 0.082\n",
"Epoch 5 Batch 432/538 - Train Accuracy: 0.942, Validation Accuracy: 0.916, Loss: 0.087\n",
"Epoch 5 Batch 433/538 - Train Accuracy: 0.923, Validation Accuracy: 0.913, Loss: 0.115\n",
"Epoch 5 Batch 434/538 - Train Accuracy: 0.917, Validation Accuracy: 0.913, Loss: 0.094\n",
"Epoch 5 Batch 435/538 - Train Accuracy: 0.930, Validation Accuracy: 0.907, Loss: 0.082\n",
"Epoch 5 Batch 436/538 - Train Accuracy: 0.898, Validation Accuracy: 0.911, Loss: 0.097\n",
"Epoch 5 Batch 437/538 - Train Accuracy: 0.920, Validation Accuracy: 0.913, Loss: 0.092\n",
"Epoch 5 Batch 438/538 - Train Accuracy: 0.927, Validation Accuracy: 0.921, Loss: 0.076\n",
"Epoch 5 Batch 439/538 - Train Accuracy: 0.937, Validation Accuracy: 0.918, Loss: 0.085\n",
"Epoch 5 Batch 440/538 - Train Accuracy: 0.915, Validation Accuracy: 0.911, Loss: 0.099\n",
"Epoch 5 Batch 441/538 - Train Accuracy: 0.922, Validation Accuracy: 0.912, Loss: 0.098\n",
"Epoch 5 Batch 442/538 - Train Accuracy: 0.927, Validation Accuracy: 0.914, Loss: 0.070\n",
"Epoch 5 Batch 443/538 - Train Accuracy: 0.928, Validation Accuracy: 0.909, Loss: 0.087\n",
"Epoch 5 Batch 444/538 - Train Accuracy: 0.915, Validation Accuracy: 0.912, Loss: 0.080\n",
"Epoch 5 Batch 445/538 - Train Accuracy: 0.939, Validation Accuracy: 0.913, Loss: 0.077\n",
"Epoch 5 Batch 446/538 - Train Accuracy: 0.940, Validation Accuracy: 0.916, Loss: 0.090\n",
"Epoch 5 Batch 447/538 - Train Accuracy: 0.909, Validation Accuracy: 0.922, Loss: 0.091\n",
"Epoch 5 Batch 448/538 - Train Accuracy: 0.930, Validation Accuracy: 0.920, Loss: 0.075\n",
"Epoch 5 Batch 449/538 - Train Accuracy: 0.941, Validation Accuracy: 0.921, Loss: 0.091\n",
"Epoch 5 Batch 450/538 - Train Accuracy: 0.912, Validation Accuracy: 0.921, Loss: 0.106\n",
"Epoch 5 Batch 451/538 - Train Accuracy: 0.906, Validation Accuracy: 0.925, Loss: 0.083\n",
"Epoch 5 Batch 452/538 - Train Accuracy: 0.933, Validation Accuracy: 0.923, Loss: 0.078\n",
"Epoch 5 Batch 453/538 - Train Accuracy: 0.920, Validation Accuracy: 0.920, Loss: 0.092\n",
"Epoch 5 Batch 454/538 - Train Accuracy: 0.917, Validation Accuracy: 0.923, Loss: 0.092\n",
"Epoch 5 Batch 455/538 - Train Accuracy: 0.935, Validation Accuracy: 0.926, Loss: 0.081\n",
"Epoch 5 Batch 456/538 - Train Accuracy: 0.932, Validation Accuracy: 0.928, Loss: 0.102\n",
"Epoch 5 Batch 457/538 - Train Accuracy: 0.915, Validation Accuracy: 0.928, Loss: 0.087\n",
"Epoch 5 Batch 458/538 - Train Accuracy: 0.923, Validation Accuracy: 0.921, Loss: 0.078\n",
"Epoch 5 Batch 459/538 - Train Accuracy: 0.925, Validation Accuracy: 0.922, Loss: 0.075\n",
"Epoch 5 Batch 460/538 - Train Accuracy: 0.897, Validation Accuracy: 0.934, Loss: 0.088\n",
"Epoch 5 Batch 461/538 - Train Accuracy: 0.937, Validation Accuracy: 0.924, Loss: 0.088\n",
"Epoch 5 Batch 462/538 - Train Accuracy: 0.920, Validation Accuracy: 0.917, Loss: 0.076\n",
"Epoch 5 Batch 463/538 - Train Accuracy: 0.902, Validation Accuracy: 0.926, Loss: 0.084\n",
"Epoch 5 Batch 464/538 - Train Accuracy: 0.919, Validation Accuracy: 0.920, Loss: 0.080\n",
"Epoch 5 Batch 465/538 - Train Accuracy: 0.918, Validation Accuracy: 0.918, Loss: 0.085\n",
"Epoch 5 Batch 466/538 - Train Accuracy: 0.904, Validation Accuracy: 0.919, Loss: 0.079\n",
"Epoch 5 Batch 467/538 - Train Accuracy: 0.928, Validation Accuracy: 0.915, Loss: 0.084\n",
"Epoch 5 Batch 468/538 - Train Accuracy: 0.941, Validation Accuracy: 0.912, Loss: 0.092\n",
"Epoch 5 Batch 469/538 - Train Accuracy: 0.922, Validation Accuracy: 0.916, Loss: 0.085\n",
"Epoch 5 Batch 470/538 - Train Accuracy: 0.932, Validation Accuracy: 0.914, Loss: 0.082\n",
"Epoch 5 Batch 471/538 - Train Accuracy: 0.938, Validation Accuracy: 0.912, Loss: 0.077\n",
"Epoch 5 Batch 472/538 - Train Accuracy: 0.956, Validation Accuracy: 0.907, Loss: 0.072\n",
"Epoch 5 Batch 473/538 - Train Accuracy: 0.901, Validation Accuracy: 0.912, Loss: 0.083\n",
"Epoch 5 Batch 474/538 - Train Accuracy: 0.946, Validation Accuracy: 0.918, Loss: 0.073\n",
"Epoch 5 Batch 475/538 - Train Accuracy: 0.932, Validation Accuracy: 0.928, Loss: 0.079\n",
"Epoch 5 Batch 476/538 - Train Accuracy: 0.924, Validation Accuracy: 0.929, Loss: 0.081\n",
"Epoch 5 Batch 477/538 - Train Accuracy: 0.916, Validation Accuracy: 0.925, Loss: 0.099\n",
"Epoch 5 Batch 478/538 - Train Accuracy: 0.925, Validation Accuracy: 0.919, Loss: 0.069\n",
"Epoch 5 Batch 479/538 - Train Accuracy: 0.930, Validation Accuracy: 0.915, Loss: 0.074\n",
"Epoch 5 Batch 480/538 - Train Accuracy: 0.937, Validation Accuracy: 0.917, Loss: 0.082\n",
"Epoch 5 Batch 481/538 - Train Accuracy: 0.945, Validation Accuracy: 0.925, Loss: 0.079\n",
"Epoch 5 Batch 482/538 - Train Accuracy: 0.917, Validation Accuracy: 0.929, Loss: 0.073\n",
"Epoch 5 Batch 483/538 - Train Accuracy: 0.909, Validation Accuracy: 0.927, Loss: 0.096\n",
"Epoch 5 Batch 484/538 - Train Accuracy: 0.932, Validation Accuracy: 0.930, Loss: 0.092\n",
"Epoch 5 Batch 485/538 - Train Accuracy: 0.936, Validation Accuracy: 0.923, Loss: 0.084\n",
"Epoch 5 Batch 486/538 - Train Accuracy: 0.946, Validation Accuracy: 0.918, Loss: 0.073\n",
"Epoch 5 Batch 487/538 - Train Accuracy: 0.925, Validation Accuracy: 0.920, Loss: 0.075\n",
"Epoch 5 Batch 488/538 - Train Accuracy: 0.938, Validation Accuracy: 0.905, Loss: 0.071\n",
"Epoch 5 Batch 489/538 - Train Accuracy: 0.903, Validation Accuracy: 0.910, Loss: 0.082\n",
"Epoch 5 Batch 490/538 - Train Accuracy: 0.930, Validation Accuracy: 0.914, Loss: 0.081\n",
"Epoch 5 Batch 491/538 - Train Accuracy: 0.902, Validation Accuracy: 0.915, Loss: 0.090\n",
"Epoch 5 Batch 492/538 - Train Accuracy: 0.923, Validation Accuracy: 0.920, Loss: 0.080\n",
"Epoch 5 Batch 493/538 - Train Accuracy: 0.904, Validation Accuracy: 0.918, Loss: 0.084\n",
"Epoch 5 Batch 494/538 - Train Accuracy: 0.923, Validation Accuracy: 0.918, Loss: 0.093\n",
"Epoch 5 Batch 495/538 - Train Accuracy: 0.925, Validation Accuracy: 0.907, Loss: 0.088\n",
"Epoch 5 Batch 496/538 - Train Accuracy: 0.924, Validation Accuracy: 0.918, Loss: 0.072\n",
"Epoch 5 Batch 497/538 - Train Accuracy: 0.937, Validation Accuracy: 0.914, Loss: 0.080\n",
"Epoch 5 Batch 498/538 - Train Accuracy: 0.930, Validation Accuracy: 0.911, Loss: 0.080\n",
"Epoch 5 Batch 499/538 - Train Accuracy: 0.911, Validation Accuracy: 0.916, Loss: 0.084\n",
"Epoch 5 Batch 500/538 - Train Accuracy: 0.948, Validation Accuracy: 0.913, Loss: 0.064\n",
"Epoch 5 Batch 501/538 - Train Accuracy: 0.938, Validation Accuracy: 0.915, Loss: 0.086\n",
"Epoch 5 Batch 502/538 - Train Accuracy: 0.927, Validation Accuracy: 0.915, Loss: 0.072\n",
"Epoch 5 Batch 503/538 - Train Accuracy: 0.941, Validation Accuracy: 0.919, Loss: 0.081\n",
"Epoch 5 Batch 504/538 - Train Accuracy: 0.937, Validation Accuracy: 0.920, Loss: 0.067\n",
"Epoch 5 Batch 505/538 - Train Accuracy: 0.941, Validation Accuracy: 0.919, Loss: 0.069\n",
"Epoch 5 Batch 506/538 - Train Accuracy: 0.911, Validation Accuracy: 0.920, Loss: 0.076\n",
"Epoch 5 Batch 507/538 - Train Accuracy: 0.910, Validation Accuracy: 0.929, Loss: 0.094\n",
"Epoch 5 Batch 508/538 - Train Accuracy: 0.916, Validation Accuracy: 0.928, Loss: 0.077\n",
"Epoch 5 Batch 509/538 - Train Accuracy: 0.919, Validation Accuracy: 0.920, Loss: 0.085\n",
"Epoch 5 Batch 510/538 - Train Accuracy: 0.927, Validation Accuracy: 0.917, Loss: 0.083\n",
"Epoch 5 Batch 511/538 - Train Accuracy: 0.916, Validation Accuracy: 0.922, Loss: 0.083\n",
"Epoch 5 Batch 512/538 - Train Accuracy: 0.938, Validation Accuracy: 0.920, Loss: 0.085\n",
"Epoch 5 Batch 513/538 - Train Accuracy: 0.898, Validation Accuracy: 0.914, Loss: 0.084\n",
"Epoch 5 Batch 514/538 - Train Accuracy: 0.932, Validation Accuracy: 0.910, Loss: 0.079\n",
"Epoch 5 Batch 515/538 - Train Accuracy: 0.919, Validation Accuracy: 0.918, Loss: 0.088\n",
"Epoch 5 Batch 516/538 - Train Accuracy: 0.879, Validation Accuracy: 0.918, Loss: 0.083\n",
"Epoch 5 Batch 517/538 - Train Accuracy: 0.935, Validation Accuracy: 0.919, Loss: 0.079\n",
"Epoch 5 Batch 518/538 - Train Accuracy: 0.914, Validation Accuracy: 0.909, Loss: 0.087\n",
"Epoch 5 Batch 519/538 - Train Accuracy: 0.928, Validation Accuracy: 0.907, Loss: 0.085\n",
"Epoch 5 Batch 520/538 - Train Accuracy: 0.925, Validation Accuracy: 0.904, Loss: 0.084\n",
"Epoch 5 Batch 521/538 - Train Accuracy: 0.935, Validation Accuracy: 0.907, Loss: 0.101\n",
"Epoch 5 Batch 522/538 - Train Accuracy: 0.926, Validation Accuracy: 0.921, Loss: 0.076\n",
"Epoch 5 Batch 523/538 - Train Accuracy: 0.930, Validation Accuracy: 0.900, Loss: 0.083\n",
"Epoch 5 Batch 524/538 - Train Accuracy: 0.929, Validation Accuracy: 0.910, Loss: 0.082\n",
"Epoch 5 Batch 525/538 - Train Accuracy: 0.932, Validation Accuracy: 0.924, Loss: 0.084\n",
"Epoch 5 Batch 526/538 - Train Accuracy: 0.926, Validation Accuracy: 0.923, Loss: 0.080\n",
"Epoch 5 Batch 527/538 - Train Accuracy: 0.921, Validation Accuracy: 0.924, Loss: 0.082\n",
"Epoch 5 Batch 528/538 - Train Accuracy: 0.921, Validation Accuracy: 0.928, Loss: 0.089\n",
"Epoch 5 Batch 529/538 - Train Accuracy: 0.893, Validation Accuracy: 0.932, Loss: 0.084\n",
"Epoch 5 Batch 530/538 - Train Accuracy: 0.899, Validation Accuracy: 0.931, Loss: 0.090\n",
"Epoch 5 Batch 531/538 - Train Accuracy: 0.930, Validation Accuracy: 0.936, Loss: 0.087\n",
"Epoch 5 Batch 532/538 - Train Accuracy: 0.921, Validation Accuracy: 0.931, Loss: 0.080\n",
"Epoch 5 Batch 533/538 - Train Accuracy: 0.919, Validation Accuracy: 0.923, Loss: 0.084\n",
"Epoch 5 Batch 534/538 - Train Accuracy: 0.930, Validation Accuracy: 0.923, Loss: 0.078\n",
"Epoch 5 Batch 535/538 - Train Accuracy: 0.933, Validation Accuracy: 0.935, Loss: 0.075\n",
"Epoch 5 Batch 536/538 - Train Accuracy: 0.943, Validation Accuracy: 0.927, Loss: 0.097\n",
"Epoch 6 Batch 0/538 - Train Accuracy: 0.943, Validation Accuracy: 0.917, Loss: 0.078\n",
"Epoch 6 Batch 1/538 - Train Accuracy: 0.941, Validation Accuracy: 0.915, Loss: 0.076\n",
"Epoch 6 Batch 2/538 - Train Accuracy: 0.920, Validation Accuracy: 0.917, Loss: 0.089\n",
"Epoch 6 Batch 3/538 - Train Accuracy: 0.930, Validation Accuracy: 0.922, Loss: 0.076\n",
"Epoch 6 Batch 4/538 - Train Accuracy: 0.927, Validation Accuracy: 0.922, Loss: 0.083\n",
"Epoch 6 Batch 5/538 - Train Accuracy: 0.913, Validation Accuracy: 0.908, Loss: 0.089\n",
"Epoch 6 Batch 6/538 - Train Accuracy: 0.929, Validation Accuracy: 0.914, Loss: 0.082\n",
"Epoch 6 Batch 7/538 - Train Accuracy: 0.938, Validation Accuracy: 0.920, Loss: 0.084\n",
"Epoch 6 Batch 8/538 - Train Accuracy: 0.919, Validation Accuracy: 0.929, Loss: 0.085\n",
"Epoch 6 Batch 9/538 - Train Accuracy: 0.905, Validation Accuracy: 0.921, Loss: 0.079\n",
"Epoch 6 Batch 10/538 - Train Accuracy: 0.923, Validation Accuracy: 0.920, Loss: 0.088\n",
"Epoch 6 Batch 11/538 - Train Accuracy: 0.938, Validation Accuracy: 0.911, Loss: 0.076\n",
"Epoch 6 Batch 12/538 - Train Accuracy: 0.924, Validation Accuracy: 0.912, Loss: 0.082\n",
"Epoch 6 Batch 13/538 - Train Accuracy: 0.929, Validation Accuracy: 0.912, Loss: 0.075\n",
"Epoch 6 Batch 14/538 - Train Accuracy: 0.932, Validation Accuracy: 0.911, Loss: 0.081\n",
"Epoch 6 Batch 15/538 - Train Accuracy: 0.943, Validation Accuracy: 0.904, Loss: 0.074\n",
"Epoch 6 Batch 16/538 - Train Accuracy: 0.915, Validation Accuracy: 0.908, Loss: 0.078\n",
"Epoch 6 Batch 17/538 - Train Accuracy: 0.915, Validation Accuracy: 0.909, Loss: 0.085\n",
"Epoch 6 Batch 18/538 - Train Accuracy: 0.944, Validation Accuracy: 0.904, Loss: 0.087\n",
"Epoch 6 Batch 19/538 - Train Accuracy: 0.924, Validation Accuracy: 0.907, Loss: 0.083\n",
"Epoch 6 Batch 20/538 - Train Accuracy: 0.926, Validation Accuracy: 0.911, Loss: 0.088\n",
"Epoch 6 Batch 21/538 - Train Accuracy: 0.948, Validation Accuracy: 0.908, Loss: 0.069\n",
"Epoch 6 Batch 22/538 - Train Accuracy: 0.906, Validation Accuracy: 0.902, Loss: 0.080\n",
"Epoch 6 Batch 23/538 - Train Accuracy: 0.900, Validation Accuracy: 0.902, Loss: 0.093\n",
"Epoch 6 Batch 24/538 - Train Accuracy: 0.942, Validation Accuracy: 0.902, Loss: 0.079\n",
"Epoch 6 Batch 25/538 - Train Accuracy: 0.919, Validation Accuracy: 0.913, Loss: 0.079\n",
"Epoch 6 Batch 26/538 - Train Accuracy: 0.915, Validation Accuracy: 0.912, Loss: 0.085\n",
"Epoch 6 Batch 27/538 - Train Accuracy: 0.934, Validation Accuracy: 0.920, Loss: 0.067\n",
"Epoch 6 Batch 28/538 - Train Accuracy: 0.913, Validation Accuracy: 0.910, Loss: 0.070\n",
"Epoch 6 Batch 29/538 - Train Accuracy: 0.921, Validation Accuracy: 0.901, Loss: 0.070\n",
"Epoch 6 Batch 30/538 - Train Accuracy: 0.911, Validation Accuracy: 0.914, Loss: 0.087\n",
"Epoch 6 Batch 31/538 - Train Accuracy: 0.943, Validation Accuracy: 0.919, Loss: 0.064\n",
"Epoch 6 Batch 32/538 - Train Accuracy: 0.930, Validation Accuracy: 0.916, Loss: 0.067\n",
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"Epoch 6 Batch 122/538 - Train Accuracy: 0.930, Validation Accuracy: 0.930, Loss: 0.070\n",
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"Epoch 6 Batch 256/538 - Train Accuracy: 0.918, Validation Accuracy: 0.930, Loss: 0.078\n",
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"Epoch 6 Batch 287/538 - Train Accuracy: 0.939, Validation Accuracy: 0.923, Loss: 0.060\n",
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"Epoch 7 Batch 145/538 - Train Accuracy: 0.932, Validation Accuracy: 0.941, Loss: 0.072\n",
"Epoch 7 Batch 146/538 - Train Accuracy: 0.944, Validation Accuracy: 0.938, Loss: 0.063\n",
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"Epoch 7 Batch 151/538 - Train Accuracy: 0.945, Validation Accuracy: 0.936, Loss: 0.060\n",
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"Epoch 7 Batch 155/538 - Train Accuracy: 0.933, Validation Accuracy: 0.942, Loss: 0.056\n",
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"Epoch 7 Batch 160/538 - Train Accuracy: 0.932, Validation Accuracy: 0.930, Loss: 0.052\n",
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"Epoch 7 Batch 163/538 - Train Accuracy: 0.941, Validation Accuracy: 0.935, Loss: 0.072\n",
"Epoch 7 Batch 164/538 - Train Accuracy: 0.929, Validation Accuracy: 0.934, Loss: 0.064\n",
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"Epoch 7 Batch 172/538 - Train Accuracy: 0.929, Validation Accuracy: 0.927, Loss: 0.053\n",
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"Epoch 7 Batch 361/538 - Train Accuracy: 0.944, Validation Accuracy: 0.939, Loss: 0.057\n",
"Epoch 7 Batch 362/538 - Train Accuracy: 0.947, Validation Accuracy: 0.938, Loss: 0.049\n",
"Epoch 7 Batch 363/538 - Train Accuracy: 0.938, Validation Accuracy: 0.930, Loss: 0.054\n",
"Epoch 7 Batch 364/538 - Train Accuracy: 0.936, Validation Accuracy: 0.935, Loss: 0.067\n",
"Epoch 7 Batch 365/538 - Train Accuracy: 0.954, Validation Accuracy: 0.943, Loss: 0.057\n",
"Epoch 7 Batch 366/538 - Train Accuracy: 0.948, Validation Accuracy: 0.938, Loss: 0.053\n",
"Epoch 7 Batch 367/538 - Train Accuracy: 0.946, Validation Accuracy: 0.943, Loss: 0.050\n",
"Epoch 7 Batch 368/538 - Train Accuracy: 0.941, Validation Accuracy: 0.946, Loss: 0.049\n",
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"Epoch 7 Batch 371/538 - Train Accuracy: 0.949, Validation Accuracy: 0.946, Loss: 0.058\n",
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"Epoch 7 Batch 373/538 - Train Accuracy: 0.928, Validation Accuracy: 0.936, Loss: 0.049\n",
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"Epoch 7 Batch 375/538 - Train Accuracy: 0.943, Validation Accuracy: 0.932, Loss: 0.053\n",
"Epoch 7 Batch 376/538 - Train Accuracy: 0.938, Validation Accuracy: 0.934, Loss: 0.051\n",
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"Epoch 7 Batch 383/538 - Train Accuracy: 0.935, Validation Accuracy: 0.946, Loss: 0.054\n",
"Epoch 7 Batch 384/538 - Train Accuracy: 0.928, Validation Accuracy: 0.949, Loss: 0.049\n",
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"Epoch 7 Batch 391/538 - Train Accuracy: 0.945, Validation Accuracy: 0.947, Loss: 0.045\n",
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"Epoch 7 Batch 393/538 - Train Accuracy: 0.962, Validation Accuracy: 0.945, Loss: 0.048\n",
"Epoch 7 Batch 394/538 - Train Accuracy: 0.936, Validation Accuracy: 0.938, Loss: 0.060\n",
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"Epoch 7 Batch 399/538 - Train Accuracy: 0.933, Validation Accuracy: 0.934, Loss: 0.059\n",
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"Epoch 7 Batch 406/538 - Train Accuracy: 0.949, Validation Accuracy: 0.939, Loss: 0.050\n",
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"Epoch 7 Batch 408/538 - Train Accuracy: 0.929, Validation Accuracy: 0.943, Loss: 0.056\n",
"Epoch 7 Batch 409/538 - Train Accuracy: 0.942, Validation Accuracy: 0.931, Loss: 0.054\n",
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"Epoch 7 Batch 416/538 - Train Accuracy: 0.957, Validation Accuracy: 0.941, Loss: 0.054\n",
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"Epoch 7 Batch 422/538 - Train Accuracy: 0.931, Validation Accuracy: 0.932, Loss: 0.059\n",
"Epoch 7 Batch 423/538 - Train Accuracy: 0.949, Validation Accuracy: 0.940, Loss: 0.059\n",
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"Epoch 7 Batch 426/538 - Train Accuracy: 0.948, Validation Accuracy: 0.934, Loss: 0.051\n",
"Epoch 7 Batch 427/538 - Train Accuracy: 0.935, Validation Accuracy: 0.928, Loss: 0.052\n",
"Epoch 7 Batch 428/538 - Train Accuracy: 0.953, Validation Accuracy: 0.930, Loss: 0.048\n",
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"Epoch 7 Batch 432/538 - Train Accuracy: 0.944, Validation Accuracy: 0.951, Loss: 0.059\n",
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"Epoch 7 Batch 434/538 - Train Accuracy: 0.930, Validation Accuracy: 0.947, Loss: 0.058\n",
"Epoch 7 Batch 435/538 - Train Accuracy: 0.938, Validation Accuracy: 0.942, Loss: 0.050\n",
"Epoch 7 Batch 436/538 - Train Accuracy: 0.927, Validation Accuracy: 0.938, Loss: 0.057\n",
"Epoch 7 Batch 437/538 - Train Accuracy: 0.945, Validation Accuracy: 0.942, Loss: 0.053\n",
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"Epoch 7 Batch 471/538 - Train Accuracy: 0.963, Validation Accuracy: 0.938, Loss: 0.044\n",
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"Epoch 8 Batch 0/538 - Train Accuracy: 0.964, Validation Accuracy: 0.946, Loss: 0.046\n",
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"Epoch 9 Batch 376/538 - Train Accuracy: 0.962, Validation Accuracy: 0.957, Loss: 0.039\n",
"Epoch 9 Batch 377/538 - Train Accuracy: 0.969, Validation Accuracy: 0.957, Loss: 0.041\n",
"Epoch 9 Batch 378/538 - Train Accuracy: 0.958, Validation Accuracy: 0.953, Loss: 0.036\n",
"Epoch 9 Batch 379/538 - Train Accuracy: 0.955, Validation Accuracy: 0.941, Loss: 0.035\n",
"Epoch 9 Batch 380/538 - Train Accuracy: 0.959, Validation Accuracy: 0.941, Loss: 0.036\n",
"Epoch 9 Batch 381/538 - Train Accuracy: 0.970, Validation Accuracy: 0.944, Loss: 0.040\n",
"Epoch 9 Batch 382/538 - Train Accuracy: 0.947, Validation Accuracy: 0.956, Loss: 0.046\n",
"Epoch 9 Batch 383/538 - Train Accuracy: 0.955, Validation Accuracy: 0.958, Loss: 0.042\n",
"Epoch 9 Batch 384/538 - Train Accuracy: 0.942, Validation Accuracy: 0.952, Loss: 0.045\n",
"Epoch 9 Batch 385/538 - Train Accuracy: 0.957, Validation Accuracy: 0.956, Loss: 0.042\n",
"Epoch 9 Batch 386/538 - Train Accuracy: 0.970, Validation Accuracy: 0.958, Loss: 0.040\n",
"Epoch 9 Batch 387/538 - Train Accuracy: 0.960, Validation Accuracy: 0.957, Loss: 0.035\n",
"Epoch 9 Batch 388/538 - Train Accuracy: 0.950, Validation Accuracy: 0.950, Loss: 0.035\n",
"Epoch 9 Batch 389/538 - Train Accuracy: 0.949, Validation Accuracy: 0.943, Loss: 0.051\n",
"Epoch 9 Batch 390/538 - Train Accuracy: 0.951, Validation Accuracy: 0.947, Loss: 0.034\n",
"Epoch 9 Batch 391/538 - Train Accuracy: 0.960, Validation Accuracy: 0.952, Loss: 0.034\n",
"Epoch 9 Batch 392/538 - Train Accuracy: 0.948, Validation Accuracy: 0.959, Loss: 0.040\n",
"Epoch 9 Batch 393/538 - Train Accuracy: 0.954, Validation Accuracy: 0.955, Loss: 0.038\n",
"Epoch 9 Batch 394/538 - Train Accuracy: 0.954, Validation Accuracy: 0.948, Loss: 0.042\n",
"Epoch 9 Batch 395/538 - Train Accuracy: 0.952, Validation Accuracy: 0.950, Loss: 0.040\n",
"Epoch 9 Batch 396/538 - Train Accuracy: 0.966, Validation Accuracy: 0.941, Loss: 0.034\n",
"Epoch 9 Batch 397/538 - Train Accuracy: 0.956, Validation Accuracy: 0.938, Loss: 0.040\n",
"Epoch 9 Batch 398/538 - Train Accuracy: 0.953, Validation Accuracy: 0.938, Loss: 0.038\n",
"Epoch 9 Batch 399/538 - Train Accuracy: 0.947, Validation Accuracy: 0.952, Loss: 0.046\n",
"Epoch 9 Batch 400/538 - Train Accuracy: 0.961, Validation Accuracy: 0.950, Loss: 0.049\n",
"Epoch 9 Batch 401/538 - Train Accuracy: 0.971, Validation Accuracy: 0.958, Loss: 0.039\n",
"Epoch 9 Batch 402/538 - Train Accuracy: 0.964, Validation Accuracy: 0.957, Loss: 0.035\n",
"Epoch 9 Batch 403/538 - Train Accuracy: 0.965, Validation Accuracy: 0.957, Loss: 0.040\n",
"Epoch 9 Batch 404/538 - Train Accuracy: 0.960, Validation Accuracy: 0.955, Loss: 0.039\n",
"Epoch 9 Batch 405/538 - Train Accuracy: 0.964, Validation Accuracy: 0.957, Loss: 0.038\n",
"Epoch 9 Batch 406/538 - Train Accuracy: 0.958, Validation Accuracy: 0.956, Loss: 0.044\n",
"Epoch 9 Batch 407/538 - Train Accuracy: 0.963, Validation Accuracy: 0.956, Loss: 0.039\n",
"Epoch 9 Batch 408/538 - Train Accuracy: 0.941, Validation Accuracy: 0.962, Loss: 0.042\n",
"Epoch 9 Batch 409/538 - Train Accuracy: 0.956, Validation Accuracy: 0.965, Loss: 0.042\n",
"Epoch 9 Batch 410/538 - Train Accuracy: 0.956, Validation Accuracy: 0.963, Loss: 0.037\n",
"Epoch 9 Batch 411/538 - Train Accuracy: 0.962, Validation Accuracy: 0.961, Loss: 0.043\n",
"Epoch 9 Batch 412/538 - Train Accuracy: 0.964, Validation Accuracy: 0.959, Loss: 0.032\n",
"Epoch 9 Batch 413/538 - Train Accuracy: 0.958, Validation Accuracy: 0.958, Loss: 0.039\n",
"Epoch 9 Batch 414/538 - Train Accuracy: 0.932, Validation Accuracy: 0.963, Loss: 0.054\n",
"Epoch 9 Batch 415/538 - Train Accuracy: 0.953, Validation Accuracy: 0.960, Loss: 0.037\n",
"Epoch 9 Batch 416/538 - Train Accuracy: 0.961, Validation Accuracy: 0.957, Loss: 0.037\n",
"Epoch 9 Batch 417/538 - Train Accuracy: 0.960, Validation Accuracy: 0.954, Loss: 0.034\n",
"Epoch 9 Batch 418/538 - Train Accuracy: 0.952, Validation Accuracy: 0.954, Loss: 0.044\n",
"Epoch 9 Batch 419/538 - Train Accuracy: 0.964, Validation Accuracy: 0.960, Loss: 0.035\n",
"Epoch 9 Batch 420/538 - Train Accuracy: 0.951, Validation Accuracy: 0.955, Loss: 0.046\n",
"Epoch 9 Batch 421/538 - Train Accuracy: 0.962, Validation Accuracy: 0.945, Loss: 0.030\n",
"Epoch 9 Batch 422/538 - Train Accuracy: 0.948, Validation Accuracy: 0.945, Loss: 0.034\n",
"Epoch 9 Batch 423/538 - Train Accuracy: 0.955, Validation Accuracy: 0.945, Loss: 0.037\n",
"Epoch 9 Batch 424/538 - Train Accuracy: 0.959, Validation Accuracy: 0.952, Loss: 0.042\n",
"Epoch 9 Batch 425/538 - Train Accuracy: 0.932, Validation Accuracy: 0.957, Loss: 0.048\n",
"Epoch 9 Batch 426/538 - Train Accuracy: 0.965, Validation Accuracy: 0.955, Loss: 0.036\n",
"Epoch 9 Batch 427/538 - Train Accuracy: 0.951, Validation Accuracy: 0.952, Loss: 0.039\n",
"Epoch 9 Batch 428/538 - Train Accuracy: 0.958, Validation Accuracy: 0.954, Loss: 0.036\n",
"Epoch 9 Batch 429/538 - Train Accuracy: 0.956, Validation Accuracy: 0.949, Loss: 0.042\n",
"Epoch 9 Batch 430/538 - Train Accuracy: 0.951, Validation Accuracy: 0.951, Loss: 0.040\n",
"Epoch 9 Batch 431/538 - Train Accuracy: 0.956, Validation Accuracy: 0.951, Loss: 0.033\n",
"Epoch 9 Batch 432/538 - Train Accuracy: 0.960, Validation Accuracy: 0.951, Loss: 0.042\n",
"Epoch 9 Batch 433/538 - Train Accuracy: 0.955, Validation Accuracy: 0.958, Loss: 0.062\n",
"Epoch 9 Batch 434/538 - Train Accuracy: 0.954, Validation Accuracy: 0.964, Loss: 0.031\n",
"Epoch 9 Batch 435/538 - Train Accuracy: 0.956, Validation Accuracy: 0.962, Loss: 0.039\n",
"Epoch 9 Batch 436/538 - Train Accuracy: 0.944, Validation Accuracy: 0.962, Loss: 0.040\n",
"Epoch 9 Batch 437/538 - Train Accuracy: 0.951, Validation Accuracy: 0.957, Loss: 0.039\n",
"Epoch 9 Batch 438/538 - Train Accuracy: 0.959, Validation Accuracy: 0.959, Loss: 0.034\n",
"Epoch 9 Batch 439/538 - Train Accuracy: 0.956, Validation Accuracy: 0.957, Loss: 0.039\n",
"Epoch 9 Batch 440/538 - Train Accuracy: 0.961, Validation Accuracy: 0.954, Loss: 0.037\n",
"Epoch 9 Batch 441/538 - Train Accuracy: 0.955, Validation Accuracy: 0.954, Loss: 0.050\n",
"Epoch 9 Batch 442/538 - Train Accuracy: 0.963, Validation Accuracy: 0.951, Loss: 0.029\n",
"Epoch 9 Batch 443/538 - Train Accuracy: 0.953, Validation Accuracy: 0.956, Loss: 0.036\n",
"Epoch 9 Batch 444/538 - Train Accuracy: 0.961, Validation Accuracy: 0.961, Loss: 0.040\n",
"Epoch 9 Batch 445/538 - Train Accuracy: 0.961, Validation Accuracy: 0.956, Loss: 0.035\n",
"Epoch 9 Batch 446/538 - Train Accuracy: 0.961, Validation Accuracy: 0.953, Loss: 0.037\n",
"Epoch 9 Batch 447/538 - Train Accuracy: 0.952, Validation Accuracy: 0.953, Loss: 0.038\n",
"Epoch 9 Batch 448/538 - Train Accuracy: 0.955, Validation Accuracy: 0.958, Loss: 0.038\n",
"Epoch 9 Batch 449/538 - Train Accuracy: 0.974, Validation Accuracy: 0.959, Loss: 0.033\n",
"Epoch 9 Batch 450/538 - Train Accuracy: 0.940, Validation Accuracy: 0.957, Loss: 0.055\n",
"Epoch 9 Batch 451/538 - Train Accuracy: 0.950, Validation Accuracy: 0.963, Loss: 0.035\n",
"Epoch 9 Batch 452/538 - Train Accuracy: 0.957, Validation Accuracy: 0.960, Loss: 0.031\n",
"Epoch 9 Batch 453/538 - Train Accuracy: 0.954, Validation Accuracy: 0.962, Loss: 0.044\n",
"Epoch 9 Batch 454/538 - Train Accuracy: 0.957, Validation Accuracy: 0.962, Loss: 0.039\n",
"Epoch 9 Batch 455/538 - Train Accuracy: 0.962, Validation Accuracy: 0.964, Loss: 0.038\n",
"Epoch 9 Batch 456/538 - Train Accuracy: 0.959, Validation Accuracy: 0.966, Loss: 0.056\n",
"Epoch 9 Batch 457/538 - Train Accuracy: 0.962, Validation Accuracy: 0.965, Loss: 0.040\n",
"Epoch 9 Batch 458/538 - Train Accuracy: 0.963, Validation Accuracy: 0.968, Loss: 0.040\n",
"Epoch 9 Batch 459/538 - Train Accuracy: 0.966, Validation Accuracy: 0.963, Loss: 0.034\n",
"Epoch 9 Batch 460/538 - Train Accuracy: 0.952, Validation Accuracy: 0.966, Loss: 0.040\n",
"Epoch 9 Batch 461/538 - Train Accuracy: 0.972, Validation Accuracy: 0.972, Loss: 0.037\n",
"Epoch 9 Batch 462/538 - Train Accuracy: 0.955, Validation Accuracy: 0.967, Loss: 0.039\n",
"Epoch 9 Batch 463/538 - Train Accuracy: 0.949, Validation Accuracy: 0.958, Loss: 0.047\n",
"Epoch 9 Batch 464/538 - Train Accuracy: 0.965, Validation Accuracy: 0.954, Loss: 0.034\n",
"Epoch 9 Batch 465/538 - Train Accuracy: 0.955, Validation Accuracy: 0.951, Loss: 0.041\n",
"Epoch 9 Batch 466/538 - Train Accuracy: 0.958, Validation Accuracy: 0.950, Loss: 0.042\n",
"Epoch 9 Batch 467/538 - Train Accuracy: 0.965, Validation Accuracy: 0.950, Loss: 0.042\n",
"Epoch 9 Batch 468/538 - Train Accuracy: 0.953, Validation Accuracy: 0.950, Loss: 0.048\n",
"Epoch 9 Batch 469/538 - Train Accuracy: 0.967, Validation Accuracy: 0.949, Loss: 0.036\n",
"Epoch 9 Batch 470/538 - Train Accuracy: 0.962, Validation Accuracy: 0.948, Loss: 0.039\n",
"Epoch 9 Batch 471/538 - Train Accuracy: 0.976, Validation Accuracy: 0.957, Loss: 0.030\n",
"Epoch 9 Batch 472/538 - Train Accuracy: 0.991, Validation Accuracy: 0.965, Loss: 0.027\n",
"Epoch 9 Batch 473/538 - Train Accuracy: 0.951, Validation Accuracy: 0.961, Loss: 0.037\n",
"Epoch 9 Batch 474/538 - Train Accuracy: 0.955, Validation Accuracy: 0.966, Loss: 0.038\n",
"Epoch 9 Batch 475/538 - Train Accuracy: 0.967, Validation Accuracy: 0.963, Loss: 0.032\n",
"Epoch 9 Batch 476/538 - Train Accuracy: 0.969, Validation Accuracy: 0.960, Loss: 0.042\n",
"Epoch 9 Batch 477/538 - Train Accuracy: 0.968, Validation Accuracy: 0.954, Loss: 0.040\n",
"Epoch 9 Batch 478/538 - Train Accuracy: 0.967, Validation Accuracy: 0.954, Loss: 0.032\n",
"Epoch 9 Batch 479/538 - Train Accuracy: 0.965, Validation Accuracy: 0.956, Loss: 0.033\n",
"Epoch 9 Batch 480/538 - Train Accuracy: 0.964, Validation Accuracy: 0.955, Loss: 0.036\n",
"Epoch 9 Batch 481/538 - Train Accuracy: 0.979, Validation Accuracy: 0.958, Loss: 0.048\n",
"Epoch 9 Batch 482/538 - Train Accuracy: 0.948, Validation Accuracy: 0.960, Loss: 0.035\n",
"Epoch 9 Batch 483/538 - Train Accuracy: 0.951, Validation Accuracy: 0.958, Loss: 0.045\n",
"Epoch 9 Batch 484/538 - Train Accuracy: 0.954, Validation Accuracy: 0.962, Loss: 0.048\n",
"Epoch 9 Batch 485/538 - Train Accuracy: 0.961, Validation Accuracy: 0.952, Loss: 0.042\n",
"Epoch 9 Batch 486/538 - Train Accuracy: 0.961, Validation Accuracy: 0.952, Loss: 0.035\n",
"Epoch 9 Batch 487/538 - Train Accuracy: 0.977, Validation Accuracy: 0.941, Loss: 0.034\n",
"Epoch 9 Batch 488/538 - Train Accuracy: 0.962, Validation Accuracy: 0.949, Loss: 0.032\n",
"Epoch 9 Batch 489/538 - Train Accuracy: 0.962, Validation Accuracy: 0.961, Loss: 0.038\n",
"Epoch 9 Batch 490/538 - Train Accuracy: 0.947, Validation Accuracy: 0.959, Loss: 0.041\n",
"Epoch 9 Batch 491/538 - Train Accuracy: 0.938, Validation Accuracy: 0.964, Loss: 0.039\n",
"Epoch 9 Batch 492/538 - Train Accuracy: 0.963, Validation Accuracy: 0.964, Loss: 0.037\n",
"Epoch 9 Batch 493/538 - Train Accuracy: 0.953, Validation Accuracy: 0.960, Loss: 0.038\n",
"Epoch 9 Batch 494/538 - Train Accuracy: 0.956, Validation Accuracy: 0.953, Loss: 0.039\n",
"Epoch 9 Batch 495/538 - Train Accuracy: 0.952, Validation Accuracy: 0.956, Loss: 0.038\n",
"Epoch 9 Batch 496/538 - Train Accuracy: 0.971, Validation Accuracy: 0.953, Loss: 0.030\n",
"Epoch 9 Batch 497/538 - Train Accuracy: 0.976, Validation Accuracy: 0.955, Loss: 0.040\n",
"Epoch 9 Batch 498/538 - Train Accuracy: 0.955, Validation Accuracy: 0.950, Loss: 0.037\n",
"Epoch 9 Batch 499/538 - Train Accuracy: 0.952, Validation Accuracy: 0.951, Loss: 0.041\n",
"Epoch 9 Batch 500/538 - Train Accuracy: 0.977, Validation Accuracy: 0.951, Loss: 0.027\n",
"Epoch 9 Batch 501/538 - Train Accuracy: 0.973, Validation Accuracy: 0.953, Loss: 0.040\n",
"Epoch 9 Batch 502/538 - Train Accuracy: 0.954, Validation Accuracy: 0.950, Loss: 0.035\n",
"Epoch 9 Batch 503/538 - Train Accuracy: 0.972, Validation Accuracy: 0.945, Loss: 0.037\n",
"Epoch 9 Batch 504/538 - Train Accuracy: 0.972, Validation Accuracy: 0.950, Loss: 0.030\n",
"Epoch 9 Batch 505/538 - Train Accuracy: 0.972, Validation Accuracy: 0.948, Loss: 0.030\n",
"Epoch 9 Batch 506/538 - Train Accuracy: 0.968, Validation Accuracy: 0.943, Loss: 0.032\n",
"Epoch 9 Batch 507/538 - Train Accuracy: 0.949, Validation Accuracy: 0.945, Loss: 0.034\n",
"Epoch 9 Batch 508/538 - Train Accuracy: 0.959, Validation Accuracy: 0.945, Loss: 0.038\n",
"Epoch 9 Batch 509/538 - Train Accuracy: 0.956, Validation Accuracy: 0.949, Loss: 0.041\n",
"Epoch 9 Batch 510/538 - Train Accuracy: 0.971, Validation Accuracy: 0.949, Loss: 0.034\n",
"Epoch 9 Batch 511/538 - Train Accuracy: 0.959, Validation Accuracy: 0.949, Loss: 0.040\n",
"Epoch 9 Batch 512/538 - Train Accuracy: 0.960, Validation Accuracy: 0.951, Loss: 0.037\n",
"Epoch 9 Batch 513/538 - Train Accuracy: 0.960, Validation Accuracy: 0.958, Loss: 0.034\n",
"Epoch 9 Batch 514/538 - Train Accuracy: 0.964, Validation Accuracy: 0.959, Loss: 0.035\n",
"Epoch 9 Batch 515/538 - Train Accuracy: 0.952, Validation Accuracy: 0.960, Loss: 0.043\n",
"Epoch 9 Batch 516/538 - Train Accuracy: 0.968, Validation Accuracy: 0.953, Loss: 0.038\n",
"Epoch 9 Batch 517/538 - Train Accuracy: 0.967, Validation Accuracy: 0.955, Loss: 0.037\n",
"Epoch 9 Batch 518/538 - Train Accuracy: 0.960, Validation Accuracy: 0.958, Loss: 0.041\n",
"Epoch 9 Batch 519/538 - Train Accuracy: 0.971, Validation Accuracy: 0.959, Loss: 0.037\n",
"Epoch 9 Batch 520/538 - Train Accuracy: 0.969, Validation Accuracy: 0.964, Loss: 0.039\n",
"Epoch 9 Batch 521/538 - Train Accuracy: 0.963, Validation Accuracy: 0.968, Loss: 0.044\n",
"Epoch 9 Batch 522/538 - Train Accuracy: 0.959, Validation Accuracy: 0.968, Loss: 0.033\n",
"Epoch 9 Batch 523/538 - Train Accuracy: 0.959, Validation Accuracy: 0.964, Loss: 0.039\n",
"Epoch 9 Batch 524/538 - Train Accuracy: 0.963, Validation Accuracy: 0.964, Loss: 0.036\n",
"Epoch 9 Batch 525/538 - Train Accuracy: 0.959, Validation Accuracy: 0.964, Loss: 0.037\n",
"Epoch 9 Batch 526/538 - Train Accuracy: 0.962, Validation Accuracy: 0.964, Loss: 0.034\n",
"Epoch 9 Batch 527/538 - Train Accuracy: 0.972, Validation Accuracy: 0.967, Loss: 0.033\n",
"Epoch 9 Batch 528/538 - Train Accuracy: 0.949, Validation Accuracy: 0.967, Loss: 0.040\n",
"Epoch 9 Batch 529/538 - Train Accuracy: 0.930, Validation Accuracy: 0.962, Loss: 0.038\n",
"Epoch 9 Batch 530/538 - Train Accuracy: 0.956, Validation Accuracy: 0.961, Loss: 0.045\n",
"Epoch 9 Batch 531/538 - Train Accuracy: 0.951, Validation Accuracy: 0.963, Loss: 0.038\n",
"Epoch 9 Batch 532/538 - Train Accuracy: 0.951, Validation Accuracy: 0.963, Loss: 0.032\n",
"Epoch 9 Batch 533/538 - Train Accuracy: 0.958, Validation Accuracy: 0.954, Loss: 0.035\n",
"Epoch 9 Batch 534/538 - Train Accuracy: 0.955, Validation Accuracy: 0.948, Loss: 0.029\n",
"Epoch 9 Batch 535/538 - Train Accuracy: 0.966, Validation Accuracy: 0.951, Loss: 0.034\n",
"Epoch 9 Batch 536/538 - Train Accuracy: 0.967, Validation Accuracy: 0.957, Loss: 0.044\n",
"Model Trained and Saved\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def get_accuracy(target, logits):\n",
" \"\"\"\n",
" Calculate accuracy\n",
" \"\"\"\n",
" max_seq = max(target.shape[1], logits.shape[1])\n",
" if max_seq - target.shape[1]:\n",
" target = np.pad(\n",
" target,\n",
" [(0,0),(0,max_seq - target.shape[1])],\n",
" 'constant')\n",
" if max_seq - logits.shape[1]:\n",
" logits = np.pad(\n",
" logits,\n",
" [(0,0),(0,max_seq - logits.shape[1])],\n",
" 'constant')\n",
"\n",
" return np.mean(np.equal(target, logits))\n",
"\n",
"# Split data to training and validation sets\n",
"train_source = source_int_text[batch_size:]\n",
"train_target = target_int_text[batch_size:]\n",
"valid_source = source_int_text[:batch_size]\n",
"valid_target = target_int_text[:batch_size]\n",
"(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\n",
" valid_target,\n",
" batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])) \n",
"with tf.Session(graph=train_graph) as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
"\n",
" for epoch_i in range(epochs):\n",
" for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\n",
" get_batches(train_source, train_target, batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])):\n",
"\n",
" _, loss = sess.run(\n",
" [train_op, cost],\n",
" {input_data: source_batch,\n",
" targets: target_batch,\n",
" lr: learning_rate,\n",
" target_sequence_length: targets_lengths,\n",
" source_sequence_length: sources_lengths,\n",
" keep_prob: keep_probability})\n",
"\n",
"\n",
" if batch_i % display_step == 0 and batch_i > 0:\n",
"\n",
"\n",
" batch_train_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: source_batch,\n",
" source_sequence_length: sources_lengths,\n",
" target_sequence_length: targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
"\n",
" batch_valid_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: valid_sources_batch,\n",
" source_sequence_length: valid_sources_lengths,\n",
" target_sequence_length: valid_targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
" train_acc = get_accuracy(target_batch, batch_train_logits)\n",
"\n",
" valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\n",
"\n",
" print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\n",
" .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n",
"\n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" saver.save(sess, save_path)\n",
" print('Model Trained and Saved')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Parameters\n",
"Save the `batch_size` and `save_path` parameters for inference."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"# Save parameters for checkpoint\n",
"helper.save_params(save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
=======
"execution_count": null,
"metadata": {},
>>>>>>> refs/remotes/udacity/master
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n",
"load_path = helper.load_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sentence to Sequence\n",
"To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n",
"\n",
"- Convert the sentence to lowercase\n",
"- Convert words into ids using `vocab_to_int`\n",
" - Convert words not in the vocabulary, to the `<UNK>` word id."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 31,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"def sentence_to_seq(sentence, vocab_to_int):\n",
" \"\"\"\n",
" Convert a sentence to a sequence of ids\n",
" :param sentence: String\n",
" :param vocab_to_int: Dictionary to go from the words to an id\n",
" :return: List of word ids\n",
" \"\"\"\n",
" sentence_ids = [vocab_to_int.get(word, vocab_to_int['<UNK>']) for word in sentence.lower().split()]\n",
" return sentence_ids\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_sentence_to_seq(sentence_to_seq)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Translate\n",
"This will translate `translate_sentence` from English to French."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 33,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input\n",
" Word Ids: [174, 53, 117, 38, 207, 54, 112]\n",
" English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n",
"\n",
"Prediction\n",
" Word Ids: [261, 136, 273, 109, 335, 224, 47, 220, 1]\n",
" French Words: ['il', 'a', 'vu', 'un', 'camion', 'noir', 'rouillé', '.', '<EOS>']\n"
]
}
],
=======
"execution_count": null,
"metadata": {},
"outputs": [],
>>>>>>> refs/remotes/udacity/master
"source": [
"translate_sentence = 'he saw a old yellow truck .'\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n",
"\n",
"loaded_graph = tf.Graph()\n",
"with tf.Session(graph=loaded_graph) as sess:\n",
" # Load saved model\n",
" loader = tf.train.import_meta_graph(load_path + '.meta')\n",
" loader.restore(sess, load_path)\n",
"\n",
" input_data = loaded_graph.get_tensor_by_name('input:0')\n",
" logits = loaded_graph.get_tensor_by_name('predictions:0')\n",
" target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n",
" source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n",
" keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
"\n",
" translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,\n",
" target_sequence_length: [len(translate_sentence)*2]*batch_size,\n",
" source_sequence_length: [len(translate_sentence)]*batch_size,\n",
" keep_prob: 1.0})[0]\n",
"\n",
"print('Input')\n",
"print(' Word Ids: {}'.format([i for i in translate_sentence]))\n",
"print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n",
"\n",
"print('\\nPrediction')\n",
"print(' Word Ids: {}'.format([i for i in translate_logits]))\n",
"print(' French Words: {}'.format(\" \".join([target_int_to_vocab[i] for i in translate_logits])))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imperfect Translation\n",
"You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n",
"\n",
"You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n",
"## Submitting This Project\n",
"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
<<<<<<< HEAD
"version": "3.5.2"
},
"widgets": {
"state": {},
"version": "1.1.2"
=======
"version": "3.6.1"
>>>>>>> refs/remotes/udacity/master
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
dianafprieto/SS_2017 | .ipynb_checkpoints/05_NB_VTKPython_Scalar-checkpoint.ipynb | 1 | 5018 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"imgs/header.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualization techniques for scalar fields in VTK + Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reminder: The VTK pipeline\n",
"\n",
"<img src=\"imgs/vtk_pipeline.png\", align=left>\n",
"$~$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing data within a recilinear grid\n",
"The following code snippets show step by step the how to create a pipeline to visualize the outline of a rectilinear grid. "
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%gui qt\n",
"import vtk\n",
"from vtkviewer import SimpleVtkViewer\n",
"#help(vtk.vtkRectilinearGridReader())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Data input (source)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# do not forget to call \"Update()\" at the end of the reader\n",
"rectGridReader = vtk.vtkRectilinearGridReader()\n",
"rectGridReader.SetFileName(\"data/jet4_0.500.vtk\")\n",
"rectGridReader.Update()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Filters\n",
"\n",
"* __Filter 1:__ [vtkRectilinearGridOutlineFilter()](http://www.vtk.org/doc/nightly/html/classvtkRectilinearGridOutlineFilter.html) creates wireframe outline for a rectilinear grid."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rectGridOutline = vtk.vtkRectilinearGridOutlineFilter()\n",
"rectGridOutline.SetInputData(rectGridReader.GetOutput())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Mappers\n",
"* __Mapper:__ [vtkPolyDataMapper()](http://www.vtk.org/doc/nightly/html/classvtkPolyDataMapper.html#details) maps vtkPolyData to graphics primitives."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rectGridOutlineMapper = vtk.vtkPolyDataMapper()\n",
"rectGridOutlineMapper.SetInputConnection(rectGridOutline.GetOutputPort())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Actors"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"outlineActor = vtk.vtkActor()\n",
"outlineActor.SetMapper(rectGridOutlineMapper)\n",
"outlineActor.GetProperty().SetColor(0, 0, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Renderers and Windows"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Option 1: Default vtk render window\n",
"renderer = vtk.vtkRenderer()\n",
"renderer.SetBackground(0.5, 0.5, 0.5)\n",
"renderer.AddActor(outlineActor)\n",
"renderer.ResetCamera()\n",
"\n",
"renderWindow = vtk.vtkRenderWindow()\n",
"renderWindow.AddRenderer(renderer)\n",
"renderWindow.SetSize(500, 500)\n",
"renderWindow.Render()\n",
"\n",
"iren = vtk.vtkRenderWindowInteractor()\n",
"iren.SetRenderWindow(renderWindow)\n",
"iren.Start()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"#Option 2: Using the vtk-viewer for Jupyter to interactively modify the pipeline\n",
"vtkSimpleWin = SimpleVtkViewer()\n",
"vtkSimpleWin.resize(1000,800)\n",
"vtkSimpleWin.hide_axes()\n",
"\n",
"vtkSimpleWin.add_actor(outlineActor)\n",
"vtkSimpleWin.add_actor(gridGeomActor)\n",
"\n",
"vtkSimpleWin.ren.SetBackground(0.5, 0.5, 0.5)\n",
"vtkSimpleWin.ren.ResetCamera()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color='red'>__Trick:__</font> The autocomplete functionality in Jupyter is available by pressing the `Tab` button."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Useful Resources\n",
"\n",
"http://www.vtk.org/Wiki/VTK/Examples/Python"
]
}
],
"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 |
jskDr/jamespy_py3 | wireless/infotheory_nb/info-theory-Copy2.ipynb | 2 | 2459 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sage.all import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LIB: entropy in wireless.infotheory"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Σ_x log(p(x))*p(x)\n"
]
}
],
"source": [
"from wireless.infotheory import entropy\n",
"entropy.test_010()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Σ_x log(p(x))*p(x)\n"
]
}
],
"source": [
"class DRV:\n",
" # descrete random variable\n",
" def __init__(self):\n",
" self.x = var('x')\n",
" self.p = function('p')\n",
" self.dist = None # not define yet\n",
"\n",
"class SUM:\n",
" def __init__(self, f, x, X=None):\n",
" self.f = f # function\n",
" self.x = x # argument for sum\n",
" self.X = X # all x\n",
" \n",
" def __repr__(self):\n",
" if self.X is None:\n",
" return f'Σ_{self.x} {self.f}'\n",
" else:\n",
" return f'Σ_{self.x} {self.f} in {self.X}'\n",
" \n",
" def __str__(self):\n",
" return self.__repr__()\n",
" \n",
"def H(X):\n",
" \"\"\"\n",
" Caculate entropy of descrete random variable X\n",
" \"\"\"\n",
" return SUM(p(X.x)*log(X.p(X.x)), X.x, X.dist)\n",
"\n",
"def test_010():\n",
" X = DRV()\n",
" print(H(X))\n",
" \n",
"test_010()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python3_Sage9",
"language": "python",
"name": "sage"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
probml/pyprobml | notebooks/book2/07/laplace_approx_beta_binom_jax.ipynb | 1 | 734 | {
"cells": [
{
"cell_type": "markdown",
"id": "6cff39e7",
"metadata": {},
"source": [
"Source of this notebook is here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/04/laplace_approx_beta_binom_jax.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:pymc_exp]",
"language": "python",
"name": "conda-env-pymc_exp-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| mit |
smsaladi/murraylab_tools | examples/Echo Setup Usage Examples.ipynb | 1 | 41841 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Welcome to the Echo package"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"echo\" subpackage of murraylab_tools is designed to automate the setup of reactions (mostly TX-TL reactions) with the Echo. The EchoRun class can produce picklists for the Echo, as well as instructions for loading a source plate to go with those instructions, when appropriate. \n",
"\n",
"There are currently three main modes of operations of the Echo package, distinguished mainly by their inputs:\n",
"- **TX-TL Setup Spreadsheet**: Takes a pair of CSV spreadsheets saved from a TX-TL setup spreadsheet (version 2.X). Use this for most TX-TL experiments. \n",
"- **Programmatic Setup**: You can build a TX-TL reaction (or, with a bit more difficulty, a non-TX-TL reaction) programmatically, without using a setup spreadsheet. There are a couple of functions for doing this, which can be combined:\n",
" - **build_dilution_series**: Takes two materials and an array of concentrations for each, and builds a 2D dilution series out of the two. Can also be used to set up 1D dilution series (by setting one of the materials to a dummy material and giving it the concentration list [0]). \n",
" - **add_material_to_block**: Adds a single ingredient to all wells in a rectangular block on the destination plate, at a fixed concentration.\n",
" - **add_material_to_well**: Like add_material_to_block, but to a single well.\n",
" \n",
"- **Association Spreadsheet**: Takes one or more spreadsheets describing the contents of an Echo source plate, plus a simple spreadsheet describing final concentrations of the materials of those source plates in each destination. Use this for non-TX-TL experiments, or if you have a TX-TL experiment with more source materials than can be handled in a TX-TL setup spreadsheet."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*A word of warning*: The quick start examples will help you jump right into setting up an experiment, but they make a number of assumptions about your experiment. Some things you'll want to check before running your own:\n",
"- **Reaction Size**: Default is 5 µL.\n",
"- **Buffer/Extract Fractions**: Defaults are 0.42/0.33 (typical for French Press extracts).\n",
"- **Buffer/Extract Aliquot Size**: Defaults are 30µL/37µL. \n",
"- **\"Master Mix Excess**: For accounting for pipetting loss. Default is 1.1 (10% excess).\n",
"- **Master Mix Composition**: Default is buffer and extract only. Really only relevant for dilution series.\n",
"- **Source Plate Type**: Default is 384_PP. You probably want this.\n",
"- **Source Plate Material**: Default is AQ_BP (buffer-like liquids).\n",
"- **Destination Plate Type**: Default is \"Nunc_384_black_glassbottom\".\n",
"- **Destination Plate Size**: The Echo package has no knowledge of the destination plate. *There is no check to keep you from defining picks off the edge of the destination plate*.\n",
"- **Controls**: TX-TL experiments come with a negative control. Most TX-TL setup spreadsheets also define a positive control.\n",
"- **Dead Volume/Max Volume**: Default dead volume is 21 µL, including loss to meniscus. Default max volume is 65 µL. \n",
"- **Volume/Aliquot of Buffer and Extract**: Default is 30 µL extract/aliquot and 37 µL buffer/aliquot.\n",
"\n",
"Additionally, be aware that the TX-TL setup scripts keep track of which source plate wells they've used in a .dat file. If you run those experiments repeatedly, they'll fill up the file and eventually error out when they run out of wells on the plate. \n",
"\n",
"You can read more about how the echo package works in the next section, \"**How it all works**\", or you can skip to the example usage sections below that. For more information on tweaking settings, see **Tweaking Settings** at the end of this notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How it all works"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Every project begins with an EchoRun object. This object holds (nearly) all of the information about an experiment, and is ultimately responsible for coordinating plates and materials, and for actually writing picklists and experimental protocols. \n",
"\n",
"Most EchoRun objects need a SourcePlate object. This object is responsible for keeping track of which wells have been used, and for assigning new wells on the source plate to materials that you'll want to transfer. It will try to assign wells in a way that keeps the same material in a contiguous block, buffered on either side by an empty well for ease of pipetting. SourcePlate objects are also typically associated with a .dat tracking file that lists which wells have been used on the plate. That way, you can use the same source plate over many experiments without having to manually program in which wells are forbidden. The SourcePlate object will read this file to learn what it has available to it, and will automatically write back to the same file whenever the EchoRun object controlling it writes a picklist. \n",
"\n",
"Materials (DNA, chemicals, water, TX-TL, etc) on a source plate are represented by EchoSourceMaterial objects. An EchoSourceMaterial has a concentration, which is always stored in nM. However, because dsDNA is usually measured in ng/uL, and dsDNA is one of the most common materials used, EchoSourceMaterials by default assume that the concentration set in their constructors is in units of ng/uL. Any EchoSourceMaterial with `length` > 0 will convert that ng/uL concentration into an internal nM concentration. Only if the length of the EchoSourceMaterial is set to 0 will it use its set concentration value directly. This convention appears in several other contexts in the echo package.\n",
"\n",
"An EchoSourceMaterial is always associated with a SourcePlate. The EchoSourceMaterial keeps track of how much of itself has been used, and will request that wells be allocated on the SourcePlate. \n",
"\n",
"EchoRun objects can also have a MasterMix object, which defines what materials will be put into the master mix, and at what concentrations. Association lists don't currently support master mixes, and TX-TL reactions built from CSVs will always pull their master mix information from the CSV, so this is currently only useful if you're using the dilution series function(s).\n",
"\n",
"To generate an Echo picklist, you will generally need to do three things:\n",
"- Building an EchoRun object. This usually just means setting the name of a source plate tracking file\n",
"- Describe the experiment. This almost always means one or more calls to `build_picklist_from_txtl_setup_csvs`, `build_dilution_series`, or `build_picklist_from_association_spreadsheet` from your EchoRun object. What this entails depends on your experiment; TX-TL setup with a setup spreadsheet and association file setups are almost completely defined by external files, while 2D dilution series experiments require some definition in your script. \n",
"- Write the picklist, which is done with a call to `write_picklist` from your EchoRun object. \n",
"\n",
"For more information, see the examples below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TX-TL Setup Spreadsheet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quick Start Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following creates an Echo picklist and experimental protocol for a variety of fluorescent protein mixes described in \"inputs/TX-TL_setup_example.xlsx\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import murraylab_tools.echo as mt_echo\n",
"import os.path\n",
"\n",
"# Relevant input and output files. Check these out for examples of input file format.\n",
"txtl_inputs = os.path.join(\"txtl_setup\", \"inputs\")\n",
"txtl_outputs = os.path.join(\"txtl_setup\", \"outputs\")\n",
"stock_file = os.path.join(txtl_inputs, \"TX-TL_setup_example_stocks.csv\") # Source materials\n",
"recipe_file = os.path.join(txtl_inputs, \"TX-TL_setup_example_recipe.csv\") # Experimental setup \n",
"plate_file = os.path.join(txtl_inputs, \"TX-TL_setup_example_plate.dat\") # Keeps track of wells used \n",
"output_name = os.path.join(txtl_outputs, \"TX-TL_setup_example\") # Output (both a picklist and a\n",
" # small protocol for building the\n",
" # source plate)\n",
"\n",
"# Build an EchoRun object\n",
"txtl_plate = mt_echo.SourcePlate(filename = plate_file)\n",
"txtl_echo_calculator = mt_echo.EchoRun(plate = txtl_plate)\n",
"\n",
"# Describe the experiment\n",
"txtl_echo_calculator.build_picklist_from_txtl_setup_csvs(stock_file, recipe_file)\n",
"# Write results\n",
"txtl_echo_calculator.write_picklist(output_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ### More Information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `build_picklist_from_txtl_setup_csvs` function is used for creating a picklist from a TX-TL setup spreadsheet (version 2.0 or later -- spreadsheets from before late 2016 will not work). You will need to feed it the names of two CSV files produced from the \"recipe\" sheet (the first sheet, containing explicit pipetting directions) and the \"stocks\" sheet (the second sheet, which details the concentrations of most of the materials used in the experiment). The standard workflow looks like:\n",
"\n",
"* Edit your TX-TL setup Excel document (modifying the \"Stocks\" sheet and the \"Layout\" sheet, and the few cells shaded purple in the \"Recipe\" sheet).\n",
"* Save the \"Recipe\" and \"Stocks\" sheets from the xls/xlsx file as CSVs.\n",
"* Run `build_picklist_from_txtl_setup_csvs`, passing it the names of the two CSVs you just saved.\n",
"\n",
"Reaction size and master mix excess ratio are read from the recipe spreadsheet. You probably should not mess with those settings \n",
"\n",
"Note that you *must* give each reaction a plate location, i.e. \"D4\" or \"E07\". In the Excel spreadsheet, plate locations can be added in the \"Layout\" tab, and will be automatically propagated to the \"Recipe\" tab. \n",
"\n",
"`build_picklist_from_txtl_setup_csvs` requires that the EchoRun object have a source plate object associated with a source plate file. It will automatically assign wells to that source plate for all the materials required to run that reaction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Programmatic construction of TX-TL Reactions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quick Start Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following creates an Echo picklist and simple experimental protcol for a two-way dilution series of a reporter plasmid and inducer. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well D2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D3; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D4; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D5; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D6; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D7; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D8; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D9; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D10; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material GFP Plasmid into well D11; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well E2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well F2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well G2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well H2; are you sure you want to do this?\n",
" \"you want to do this?\")\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:168: UserWarning: Requesting 0 volume from material ATc into well I2; are you sure you want to do this?\n",
" \"you want to do this?\")\n"
]
}
],
"source": [
"import murraylab_tools.echo as mt_echo\n",
"import os.path\n",
"\n",
"# Relevant input and output files. Check these out for examples of input file format.\n",
"dilution_inputs = os.path.join(\"2D_dilution_series\", \"inputs\")\n",
"dilution_outputs = os.path.join(\"2D_dilution_series\", \"outputs\")\n",
"plate_file = os.path.join(dilution_inputs, \"dilution_setup_example_plate.dat\") # Keeps track of wells used\n",
"output_name = os.path.join(dilution_outputs, \"dilution_setup_example\") # Output (both a picklist and a\n",
" # small protocol for building the\n",
" # source plate)\n",
"\n",
"# Build an EchoRun object\n",
"dilution_plate = mt_echo.SourcePlate(filename = plate_file)\n",
"default_master_mix = mt_echo.MasterMix(plate = dilution_plate)\n",
"dilution_echo_calculator = mt_echo.EchoRun(plate = dilution_plate, master_mix = default_master_mix)\n",
"\n",
"# Set final concentrations of two materials\n",
"gfp_final_concentrations = range(0,6,1) # in nM\n",
"atc_final_concentrations = range(0,100,10) # in ng/uL\n",
"\n",
"# Define reporter plasmid material\n",
"gfp_conc = 294 # Concentration in ng/uL\n",
"gfp_len = 3202 # Size of DNA in bp\n",
"gfp = mt_echo.EchoSourceMaterial('GFP Plasmid', gfp_conc, gfp_len, dilution_plate)\n",
"\n",
"# Define inducer material\n",
"atc_conc = 1000 # Concentration in ng/uL (important that this matches the units of the final concentrations)\n",
"atc_len = 0 # This isn't dsDNA, so it has 0 length. \n",
"atc = mt_echo.EchoSourceMaterial(\"ATc\", atc_conc, atc_len, dilution_plate)\n",
"\n",
"# Plan out the experiment\n",
"starting_well = \"D2\"\n",
"dilution_echo_calculator.build_dilution_series(gfp, atc, gfp_final_concentrations,\n",
" atc_final_concentrations, starting_well)\n",
"# Write results\n",
"dilution_echo_calculator.write_picklist(output_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the warnings -- a lot of mistakes you might make will cause you to pipette 0 nL at a time, so the code will warn you if you do so. In this case, those 0 volume pipetting steps are normal -- you just added a material to 0 concentration. Similar warnings will appear if you under-fill a reaction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also manually add a single material to a well (`add_material_to_well`) or a rectangular block of wells (`add_material_to_block`) by specifying the material (an EchoSourceMaterial object), a final concentration, and a location. For example, if we set up a dilution series using the variables above..."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Build an EchoRun object\n",
"dilution_plate = mt_echo.SourcePlate(filename = plate_file)\n",
"default_master_mix = mt_echo.MasterMix(plate = dilution_plate)\n",
"dilution_echo_calculator = mt_echo.EchoRun(plate = dilution_plate, master_mix = default_master_mix)\n",
"\n",
"# Plan out a dilution series\n",
"starting_well = \"D2\"\n",
"dilution_echo_calculator.build_dilution_series(gfp, atc, gfp_final_concentrations,\n",
" atc_final_concentrations, starting_well)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...then we can add, say, bananas, to a 2x2 square in the top-left corner of the reaction."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Bananas at 100 nM\n",
"bananas = mt_echo.EchoSourceMaterial('Cavendish', 100, 0, None)\n",
"dilution_echo_calculator.add_material_to_block(bananas, 3, 'D2', 'E3')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also add to a single well, if you really need to."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"old_bananas = mt_echo.EchoSourceMaterial('Gros Michel', 100, 0, None)\n",
"dilution_echo_calculator.add_material_to_well(old_bananas, 3, 'F5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### More Information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `build_dilution_series` function of EchoRun is useful for quickly building a grid of dilutions of two materials, one on each axis. This is useful for double titrations of, for example, plasmid vs. inducer, or titration of two inputs. If you want to do anything more complex, you'll probably need to move to a TX-TL setup spreadsheet. \n",
"\n",
"This function will always output reactions in solid blocks, with the upper-left-most well specified by the starting well argument of `build_dilution_series` (last argument). The function will also add a negative control well one row below the last row of the dilution series block, aligned with its first column. You will *not* get a positive control reaction -- you'll have to add that yourself, if you want it. \n",
"\n",
"Note that you must manually define source materials for 2D dilution setup. An EchoSourceMaterial object has four attributes -- a name, a concentration, a length, and a plate object. \n",
"* The name can be whatever you want, but note that the names \"water\" and \"txtl_mm\" are reserved for water and TX-TL master mix, respectively. If you make one of your materials \"water\" or \"txtl_mm\", be aware that they're going to be assumed to actually be water and TX-TL master mix. \n",
"* Concentration and length attributes of an EchoSourceMaterial follow specific unit conventions. In brief, if the material is dsDNA, length is the number of base pairs and concentration is in units of ng/µL; otherwise, length is 0 and concentration is in units of nM. See \"**How it all works**\" above for more details. \n",
"* The EchoSourcePlate object should be the same EchoSourcePlate associated with the EchoRun object you're going to use. \n",
"\n",
"If you want to set up multiple dilutions series, you can call `build_dilution_series` multiple times on the same EchoRun object. All dilution series will be put on the same plate, and source wells for materials with the same names (including the master mix and water) will be combined. Just make sure to start each dilution series in a different well! \n",
"\n",
"`build_dilution_series` requires that the EchoRun object have a source plate object associated with a source plate file. It will automatically assign wells to that source plate for all the materials required to run that reaction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Association Spreadsheet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quick Start Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following creates an Echo picklist for an automated PCR setup with three sets of primers to be applied individually to three different plasmids, as defined in a pair of CSV files (one defining the source plate, one describing what should go in the destination plates). "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well A1 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well A2 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well A3 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well B1 has 50000 nL volume but only contains 5050.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well B2 has 50000 nL volume but only contains 5050.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well B3 has 50000 nL volume but only contains 5050.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well C1 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well C2 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n",
"/Users/sclamons/anaconda/lib/python3.6/site-packages/murraylab_tools/echo/echo_functions.py:1053: Warning: Reaction in well C3 has 50000 nL volume but only contains 5075.00 nL of ingredients. Are you sure you want to underfill this reaction?\n",
" warnings.warn(warn_string, Warning)\n"
]
}
],
"source": [
"import murraylab_tools.echo as mt_echo\n",
"import os.path\n",
"\n",
"# Relevant input and output files. Check these out for examples of input file format.\n",
"assoc_inputs = os.path.join(\"association_list\", \"inputs\")\n",
"assoc_outputs = os.path.join(\"association_list\", \"outputs\") \n",
"stock_file = os.path.join(assoc_inputs, 'association_source_sheet.csv')\n",
"assoc_file = os.path.join(assoc_inputs, 'association_final_sheet.csv')\n",
"assoc_name = os.path.join(assoc_outputs, 'association_example')\n",
"\n",
"# Build an EchoRun object\n",
"assoc_echo_calculator = mt_echo.EchoRun()\n",
"assoc_echo_calculator.rxn_vol = 50000 # PCR is large-volume!\n",
"\n",
"# Define which column of the source file is what\n",
"name_col = 'B'\n",
"conc_col = 'C'\n",
"len_col = 'D'\n",
"well_col = 'A'\n",
"plate_col = 'E'\n",
"\n",
"# Define the source plate based on the stock file.\n",
"assoc_echo_calculator.load_source_plate(stock_file, name_col, conc_col,\n",
" len_col, well_col, plate_col)\n",
"\n",
"# Build a protocol, based on the association file.\n",
"assoc_echo_calculator.build_picklist_from_association_spreadsheet(assoc_file,\n",
" well_col)\n",
"\n",
"# Write the picklist\n",
"assoc_echo_calculator.write_picklist(assoc_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### More Information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `build_picklist_from_association_spreadsheet` function is used for arbitrary mappings of source plate wells to destination wells, using 1) a spreadsheet describing the contents of each source plate, and 2) a second spreadsheet describing what materials should be put in what wells, and at what concentration. You must always set up a source plate first. This should be done by calling `load_source_plate` with the name of the source plate spreadsheet and information about its organization. Alternatively, you could build your own manually. That is not recommended. \n",
"\n",
"The first 'source' spreadsheet (describing the source plate) is a CSV-format spreadsheet with, at minimum, columns with the following information. You can add any number of additional columns to the source plate spreadsheet, which will be ignored by this function. This is the spreadsheet read in with the `load_source_plate` command.\n",
"* Location: This is the well number of the material, i.e. \"C4\" or \"E08\". If the same material is found in multiple wells, it will need one row for each well.\n",
"* Name: Brief string describing the material. This name will be used in the recipe output of EchoRun. It can be any string, but be aware that the names \"water\" and \"txtl_mm\" are reserved for describing water and TX-TL master mix, respectively. For this function, that won't matter, but if you try to combine other setup commands with this one, you should avoid using \"water\" and \"txtl_mm\" as material names. \n",
"* Concentration: If the material is dsDNA, this should be the concentration of the DNA in ng/µL. Otherwise, it should be the concentration of the material in whatever units you want (nM recommended).\n",
"* Length: If the material is dsDNA, this should be the number of base pairs in the DNA. Otherwise, length should be 0. This is important for correct unit usage.\n",
"* Plate: A single source plate spreadsheet can contain materials from different source plates, so a column is required to determine which plate the material is coming from. Name of the source plate. Put a number N here, and the plate will be auto-named \"Plate[N]\" (recommended usage). Alternatively, you can give the plate a custom name.\n",
"\n",
"The second 'association' spreadsheet (describing the what materials go together) is also a CSV-format spreadsheet. This spreadsheet determines what goes into the destination well. One column of the association spreadsheet determines that row's well on the destination plate. The EchoRun object will scan through every column, ignoring the well column, taking pairs of columns from left to right. There can be any number of pairs of columns; each one will cause one material to be moved from the source plate to the destination plate. The first clumn in each pair holds the name of a material. This name must exactly match one of the material names listed in the source spreadsheet, and determines where material will be taken from. The second column in each pair describes the *final* concentration of that material. If the material is dsDNA (has non-zero length), the units of final concentration are assumed to be nM. Otherwise, the units of final concentration are the same as the units of concentration used for that material in the source plate.\n",
"\n",
"Note that unlike the other two experimental settings of EchoRun, `build_picklist_from_association_spreadsheet` does *not* require that its EchoRun object be associated with a source plate file or SourcePlate object prior to the function being called -- a new SourcePlate object will be manufactured from the input spreadsheet when you call `load_source_plate`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tweaking Settings"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import murraylab_tools.echo as mt_echo\n",
"import os.path\n",
"\n",
"plate_file = os.path.join(\"tweaking\", \"plate_file_example.dat\")\n",
"\n",
"# Build an EchoRun object\n",
"example_plate = mt_echo.SourcePlate(filename = plate_file)\n",
"example_master_mix = mt_echo.MasterMix(example_plate)\n",
"example_echo_calculator = mt_echo.EchoRun(plate = example_plate, master_mix = example_master_mix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To change the reaction volume:\n",
"\n",
"Reaction volume is a property of an EchoRun object."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"example_echo_calculator.rxn_vol = 10.5 * 1e3 # Volume IN nL!!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure to run this before running `build_picklist_from_association_spreadsheet` or `build_dilution_series`. You almost certainly shouldn't do this at all when using `build_picklist_from_txtl_setup_csvs`, because that function will automatically extract a reaction volume from the setup spreadsheet."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To change the master mix composition/extract fraction:\n",
"\n",
"This is really only relevant for the 2D dilution series TX-TL setup (`build_dilution_series`) -- TX-TL setup from a spreadsheet pulls the extract fraction from the spreadsheet, and the association spreadsheet method has no knowledge of TX-TL. Accordingly, the extract fraction is an optional argument in `build_dilution_series`. To modify the master mix of a reaction, you'll have to set its MasterMix object, which is most safely done with the `add_master_mix` function. \n",
"\n",
"Changing the buffer/extract composition can be accomplished in the constructor of the new MasterMix object. Be sure to add the new MasterMix object before calling `write_picklist`! Also be sure that the new MasterMix object has the same reaction volume (`rxn_vol`) as the EchoRun object. Otherwise you'll get an error."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_master_mix = mt_echo.MasterMix(example_plate, extract_fraction = 0.40, \n",
" rxn_vol = example_echo_calculator.rxn_vol)\n",
"example_echo_calculator.add_master_mix(new_master_mix)\n",
"example_echo_calculator.build_dilution_series(gfp, atc, gfp_final_concentrations,\n",
" atc_final_concentrations, starting_well)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also add arbitrary components to the master mix. For example, the following code ads the dye DFHBI-1T to every well at a final concentration of 10 µM, from a 2 mM stock:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_master_mix = mt_echo.MasterMix(example_plate, \n",
" rxn_vol = example_echo_calculator.rxn_vol)\n",
"dfhbi = mt_echo.EchoSourceMaterial(\"DFHBI-1T\", 2000, 0, example_plate)\n",
"new_master_mix.add_material(dfhbi, 10)\n",
"example_echo_calculator.add_master_mix(new_master_mix)\n",
"example_echo_calculator.build_dilution_series(gfp, atc, gfp_final_concentrations,\n",
" atc_final_concentrations, starting_well)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**To change buffer/extract aliquot size:**\n",
"\n",
"Buffer and extract aliquot size are controlled by the MasterMix object. Like extract percentage, aliquot sizes can be changed in the MasterMix's constructor.\n",
"\n",
"Note that both aliquot sizes are in units of nL, not uL."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_master_mix = mt_echo.MasterMix(example_plate,\n",
" extract_per_aliquot = 50000,\n",
" buffer_per_aliquot = 70000,\n",
" rxn_vol = example_echo_calculator.rxn_vol)\n",
"example_echo_calculator.add_master_mix(new_master_mix)\n",
"example_echo_calculator.build_dilution_series(gfp, atc, gfp_final_concentrations,\n",
" atc_final_concentrations, starting_well)\n",
"# ...\n",
"# calculator_with_odd_destination.write_picklist(...)\n",
"#..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**To run a (dilution series) reaction without master mix:**\n",
"You can also make a dilution series without any master mix by either creating a new EchoRun object with `None` for its MasterMix, or by removing the MasterMix on an existing EchoRun object with a call to `remove_master_mix`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"dye1 = mt_echo.EchoSourceMaterial('A Dye', 100, 0, dilution_plate)\n",
"dye2 = mt_echo.EchoSourceMaterial('Another Dye', 122, 0, dilution_plate)\n",
"dye_concentrations = [x for x in range(10)]\n",
"\n",
"example_echo_calculator.remove_master_mix()\n",
"example_echo_calculator.build_dilution_series(dye1, dye2, dye_concentrations, \n",
" dye_concentrations, starting_well)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To change the source plate type/material type:\n",
"\n",
"Source plate types and material types are set as optional arguments in the constructor of a SourcePlate object. The type and material of a source plate are both set by a string, which can be any string that the Echo Plate Reformat software will recognize."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plate_type = \"384PP_AQ_BP\" # This is actually the default plate value\n",
"retyped_example_plate = mt_echo.SourcePlate(filename = plate_file, SPtype = plate_type)\n",
"another_example_echo_calculator = mt_echo.EchoRun(plate = retyped_example_plate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To change the source plate name:\n",
"\n",
"Source plate names are set much like source plate types with the argument `SPname`. In addition, as a shorthand, you can set `SPname` to be a number N, in which case the plate will be named `Source[N]`. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plate_name = \"FirstPlate\"\n",
"renamed_example_plate = mt_echo.SourcePlate(filename = plate_file, SPname = plate_name)\n",
"yet_another_example_echo_calculator = mt_echo.EchoRun(plate = renamed_example_plate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**To change the destination plate type:**\n",
"\n",
"Destination plate types are determined and stored directly in the EchoRun object. The destination plate type can be set by the optional argument `DPtype` in the constructor of an EchoRun object, or set manually any time before calling `write_picklist` on that EchoRun."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"calculator_with_odd_destination = mt_echo.EchoRun(plate = example_plate, DPtype = \"some_96_well_plate\")\n",
"calculator_with_odd_destination.DPtype = \"Nunc_384_black_glassbottom\" # Just kidding!\n",
"# ...\n",
"# calculator_with_odd_destination.write_picklist(...)\n",
"#..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**To change dead volume and max volume**:\n",
"\n",
"You probably shouldn't do this. If you absolutely must squeeze every last bit of efficiency out of your source wells, you can set the `dead_volume` and `max_volume` variables, which are static variables in the murraylab_tools.echo package. If you change them, also make sure to set the static variable `usable_volume`, which defines the volume of material in a well that can actually be used by the Echo (this is normally calculated from `dead_volume` and `max_volume` at package import). Also, you should do this before running any experimental protocol function."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from murraylab_tools.echo.echo_functions import dead_volume, max_volume, usable_volume\n",
"dead_volume = 10000 # Volume in nL!\n",
"max_volume = 75000 # Volume in nL!\n",
"usable_volume = max_volume - dead_volume # Don't forget to re-calculate this!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
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